Liquidity Preference and Cross-Sectional Turnover Persistence · Their liquidity preference theory...

Liquidity Preference and Cross-Sectional Turnover Persistence

Sheng-Syan Chen

National Taiwan University

[email protected]

Ji-Chai Lin

Louisiana State University

[email protected]

Xuan-Qi Su

National Kaohsiung First University of Science and Technology

[email protected]

Chin-Te Yu

National Taiwan University

[email protected]

ABSTRACT: While numerous studies have examined the determinants of trading

volume, we document new evidence that stocks with relatively high (low) turnover in a

given year tend to maintain relatively high (low) turnover for the subsequent 20 years.

This persistent preference for trading high turnover stocks suggests that investors have

strong liquidity preference. To further show that investor trading preference on

individual firms is largely formed very early on when their stocks became publicly

traded, we use initial turnover and find that it dominates all the previously identified

trading volume determinants in explaining future turnover. Our findings imply that

Amihud and Mendelson‟s (1986) clientele effect is very persistent, i.e., short-term

investors persistently trade liquidity stocks, while long-term investors persistently hold

illiquid stocks. Also, Booth and Chua‟s (1996) liquidity-purchasing theory of IPO

underpricng is relevant because initial turnover has long-lasting impacts on future

liquidity.

Comments welcome

This draft: April 15, 2012

mailto:[email protected]





ABSTRACT

While numerous studies have examined the determinants of trading volume, we

document new evidence that stocks with relatively high (low) turnover in a given year

tend to maintain relatively high (low) turnover for the subsequent 20 years. This

persistent preference for trading high turnover stocks suggests that investors have

strong liquidity preference. To further show that investor trading preference on

individual firms is largely formed very early on when their stocks became publicly

traded, we use initial turnover and find that it dominates all the previously identified

trading volume determinants in explaining future turnover. Our findings imply that

Amihud and Mendelson‟s (1986) clientele effect is very persistent, i.e., short-term

investors persistently trade liquidity stocks, while long-term investors persistently hold

illiquid stocks. Also, Booth and Chua‟s (1996) liquidity-purchasing theory of IPO

underpricng is relevant because initial turnover has long-lasting impacts on future

liquidity.

Keywords: Initial Turnover; Liquidity Preference; Cross-Sectional Turnover

Persistence.

JEL Classification: G12, G14, C23


1. Introduction

Keynes (1935) and Tobin (1958) assert that investors prefer to maintain their funds in liquid

assets, such as cash or checking accounts, and that, in order to attract investors to switch to hold

relatively illiquid securities, they must offer a sufficiently large premium. Their liquidity preference

theory helps us better understand the term structure of interest rates. Amihud and Mendelson (1986)

extend the liquidity preference theory to capital assets, and propose a clientele effect in stock

markets in which short-term investors prefer to trade liquid assets (to minimize transaction costs),

while investors with long expected holding periods prefer to hold less liquid assets (to earn liquidity

premiums).

These seminal papers motivate us to ask the following related questions: How strong is

investors‟ liquidity preference in U.S. stock markets? Does the clientele effect persist for a long

period of time? What are implications of strong liquidity preference and a persistent clientele effect

for corporate finance? And, what are their implications for asset pricing?

These questions are important because if investors have strong liquidity preference and the

clientele effect persists long-term, then, to lower liquidity premiums and create value in the long run,

firms need to create liquidity at the initial trading stage in order to meet investors‟ liquidity

preference and to attract liquidity trading for years to come. The notion that it takes liquidity to

create liquidity for the future would make Booth and Chua‟s (1996) liquidity-purchasing theory of

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IPO underpricng relevant in IPO pricing, simply because it is important for young firms to establish

high liquidity in the beginning.

