The Sovereign Wealth Fund Discount: Evidence from Public Equity … · 2013-09-19 · 3 The...

The Sovereign Wealth Fund Discount: Evidence from Public Equity Investments

Bernardo Bortolotti Professor of Economics, Università di Torino

Director, Sovereign Investment Lab, Paolo Baffi Center, Università Bocconi

Veljko Fotak Assistant Professor of Finance, University at Buffalo

Researcher, Sovereign Investment Lab, Paolo Baffi Center, Università Bocconi

William L. Megginson Professor and Rainbolt Chair in Finance, University of Oklahoma

KFUPM Chair Professor in Finance, King Fahd University of Petroleum & Minerals Visiting Professor, Université Paris Dauphine

Current draft: September 5, 2013

Abstract

Using a sample of 1,018 Sovereign Wealth Fund (SWF) equity investments in publicly traded firms and a control sample of 5,975 transactions by private-sector financial institutions over 1980-2012, we find that announcement-period abnormal returns of SWF investments are positive, but lower than those of comparable private-sector investments by approximately 2.67 percentage points. We do not find evidence of long-term stock price performance of SWF investment targets differing from that of private-sector investment targets. We do find, however, significant differences among SWFs which are only partially captured by the short-term market reaction: firms acquired by passive funds tend to underperform over the following three years, while positive abnormal returns are associated with actively monitoring SWFs. We conclude that SWFs’ corporate governance role tends to affect the value of target firms. JEL Classification: G32, G15, G38 Keywords: Sovereign wealth funds, International financial markets, Government policy and regulation Please address correspondence to:

William L. Megginson Price College of Business 307 West Brooks, 205A Adams Hall The University of Oklahoma Noman, OK 73019-4005 Tel: (405) 325-2058; Fax: (405) 325-7688 e-mail: [email protected]

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The Sovereign Wealth Fund Discount: Evidence from Public Equity Investments*

Abstract

Using a sample of 1,018 Sovereign Wealth Fund (SWF) equity investments in publicly traded firms and a

control sample of 5,975 transactions by private-sector financial institutions over 1980-2012, we find that

announcement-period abnormal returns of SWF investments are positive, but lower than those of

comparable private-sector investments by approximately 2.67 percentage points. We do not find evidence

of long-term stock price performance of SWF investment targets differing from that of private-sector

investment targets. We do find, however, significant differences among SWFs which are only partially

captured by the short-term market reaction: firms acquired by passive funds tend to underperform over

the following three years, while positive abnormal returns are associated with actively monitoring SWFs.

We conclude that SWFs’ corporate governance role tends to affect the value of target firms.

JEL Classification: G32, G15, G38

Keywords: Sovereign wealth funds, International financial markets, Government policy and regulation

September 5, 2013

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* We thank Kate Holland, Laura Pellizzola, Valentina Milella, Armando Rungi, and Timothée Waxin for research assistance with this project. We also benefited from comments offered by George Allayanis, Wolfgang Bessler, Cecilia Chen, Mihaljek Dubravko, John Earle, Espen Eckbo, Oral Erdogan, Mara Faccio, Joseph Fan, Chitru Fernardo, Igor Filatotchev, Nuno Fernandes, Edith Ginglinger, Andrea Goldstein, Peter Hahn, Cam Harvey, Ron Jarmin, Stefanie Kleimeier, Kai Li, Donato Masciandaro, Anthony May, Meijun Qian, Vikas Raman, Vanessa Rossi, Andrew Rozanov, Karl Sauvant, Koen Schoors, Myron Slovin, Paola Subacchi, Georges Sudarskis, Marie Sushka, Vahap Uysal, Paul Wachtel and Pradeep Yadav. The current version of this study has benefited from comments offered by participants in the 2013 ECCE-USM Financial Globalization and Sustainable Finance conference at the Stellenbosch Business School, Cape Town, South Africa (keynote speech), the Sovereign Investment Laboratory’s workshop on Sovereign Wealth Funds, Long-Term Investment, and Growth: Unlocking Potential (Venice, June 2013), and the Université Paris Dauphine (June 2013). We also appreciate suggestions offered on a predecessor version of this paper by participants in the Fulbright Tocqueville Distinguished Chair lecture (Université Paris-Dauphine, March 2008), the Comparative Analysis of Enterprise Data 2008 conference (Budapest), the 2008 Financial Management Association European meeting (Prague), the 2008 Barbarians at the Gate conference at Cass Business School (London), the University of British Columbia 2008 Summer Corporate Finance conference, the Chatham House International Economics Research Workshop (London, December 2008), the 2008 Australasian Finance and Banking Conference (Sydney), the 2009 Conference of the Autorité des Marches Financiers Scientific Advisory Board (Paris), the 8th Annual Darden International Finance Conference (Singapore, May 2009), the 15th Dubrovnik Economic Conference (June 2009), the 2009 European Finance Association meeting (Bergen), the Post-Crisis Era and the Expanding Role of the State conference at the Korea Advanced Institute of Science and Technology (Seoul, November 2009) and seminar participants at the Chinese University of Hong Kong, the University of Maastricht, the Ludwig-Maximilians-Universität München, the University of Southern Mississippi, HEC-Montreal (Quebec), the University of Oklahoma, the Fondazione Eni Enrico Mattei (FEEM, Milan), Saudi Aramco, and King Fahd University of Petroleum and Minerals. We also appreciate the financial support provided by the Fulbright Commission, the Center for Financial Studies at the University of Oklahoma’s Price College of Business, the Sovereign Investment Lab at Università Bocconi, the University of Buffalo, and the KFUPM Chair Professor in Finance, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia.

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The Sovereign Wealth Fund Discount: Evidence from Public Equity Investments

The past fifteen years have seen the emergence of state-owned investors as major net purchasers of listed

common stock. Borisova, Fotak, Holland, and Megginson (2012) report that, since 2000, governments

have acquired more assets through stock purchases (USD 969 billion) than they have sold through share

issue privatizations and direct sales (USD 765 billion). Among state-owned investors, sovereign wealth

funds (SWFs) play an especially prominent role and their investable assets are estimated as totaling over

USD 3 trillion. Assets under SWF management are also growing rapidly--since 2010, more rapidly than

any other institutional investor group, including hedge funds and private-equity funds.1

SWFs differ from the other major government-owned purchasers of listed stock, state-owned

enterprises, in that SWFs do not produce goods or provide services, but instead make purely financial

investments, often abroad (Bortolotti, 2013). In this respect, SWFs are the type of state-owned investor

most comparable to the privately owned institutional investors (such as pension and mutual funds,

insurance companies, and hedge funds) that have long been active in global capital markets and have been

researched extensively by financial economists. Yet, as state-owned entities, SWFs are organized and

managed differently than private-sector investment funds and might have social rather than purely

economic objectives, which could affect the performance and valuation of their investment targets. These

government-owned funds also make large, risky, cross-border investments in politically sensitive

industries such as banking, telecommunications, and energy. For all these reasons, understanding how

SWFs impact target firm values--and how their impact differs from that of private-sector institutional

investors--has assumed first order importance. Our study aims at contributing to that understanding.

1 The exact value of SWFs’ assets is not known and widely debated, as in Balding (2008), Truman (2011), and Kotter and Lel (2011). Bernstein, Lerner and Schoar (2013) estimate that SWF assets under management have increased ten-fold in the last two decades and have grown at about 24 percent annually over the past three years.

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By some measures, SWFs have already been extensively researched.2 Perhaps most important

from our perspective, however, existing empirical research on SWFs offers incomplete evidence about the

impact of SWF investments on the value of publicly traded companies. All of the studies that examine

such SWF investments using event study techniques (Dewenter, Han, and Malatesta, 2010; Kotter and

Lel, 2011; Karolyi and Liao, 2011; and Knill, Lee, and Mauck, 2012) find significantly positive

announcement period returns of between 0.8% (Karolyi and Liao) and 2.25% (Kotter and Lel), suggesting

that SWFs have a positive impact on firm value as investors. Unfortunately, these results offer little

insight into how SWFs differ from private-sector investors, as corporate finance research consistently

documents positive announcement period abnormal returns for all types of direct stock purchases by

institutional or individual investors. Mikkelson and Ruback (1985), Holthausen, Leftwich, and Mayers

(1987, 1990), and Hertzel and Smith (1992) all find positive stock price reactions to corporate purchases

of another company’s equity securities, while Hertzel, Lemmon, Link, and Rees (2002) and Wruck and

Wu (2009) find that public firms that place equity privately experience positive announcement effects.

Also, a large literature finds positive target stock price reactions to share acquisitions by hedge funds

(Brav, Jiang, Partnoy, and Thomas, 2008; Klein and Zur, 2009) and other institutional investors,

presumably due to anticipated monitoring or in anticipation of an acquisition. Therefore, the existing

findings of a positive short term target stock price reaction to announcements of SWF investments is

similar to the responses documented for targeted stock purchases by all types of private investors. To

determine how the identity of the investing SWF impacts target firm value, we must compare SWF stock

purchases to those made by private financial investors. That is a key purpose of our study.

Given the lack of transparency regarding SWF investments and the consequent scarcity of

available data, we manually construct a dataset of 1,018 investments by SWFs (or by SWF-owned

2 Multiple articles and books--including Truman (2008, 2011), Balding (2008, 2012), Butt, Shivdasani, Stendevad, and Wyman (2008), Bolton, Samama, and Stiglitz (2011), and Clark, Dixon and Monk (2013)--have described the evolution of SWFs from their humble beginning in 1953 into today’s massive funds, while Bernstein, Lerner, and Schoar (2013), Scherer (2009), Chhaocharia and Laeven (2009), Fernandes (2011), Dyck and Morse (2011), Bodie and Briére (2011), Johan, Knill, and Mauck (2013), and Avendaño and Santiso (2012) all examine the investment policies of SWFs, either theoretically or empirically.

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investment subsidiaries) in publicly traded firms, with a total value of USD 352 billion, completed over

the 1980-November 2012 period. We generate a “benchmark” control sample of stock purchases by

financial investors from the same home countries as our sample of SWFs, targeted at firms headquartered

in the same countries as SWF investment targets, and executed over the same time period.3 We find that

announcements of SWF investments are associated with mean abnormal returns of 0.9% over (-1,+1), but

these are lower than the 5.02% mean abnormal returns generated by the private benchmark investors.

Such weaker market reaction could be due to differences in target or deal characteristics.

Descriptive statistics indicate that SWFs tend to target larger (higher total assets) and more profitable

(higher ROA) firms than do private investors, but benchmark targets display higher sales growth. Further,

SWF targets tend to be headquartered in more democratic countries. Given these differences, we first

investigate possible investment selection biases by constructing a propensity-score-matched (PSM)

sample from the universe of benchmark transactions. Using this matched sample, we compare the market

reaction to news of a SWF investment to news of a PSM private investment, and find the mean abnormal

stock return to SWF investments is 1.57 percentage points lower.4 As SWFs tend to acquire smaller stakes

than benchmark investors, we verify this result holds regardless of the fraction of the target firm acquired:

samples including only small (less than one or five percent) stake acquisitions, as well as those with larger

(five to fifty percent) and controlling stake (greater than fifty percent) purchases also show significantly

lower reactions to SWF than to private investor purchases. These findings also hold for other data subsets,

including domestic versus foreign investments and initial versus follow-on investments.

While this ‘matched-differences’ approach controls for the selection bias of SWFs, it does not

account for differences in deal characteristics. Accordingly, we incorporate both controls for selection

3 In similar spirit, Kotter and Lel (2011) observe that the “magnitude of the market reaction is similar to the announcement effects of investments by institutional investors on stock returns for a comparable event window,” but they offer no formal comparison. A working paper by Karolyi and Liao (2011) compares the market reaction to news of acquisitions by state-owned firms to acquisitions by state-owned funds (including SWFs) and by private-sector acquirers. Consistent with our findings, they find that the market reaction to cross-border investments by state-owned funds is smaller than the market reaction to private-sector investments. 4 Here and in the rest of the paper, cited results are statistically significant at the 1% level, unless otherwise stated.

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biases and controls for deal characteristics in propensity-score-weighted regression analysis. Even after

the more extensive set of year, country, industry, firm, and deal level controls are included in this

analysis, our main findings are confirmed. The market reaction to SWF investments is lower than that of

comparable private-sector investments, by approximately 2.67 percentage points. We verify the

robustness of these results in data subsets, as above, and for both financial and non-financial targets.

In order to establish possible channels linking SWFs’ characteristics to performance, we classify

SWFs into three types based on perceived levels of fund independence and investment activism. The

categories are ‘passive’ funds that, by policy, buy small stakes and/or have self-imposed ownership limits;

‘active government’ funds that are subject to sponsor-government interference in day-to-day

management, based on Truman’s (2008) classification; and potential ‘monitor’ funds that make non-

passive investments and whose managers are largely independent from sponsor-government influence.

Compared to matched private-sector investors, the largest price response discounts are associated with

investments by ‘passive’ SWFs, though regression analysis does not always find systematic differences

among the three fund types.

While our study focuses on abnormal returns at the time of the announcement of a SWF

investment, we also analyze the target firm’s long-term stock price performance over various time

intervals, up to three years. Most event study analyses implicitly assume that the market reaction at the

time of an announcement fully captures the impact of the investment on firm value. Yet, the impact of

SWFs on firm value might not be fully understood and priced by market participants, since these funds

are fairly opaque investment vehicles which have only recently attracted widespread interest. Long-term

event studies reveal negative (albeit, not statistically significant) abnormal target stock price performance

following SWF investments--at least once we exclude investments by Norway’s SWF--but stock price

performance is also negative for targets of private-sector investments and the magnitude of the estimated

effect is even greater. Regression analysis shows that the long-term stock-price returns of SWF

investment targets are not significantly different from matched private-sector investments, although

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targets of ‘passive’ and ‘active government’ SWFs display lower returns than matches, while targets of

‘monitor’ SWF actually display higher returns.

Our findings suggest that there is a ‘stigma’ associated with the announcement of the ‘sovereign

wealth fund’ identity of the investor, and the stigma affects all SWFs, regardless of their type.5 However,

our results also suggest that significant, and systematic, differences exist among SWFs which are only

partially captured by the short-term market reaction. Firm value seems to be positively affected over the

long term by improved monitoring provided by ‘monitor’ SWFs, but the sovereign discount persists for

all types of sovereign funds.

This research contributes directly to the emerging literature employing event studies to evaluate

the impact on firm value of having a SWF as a shareholder. Since all previous studies find significantly

positive announcement period returns, the interpretation has thus far been that the market welcomes

SWFs as investors.6 We show that there is in reality a discount associated with the sovereign identity of

the shareholder. This study also contributes to the wider literature regarding ownership structure--in

particular, government ownership--and firm performance. Our results are mainly consistent with a broad

stream of privatization literature, finding that, all else equal, a private-sector investor has a more

beneficial impact on firm value than a government-owned investor, yet this study’s findings also show

that not all government entities have the same impact. Within the literature on institutional shareholding,

we add evidence on a new, large, and increasingly important category of institutional investor.

This manuscript is structured as follows. Section 1 presents the theoretical framework, while

Section 2 describes the data sources and sample construction procedures and offers descriptive statistics

for both the SWF and the benchmark samples. Section 3 describes the basic event studies and related

5 The only exception to this is the Norwegian Government Pension Fund Global (GPFG), as we find that the market reaction to investments by GPFG does not differ from the market reaction to investments by private-sector financial institutions. Extant research (Caner and Grennes, 2009; Ang, Goetzmann, and Schaefer, 2009; Chambers, Dimson, and Ilmanen, 2012) finds that GPFG is the most transparent and most professionally-managed of all SWFs. It also differs from other funds in that its management appears to be better insulated from political influence and because it invests exclusively in foreign targets, in which it typically acquires equity stakes below one percent. 6 Dewenter, Han and Malatesta (2010) note “These results suggest that SWF acquisitions convey positive information about, or are expected to have generally benign effects on, the firms in which they invest.”

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results. Section 4 focuses on the differences between SWF and benchmark sample target selection criteria,

describes the propensity-score matching process, and examines differences in deal characteristics. Section

5 documents and analyzes differences in announcement-period market reactions between SWF and

benchmark-sample investments. Section 6 offers an analysis and discussion of the long-term performance

of SWF and benchmark sample investments targets. Finally, we summarize our conclusions and discuss

avenues for future research in Section 7.

1. Hypotheses Regarding the Predicted Impact of SWF Investments on Target Firm Stocks

Existing theory and empirical evidence suggests that politicians are “bad owners” of corporations as they

typically impose political objectives that destroy shareholders’ value (Shleifer and Vishny, 1994;

Megginson and Netter, 2001). Being ultimately owned and controlled by governments, the same logic

could apply when SWFs become shareholders in target firms. However, the lion’s share of SWFs’

investment takes place abroad, and this may align the incentives of sovereign owners with other

shareholders’. Indeed, incumbent politicians from the investing countries are certainly unwilling to

provide foreign citizens the financial benefits of state ownership, and this may induce them to focus on

shareholder wealth maximization rather than political redistribution to specific constituencies. According

to this view, SWFs simply pursue commercial objectives (an oft-stated motivation in their official

statements) and operate as any other type of financial investor. Consequently, no differences should

emerge between the financial performance of targets acquired by SWFs and by private institutions.

However, there are also well-grounded theories that posit that the impact of SWFs on acquisition

targets could differ from that of private-sector investors. Shleifer and Vishny (1986) hypothesize that

blockholders have the proper incentives to monitor portfolio firm managers and the capability to intervene

to punish or replace poorly performing executives. Furthermore, empirical research (Chen, Hardford, and

Li, 2007; Brav, Jiang, Partnoy, and Thomas, 2008; Klein and Zur, 2009; Ferreira and Matos, 2008;

Ferreira, Massa, and Matos, 2010; Becht, Franks, Mayer, and Rossi, 2009; Cronqvist and Fahlenbrach,

2009) shows that not all blockholders have the same impact and that at least one class of large

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institutional investors, hedge funds, is often successful at improving governance of portfolio firms. Due to

their size and lack of explicit liabilities, SWFs typify the patient, long term shareholder that should have

the power and incentives to monitor portfolio firm managers and discipline under-performers (Edmans,

2009; Cai, Garner, and Walkling, 2009). Additionally, studies of cross-border investments find that

foreign institutions play a bigger role in pushing for improvements in corporate governance than do

domestic institutions (Gillian and Starks, 2003; Ferreira and Matos, 2008), particularly after a state-owned

firm is privatized (Estrin, Hanousek, Kočenda, and Svejnar, 2009). In short, as large, long-term, foreign

investors, SWFs have the potential to become exactly the type of value-increasing blockholders described

in the literature. In addition, SWF shareholding could carry benefits not associated with private-sector

investments--such as access to capital provided by state-owned banks or preferences for government

contracts. We thus set forth the SWF Premium Hypothesis, predicting that SWFs will have a stronger and

more beneficial impact on the governance and valuation of investment targets than will other, private-

sector financial investors.

An alternative hypothesis posits that, on the contrary, SWFs might refrain from taking an active

corporate governance role in target companies in order not to generate political opposition or a regulatory

backlash. Extant evidence indicates that SWFs play little visible role in target firm corporate governance

and rarely take seats on target firm boards (Mehropouya, Huang, and Barnett, 2009; Rose, 2008).7 In

addition, the monitoring role of SWFs might be further reduced by their reluctance to divest, as the selling

of a large block of shares could also trigger political reactions and resentment amongst local management,

regulators and market participants. Accordingly, SWFs might be expected to act like ‘quiet leviathans’

and be unlikely even to exercise the type of governance through threat of exit discussed by Parrino, Sias,

and Starks (2003) and Admati and Pfleiderer (2009), or to withhold their votes as a sign of displeasure

with current managers (Del Guercio, Seery, and Woitdke, 2008; Edmans, 2009), for fear of upsetting

target firm governments and public opinion.

7 We should note that not all commentators agree. For example, Dewenter, Han and Malatesta (2010) interpret their results as indicating an active governance role for SWFs.

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Yet, passivity is not the only possible danger associated with SWF investments: there is also a

widespread fear that SWFs will not act as strictly commercially-minded investors, seeking only the

highest possible financial return, but will instead be forced to invest strategically by home-country

governments seeking political influence or access to foreign technology. A large body of empirical

research on state owned enterprises and privatization, summarized in Megginson and Netter (2001) and

Estrin, Hanousek, Kočenda, and Svejnar (2009), suggests that governments are usually bad operating

managers and that firm performance improves with privatization. Another stream of literature has looked

at ‘mixed ownership’ firms (Boardman and Vining, 1989; Shirley and Walsh, 2001), generally finding

that mixed ownership also has a negative impact on firm value as it imposes goals (such as employment

maximization) inconsistent with shareholder wealth maximization. However, there has been little

investigation of whether states can be value-creating investors. This concern is exacerbated by the fact

that, with the important exception of Norway’s Government Pension Fund Global, the largest SWFs are

all controlled by undemocratic governments.8 Accordingly, a negative impact of SWF investments on

target firm performance could result from the imposition of a “neo-mercantilist” political agenda by

SWFs investing in target firms, diverting resources from shareholder value maximization to the benefit of

SWF-sponsor countries. From these arguments, we set forth the SWF Discount Hypothesis, predicting

that SWFs will have a less positive impact on investment targets than will comparable private-sector

investors. This could occur either because SWFs are completely passive shareholders who exercise no

oversight or monitoring of target firm managers, or funds might instead take an active role in pursuing

strategic objectives and a politically motivated agenda.

Given this set of conflicting theoretical predictions, the net impact of SWF investments on firm

value can only be determined through empirical examination, which this paper aims to perform. We

emphasize that our main objective is not to document the raw impact itself, but to compare and contrast it

to that resulting from similar, but private-sector investments. We employ standard event study techniques,

8 According to recent accounts, almost half of all SWF investments since 2006 originate from countries classified by The Economist as authoritarian regimes or flawed democracies (Barbary and Bortolotti, 2012).

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supplemented by regression analysis, to achieve this and focus on measuring target firms’ abnormal

stock-price reaction at the announcement of SWFs’ and private shareholders’ investments. Hence,

positive abnormal stock-price returns surrounding announcement of SWF investments greater than those

surrounding announcements of benchmark private-sector investments are consistent with the SWF

Premium Hypothesis, while smaller abnormal returns are consistent with the SWF Discount Hypothesis.

2. Data and Descriptive Analysis

2.1. Sovereign Wealth Fund List

There is no consensus on exactly what constitutes a ‘sovereign wealth fund’. While SWFs are a

heterogeneous group, most evolved from funds set up by governments whose revenue streams were

dependent on the value of one underlying commodity and thus wished to diversify investments with the

goal of stabilizing revenues. Accordingly, most SWFs have been established in countries that are rich in

natural resources, with oil-related SWFs being the most common and most important. These include the

funds sponsored by Arab Gulf countries, the ex-Soviet republics, Brunei, and Norway. The other

important group of SWFs includes those that have been financed out of accumulated foreign currency

reserves resulting from persistent and large net exports, especially the funds based in Singapore, Korea,

China, and other East-Asian exporters. Because definitions vary and because few funds have disclosed

key organizational details, heterogeneous funds are often grouped into the SWF category, even though

there are significant differences between funds with respect to organizational structure (separately-

incorporated holding companies versus pure state ministries), investment objectives (preservation of

capital versus wealth diversification and growth), compensation policies and status of fund managers

(incentivized professionals versus fixed-wage bureaucrats), and degree of financial transparency

(Norway’s Government Pension Fund-Global versus almost all other large funds).

Most definitions of “sovereign wealth fund” suggest these are state-owned investment funds (not

operating companies) that make long-term domestic and international investments in search of

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commercial returns.9 Some definitions are broader than this, as in Truman (2008), who defines a

sovereign wealth fund as “a separate pool of government-owned or government-controlled financial

assets that includes some international assets.” On the other hand, Balding (2008) shows that a more

expansive definition encompassing government-run pension funds, development banks, and other

investment vehicles would yield a truly impressive total value of “sovereign wealth.”10

This study employs the selection criteria presented in Miracky and Bortolotti (2009), which

defines a SWF as: (1) an investment fund rather than an operating company; (2) that is wholly owned by a

sovereign government, but organized separately from the central bank or finance ministry to protect it

from excessive political influence; (3) that makes international and domestic investments in a variety of

risky assets; (4) that is charged with seeking a commercial return; and (5) which is a wealth fund rather

than a pension fund--meaning that the fund is not financed with contributions from pensioners and does

not have a stream of liabilities committed to individual citizens. While this sounds clear-cut, ambiguities

remain. Several funds headquartered in the United Arab Emirates are defined as SWFs, even though these

are organized at the emirati rather than the federal level, because the emirates are the true decision-

making administrative units.11 We also include Norway’s Government Pension Fund-Global, since,

despite its name, it is financed through oil revenues rather than through contributions by pensioners and it

does not have any explicit pension liabilities. These criteria yield a sample of 36 sovereign wealth funds

from 24 countries; while we identify and list all the entities that meet our definition of ‘sovereign wealth

9 In addition, most definitions exclude funds directly managed by central banks or finance ministries, as these often have very different priorities, such as currency stabilization, funding of specific development projects, or the development of specific economic sectors. 10 In ongoing research employing the SDC and other databases, we identify over 12,100 investments, worth over USD 1.67 trillion, just in listed-firm stocks by state-owned investment companies, stabilization funds, commercial and development banks, pension funds, and state-owned enterprises. If we add state purchases of government and corporate bonds, plus SWF holdings and foreign exchange reserves of roughly USD 12 trillion, the total value of state-owned financial assets may already exceed USD 20 trillion. 11 The sub-national UAE funds included are the Abu Dhabi Investment Authority (the world’s second-largest SWF), the Investment Corporation of Dubai (and its subsidiary Istithmar World), Mubadala Development Company, DIFC Investments (Company) LLC, the International Petroleum Investment Corporation (IPIC), and Ras Al Khaimah Investment Authority.

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funds’, we find public equity investments with sufficient data for only 19 of those funds, originating from

15 countries.

Table 1 presents our list of SWFs, along with estimates of their size in the spring of 2013, their

inception dates, and the principal source of their funding. This table is based on a more comprehensive

description of SWF organization, investment strategy, and mission presented in Barbary (2010). Table 1

shows total assets for all SWFs of USD 3.366 trillion, with oil and gas-financed SWFs managing total

assets of USD 2.372 trillion and non-oil SWFs managing assets worth USD993 billion.12

**** Insert Table 1 about here****

2.2. SWF Sample Construction

Our sample of SWF investments originates from the Sovereign Investment Laboratory (SIL)

SWF Database. This database was assembled and is constantly updated by a team of researchers,

including the authors of this paper, and covers domestic and international investments made by SWFs, by

SWF-majority-owned subsidiaries, or by other SWF investment vehicles between January 1980 and

November 2012.13 The data includes investments in listed equity, unlisted equity, commercial real estate,

private equity funds, and joint ventures in which one of the SWFs listed in Table 1 (or one of its majority-

owned subsidiaries) is an investor. These data are assembled using multiple public sources. Information

from five financial databases (Thomson One Banker, Bloomberg, the Thomson Reuters SDC Mergers and

Acquisitions database, the Zephyr M&A database, and Zawya.com) is integrated with data from fund

12 Many smaller SWFs are both notoriously opaque in their investments and, at least partly due to their small size, less active. Our dataset includes investments by 15 of the largest 17 SWFs (as ranked by AUM). 13 SWFs operate frequently through subsidiaries and investment vehicles: we identify over 150 majority-owned subsidiaries. Therefore, in the remainder of the paper, any reference to ‘SWF investments’ includes investments by majority-owned subsidiaries, and any transactions by ‘SWF acquirers’ includes transactions in which the acquirer is either a SWF or a SWF-majority-owned subsidiary. Given that the main purpose of the study is to determine the impact of SWFs on investment targets, we believe it is appropriate to include, in our sample, all investments executed under the control of SWFs. In making such determination, we elect to use a controlling stake (equity ownership exceeding 50%) in the subsidiary as a proxy for SWF control and decision-making. Yet, we note that investment vehicles that are majority, but not fully, SWF-owned are rare in our sample, and the overwhelming majority of subsidiary investments in the sample originate from fully-owned subsidiaries. In unreported analysis, we exclude investments by all not fully owned subsidiaries and the results are largely unaffected.