Furthermore, Amihud, Mendelson, and Petersen (2005) survey studies on the effects of

liquidity and liquidity risk on asset pricing and show that liquidity premiums are pervasive in many

markets.1 Conversely, Fama and French (1992) show that the CAPM beta is not priced in the U.S.

markets. The findings raise an intriguing question: Why is it relatively easy to detect a liquidity

premium? We argue that strong liquidity preference and long-term cross-sectional liquidity

persistence are important elements for illiquidity to be priced. This is because if investors have

strong liquidity preference and if stock illiquidity persists long-term, illiquid stocks must offer

higher returns to attract long-term investors to hold them. For stock illiquidity to persist for a long

period of time, Amihud and Mendelson‟s (1986) clientele effect must be very persistent, i.e.,

short-term investors must persistently trade liquidity stocks, while long-term investors must

persistently hold illiquid stocks.

While the literature has suggested that liquidity premium reflects investors‟ liquidity

preference, in this paper we propose a more direct measure of investors‟ liquidity preference in a

stock market, namely long-term cross-sectional persistence in share turnover of common stocks.

Our reasoning is as follows. Since a large part of liquidity could be latent and elusive (Amihud

(2002), Mahanti et al. (2008) and Bao et al. (2011)), we assume and will show later on that annual

turnovers of individual stocks contain useful information about their future liquidity. If investors

1 Numerous studies have shown evidence of liquidity premium; see, for example, Amihud (2002), Pastor and

Stambaugh (2003), Acharya and Pedersen (2005), Eckbo and Norli (2005), Sadka (2006), Liu (2006), Bekaert, Harvey,

and Lundblad (2007), Korajczyk and Sadka (2008), and Watanabe and Watanabe (2008).

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have strong liquidity preference, there would be more demand for and hence more trading on liquid

stocks, compared to illiquid stocks. Stocks with high turnover, indicative of high current and future

liquidity, will continue to attract trading and have high turnover, while low-turnover stocks, being

less liquid and offering higher returns, will continue to have low turnover. Thus, strong long-term

cross-sectional turnover persistence is a natural manifestation of strong liquidity preference by

investors.

Strong long-term cross-sectional turnover persistence is also a good indication of a persistent

clientele effect. If short-term traders prefer stocks with low transaction costs and long-term traders

prefer stocks with high transaction costs, as Amihud and Mendelson (1986) suggest, then stocks

that already had a higher turnover will continue to have higher turnover. This turnover persistence

occurs because (1) stocks with lower transaction costs tend to have higher turnover, and (2)

short-term traders will trade more and generate more turnover than long-term traders. Thus, strong

long-term cross-sectional turnover persistence would imply a persistent clientele effect.

The purpose of this study is to examine the evolution of annual share turnover in the

cross-section of stocks listed on the NYSE/AMEX from 1965 to 2009 and on the NASDAQ from

1983 to 2009, and shed light on the degree of liquidity preference and the clientele effect in the U.S.

markets. While numerous studies have investigated the determinants of trading volume,2 we

document new evidence that self-perpetuating patterns exist in the cross section of share turnover.

2 See, for example, Lakonishok and Smidt (1986), Karpoff (1987), Merton (1987), Hiemstra and Jones (1994), Lo and

Wang (2000), Chordia, Subrahmanyam and Anshuman (2001), Chae (2005), Chordia, Huh and Subrahmanyam (2007),

Cremers and Mei (2007), Griffin, Nardari and Stulz (2007), French (2008), Kaniel, Saar and Titman (2008), Brown,

Crocker and Foerster (2009), Chordia, Roll and Subrahmanyam (2011).

4

In other words, even being in the same industry and similar in many characteristics--such as

visibility, different opinions across investors, analyst coverage, institutional holdings, and market

and idiosyncratic risks--stocks with relatively high (low) turnover in a given year tend to maintain

relatively high (low) turnover for the subsequent 20 years.

To further show that investor trading preference on individual stocks is largely formed very

early on when they become publicly traded, we use initial turnover to predict future turnover, and

find that a one standard deviation change in a stock‟s initial turnover leads to a change of 0.449

standard deviations in future turnover for the NYSE/AMEX sample; and, it leads to a change of

0.671 standard deviations in future turnover for the NASDAQ sample. The numbers suggest that

initial turnover is a very important determinant of future turnover. In fact, in terms of explanatory

power on future turnover, initial turnover dominates all the well-known turnover determinants

identified by previous studies.