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websites and from various news sources.14 The dataset contains, amongst other variables, information

regarding the acquirer (name and country of the SWF and of the eventual acquiring subsidiary), the

investment’s target (name, country, industrial classification), and deal characteristics (announcement and

completion dates, monetary value, size of the stake acquired and owned, details on the type of securities

involved, and an identifier for capital injections). From this, a subset of investments containing

investments by SWFs (or their majority-owned subsidiaries) in publicly traded firms is selected. The

analysis is limited to publicly traded firms as we require firm-level data (both ‘accounting’ disclosures

and stock price information). As reported in Table 1, the final sample contains 1,018 investments by

SWFs (or majority-owned subsidiaries) in publicly-traded targets, for a total value of USD 352 billion.15

2.3. The Benchmark Sample

We construct a ‘benchmark sample’ to draw a comparison between SWF investments and similar

investments by other, non-government owned financial firms. We obtain this sample by downloading,

from the Thomson Reuters SDC Platinum Mergers & Acquisitions Database (SDC), a dataset including

all investments with announcement dates between December 1, 1980 and November 1, 2012 with a

publicly-traded target, and with the acquirer having a SIC code between 6000 and 6999, as an identifier

for financial firms. As a first filter, we only keep transactions in which the acquirer originates from one of

14 The sources include the Lexis-Nexis database and the archives of Financial Times, New York Times, Wall Street Journal, GulfNews, the Associated Press, Reuters, and others. We must employ an additional methodology to collect a second sample of investments, those made by Norway’s GPFG. Since this fund almost always accumulates small stakes in listed companies through open market share purchases, its investments are rarely documented in the press and are almost never recorded as direct share acquisitions by commercially-available databases. GPFG does, however, post annual listing of all its equity holdings around the world, and investments in U.S.-listed stocks made by Norges Bank Investment Management (NBIM), the asset management arm of the GPFG, are publicly disclosed on a quarterly basis beginning in the fourth quarter of 2006. Using this, we generate a list of new NBIM investments in U.S.-listed companies by tracking the annual investment lists and determining when NBIM makes an initial investment, which we define as an investment that did not appear in the previous year’s listing. We then follow NBIM’s holdings after the initial investment and record increases in their holdings as follow-on investments. We take the filing date—the day when NBIM files a Form 13F-HR with the U.S. SEC detailing its shareholdings in a listed firm—as the announcement date for performing event studies, since this is the date that the stock ownership information is first disclosed. Given our reliance on Form 13F-HR as a data source, we have this data only for U.S. listed firm investments by Norway’s GPFG. 15 Here and in the rest of the paper, monetary values are all converted to USD, using daily historical exchange rates from the Thomson Reuters Datastream database, and are adjusted to the base year 2000, using the Consumer Price Index reported by the US Bureau of Labor Statistics.

15

the 15 countries in which SWF acquirers in our sample are based and where the target firm is

headquartered in one of the 54 countries in which SWF investment targets are headquartered.

All transactions classified as leveraged buyouts, recapitalizations, self-tender offers, exchange

offers, repurchases, and privatizations are excluded, as are all instances of debt restructurings (identified

as transactions with an acquisition technique labeled as ‘debt restructuring’ or with an acquirer labeled as

‘creditor’).16 Transactions with status listed as ‘rumor’, ‘discontinued rumor’, ‘status unknown’, ‘seeking

buyer’, or ‘seeking buyer withdrawn’ are also excluded, as are all deals with SWF involvement, either

marked as ‘SWF Involvement Buyside’ or ‘SWF Involvement Sellside’, or manually identified as having

a SWF buyer or seller, using our own list of SWFs, SWF subsidiaries, and SWF investment vehicles. We

further exclude all deals in which the immediate or ultimate parent of either the target or the buyer is

identified as ‘government’. Finally, we exclude all transactions for which the target does not have a

Datastream code, and all transactions with individuals as acquirers. The resulting benchmark sample

contains 5,975 observations with a total deal value of USD 224 billion.

2.4. Variable Definitions and Descriptive Analysis

A table listing all variables, variable definitions, and data sources is included in Appendix A.

Deal-based variables (such as Deal Value and Deal Stake) originate from the SIL SWF Database, the

construction of which is described in Section 2.2. Target-specific variables (Total Assets, Return on

Assets, Quick Ratio, Closely Held Shares, Sales Growth, Debt to Assets, and Market to Book) are obtained

from the Thomson Reuters Worldscope (Worldscope) database, in US dollars; in the descriptive statistics

and in the matching procedures, we present and employ target metrics as of December 31 of the year prior

to the investment. Dollar-denominated metrics (as opposed to ratios) are scaled to ‘2000 USD’ by using

the Consumer Price Index (All Urban Consumers) from the Bureau of Labor Statistics. Daily stock-price

performance data and daily local equity index values are obtained from the Thomson Reuters Datastream

16 These filters are standard in empirical studies using the SDC database. For example, the same filters are used in Ferreira, Massa, and Matos (2010), but they further exclude all minority acquisitions. Similarly, the same filters are applied by Karolyi and Liao (2011), but they further exclude all domestic deals.

16

database; in particular, we employ the Total Return Index, in USD, to compute daily returns for both

individual equities and associated market indices.17 Finally, we collect country-specific data for both

acquirer and target nations from various sources. GDP Per Capita (defined as the target-country GDP in

2000 USD divided by national population), GDP Growth, and Market Capitalization to GDP (defined as

the total market capitalization of all publicly listed domestic firms divided by GDP) are from the World

Bank. Variables measuring legal origin (Acquirer Country Common and Target Country Common,

identifying ‘common law’ legal origin) are from La Porta, et al. (1998) and variables measuring the level

of democracy come from the Polity IV Project (Acquirer Country Democracy and Target Country

Democracy, defined as the difference between the ‘Democracy’ and ‘Autarchy’ scores).18

To capture differences in investment styles across SWFs, we classify funds as ‘passive’, ‘active

government’, or ‘monitor’, as follows. SWFs labeled ‘passive’ are those that do not aim to exercise

influence on the management of the investment target firms. This definition refers to the general

investment strategy of the fund, which could be motivated by either investment philosophy, limitations

dictated by skill or workforce size, or restrictions self-imposed to avoid excessive resistance (by

shareholders, media, and regulators) when investing in foreign targets.19 The remaining funds, which

presumably have an ‘active’ investment style, are classified as either ‘active government’ or ‘monitor’

funds. ‘Active government’ funds are those in which the sponsoring government is closely involved with

day-to-day SWF operations, while in ‘monitor’ funds managers are largely free from governmental

17 The ‘total return’ indices for equity and related local market indices in Datastream, respectively labeled as ‘RI’ and ‘LI’, are obtained from stock prices adjusted for dividends and splits and scaled so that, on the day of listing, the value of the index is 100 for each security. 18 The same metric of the level of democracy derived from the Polity IV dataset has been widely used in the ‘law and finance’ literature, as in Ayyagari, Demirgüç-Kunt, and Maksimovic (2006) and Rodrik and Wacziarg (2005). 19 We first rely on an analysis by Truman (2008), who identifies funds which have self-imposed stake size limits and those that, by policy, avoid controlling stakes. Six of the funds included in our analysis are not scored by Truman; for those, we identify the mean and maximum stake acquired. If the maximum stake acquired in any transaction does not exceed 5%, we identify the SWF as ‘passive’; the six funds meeting this criterion are, accordingly, classified as ‘passive’. For robustness, analyses are replicated without these six funds, and the main results are unaffected. The unavoidable underlying assumption in our classification is that the investment style of each SWF does not change over time.

17

interference.20 This distinction allows for the testing of cross-sectional differences in the impact SWFs

have on investment targets, and provides insights into the causality of the impact. If SWFs exercise a

positive impact on governance through monitoring, this effect should be associated primarily with

‘monitor’ funds that are actively involved in the governance of their investment targets and which are free

to pursue value-maximizing objectives, rather than political priorities. On the other hand, ‘active

government’ funds might negatively impact firm governance, and thus firm value, because they impose

political goals on target firms that interfere with shareholder wealth-maximization. Finally, ‘passive’

SWFs are most likely to exacerbate conflicts between managers and minority shareholders by freeing the

former from effective oversight.

Banking crises are identified using the methodology described in Laeven and Valencia (2010).

Their dataset lists banking crises around the world from 1970 to 2012, and identifies the country/years

during which a banking crisis took place, based on two conditions: “(1) Significant signs of financial

distress in the banking system (as indicated by significant bank runs, losses in the banking system, and

bank liquidations); and (2) Significant banking policy intervention measures in response to significant

losses in the banking system” (Laeven and Valencia, 2010).21

Since we focus on comparing the impact of SWF investments on firm value to the impact of

private-sector financial investors, it is important to first understand if, and how, SWF investments differ

from private-sector investments. Simple descriptive statistics, while not conclusive, help identify possible

selection biases in SWF investments and associated endogeneity concerns. Table 2, Panel A reports mean,

median, and the number of observations for all continuous variables for both the SWF and benchmark

samples. This Panel also presents t-statistics for a test of differences between SWF and benchmark means,

20 Truman (2008) offers, for each fund, a score on a scale of zero to one, in quarter-point increments, reflecting the level of independence of management from governmental interference (with one indicating full managerial independence). Approximately half of the investments in our sample with non-passive SWF acquirers are associated with a managerial-independence score of one, so we classify all non-passive funds with a managerial-independence score of less than one as ‘active government’ funds and all other non-passive funds as ‘monitor’. 21 An updated version of the dataset is described in Laeven and Valencia (2012) and Luc Laeven makes the dataset available at http://www.luclaeven.com/Data.htm

18

computed with standard errors clustered at the investment target level. The mean (median) value of SWF

investments, USD 408.45 million (USD 18.65 million), is significantly greater than the USD 49.56

million average (USD 8.19 million median) value of benchmark investments, but the 8.45% (1.23%)

mean stake acquired by SWFs is significantly smaller than the 22.60% (12.09%) mean stake acquired by

private-sector financial investors.22 Consistently, SWFs tend to invest in larger firms: the mean total value

of assets of SWF investment targets is USD 82.83 billion (USD 3.46 billion), compared to USD 1.77

billion (USD 96.68 million) for the benchmark sample. SWF investment targets also tend to have higher

ROA, but lower liquidity and lower sales growth, and SWFs tend to invest in countries that are more

developed (higher democracy score and higher GDP per capita) but with lower GDP growth.


Panel B of Table 2 reports descriptive statistics for binary variables in both the SWF and

benchmark samples, with the related z-statistic from a binomial test of differences in proportions between

the two samples. Out of the 1,018 (5,975) investments in the SWF (benchmark) sample, 12.24% (83.36%)

are domestic, 4.4% (12.71%) involve acquisition of a controlling stake exceeding 50% of shares

outstanding, 62.57% (59.55%) are initial investments in a specific target, 45.58% (62.85%) involve an

acquirer from a common-law country, 13.27% (13.46%) are capital injections and 49.36% (3.87%) are

initiated during a banking or financial crisis.

Almost two-fifths (39.49%) of SWF investments originate from the Norwegian Government

Pension Fund Global, so to prevent analysis of our findings being dominated by results related to

Norway, we perform robustness checks that exclude or isolate GPFG investments. Overall, 57.07% of all

SWF investments are attributed to ‘passive’ funds, which includes Norway’s GPFG, while 25.93% and

11.2% of SWF investments are attributed to ‘monitor’ and ‘active government’ funds, respectively.

22 Investments by Norway’s GPFG are generally smaller than those of other SWFs, both in terms of size of the stake acquired and deal value, and tend to affect the related statistics. Excluding Norway from the SWF sample increases mean (median) stake acquired to 15.33% (7.8%) and the mean (median) deal value to USD 741.23 million (USD 116.41 million).

19

The industrial distribution of investments is described in Table 2, Panel C. SWF investments are

heavily focused on the financial industry (29.54% of all investments) and on industrials (18.17%). The

benchmark sample reveals similar patterns, with 25.52% of all investments in financials and 19.02% in

industrials. Panel D of Table 2 outlines the temporal distribution of investments from 1983 to 2012.

While both the SWF and benchmark samples are heavily biased towards more recent years, such bias is

much more pronounced in the SWF sample--with approximately half of these observations originating

during the five years spanning January 2008 to November 2012. Finally, Panel E of Table 2 tabulates the

geographic distribution of investments, and shows that SWF investments are concentrated in the United

States (44.32% of the total), though this largely reflects the disproportionate impact of Norway’s GFPG;

excluding these, investments in US-headquartered target firms account for 11.88% of the SWF total.

China, Singapore, India, and the United Kingdom are the next most common targets of SWF investments,

with the first two of these involving mostly domestic deals. The perhaps surprising distribution of

investments in the benchmark sample (the three leading investment destinations are Australia, China and

South Korea) results from constraining acquirer and target countries in this sample to be from the same

list of acquirer and target countries as in the SWF sample, as previously discussed.

3. Event Studies

We employ both short-term and long-term event study methodologies to gauge the impact of SWF

investments on firm value, but focus initially and principally on short-term analyses. Abnormal daily

returns are computed by subtracting the total return for the local market index from the total return on the

security of interest.23 Cumulative abnormal returns (CARs) are reported over various intervals on and

around the day on which the announcement of the investment occurred (day 0), with the largest interval

spanning the 21-day interval around the announcement. In most analyses, we emphasize the three-day

23 Total returns for securities and local market indices are obtained from Datastream and are adjusted for dividends and splits. While returns are computed in USD, for both individual securities and local-market indices, unreported robustness tests verify that results are unaffected by this conversion.

20

window (-1,+1) to capture the effect of possible previous-day leakage of information or next-day reaction

(common when announcements occur ‘after hours’). Results for the full sample of SWF investments are

presented in Table 3, Panel A, and the mean (median) three-day CAR is 0.89% (0.25%). The statistical

significance of mean abnormal returns is tested using the bootstrapped, skewness-adjusted t-test described

by Hall (1992) and Lyon, Barber and Tsai (1999) to correct for the skewness of abnormal return, and a

generalized sign test is employed for medians. Both mean and median CARs are positive and statistically

significant at the 1% level over the one-day and three-day event windows based on both tests; over the

21-day event window, the mean abnormal returns are significant at 1%, but the medians only at 10%.24


Norway’s GPFG is often cited as being different from other SWFs, both in terms of internal

organization and investment style.25 Accordingly, Dewenter, Han and Malatesta (2010) check whether

their results are driven by GPFG investments by presenting core estimates from subsamples excluding

GPFG. This is even more important here, as investments by GPFG account for almost half of the total

number of investments in our sample. Accordingly, Panel B replicates the event study analysis while

excluding investments by Norway’s GPFG. This increases CARs substantially: over the 3-day event

window, the mean CAR is 2.45% and the median CAR is 0.60% and the results are even stronger over the

longer event window (the 21-day mean and median CARs are 3.27% and 1.81%, respectively).

Much of the debate surrounding SWFs has focused on foreign (cross-border) investments, as

local managers, politicians, regulators, and commentators are often hostile to ownership by any entity that

24 For robustness, we also employ the standard ‘Patell z’ test for significance of mean CARs, the crude-dependence adjusted (CDA) t-statistic proposed by Brown and Warner (1985) for means, and a non-parametric Wilcoxon sign-rank test for the significance of medians. All tests indicate high levels of statistical significance for the one-day and three-day event windows, but offer mixed evidence over the longer window. 25 Norway’s GPFG is often hailed as being the most professionally managed and most transparent SWF. Various studies have focused on its structure and behavior (Caner and Grennes, 2009; Ang, Goetzmann, and Schaefer, 2009; Chambers, Dimson, and Ilmanen, 2012), finding that its management, while reporting periodically to the government, appears to be better insulated from political interference than any other SWF leadership team. In terms of investment style, GPFG makes exclusively foreign investments and has always committed to acquiring small stakes (although the exact definition of ‘small’ has varied over time, at once signifying no more than 1% of equity, now mostly restricted to below 5%). While being for long seen as the prototypical passive investor, there are recent signs of increased activism, perhaps as a response to losses sustained over the 2007-2009 financial crisis.

21

is owned by a foreign government. Accordingly, we investigate the impact of SWF investments on

foreign targets separately, and results are reported in Panel C of Table 2.26 These are generally very

similar to the overall sample, in terms of both magnitude and statistical significance of CARs.

It is also plausible that a particular investor’s stock purchase might elicit the strongest reaction,

especially if the identity of the investor is a source of controversy. Accordingly, the findings reported in

Panel D of Table 2 show a stronger response to initial investments than to follow-on stock purchases,

with three-day mean and median CARs of, respectively, 2.38% and 0.89% for initial purchases versus

0.89% and 0.25% for all SWF investments.

Overall, these results indicate that the market reaction to SWF investments is, on an average,

positive and significant in both statistical and economic terms. These results are consistent with, but

somewhat lower than those in Dewenter, Han, and Malatesta (2010), who report mean three-day CARs of

1.52% (versus our 0.89%). Our estimates are more similar to those reported by Kotter and Lel (2011),

who estimate three-day CARs of 2.25%, close in magnitude to our estimate of 2.45% excluding Norway,

which is the best comparison as their sample contains only one investment by the Norwegian SWF.

Panel E of Table 2 focuses on the benchmark sample. Three-day mean and median CARs are,

respectively, 5.02% and 1.29%; 21-day mean and median CARs are 9.47% and 4.69%, respectively.

While we do not test the statistical significance of differences between the market reaction to SWF

investments and the market reaction to benchmark investments here, we note that the difference is

magnitude appears substantial, with the reaction to SWF investments appearing far smaller.

For robustness, we replicate but do not report the same analysis by computing raw (unadjusted,

rather than abnormal) returns and by computing buy-and-hold (rather than cumulative) abnormal returns.

Given the short event windows involved, the main results are similar, with both samples displaying

positive and statistically significant abnormal returns. We further compute market-model (rather than

market-adjusted) abnormal returns, by first estimating security betas using daily returns over the year

26 A stream of the literature on government-owned investors has actually chosen, for the same reasons, to focus uniquely on foreign transactions. A recent example is Karolyi and Liao (2011).

22

ending 20 trading days prior to the announcement date. The use of a market model similarly leads to

virtually identical short-run results.

4. Target and Deal Characteristics

The descriptive results presented in Section 2 suggest that substantial differences exist between the

investments by SWFs and investments by other, private-sector financial investors which could, in turn,

affect the market reaction, creating potential problems in attributing causation. Accordingly, we first

identify potential biases in the investment target selection process by SWFs and then control for those

biases by propensity-score matching and propensity-score weighted regressions.

4.1. Selection Bias (Probit Model)

In order to model the selection bias in SWF investments, we estimate a probit model, in which the

response is a binary variable that assumes the value of one when the investor is a SWF and the value of

zero when the investor is a non-government-owned financial entity. The set of predictors is limited to

firm and country characteristics--which we consider exogenous in the SWF selection process--as opposed

to deal characteristics, which are determined in part by the fund itself. In selecting the exact metrics to

use, we attempt to replicate, as much as data-availability constraints permit, the set of predictors that

previous literature on foreign investors has found meaningful. In particular, we attempt to replicate the

variables used by Kotter and Lel (2011).27 Target-level characteristics include metrics for size (the natural

logarithm of Total Assets), profitability (ROA), leverage (Debt to Assets), valuation (Market to Book), and

the proportion of shares that are closely held (Closely Held Shares). All target-level characteristics are

included as of December 31 of the year prior to the investment, to ensure that results are not affected by

the investment itself. The one-year pre-investment buy and hold abnormal return, BHAR Previous Year,

computed as the difference between the total return for the target firm and the total return for the local

27 Kotter and Lel (2011) similarly investigate selection biases in SWF investments. The implementation is, however, fundamentally different. Kotter and Lel’s benchmark includes all firms in the Worldscope database for which they are able to collect data. We are interested in finding a comparable set of investments, rather than a comparable set of target firms, so our ‘universe’ comprises all firms that are subject to investments by financial acquirers.

23

market index, is added as a metric of recent stock-price performance. The set of country-level predictors

employed includes measures of individual wealth (the natural logarithm of GDP per Capita), economic

growth (GDP Growth), and stock-market development (Market Capitalization to GDP), as well as a

binary variable identifying targets headquartered in common-law countries (Target Common) and the

Polity IV variable measuring the level of political freedom (Target Democracy). The models include

industry and year controls, while standard errors are clustered at the investment-target level, and all

continuous variables are winsorized at the first and ninety-ninth percentile of the distribution to mitigate

the impact of outliers.28

Estimation results, presented in Table 4, indicate that the probability of an acquirer being a SWF

increase if the target is large (greater Total Assets) and if the target is foreign, based in a common-law

country, and with abnormally high stock returns over the previous year. Further, the probability of an

acquirer being a SWF increases during a banking or financial crisis. These results are consistent with

some of the findings of Kotter and Lel (2011), who also document a SWF preference for large targets

headquartered in developed countries and for crisis periods, but while Kotter and Lel (2011) find that

SWFs invest in financially constrained firms with weak abnormal stock price performance over the prior

year, we find that a strong abnormal stock price performance increases the probability of an acquirer

being a SWF, rather than a private-sector financial investor. Overall, the perhaps unsurprising key finding

here, echoed by both Kotter and Lel (2011) and Karolyi and Liao (2011), is that the strongest predictor of

investments by SWFs is the size of the firm, with large firms being much more likely SWF targets.


It is possible that SWF target selection might differ for domestic versus foreign deals, as political

pressures to invest and freedom to invest decisively are both likely to be stronger domestically. We

28 The coefficient estimates and statistical significance testing are executed by using SAS and, in particular, the ‘proc surverylogistic’ procedure that employs maximum-likelihood estimates and resampling methods to estimate standard errors in the presence of clustered data and categorical explanatory variables. This allows us to estimate consistent probit coefficients with year and industry controls and to test for significance in the presence of clustering. For more information, please refer to: http://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm#statug_surveylogistic_sect001.htm.

24

replicate the analysis above for a subset of investments including only foreign targets and for a subset

including only domestic targets. The results are presented in the second and third column of coefficient

estimates in Table 4. Results indicate that, compared to private-sector financial investors, SWFs are more

likely to invest during a banking or financial crisis both domestically and abroad. This counter-cyclical

investment pattern is consistent with the substantial capital injections SWFs made into the faltering US

and European banking industries at the onset of the 2007-2009 financial crisis. Compared to private-

sector financial firms, SWFs’ bias towards large targets is confirmed for the subset of foreign

transactions, but not for the domestic sample. Similarly, strong prior-year stock price performance

appears to attract SWFs investing in foreign targets, though the analysis does not reveal a similar

domestic preference. Domestically, a firm is more likely to receive an investment by a SWF, rather than

by a private-sector financial firm, if it has a high fraction of closely-held-shares.

We replicate the same analysis for two subsets including, respectively, only initial investments

and follow-on investments. Results for the subset of initial investments mirror closely the results for the

overall sample, revealing that determinants of SWF investments are positively related to firm size, target-

firm headquarters in a common-law country, and strong prior-year stock performance; as in the previous

samples, SWFs are more likely to invest during a crisis. Follow-on investments reveal a similar

preference for foreign-target investments during crises, but here prior-year performance is negatively

related to the probability of a SWF investor, though the coefficient estimate is not statistically significant.

4.2. Deal Characteristics

Since the previous analysis revealed that the SWF target selection process differs from that of

private-sector investors, we control for the sample selection bias in subsequent analyses. Accordingly, we

construct a propensity-score matched sample (labeled ‘PSM sample’) by finding, for each SWF

investment, a private-sector investment with a similar target--effectively, building a control sample with a

25

‘similar selection bias’.29 To accomplish this, from the ‘main’ probit model estimates (the first column of

coefficients presented in Table 4 and the omitted fixed-effect estimates) we compute the probability of the

investor being a SWF for each observation in the sample. We then proceed to find, for each SWF

investment, the private-sector investment with the closest (lowest absolute value) difference in probability

estimate, and do so with replacement.30

The mean probability scores of the SWF and PSM matched samples are not statistically different.

In other words, the PSM process has created a ‘similar’ set of targets. But it is important to understand the

limitations of the methodology: the matching process does not include deal characteristics, as those are

potentially co-determined during the investment process and are not exogenous (unlike firm and country

characteristics). We next investigate differences between deal-level variables in the two samples to see

how investments by SWFs differ from private-sector investments, after controlling for differences in

selection of investment targets. We compute means and medians of deal-level variables for the SWF and

PSM matched samples and test for differences by computing t-tests for differences between paired

samples, clustered at the target-firm level. To mitigate the impact of outliers, all continuous variables are

winsorized at the first and ninety-ninth percentile of the distribution. Results are reported in Table 5.

SWFs inject capital about as often as private investors (10% of SWF investments are capital injections vs.

11% for the matched sample), and the difference is not statistically significant, but SWFs acquire control

significantly less often (in 4% of deals vs. 18%) and SWFs buy significantly smaller stakes (mean stake

of 8% vs. 25%) and, consequently, hold smaller stakes (12% vs. 29%). Yet, average SWF and private

investments are of a very comparable size (USD 160 million vs. USD 174 million).

29 Propensity-score matching is widely used in both the economics and finance literature. Examples of applications in empirical research in corporate finance include Mikkelson and Partch (2003), Lowry, Officer and Schwert (2010), and Irani and Oesch (2013). 30 There is an obvious tradeoff between bias and volatility of model estimates, discussed by Caliendo and Kopening (2008). Sampling without replacement leads to lower-quality matches which might not sufficiently mitigate sample selection biases. Sampling with replacement could lead to the same control-sample observation being over-represented in the benchmark sample, thus increasing the volatility of estimates. Roberts and Whited (2012) provide guidance on the matter and suggest using sampling with replacement. For robustness, in unreported results, we repeat the matching procedure without replacement, thus ensuring unique matches, and verify that all the main results that follow are virtually unaffected.

26


5. Market Reactions of SWFs versus Private-sector Investments

5.1. Differences from Propensity-Score-Matches

With comparable SWF and PSM benchmark samples, we can control for selection bias while

testing for differences in three-day market-adjusted cumulative abnormal returns using, as previously,

matched-sample t-tests with standard errors clustered at the firm level. As before, to mitigate the impact

of outliers, abnormal returns are winsorized at the first and ninety-ninth percentile of the distribution.

Results are presented in Table 6, Panel A. For the entire sample, the mean reaction to SWF investments is

0.43% vs. 2.83% for the PSM sample, and the mean -1.57 percentage point difference is statistically

significant at 1%.31 Excluding Norway, the mean reaction to SWF investments is 1.61% vs. 4.58% for the

PSM sample, and the -2.26 percentage point difference is again significant at 1%. These results indicate

that, while the market reaction to SWF investments is positive, it is far weaker than the market reaction to

other, private-sector investors. The identity of the investor--the fact that the investor is a SWF--is

interpreted as relatively ‘bad news’ by the market. Of course, this analysis controls for target

characteristics (through matching) but not for differences in deal characteristics, as will be done in

subsequent tests.


Similarly to the data sub-setting performed earlier in the event study analysis, the next step is to

compare mean abnormal returns around SWF and private-sector investments in data sub-samples. For

each sub-sample presented, we first repeat the probit estimation and the propensity-score matching on a

reduced dataset, then compare mean abnormal returns. The first analysis is of a sub-sample including only

foreign targets: the mean reaction to SWF investments in foreign targets is 0.30% vs. 2.39% for the PSM

31 Please note that the mean difference is not equal to the difference between the means of the SWF and PSM samples. This is due to the fact that, in computing the means for the SWF and PSM samples, we use all observations with available data in each case; to compute the mean difference, we include only pairs for which abnormal returns are available for both the SWF and PSM observations.

27

sub-sample, and the mean -1.86 percentage point difference is significant at 1%.32 For the sample of SWF

investments in domestic targets, the mean 1.25% market reaction is positive and significant, but the mean

difference with the PSM sub-sample is -3.61 percentage points, statistically significant at 1%.

We compute the mean market reaction for the three groups of SWFs previously identified:

‘monitor’, ‘active government’, and ‘passive’. There is a positive and statistically significant market

reaction for investments by the ‘monitor’ group of SWFs (2.1%), but not for ‘passive’ (-0.01%, not

significant) and ‘active government’ (1.43%, not significant) SWFs. The difference between the SWF and

PSM samples is negative for all three SWF types, but statistically significant (-2.59 percentage points),

only for ‘passive’ SWFs. Interestingly, the discount appears to be mostly associated with investments by

‘passive’ SWFs. The same type of analysis is also presented for two more investment subsets: a sample

including only first investments and a sample including only follow-on investments. In each case, the

mean difference between the SWF and the PSM sample is negative and statistically significant.