Moreover, stocks with higher turnover in a given year tend to have lower future trading costs

(as proxied by Hasbrouck‟s (2009) Gibbs trading cost and Amihud‟s (2002) illiquidity measure) and

lower future liquidity risk (as measured by Pastor and Stambaugh‟s (2003) liquidity beta), which

also persist for over 20 years. Given the pervasive evidence of liquidity premium (Amihud,

Mendelson, and Petersen (2005)), our findings imply that a firm‟s annual turnover contains very

useful information about future liquidity and thus can play an important role in inferring the cost of

equity capital (see Liu (2006)).

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Our findings are robust to a time-honored subsample of stocks that have survived at least 20

years. Overall, our results confirm that cross-sectional long-term persistence behavior exists in

trading volume and that the persistence is unlikely to be due to survivorship bias. This persistent

preference for trading individual stocks suggests that investors in U.S. markets have strong liquidity

preference, and that Amihud and Mendelson‟s (1986) clientele effect is very persistent.

Our finding of the long-term cross-sectional persistence in turnover differs from the short-term

persistence documented by Lo and Wang (2000). They show that “there is some persistence in

turnover deciles from week to week--the largest- and smallest-turnover stocks in 1 week are often

the largest- and smallest-turnover stocks, respectively, the next week…” As Gervais, Kaniel, and

Mingelgrin (2001) explain, shocks in the trading activity that create unusually high (low) trading

volume on a stock over a short period, like a day or a week, affect its visibility, and in turn the

subsequent demand and price for that stock. Unlike the liquidity effect in which higher turnover

stocks normally have lower long-run returns, stocks with higher visibility tend to experience higher

short-run returns. Another difference is that the visibility effect dissipates over time and is thus a

short-lived phenomenon.

To further confirm our findings on long-term cross-sectional turnover persistence, we analyze

a sample of IPO stocks from 1975 to 2009, and find that, after controlling for IPO firms‟

characteristics known to affect turnover, stocks that begin their lives with a relatively high (low)

turnover tend to maintain as such for the subsequent 20 years in event time. This evidence implies

that since firms tend to stay at their IPO rankings of trading volume after IPOs, stocks with higher

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liquidity in the beginning are likely to have higher liquidity for many years to come. This further

illustrates the importance of establishing high liquidity at the start.

How could firms enhance their liquidity at the beginning? Booth and Chua (1996) posit that

IPO underpricing is a mechanism for promoting ownership dispersion, which would lead to an

increase in aftermarket liquidity.3 Consistent with their theory, Amihud, Hauser, and Kirsh (2003)

find evidence in IPOs listed on the Tel Aviv Stock Exchange, Pham, Kalev, and Steen (2003)

document evidence in Australian IPOs and Zheng and Li (2008) present evidence in U.S. IPOs that

more IPO underpricing leads to greater aftermarket liquidity and higher share turnover. Furthermore,

to explain why IPO stocks tend to yield relatively low returns, Eckbo and Norli (2005) show that,

compared to seasoned firms, IPO firms tend to have greater share turnover, which reduces liquidity

risk and lowers investors‟ required rate of return. Taken together, these studies show that IPO

underpricing provides certain benefits to the firms. Our study further raises the importance of Booth

and Chua‟s (1996) liquidity-purchasing theory of IPO underpricing, since higher initial turnover

yields long-term benefits to the firms.

The remainder of this paper is organized as follows. Section 2 describes our sample selection

and presents descriptive statistics of the variables used in our study. Section 3 investigates the

evolution of the cross-sectional turnover and presents the essence of our findings that stocks with

relatively high (low) turnover tend to maintain relatively high (low) turnover for many years into

the future. Section 4 uses Fama-MacBeth regression analysis to illustrate the importance of initial

3 Similarly, Habib and Ljungqvist (2001) argue that IPO firms could treat underpricing as promotion and marketing

expenses for attracting investor attention and trader interest, which would raise stock liquidity.

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turnover in predicting future turnover. Section 5 provides robustness checks using an IPO sample

and a sample of time-honored stocks. Section 6 shows that firms‟ historical turnover rates contain

useful information about their future liquidity and liquidity risk. Finally, our paper ends with

concluding remarks in Section 7.

2. Data, Variables and Sample Selection

2.1 Sample Construction and Turnover Measure

Our initial sample is comprised of NYSE/AMEX-listed stocks from 1965 to 2009 and

NASDAQ-listed stocks from 1983 to 2009. We conduct our analyses separately for NYSE/AMEX

stocks and NASDAQ stocks because of concerns about the differences in market structure and the

double-counting issue in NASDAQ volume (e.g., Atkins and Dyl (1997) and Anderson and Dyl

(2005)). We restrict our sample to common stocks only (CRSP share codes 10 and 11), excluding

ADRs, Americus Trust components, closed-end funds, preferred stocks and REITs (Lo and Wang

(2000), Chordia, Roll, and Subrahmanyam (2001), and Cremers and Mei (2007)).

Since the focus of our study is to see to what extent investors‟ trading preference depends on

individual stocks‟ past turnover, we analyze only firm-year observations in our sample that have

nonmissing values on raw annualized turnover (TURN). Using a time aggregation procedure

suggested by Lo and Wang (2000), we create the raw annualized turnover series of a firm by

accumulating its monthly turnovers across months in each calendar year, where turnover in a given

month is measured as total trading volume in that month divided by the number of shares

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outstanding at the end of the month. We obtain monthly trading volume and shares outstanding

from CRSP. To address the concern that some stocks are likely to not have all 12 monthly

observations in their first year, we delete the first firm-year observation for each stock that does not

have a complete turnover data in that year.4 Moreover, as Chordia et al. (2007), French (2008), and

Chordia et al. (2011) point out, there exists an upward trend in share turnover in U.S. markets. To

better analyze cross-sectional dynamics, we follow Gallant, Rossi, and Tauchen (1992) to filter out

the time-trend component of the turnover series for each sample stock, and refer to the detrended

turnover as TURNGRT

in our analysis.

Table 1 summarizes the descriptive statistics of annualized share turnover for NYSE/AMEX

stocks during 1965-2009 and NASDAQ stocks during 1983-2009. Panel A shows that, consistent

with French (2008) and Chordia et al. (2011), there is an appreciable increase in the average

turnover over time. Specifically, for NYSE/AMEX stocks, the average (median) turnover is 0.438

(0.286) in the first subperiod (1965–1970) and it progresses to 2.494 (2.017) over the last subperiod

(2006–2009); whereas the average (median) turnover for NASDAQ stocks is 0.557 (0.394) in the

first subperiod (1983–1985) and it grows to 1.980 (1.327) during the last subperiod (2006–2009).

<Table 1 is inserted about here>

Panel B of Table 1 reports the top ten and the bottom ten industries in annualized share

turnover (sorted by the median level). It reveals considerable variation in trading activity across

industries, ranging from 0.359 for the Agriculture industry to 1.170 for the Coal industry in the

4 The overall results are quite similar when we proxy first firm-year observation of annualized turnover by aggregating

the first 12 monthly observations of turnover in our sample period.

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NYSE/AMEX sample, and from 0.302 for the Real Estate industry to 2.406 for the Defense

industry in the NASDAQ sample. The results indicate that trading volumes across industries are

distinctively dispersive. This suggests that it is important to control for industry effects when we

analyze cross-sectional variation in turnover.

2.2 Turnover Determinants

The literature has identified a handful of well-known determinants of trading volume,

including portfolio rebalancing demands; stock visibility; mass of informed trades; different opinion

among investors; traders‟ learning effect on fundamental values and about the return generating

process; trading costs; dividend-capture trades; and industry turnover level (e.g., Lo and Wang

(2000) and Chordia et al. (2007)). To proxy for these well-known turnover determinants, we utilize

19 firm-level characteristics and one industry-level characteristic as control variables in our tests of

cross-sectional turnover persistence. We detail the variables and the rationales to include them in

the subsections below.