The evidence presented thus far suggests a weaker stock price reaction to SWF investments than

to private-sector investments in similar targets, but it also indicates that the size of the stake acquired by

SWFs is, for similar targets, smaller--so the SWF discount might well be due to the smaller stakes the

funds acquire. While stake size is controlled for in the regression analyses presented in the next section,

we make a first attempt at investigating this possibility by further sub-setting the data by the size of the

stake acquired. Accordingly, Panel B of Table 6 presents results for four data sub-samples, including

investments for a stake, respectively, (1) smaller than or equal to one percent, (2) smaller than or equal to

five percent, (3) greater than five percent but smaller than or equal to fifty percent, and (4) exceeding fifty

percent. In each case, the probit model is re-estimated on the data subset so, for (1), we estimate the probit

model and replicate the matching procedure by including only investments by SWFs and private-sector

investors with stakes smaller than or equal to one percent. This is a form of stratified propensity-score 32 The mean abnormal return at the announcement of foreign SWF investments is not statistically significant, in contrast to the results from the event studies. The difference lies in the fact that event-study results are based on non-winsorized data, and are accordingly more likely to be affected by outliers and skewed abnormal returns. Overall, these results point to the fact that much of the positive market reaction to SWF investments is due to domestic investments.

28

matching that allows for more rigorous control for a variable that is considered especially relevant--in this

case, the size of the stake—and our procedure mirrors the stratified sampling by Heckman, Ichimura, and

Todd (1997).. In all cases, the mean difference between the SWF and benchmark sample is negative and

statistically significant, suggesting that our findings are not driven by the smaller stakes acquired by

SWFs.

5.2. Regressions with Propensity-Score Weights

While the previous section’s analyses controlled for the selection bias in SWF investments, we

now employ cross-sectional regressions to also control for differences in deal characteristics. Coefficients

are estimated by a weighted-least-squares procedure in which the weights are inversely proportional to the

probability of an investment belonging to the SWF or benchmark sample.33 For each transaction, the

‘propensity score’ is an estimate of the conditional probability of finding that the acquirer is a SWF.

Probability estimates are derived from the probit model presented in Section 4.1. A transaction with a

SWF acquirer receives a weight , while a transaction with a private-sector (benchmark)

acquirer receives a weight . The regression is ‘weighted’ in so for that the parameter

estimates minimize the weighted sum of squares. In the first specification, the response variable is the

three-day CAR. The predictor of interest is a binary variable equal to one for investments by SWFs, zero

otherwise. Deal based variables (Capital Injection, Stake and Control), target-level controls (Total Assets,

Return on Assets, Closely Held Shares, Debt to Assets, Market to Book and buy-and-hold abnormal

returns computed over the year prior to the announcement of the SWF investment), and country-level

metrics (Acquirer Democracy, Acquirer Common, and Crisis) are added as controls. Industry, country,

and year fixed-effects are included, and standard errors are clustered at the investment-target level. As in

previous analyses, to mitigate the impact of outliers, all continuous variables are winsorized at the first

and ninety-ninth percentile of the distribution.

33 Caliendo and Kopening (2008) discuss applications of propensity-score weighting techniques and the related literature.

29

In the first column of Table 7, we present results obtained by simple least-squares estimation. The

coefficient estimate for the SWF binary variable indicates that, all else equal, SWF investments are

associated with a market reaction that is 1.98 percentage points lower than for comparable private-sector

investors.34 We repeat the same analysis and present results in the second column of the same table by

estimating coefficients using weighted-least-squares (with propensity-score-based weights), as described

above. This analysis confirms our findings that the SWF identity leads to a weaker market reaction. If

anything, after mitigating selection biases through propensity-score weighting, the magnitude of the

estimated SWF effect increases to -2.67 percentage points.


As in previous analysis, we are concerned about the possible impact of investments by Norway’s

GPFG on our estimates--and have reason to suspect that the SWF effect should be weaker for Norway’s

fund, as it is often perceived as a responsible and politically-independent investor. The third column of

Table 7 repeats the same analysis as above, but distinguishes between the Norwegian GPFG and all other

SWFs by adding two binary variables. For GPFG deals, the coefficient estimate is negative (-0.61

percentage points) but tiny and not statistically significant; for all other SWFs, the estimated coefficient is

negative and statistically significant (-3.02 percentage points).

Dewenter, Han and Malatesta (2010) document that the market reaction to SWF investments is

stronger, the larger the size of the stake acquired. Each set of results thus far presented finds a positive

and statistically significant coefficient associated with the size of the stake acquired in the investment is

observed--but these analyses do not show whether the result is specific to SWF investments or is common

to all financial investors. Accordingly, the fourth column of Table 7 reports results with the addition of

two interactions: first, between the SWF binary variable and the variable measuring the size of the stake

acquired (SWF Stake) and, second, between the SWF binary variable and the binary variable identifying

34 For the sake of brevity, results related to control variables are only briefly discussed. We note that coefficient estimates indicate that the market reaction is stronger the larger the stake acquired. The market reaction is also weaker during a financial or banking crisis. Finally, targets with higher leverage and with stronger prior-year stock price performance experience weaker market reactions to the news of investments.

30

controlling stakes (SWF Control). These variables allow for testing whether the relationship between

market reaction and the size of the stake acquired is different for SWFs than for other, private-sector,

acquirers. Statistically insignificant coefficient estimates are obtained for each interaction variable,

indicating that the direct relationship between the size of the stake acquired and the market reaction is not

specific to SWFs and exists for both sovereign and private-sector investors.

Finally, the fifth and last columns of Table 7 present parameter estimates including binary

variables identifying ‘monitor’ and ‘active government’ SWFs. The addition of those two binary variables

allows for testing whether the market reaction to the presence of those two SWF types as investors differs

from the market reaction to the ‘base case’ of ‘passive’ SWFs. The coefficient estimate is negative and

statistically significant for ‘passive’ SWFs (-2.48 percentage points), implying that acquisitions by

passive funds are usually interpreted as “bad news” compared to investments by private-sector acquirers.

However, the lack of statistical significance for the coefficient estimates associated with ‘monitor’ and

‘active government’ SWFs suggests the market does not react differently to the three types of SWF

acquirers here identified.

In unreported results, additional interaction variables are added to test whether the market

reaction depends on the level of democracy or legal origin of the acquiring SWF, but coefficient estimates

are not statistically significant. Further, the interaction between the Crisis and SWF variables is analyzed

to test whether SWF investments elicit ‘less unfavorable’ responses during crises, but, once more,

coefficient estimates are not statistically significant. In (also unreported) robustness tests, we remove

country fixed effects and add the following country-level variables, which have been documented to

affect cross-country investments in prior studies: Target Democracy (the Polity IV measure for the target

country), Target Common (a binary variable identifying common-law legal origin), GDP Per Capita,

GDP Growth, and Market Capitalization to GDP. No robust relationships are observed between the

abnormal stock return and those target-country level variables, and the main results documented in Table

7 are unaffected.

31

Table 8 presents similar propensity-score-weighted regression estimates for data subsets

including, respectively, only first investments, only follow-on investments, only foreign investments and

only domestic investments. In each case, SWF purchases are associated with a significant discount: -1.89

percentage points for first investments, -2.75 percentage points for follow-on investments, -1.82

percentage points for foreign investments, and -4.48 percentage points for domestic investments.


Finally, we test whether the SWF discount findings could result from SWF investments in the

financial industry.35 Not only is the number of such investments quite large (almost a third of the sample,

by count), but a significant fraction of these consists of bailouts of financial institutions, both domestic

and foreign, during the 2007-2009 financial crisis. Accordingly, we subset the data into two more sub-

samples, one excluding investments in financial firms and the other including only investments in

financial firms. Table 8 reveals a SWF discount in both sets of observations: SWF investments elicit a

response that is -2.32 (-3.17) percentage points lower than comparable private-sector investments when

excluding (including only) financial targets.

6. Long-Term Stock-Price Performance of SWF Investment Targets

6.1. Long-Term Event Studies

It is legitimate to question whether the ‘sovereign discount’ documented in the short-term market

reaction fully anticipates abnormal stock price performance over the long-term following SWF

investments. Accordingly, while noting that long-term event studies are both controversial and, often, not

robust to the selection of alternative methodologies (Lyon, Barber and Tsai, 1999), Panel A of Table 9

presents median buy-and-hold abnormal returns (BHAR) over six month, one year, two year, and three

year holding periods following investment completion dates.36 BHARs are computed as the difference

35 We thank an anonymous referee for suggesting this line of inquiry. 36 Medians are reported rather than means due to the skewness typical of mean long-term abnormal returns, as discussed in Barber and Lyon (1997).

32

between the buy-and-hold return for the stock and the buy-and-hold return for the local market total return

index. Nonparametric tests for significance indicate that median abnormal returns are not statistically

significant over any time interval, except for the three-year median abnormal return of 5.33%, which is

statistically significant at 1%.

As was done for the short term analyses, and mirroring Dewenter, Han and Malatesta (2010), we

re-estimate median abnormal returns excluding investments by Norway’s SWF. The estimated median

abnormal returns are negative over all time intervals, but not statistically significant, nor economically

large: the three-year median BHAR is -3.49%, or approximately -1.15% per year. Overall, this analysis of

SWF investments paints a mixed picture, with weak evidence of positive abnormal performance mostly

driven by Norway’s GPFG and only over the longer time horizons. For the other SWFs, the negative

medians indicate that most investments underperform--though we must stress that these results are neither

statistically significant nor robust to alternative methodologies tested.37 In this respect, our findings are

consistent with Dewenter, Han and Malatesta (2010), who estimate cumulative, buy-and-hold, and

calendar-time abnormal returns for up to five years following SWF investments and find “little basis for

rejecting the hypothesis that the shares of firms that attract SWF investments earn abnormal returns, on

average, over long periods.” Similarly, Kotter and Lel (2011) find negative median buy-and-hold

abnormal returns, but the results are not statistically significant; the authors interpret their own analysis as

indicative of “no substantial effect on firm performance and governance in the long run.”

We replicate the analysis above using the benchmark sample. Over all time horizons, median

abnormal returns are persistently negative--albeit statistically significant only over the shortest time

interval, six-months. While we defer a formal comparison of median long-term abnormal returns of SWF

investment targets and private-sector targets to the regression analysis in the following section, a naïve

comparison of abnormal returns indicates that the long-term performance of targets of private-sector

37 In unreported results, calendar-time abnormal returns are computed but no evidence of abnormal performance is observed. On the other hand, use of market-model or matched-firm methodologies reveals negative abnormal stock-price performance associated with SWF investments.

33

investments appears to be worse than that of SWF investment targets. The three-year BHAR for the SWF

sample excluding Norway is -3.49%, while for the benchmark sample it is -18.15%. In unreported

analyses of target-firm operating performance, both targets of SWF and private-sector investments are

found to suffer a decline in ROA (-1.76 percentage points for SWF targets and -1.88 percentage points for

private-sector targets) over time horizons spanning up to three-years post investment, and the magnitude

of this decline is not statistically different across the two sets.


The overall results presented above, documenting negative long-term abnormal returns following

positive short-term abnormal returns after equity purchases, are consistent with Hertzel, Lemmon, Link,

and Rees (2002), who find that public firms that place equity privately experience significantly positive

announcement effects, as well as significantly negative excess returns over longer holding periods. Also,

the literature on hedge funds and other institutional investors finds positive reactions to the announcement

of share acquisitions by hedge funds (Brav, Jiang, Partnoy, and Thomas, 2008; Klein and Zur, 2009),

presumably due to anticipated monitoring or in anticipation of an acquisition, that dissipate longer term if

no takeover attempt is actually mounted (Greenwood and Schoar, 2009).

6.2 Long-Term Abnormal Returns, Regressions

The estimations presented in Table 10 utilize regression models similar to those presented in

Table 7, but here the response variables are buy-and-hold abnormal returns over one, two, and three year

windows. In the first specification, the variable of interest is the SWF binary variable equal to one when

the investor is a SWF; control variables are the same as those employed in the cross-sectional analysis of

short-term abnormal returns. The estimated coefficients on the SWF binary variable are not statistically

significant at any time horizon, indicating that the performance of investment targets does not depend on

the acquirer being a SWF, rather than a private-sector investor. Performance is, however, negatively

related to the size of the target and to prior-year stock price returns, but positively related to ROA.


34

The fourth, fifth, and sixth columns of Table 10 distinguish between the three types of SWFs

previously identified (‘passive’, ‘active government’, and ‘monitor’), by adding two binary variables

identifying investments by ‘monitor’ and ‘active government’ SWFs. If the short-term market reaction

does not fully incorporate the negative impact on firm value associated with the monitoring gap induced

by ‘passive’ SWFs, negative coefficient estimates should be associated with the SWF binary variable.

Conversely, any difference in long-term performance between SWFs that has not been fully incorporated

in the short-term market reaction should be captured by the coefficient estimates associated with the SWF

Monitor and SWF Active Government binary variables. The coefficients associated with SWFs

(identifying the base case, passive funds) are negative and statistically significant for the one, two, and

three year windows (respectively, -11.37%, -26.53%, and -29.37%), indicating that stock returns for

targets of passive SWFs are abnormally low over the long-term horizons tested here. The coefficients

associated with the incremental effect of ‘active government’ fund investments are not statistically

significant, indicating performance not different from that of ‘passive’ SWFs. Conversely, the coefficient

estimates for the ‘monitor’ group of SWFs indicate that mean BHARs are 32.8 percentage points and 80.6

percentage points higher than those for the ‘passive’ group, respectively, over the two-year and three-year

windows.

These results suggest that SWF investment targets do not on average perform differently from the

targets of other, private-sector investors. In reality, this level of aggregation masks one important

difference: while the targets of ‘passive’ and ‘active government’ SWFs underperform, the targets of

‘monitor’ SWF display performance in line with, or even superior to, those of the private-sector

benchmark sample. Based on the three-year results, investment targets of ‘passive’ and ‘active

government’ SWFs display abnormal returns that are approximately 10 percentage points lower, per year,

than those of comparable benchmark-sample investments. The targets of ‘monitor’ SWFs, on the other

side, have abnormal returns that are approximately 17 percentage points higher, per year, than those of

35

comparable benchmark-sample investments.38 These results reveal, first, a high degree of heterogeneity

amongst SWFs--not only do some SWFs have a positive monitoring role, but others negatively affect

investment targets presumably by creating a governance gap (the ‘passive’ funds) or by pursuing a

politically motivated agenda (the ‘active government’ funds). Second, by not fully capturing such

differences and reactively negatively to the ‘SWF’ moniker, markets fail to fully capture these nuanced

long-term effects in their short-term reactions.

7. Conclusions

Sovereign wealth funds are key actors in the global financial landscape of the twenty-first century. Yet,

given their high level of heterogeneity and general lack of transparency, extant research has provided an

incomplete view of their impact. These are large institutional investors owned directly by sovereign

governments, but which are--at least according to official statements--managed independently to invest in

a diverse set of financial asset classes in pursuit of commercial returns. As such, SWFs should operate as

any other type of financial investor, and thus no differences should emerge between the financial

performance of targets acquired by SWFs and by private institutional investors. This study questions that

view, and finds that SWFs have a relatively negative impact on target firm value.

We find that markets react positively to the news of any investment by a financial investor, but

the fact that the purchaser is a SWF leads to a weaker (though still positive) reaction indicative of a

‘sovereign wealth fund discount’. The crucial implication is that, while investments by financial acquirers

are typically value-increasing, the fact that the investor is a SWF leads to a lower firm value. These

results are robust to controls for target selection bias by SWFs and for a large set of potentially related

38 The difference in performance between targets of ‘monitor’ SWFs and targets of private-sector investors is estimated as the sum of the base case coefficient (on the SWF dummy) and the incremental effect for ‘monitor’ funds. Over the three-year window, that sum is approximately 51% (the sum of -29.37% and 80.55%), or approximately 17% per year. In separate analyses, we verify that the difference is statistically significant.

36

factors at the country, year, industry, firm, and deal level. The market also reacts differently depending on

the identity of the SWF involved. While the market reacts negatively to the news of a sovereign investor

in general, this result does not hold for investments by Norway’s GPFG, widely seen as a more

sophisticated and non-threatening investor than many of its peers. We find some evidence of investments

by other ‘passive’ SWFs being associated with the largest discounts, but these results are not confirmed in

all tests.

We also analyze long-term stock price performance of SWF investment targets over event

windows spanning up to three years post-investment. Overall, no significant performance differences are

observed between SWF and private-sector investment targets in aggregate analyses. Yet, distinguishing

between different types of SWFs reveals that buy-and-hold abnormal returns associated with ‘passive’

SWFs and active SWFs whose managers are not truly independent are about 10% per year lower than

those of the private benchmark sample, while those of ‘monitor’ SWFs are actually greater (by about 17%

per year). While we recognize the limitations of long-term event study analysis, these results suggest that

significant, and systematic, differences exist regarding SWFs’ impact on investment targets, which are

only partially captured by the short-term market reaction.

Taken together, this study’s findings carry strong policy implications. In particular, they highlight

the dangers of a one-size-fits-all regulatory approach towards sovereign investors. Just like private

institutional investors, sovereign entities come in all shapes and sizes. While some appear to have a

harmful impact on investment target performance, others are highly sophisticated and transparent

investors capable of exercising a monitoring role typically associated with institutional blockholders.

Additionally, this research highlights the dangers of forcing SWFs into a purely passive stance, as so

often advocated by Western media and regulators. By being forced into passive shareholding, SWFs not

only do not create value through monitoring, but may exacerbate conflicts between managers and

minority shareholders by freeing the former from effective oversight.

The research on SWFs has, to date, treated these funds as one, large, homogenous group and

consequently has struggled to find strong, generalizable results. More recent years have seen the

37

emergence of fund-specific (or sponsor country-specific) analyses, which often suffer from the drawbacks

of all case studies--specificity and detailed analysis that leads to difficulties in generalizing results. We

attempt to recognize the heterogeneity between funds, while identifying large groups which reflect

systemic differences in investment style. Future research should aim at creating more refined

classifications, while more clearly defining the boundaries between the different types of sovereign

investors, to provide better understanding of one of the most important trends in the post-crisis global

financial system.

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Table 1. List of Sovereign Wealth Funds The table includes a list of the 36 funds that meet the Sovereign Investment Laboratory (SIL) definition of a SWF, and offers information regarding country of origin; fund name; the year in which the fund was established; the principal source of funding for the fund; and the estimated fund size in USD as of May 17, 2013. The right four columns present the number of investments, the total value and average value of investments, and the average size of the stake acquired in the sample extracted from the SIL SWF Database.

SIL Database

Country Fund Name Inception Year Source of Funds

Total Assets USD

Billion Obs

Total Deal Value

USD M

Average Deal Value

USD M

Average Deal

Strake Norway Government Pension Fund – Global 1990 Commodity (Oil) 746.30 402 $6,649.84 $16.79 0.34% China China Investment Corporation 2007 Trade Surplus 482.20 46 $98,478.90 $2,525.10 12.00% UAE-Abu Dhabi Abu Dhabi Investment Authority 1976 Commodity (Oil) 450.00 26 $11,523.48 $606.50 8.89% Kuwait Kuwait Investment Authority 1953 Commodity (Oil) 296.00 27 $15,207.92 $800.42 6.25%

Singapore Government of Singapore Investment Corporation 1981 Trade Surplus 220.00

102 $30,717.39 $388.83 7.01% Singapore Temasek Holdings 1974 Trade Surplus 157.90 196 $59,030.75 $385.82 19.06%

Russia National Wealth Fund and Reserve Fund 2008 Commodity (Oil) 172.90

NA NA NA NA China National Social Security Fund 2000 Trade Surplus 141.40 NA NA NA NA Qatar Qatar Investment Authority 2005 Commodity (Oil) 135.00 66 $63,724.28 $1,481.96 10.96% Australia Australian Future Fund6 2006 Non-Commodity 83.10 4 $628.90 $157.22 1.13% UAE - Dubai Investment Corporation of Dubai 2006 Commodity (Oil) 70.00 1 $1,245.90 $1,245.90 0.03%

UAE-Dubai International Petroleum Investment Company 1984 Commodity (Oil) 60.00

28 $29,556.56 $1,343.48 24.09% Libya Libyan Investment Authority 2006 Commodity (Oil) 64.20 20 $1,368.55 $124.41 14.96%

UAE-Abu Dhabi Mubadala Development Company PJSC 2002 Commodity (Oil) 53.10

16 $5,658.77 $565.88 33.84% Kazakhstan Kazakhstan National Fund 2000 Commodity (Oil) 52.30 37 $8,594.41 $286.48 21.94% Republic of Korea Korea Investment Corporation 2005 Government-Linked Comps 43.00 4 $2,889.72 $963.24 8.47% Brunei Brunei Investment Agency 1983 Commodity (Oil) 39.00 3 $234.77 $117.38 25.20% Malaysia Khazanah Nasional Berhard 1993 Government-Linked Firms 40.20 NA NA NA NA Azerbaijan State Oil Fund of Azerbaijan 1999 Commodity (Oil) 34.30 NA NA NA NA Ireland National Pension Reserve Fund 2001 Non-Commodity 19.60 NA NA NA NA

43

SIL Database

Country Fund Name Inception Year Source of Funds

Total Assets USD

Billion Obs

Total Deal Value

USD Million

Average Deal Value

USD M

Average Deal

Strake New Zealand New Zealand Superannuation Fund 2003 Non-Commodity 17.90 NA NA NA NA UAE - Dubai Istithmar World 2003 Government-Linked Firms 11.50 21 $4,464.16 $297.61 25.60% Bahrain Mumtalakat Holding Company 2006 Government-Linked Firms 11.20 1 $199.23 $199.23 6.67% East Timor Timor-Leste Petroleum Fund 2005 Commodity (Oil & Gas) 13.00 NA NA NA NA UAE Emirates Investment Authority 2007 Commodity (Oil) 10.00 NA NA NA NA UAE-Abu Dhabi Abu Dhabi Investment Council 2007 Commodity (Oil) 10.00 NA NA NA NA Oman State General Reserve Fund 1980 Commodity (Oil & Gas) 8.20 8 $1,158.85 $193.14 12.80% Angola Fundo Soberano Angolano 2012 Commodity (Oil) 5.00 NA NA NA NA UAE-Ras Al Khaimah

Ras Al Khaimah Investment Authority 2005 Commodity (Oil) 2.00

NA NA NA NA Vietnam State Capital Investment Corporation 2006 Government-Linked Firms 0.60 NA NA NA NA Kiribati Revenue Equalization Reserve Fund 1956 Commodity (Phosphates) 0.50 NA NA NA NA Equatorial Guinea Fund for Future Generations 2002 Commodity (Oil) 0.08 NA NA NA NA São Tomé & Principe National Oil Account 2004 Commodity (Oil) < 0.01

NA NA NA NA Oman Oman Investment Fund 2006 Commodity (Oil & Gas) NA NA NA NA NA Gabon Fund for Future Generations 1998 Commodity (Oil) NA NA NA NA NA UAE - Dubai Dubai International Financial Center 2006 Government-Linked Firms NA 10 $10,752.48 $1,194.72 19.43% Total, oil-based funds (US$ billion) $2,372.50 Total, non-oil based funds (US$ bn) $993.30 Total, all funds (US$ billion) $3,365.80 1018 $352,084.86

Table 2. Characteristics of the Sample of SWF Investments and the Benchmark Sample of Investments in Publicly-Traded Firms The table includes descriptive statistics for the sample of SWF investments (SWF) and the related benchmark sample of private-sector investments (Benchmark). Panel A contains mean, median, number of observations for each of the continuous variables, and results from a t-test for differences in means, with standard errors clustered at the target-firm level. Panel B contains the proportion (out of the total number of observations) and count for the instances in which a binary variable assumes the value of one and the results from a binomial test for differences in proportions. Panels C, D, and E detail the proportion (out of the total number of observations) of investments with announcements during a specific year (Panel C), with targets operating in a specific industry (based on Industry Classification Benchmark by FTSE International, in Panel D), and with targets headquartered in a specific country (Panel E). Variables are as defined in Appendix A. Significance levels are denoted as follows: “*” indicates significance at the 0.10 level; “**” indicates significance at the 0.05 level; “***” indicates significance at the 0.01 level. Panel A: Continuous variables

SWF Benchmark Variable Mean Median N Mean Median N t Deal Value (USD M) $408.45 $18.65 862 $49.56 $8.19 4528 5.84 *** Deal Stake 8.45% 1.23% 863 22.60% 12.09% 4310 -16.84 *** Stake Owned 12.95% 0.77% 686 29.94% 16.40% 4416 -9.88 *** Acquirer Country Democracy 1.56 -2.00 988 3.36 6.00 4923 -3.48 *** Target Country Democracy 6.18 10.00 980 4.77 8.00 5577 2.77 *** Target Country GDP Per Capita (USD) $25,992.60 $31,247.00 1001 $17,871.06 $20,387.77 5975 7.88 *** Target Country GDP Growth 1.19% 0.88% 989 3.43% 2.87% 5576 -8.06 *** Target Country Market Cap to GDP 106.05% 82.55% 998 110.14% 96.23% 5975 -1.02 Total Assets (USD M) $82,826.71 $3,464.68 912 $1,773.87 $96.68 5024 3.63 *** ROA 4.48% 5.70% 876 -15.33% 1.86% 4732 3.10 *** Quick Ratio 1.70% 1.01% 686 3.28% 0.90% 4084 -4.10 *** Closely Held Shares 32.92% 25.80% 736 36.49% 5.80% 4704 -0.92 Sales Growth 20.86% 13.54% 884 44.54% 45.26% 3144 -14.88 *** Debt to Assets 26.26% 23.19% 903 28.65% 19.79% 4815 -1.15 Market to Book 1.19 1.92 835 3.15 1.35 4807 -1.00

45

Panel B: Binary variables

SWF Benchmark Variable Proportion N Proportion N z Domestic 12.24% 124 83.36% 4981 -39.14 *** Control 4.40% 38 12.71% 548 -8.57 *** First Investment 62.57% 637 59.55% 3558 1.30 Acquirer Country Common 45.58% 464 62.85% 3755 -5.47 *** Target Country Common 75.34% 767 62.41% 3729 4.46 *** Capital Injection 13.27% 135 13.46% 804 -0.13 Crisis 49.36% 502 3.87% 231 14.31 *** SWF Norway 39.49% 402 SWF Passive 57.07% 581 SWF Active Government 11.20% 114 SWF Active Monitor 25.93% 264 Panel C. Industrial distribution of investments

SWF SWF Benchmark Benchmark Industry Proportion N Proportion N Oil and Gas 7.11% 72 3.51% 210 Basic Materials 6.80% 69 13.88% 829 Industrials 18.17% 185 19.02% 1136 Consumer Goods 8.32% 85 12.01% 718 Health Care 5.28% 54 4.10% 245 Consumer Services 11.17% 114 11.51% 688 Telecommunications 3.65% 37 1.11% 66 Utilities 3.65% 37 1.58% 94 Financials 29.54% 301 25.52% 1525 Technology 6.29% 64 7.78% 465 Missing 3.24% 33 0.37% 22

Panel D. Annual distribution of investments

SWF SWF Benchmark Benchmark Year Proportion N Proportion N 1983 0.00% 0 0.02% 1 1985 0.10% 1 0.02% 1 1986 0.00% 0 0.13% 8 1987 0.20% 2 0.22% 13 1988 0.40% 4 0.22% 13 1989 0.10% 1 0.55% 33 1990 0.20% 2 0.50% 30 1991 0.60% 6 0.89% 53 1992 0.40% 4 0.85% 51 1993 0.30% 3 1.62% 97 1994 0.99% 10 1.89% 113 1995 0.20% 2 2.08% 124 1996 0.40% 4 2.28% 136 1997 0.50% 5 2.31% 138 1998 0.40% 4 1.59% 95 1999 0.40% 4 2.88% 172 2000 1.39% 14 2.68% 160 2001 1.39% 14 2.76% 165 2002 1.89% 19 3.75% 224 2003 2.28% 23 7.33% 438 2004 3.77% 38 5.94% 355 2005 3.87% 39 5.02% 300 2006 5.96% 60 6.28% 375 2007 12.61% 127 7.73% 462 2008 31.18% 314 8.27% 494 2009 17.58% 177 8.65% 517 2010 6.55% 66 8.92% 533 2011 5.16% 52 7.98% 477 2012 1.19% 12 6.64% 397

47

Panel E. Geographic distribution of investments

SWF Benchmark SWF Benchmark Country Proportion N Proportion N Country Proportion N Proportion N Argentina 0.10% 1 0.00% 0 Malta 0.10% 1 0.00% 0 Australia 1.88% 19 33.91% 2026 Monaco 0.10% 1 0.00% 0 Austria 0.49% 5 0.02% 1 Morocco 0.10% 1 0.00% 0 Bahamas 0.20% 2 0.00% 0 Netherlands 0.69% 7 0.10% 6 Bahrain 0.39% 4 0.30% 18 New Zealand 0.49% 5 0.74% 44 Belgium 0.10% 1 0.03% 2 Niger 0.10% 1 0.00% 0 Bermuda 0.59% 6 0.02% 1 Norway 0.10% 1 5.51% 329 Brazil 0.39% 4 0.03% 2 Oman 0.20% 2 0.45% 27 Bulgaria 0.10% 1 0.00% 0 Pakistan 0.59% 6 0.17% 10 Canada 2.57% 26 0.77% 46 Papua New Guinea 0.10% 1 0.02% 1 Cayman Islands 0.20% 2 0.00% 0 Philippines 0.10% 1 0.49% 29 China 7.70% 78 13.32% 796 Portugal 0.49% 5 0.03% 2 Hong Kong 2.37% 24 2.29% 137 Qatar 1.68% 17 0.05% 3 Colombia 0.10% 1 0.00% 0 Russia 0.10% 1 0.02% 1 Egypt 0.20% 2 0.10% 6 Saudi Arabia 0.10% 1 0.07% 4 France 1.78% 18 0.08% 5 Singapore 5.53% 56 9.24% 552 Germany 1.09% 11 0.15% 9 South Africa 0.30% 3 0.07% 4 India 4.54% 46 0.89% 53 Spain 0.69% 7 0.07% 4 Indonesia 1.88% 19 1.39% 83 Sweden 0.49% 5 0.44% 26 Ireland 0.39% 4 0.07% 4 Switzerland 0.69% 7 0.05% 3 Italy 1.68% 17 0.02% 1 Taiwan 0.20% 2 0.05% 3 Japan 0.59% 6 0.79% 47 Thailand 1.09% 11 0.87% 52 Jordan 0.30% 3 0.42% 25 Turkey 0.39% 4 0.05% 3 Kazakhstan 0.10% 1 0.00% 0 UAE 1.48% 15 0.18% 11 South Korea 0.99% 10 13.21% 789 UK 4.44% 45 1.27% 76 Kuwait 0.30% 3 0.65% 39 USA 44.32% 451 1.37% 82 Malaysia 4.05% 41 10.18% 608 Vietnam 0.39% 4 0.08% 5

Missing 0.39% 4 0.00% 0

Table 3. Short-Term Market Reaction to Announcements of SWF and Benchmark Investments This table includes cumulative abnormal stock returns, in USD, for target firms’ common equity on the days surrounding the announcement of an investment. Daily abnormal returns are computed as the difference between the total return for the stock and the local market total return index. Interval indicates the time interval of interest relative to the date of the announcement of the investment (day 0). N reports the number of observations. Mean Cumulative Abnormal Return and Median Cumulative Abnormal Return report, respectively, average and median abnormal cumulative returns. Bootstrapped, Skewness Adjusted t presents the t-statistic associated with the bootstrapped, skewness adjusted test for means employed by Hall (1992). Generalized Sign z reports the p-value of a generalized nonparametric sign test for medians. Significance levels are denoted as follows: “*” indicates significance at the 0.10 level; “**” indicates significance at the 0.05 level; “***” indicates significance at the 0.01 level. Panel A includes all announcements of SWF investments in publicly traded companies; Panel B reports the same values for all SWF investments announcements, excluding those made by Norway’s SWF; Panel C presents only investment announcements associated with SWF investments in foreign targets; Panel D includes only ‘first investments’ by SWFs – that is, investments in firms in which the SWF did not previously own any equity stake; Panel E reports results for the benchmark sample of investments by private-sector financial firms. Panel A. All Sovereign Wealth Fund investments

Interval N Mean Cumulative Abnormal Return

Bootstrapped, Skewness-Adjusted t

Median Cumulative Abnormal Return

Generalized Sign z

(0,0) 795 0.89% 3.48 *** 0.15% 3.51 *** (-1,+1) 796 0.89% 2.48 *** 0.25% 2.98 ***

(-10,+10) 799 1.18% 1.57 * 0.72% 3.86 *** Panel B. Sovereign Wealth Fund investments, excluding Norway




Generalized Sign z

(0,0) 399 1.90% 4.07 *** 0.24% 3.72 *** (-1,+1) 400 2.45% 4.37 *** 0.60% 3.97 ***

(-10,+10) 403 3.27% 2.69 *** 1.81% 4.72 *** Panel C. Sovereign Wealth Fund investments, foreign targets




Generalized Sign z

(0,0) 706 0.90% 3.04 *** 0.11% 2.85 *** (-1,+1) 707 0.75% 1.78 ** 0.24% 2.59 ***

(-10,+10) 710 0.78% 0.95 0.56% 3.16 *** Panel D. Sovereign Wealth Fund investments, initial investments




Generalized Sign z

(0,0) 464 1.28% 2.94 *** 0.19% 3.49 *** (-1,+1) 466 2.38% 3.81 *** 0.89% 3.95 ***

(-10,+10) 466 2.37% 2.60 *** 1.51% 3.86 *** Panel E. Benchmark investments




Generalized Sign z

(0,0) 4823 2.56% 17.54 *** 0.22% 13.86 *** (-1,+1) 4830 5.02% 26.16 *** 1.29% 20.90 ***

(-10,+10) 4852 9.47% 23.23 *** 4.69% 26.52 ***

Table 4. Probability of SWF as an Acquirer Determined from Probit Models The table includes coefficient estimates from probit models. The response is a binary variable assuming the value of one if the investor is a SWF or a SWF-majority-owned subsidiary, zero otherwise. All predictors are as defined in Appendix A. Industry and year controls are added, as indicated, but related coefficient estimates are omitted. Standard errors are clustered at the investment target level; Wald’s Chi-Square test statistics are reported in gray italics, below the related coefficient estimates. The significance levels are denoted as follows: “*” indicates significance at the 0.10 level; “**” indicates significance at the 0.05 level; “***” indicates significance at the 0.01 level. The first column reports results for all investments; columns two to five report results for, respectively, investments including only foreign targets, domestic targets, initial investments, and follow-on investments, as indicated in the column headings.

Variable All Foreign Domestic Initial Investments

Follow-On Investments

Intercept -5.9922 *** -6.5772 *** -9.9749 *** -5.6023 *** -7.6289 *** 58.89 44.06 53.89 39.19 24.93

Domestic -1.557 *** ~0.0001 -1.7509 *** -1.1971 *** 144.85 . 143.91 19.97

Crisis 1.2471 *** 1.1932 *** 0.5585 *** 1.0344 *** 0.5692 *** 64.97 36.08 46.90 35.47 49.19

Total Assets (log) 0.393 *** 0.3989 *** 0.0076 0.3553 *** 0.0159 127.13 98.05 0.16 96.33 0.96

ROA 0.0047 0.0048 -0.0035 0.0047 0.0015 0.57 0.54 0.10 0.50 0.04

Debt to Assets -0.0015 0.0019 -0.0194 -0.0029 0.0084 0.16 0.23 0.44 0.42 0.07

Market to Book 0.0257 0.0232 0.0008 0.0248 0.0008 * 1.74 0.93 2.29 0.67 2.78

Closely Held Shares 0.0001 ~0.0001 0.3867 *** 0.0003 -0.1108 0.05 0.01 9.06 1.17 0.38

0.2286 *** 0.1892 ** -0.0776 0.3384 *** -0.1704 BHAR Previous Year

9.66 4.12 0.20 15.69 1.14 -0.0156 -0.0078 0.0769 0.0113 0.0246 Target Country GDP

Per Capita (log) 0.05 0.01 2.46 0.02 0.28 0.0290 0.0586 -0.0033 * 0.0216 0.0013 Target Country GDP

Growth 1.06 2.41 2.88 0.41 0.19 -0.0011 -0.0002 2.5171 *** -0.0011 1.2151 *** Target Country

Market Capitalization to

GDP 0.73 0.01 18.47 0.53 10.05

0.7603 *** 0.468 ** -0.125 *** 0.5629 *** -0.0278 Target Country Common 19.37 4.54 24.68 8.35 1.03

-0.0186 0.0222 -0.2416 -0.0151 2.0128 *** Target Country Democracy 2.05 1.58 0.33 0.95 28.24

N 3807 981 2826 2266 1581 Industry FE Yes Yes Yes Yes Yes

Year FE Yes Yes Yes Yes Yes

Table 5. Deal Characteristics – SWF Acquirers Versus Propensity-Score-Matched Deals The table reports mean values for the variables listed in the first column for the sample of SWF investments (SWF), the propensity-score-matched observations (PSM), and the set of paired differences between the SWF and PSM samples. The table includes the number of observations (N) and the t-statistic from a test for differences in means, computed with standard errors clustered at the investment-target level. All variables are as defined in Appendix A. The significance levels are denoted as follows: “*” indicates significance at the 0.10 level; “**” indicates significance at the 0.05 level; “***” indicates significance at the 0.01 level.

Variable SWF Benchmark Difference Capital Injection 10.35% *** 11.01% *** -0.66%

t 10.01 9.61 -0.44 N 908 908 908

Control 4.43% *** 18.43% *** -13.61% *** t 6 10.47 -6.13 N 790 613 529

Stake Owned 12.30% *** 29.02% *** -13.21% *** t 9.27 18.77 -4.96 N 634 634 435

Stake 8.25% *** 25.02% *** -15.94% *** t 12.91 16.23 -8.15 N 790 613 529

Deal Value (USD M) 160.45 *** 174.20 *** -28.61 t 12.18 13.33 -1.2 N 773 570 468

51

Table 6. Short-Term Market Reactions and Propensity-Score-Matched Differences The table reports mean cumulative abnormal returns over the three-day window surrounding and investments announcement for: the sample of SWF investments (SWF), the propensity-score-matched observations (PSM), and the set of paired differences between the SWF and PSM samples. Daily abnormal returns are computed as the difference between the total return for the stock and the local market total return index. The table includes the number of observations (N) and the t-statistic from a test for differences in means, computed with standard errors clustered at the investment-target level. The significance levels are denoted as follows: “*” indicates significance at the 0.10 level; “**” indicates significance at the 0.05 level; “***” indicates significance at the 0.01 level. Results are presented for the entire sample and for sample subsets, as described in text. Panel A and Panel B include different data subsamples, as labeled. Panel A.

Sample SWF Benchmark Difference ALL 0.43% * 2.83% *** -1.57% ***

t 1.71 7.03 -3.32 N 744 857 711

No Norway 1.61% *** 4.58% *** -2.26% *** t 3.88 9.1 -3.1 N 357 475 335

Foreign 0.3% 2.39% *** -1.86% *** t 1.11 5.76 -3.61 N 669 739 634

Domestic 1.25% ** 3.77% ** -3.61% ** t 2.22 2.57 -2.2 N 71 91 67

SWF Monitor 2.1% *** 4.75% *** -0.95% t 3.21 6.15 -0.93 N 166 250 158

SWF Passive -0.01% 2.82% *** -2.59% *** t -0.04 6.33 -5.18 N 541 569 519

SWF Government 1.43% 4.17% *** -2.05% t 1.56 3.32 -1.38 N 101 133 97

First Investments 0.97% *** 3.58% *** -3.17% *** t 2.93 7.6 -5.13 N 426 517 406

Follow-On Investments -0.29% 3.59% *** -3.59% *** t -0.77 5.68 4.82 N 318 313 294

Panel B.

Sample SWF Benchmark Difference Stake ≤ 1% -0.49% * 7.42% *** -7.98% ***

t -1.85 8.78 -25.24 N 395 306 306

Stake ≤ 5% -0.0013 4.00% *** -3.27% *** t -0.52 11.89 -7.41 N 557 646 557

5% < Stake ≤ 50% 1.57% *** 4.81% *** -2.51% *** t 2.95 15.68 -3.68 N 172 227 172

Stake >50% 8.02% 17.82% *** -17.37% ** t 1.6 4.12 -2.18 N 15 32 14

52

Table 7. Regression Analyses of Short-Term Market Reactions The table includes coefficient estimates obtained by least-square (in the first column; labeled ‘PS Weighted: No’) and by weighted-least-square optimization with propensity-score derived weights (in the other columns; labeled ‘PS Weighted: Yes’). The response variable is the market-adjusted cumulative abnormal return over the three-day window surrounding an investments announcement. All predictors are as described in Appendix A. Industry, country, and year-level fixed effects are included, as indicated, but related coefficient estimates are omitted. Standard errors are clustered at the investment target level; t-statistics are reported in gray italics, below the coefficient estimates. Significance levels are denoted as follows: “*” indicates significance at the 0.10 level; “**” at the 0.05 level; “***” at the 0.01 level. Variable CAR (-1,+1) CAR (-1,+1) CAR (-1,+1) CAR (-1,+1) CAR (-1,+1)

0.0708 *** 0.0954 *** 0.0803 * 0.0845 ** 0.0904 *** Intercept 2.65 2.86 1.85 2.52 2.60 -0.0198 ** -0.0267 *** -0.0191 * -0.0248 * SWF -2.11 -3.09 -1.73 -1.65

-0.0061 SWF Norway -0.21 -0.0302 *** SWF Not Norway -3.28 -0.0002 SWF Stake -0.17 -0.0654 SWF Control -1.05 -0.0042 SWF Monitor -0.22 -0.0142 SWF Active

Government -0.64 0.0003 0.0001 -0.0004 -0.0001 -0.0001 Acquirer Country

Democracy 0.35 0.10 -0.49 -0.10 -0.07 -0.0049 -0.0248 ** -0.0148 -0.0219 * -0.0217 * Acquirer Country

Common -0.40 -2.00 -0.92 -1.89 -1.74 0.0136 0.0026 0.0031 0.0082 0.0001 Capital Injection 1.39 0.20 0.24 0.75 0.01

0.0013 *** 0.0011 *** 0.0011 *** 0.0011 *** 0.0011 *** Stake 4.77 2.99 3.03 3.23 3.22 -0.0159 -0.0115 -0.0120 0.0041 -0.0136 Control -0.73 -0.38 -0.40 0.16 -0.46 -0.0034 -0.0155 -0.0129 -0.0154 -0.0157 Domestic -0.29 -1.40 -1.17 -1.37 -1.38

-0.0221 * -0.0405 *** -0.0406 *** -0.0387 *** -0.0404 *** Crisis -1.71 -3.03 -3.05 -2.94 -3.15 -0.0009 0.0002 0.0003 0.0004 0.0006 Total Assets (log) -0.65 0.11 0.22 0.29 0.40 0.0002 0.0002 0.0002 0.0002 * 0.0002 * ROA 1.62 1.57 1.54 1.68 1.73

-0.0003 ** -0.0006 *** -0.0005 *** -0.0005 *** -0.0005 *** Debt to Assets -2.01 -5.07 -4.97 -4.62 -4.33 -0.0007 -0.0007 -0.0007 -0.0007 -0.0006 Market to Book -1.22 -1.26 -1.25 -1.20 -1.07

~-0.0001 ~-0.0001 ~-0.0001 ~-0.0001 ~-0.0001 Closely Held Shares -1.22 -1.39 -1.37 -1.26 -1.25

-0.0064 * -0.0091 ** -0.009 ** -0.0072 * -0.0082 ** BHAR Previous Year -1.81 -2.46 -2.44 -1.90 -2.27

N 2540 2530 2530 2530 2530 Adj. R-Squared 8.99% 20.05% 20.08% 1.82% 20.66% PS Weighted No Yes Yes Yes Yes Industry FE Yes Yes Yes Yes Yes Country FE Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes

53

Table 8. Regression Analyses of Short-Term Market Reactions for Data Subsets The table presents coefficient estimates obtained by weighted-least-square optimization with propensity-score derived weights, for various subsets, as indicated. The response variable is the market-adjusted cumulative abnormal return over the three-day window surrounding an investments announcement. All predictors are as described in Appendix A. Industry, country, and year-level fixed effects are included, as indicated, but related coefficient estimates are omitted. Standard errors are clustered at the investment target level; t-statistics are reported in gray italics, below coefficient estimates. The significance levels are denoted as follows: “*” indicates significance at the 0.10 level; “**” indicates significance at the 0.05 level; “***” indicates significance at the 0.01 level.

First Investments

Follow-On Investments Foreign Domestic No

Financials Financials

Only Variable CAR (-1,+1) CAR (-1,+1) CAR (-1,+1) CAR (-1,+1) CAR (-1,+1) CAR (-1,+1)

0.0578 * 0.0939 * 0.0819 0.0162 0.1031 ** 0.0816 * Intercept 1.66 1.65 1.35 0.28 2.47 1.78 -0.0189 ** -0.0275 * -0.0182 * -0.0448 *** -0.0232 ** -0.0317 ** SWF -2.16 -1.81 -1.74 -4.12 -2.48 -2.46

0.0006 -0.0005 0.0003 -0.0096 * 0.0007 -0.0001 Acquirer Country Democracy 0.73 -0.24 0.35 -1.76 0.73 -0.06

-0.0267 ** 0.0123 -0.0372 *** -0.1614 *** -0.0386 *** 0.0186 Acquirer Country Common -2.46 0.48 -2.70 -6.44 -3.15 1.12

0.0015 0.0029 0.0122 0.0119 0.0184 -0.0183 Capital Injection 0.11 0.20 0.94 0.95 1.45 -1.37 0.0012 *** 0.0019 *** 0.0017 ** 0.0011 *** 0.0011 *** 0.0014 *** Stake 3.02 4.06 2.37 3.45 2.93 3.10

-0.0486 -0.0026 -0.0102 -0.0315 0.0054 -0.0491 Control -1.55 -0.07 -0.19 -1.21 0.17 -1.41 -0.0204 -0.0088 -0.0104 -0.0053 Domestic -1.39 -0.52 -0.84 -0.30

-0.0275 ** -0.0419 -0.0345 ** -0.0413 -0.0519 *** -0.0237 Crisis -1.97 -1.35 -2.39 -1.59 -3.33 -1.16 0.0013 0.0011 0.0001 0.0027 ~0.0001 0.0021 Total Assets (log) 0.68 0.46 0.05 1.43 0.02 1.01 0.0002 0.0002 0.0001 0.0002 ** 0.0002 0.0004 ROA 0.93 1.36 0.15 2.05 1.39 1.51

-0.0005 *** -0.0005 *** -0.0004 -0.0005 *** -0.0005 *** -0.0003 Debt to Assets -3.12 -2.93 -1.60 -3.85 -4.28 -1.07 0.0003 -0.0007 -0.0022 * -0.0002 -0.0007 -0.0006 Market to Book 0.35 -0.96 -1.80 -0.27 -1.09 -0.41

~-0.0001 ~0.0001 ~-0.0001 ~-0.0001 ~-0.0001 ~0.0001 Closely Held Shares -1.65 0.64 -0.67 -0.94 -1.14 0.03

-0.0056 -0.0059 0.0073 -0.0097 *** -0.0098 *** 0.0009 BHAR Previous Year -1.30 -1.12 0.94 -2.67 -3.03 0.10

N 1195 1135 794 1736 1921 609 Adj. R-Squared 18.32% 21.37% 32.67% 16.21% 19.30% 19.56% PS Weighted Yes Yes Yes Yes Yes Yes Industry FE Yes Yes Yes Yes Yes Yes Country FE Yes Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Yes

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Table 9. Long-Term Stock Price Performance of SWF Investment Targets The table reports market-adjusted buy-and-hold abnormal returns (BHAR) computed on the basis of the stock price, in USD, of target firms over various time intervals following completion of an investment (as indicated under the heading Interval). N reports the number of observations. Median BHAR reports median buy-and-hold abnormal returns. Generalized Sign z reports the test statistic of a generalized nonparametric sign test. Significance levels are denoted as follows: “*” indicates significance at the 0.10 level; “**” indicates significance at the 0.05 level; “***” indicates significance at the 0.01 level. Panel A includes all announcements of SWF investments in publicly traded companies; Panel B reports the same values for all investments announcements, excluding those made by Norway’s SWF; Panel C reports results for the benchmark sample of investments by private-sector financial firms. Panel A – SWFs

Interval N Median BHAR Generalized Sign z 6 months 758 -1.97% -0.91 1 year 735 -3.66% -0.74 2 years 678 0.47% 1.50 3 years 619 5.33% 3.40 ***

Panel B – SWFs, No Norway

Interval N Median BHAR Generalized Sign z 6 months 368 -2.86% -1.37 1 year 349 -6.96% -1.13 2 years 297 -7.61% -1.08 3 years 250 -3.49% 0.58

Panel C - Benchmark

Interval N Median BHAR Generalized Sign z 6 months 2985 -3.22% 3.28 *** 1 year 2754 -6.42% 2.27 ** 2 years 2362 -11.75% 0.37 3 years 2017 -18.15% -0.75

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Table 10. Regression Analyses of Long-Term Stock Price Performance of SWF Investment Targets The table presents coefficient estimates obtained by weighted-least-square optimization with propensity-score derived weights. The response variable is, in each case, the market-adjusted buy-and-hold abnormal return over the indicated time interval (one, two, or three years) following completion of an investment. Fixed effects are added, as indicated, but related coefficient estimates are omitted. All predictors are as described in Appendix A. Standard errors are clustered at the investment target level; t-statistics are reported in gray italics, below coefficient estimates. The significance levels are denoted as follows: “*” indicates significance at the 0.10 level; “**” indicates significance at the 0.05 level; “***” indicates significance at the 0.01 level.

Variable BHAR 1 yr

BHAR 2 yrs

BHAR 3 yr

BHAR 1 yr

BHAR 2 yrs

BHAR 3 yr

0.1954 0.4362 0.6735 0.2498 0.598 * 1.1177 ** Intercept 1.15 1.46 1.37 1.39 1.93 2.25

-0.0527 -0.0941 0.1304 -0.1137 * -0.2653 *** -0.2937 ** SWF -1.18 -1.08 1.18 -1.93 -2.67 -2.00

0.1215 0.3278 ** 0.8055 *** SWF Monitor 1.39 2.02 3.31 -0.0165 0.1411 0.0748 SWF Active

Government -0.11 0.65 0.28 -0.0019 -0.0013 0.0045 -0.0015 0.0018 0.0100 Acquirer Country

Democracy -0.41 -0.16 0.37 -0.33 0.24 0.80 0.0861 0.1315 0.2717 * 0.0529 0.0533 0.0696 Acquirer Country

Common 1.26 1.20 1.73 0.77 0.51 0.46 0.1024 0.0810 0.1104 0.1076 0.1012 0.1543 Capital Injection 1.06 0.51 0.44 1.11 0.63 0.61

-0.0022 -0.0022 -0.0047 -0.0022 -0.0023 -0.0049 Stake -1.19 -0.66 -1.06 -1.22 -0.70 -1.11

0.0505 0.1249 0.1880 0.0720 0.1759 0.3423 Control 0.40 0.47 0.56 0.56 0.66 1.00

-0.0284 0.1198 0.2320 -0.0517 0.0606 0.0277 Domestic -0.42 1.16 1.61 -0.76 0.61 0.18

0.1034 0.2626 ** 0.3470 0.1058 0.2596 ** 0.369 * Crisis 1.43 2.22 1.55 1.44 2.20 1.67

-0.0188 ** -0.0214 -0.0542 ** -0.0187 ** -0.0226 -0.0631 *** Total Assets (log) -1.96 -1.19 -2.29 -1.97 -1.27 -2.68

0.0021 ** 0.0048 *** 0.0087 *** 0.0021 ** 0.0049 *** 0.0091 *** ROA 2.29 2.95 4.23 2.31 2.99 4.41

-0.0005 -0.003 * -0.0023 -0.0006 -0.0033 * -0.0030 Debt to Assets -0.59 -1.77 -1.16 -0.69 -1.90 -1.47

-0.0028 0.0022 0.0065 -0.0027 0.0024 0.0069 Market to Book -0.69 0.34 0.62 -0.68 0.38 0.66

~0.0001 -0.0003 -0.0006 *** ~0.0001 -0.0002 -0.0006 *** Closely Held Shares 0.17 -1.42 -3.28 0.19 -1.37 -3.21

-0.0716 ** -0.0635 -0.1647 ** -0.0728 ** -0.0655 -0.168 ** BHAR Previous Year -2.22 -1.28 -2.01 -2.22 -1.30 -2.00 N 2158 1913 1662 2158 1913 1230 Adj. R-Squared 10.30% 20.23% 42.50% 10.34% 20.37% 43.11% Industry FE Yes Yes Yes Yes Yes Yes Country FE Yes Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Yes

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Appendix A. Description of the Explanatory Variables Used in the Empirical Analyses The table includes the source and brief definition of each variable used in the analysis. Definitions of Worldscope variables are included in the Worldscope Database Datatype Definitions Guide (www.thomson.com/financial)

Variable Source Definition Deal Value SIL SWF Database/SDC Total value of the equity investment, in 2000 USD (adjusted using CPI) Deal Stake SIL SWF Database/SDC Proportion of the investment target equity acquired in the deal by the SWF Stake Owned SIL SWF Database/SDC Size of the stake owned by the SWF in the investment target after the transaction Control SIL SWF Database/SDC Binary variable, equal to one if the 'Stake Owned' exceeds 50% Acquirer Country Democracy/Target

Country Democracy Polity IV Project 'Democracy' minus 'Autarchy' score for the relevant country

Acquirer Country Common/Target Country Common La Porta et al. (1998) Binary variable, equal to one if the relevant country is of common law origin

Crisis Laeven and Valencia (2010) and related website

Binary variable, equal to one if the country of the target headquarters is undergoing a banking crisis in the year of loan initiation

Target Country GDP Per Capita World Bank GDP Per Capita for the country in which the target's headquarters are located, in 2000 USD (adjusted using CPI)

Target Country GDP Growth World Bank Year-to-Year Change in GDP Per Capita for the country in which the target's headquarters are located, in 2000 USD (adjusted using CPI)

Target Country Market Cap to GDP World Bank

The sum of share price times the number of shares outstanding of all listed domestic companies (excluding investment companies, mutual funds, or other collective investment vehicles) divided by the total GDP, for the country in which the target’s headquarters are located

Total Assets (TA) Worldscope, WC02999 Total assets, adjusted to the base year 2000 by using the USA CPI

Return on Assets (ROA) Worldscope, WC08326 The exact definition varies by industry; please refer to the Worldscope Database Datatype Definitions Guide, available at www.thomson.com/financial

Quick Ratio Worldscope, WC08101 Cash and Equivalents plus net receivables, divided by total current liabilities

Closely-Held Shares (CHS) Worldscope, WC08021 The number of closely held shares divided by common shares outstanding. 'Closely Held Shares' represents shares held by insiders, other corporations, pension and benefit plans, and any individual holdings more than 5% of shares outstanding

Sales Growth Worldscope, WC08698 Net Sales' or 'Revenue' divided by the previous year's 'Net Sales' or 'Revenue' Debt to Assets (DtoA) Worldscope, WC08236 'Total Debt' divided by 'Total Assets' Market to Book (MtoB) Worldscope, WC09704 Market capitalization of the firm divided by common equity Domestic SIL SWF Database/SDC Binary variable, set equal to one if the acquirer country and target country are the same

SWF SIL SWF Database Binary variable, set equal to one if the acquirer is a SWF (or a majority-owned SWF subsidiary)

SWF Norway/SWF Not Norway SIL SWF Database Binary variable, set equal to one if the acquiring SWF is (is not) the Norwegian Government Pension Fund Global

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SWF Monitor SIL SWF Database; Truman (2008)

Binary variable set equal to one if the acquiring SWF is classified as an active shareholder with limited government interference in the fund's management; for more detail, see text

SWF Active Government SIL SWF Database; Truman (2008)

Binary variable set equal to one if the acquiring SWF is classified as an active shareholder with significant government interference in the fund's management; for more detail, see text

SWF Passive SIL SWF Database; Truman (2008)

Binary variable set equal to one if the acquiring SWF is classified as a passive shareholder; for more detail, see text

First Investment/Follow-On Investment SIL SWF Database Binary variable, set equal to one if 'Stake Acquired' is equal to (is different from) 'Stake

Owned' Return Datastream, RI Daily percentage change in the total return index (RI), in USD

Local-Index Return Datastream, LI Daily percentage change in the total return index for the local market index identified by Datastream (LI), in USD

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