Regarding the data sources, we obtain financial variables from Compustat (e.g., debt ratio,

dividend yields, book-to-market equity ratio, earnings volatility, and others), and take the Gibbs

trading cost measure from Joel Hasbrouck‟s website.5 We also include analyst coverage and analyst

forecast dispersion variables from I/B/E/S beginning in 1976 and institutional holding reported in

Thomson Financial 13F Data beginning in 1980. Appendix 1 summarizes the definitions of the key

5 We sincerely thank Joel Hasbrouck for providing Gibbs trading cost at his website:

http://people.stern.nyu.edu/jhasbrou/.

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variables and their data sources. To reduce the effect of outliers, all variables are winsorized at the

upper and the lower 1-percentiles.

2.2.1 Liquidity Trading: Portfolio Rebalancing Needs and Stock Visibility

The theoretical models of De Long et al. (1990), Hong and Stein (1999), and Hirshleifer et al.

(1994, 2006) predict that past returns affect trading volume. Lo and Wang (2000) also argue that the

excess expected return, generated by the CAPM, may involve a premium related to liquidity and

heterogeneous information. The excess expected return could thus proxy for illiquidity (see also

Amihud and Mendelson (1986) and Wang (1994)). F Furthermore, Chordia et al. (2007) suggest that

investors are likely to trade for portfolio rebalancing needs. Indeed, Griffin, Nardari and Stulz (2007)

employ global data to provide evidence that many stock markets experience a strong positive

relation between past returns and turnover.

Thus, we employ RETPos

, RETNeg

, and CAPM to capture the effects of past returns on turnover.

Specifically, to deal with the potential asymmetric effects due to short-selling constraints or the

disposition effect, we follow Grinblatt and Keloharju (2001) and Chordia et al. (2007) to define

RETPos

(RETNeg

) as the annualized return of a stock if it is positive (negative), and zero otherwise;

where annualized return is obtained by compounding monthly stock returns in each calendar year.

The CAPM is the intercept term from the CAPM regression based on weekly excess returns in each

calendar year.6

6 We generate weekly stock returns from CRSP daily files by following Lo and MacKinlay (1988).

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As Merton (1987), Chordia et al. (2007) and Goetzmann and Kumar (2008) observe, market

participants are frequently attracted to stocks with high visibility. We employ book-to-market equity

ratio (BM), market capitalizations (SIZE), firm age (AGE), and S&P 500 membership (SP500) to

proxy for stock visibility. Our reasoning is as follows. First, Chordia et al. (2007) suggest that

stocks with lower book-to-market ratios tend to be growth stocks, which tend to be more visible.

We define BM as the book value of equity at the end of each fiscal year divided by the year-end

market capitalization (price per share multiplied by total number of shares outstanding).

Chordia et al. (2007) also suggest that young firms will receive more attention, which leads to

more trading activities. Thus, they predict a negative relation between firm age and trading volume.

AGE is defined as the value of ln(1+A), where A represents firm age measured as the number of

years since a stock was listed on an exchange as reported in the CRSP.

The impact of S&P 500 membership on trading volume has been well documented in the

literature. For example, Harris and Gurel (1986) show that a firm‟s trading volume increases after

its inclusion into the S&P 500 index. According to Merton (1987), the S&P 500 member firms are

expected to experience wider ownership and thus more trading activity. We define SP500 as a

dummy variable equal to one if the firm-year is included in S&P 500 index, and zero otherwise.

Moreover, Merton (1987) predicts that larger-capitalization firms tend to exhibit more diverse

ownership, which leads to more trading. Chordia et al. (2007) confirm a positive relation between

firm size and trading volume. We define SIZE as the natural logarithm of year-end market

capitalization in each calendar year.

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2.2.2 Mass of Informed Trades

Rational expectations theories often posit that trading occurs due to non-informational reasons

as well as due to the profit motive of privately informed investors.7

F Following Chordia et al. (2007),

we employ analyst coverage (ACOV) as a proxy for information production. ACOV is defined as the

maximum number of analysts that make annual earnings forecasts in any month over each year. We

set the observation to zero if data is unavailable. F Chordia et al. (2007) suggest a positive relation

between turnover and ACOV.

Institutional holdings is another well-known predictor of turnover. In general, the literature has

suggested that institutional investors are better-informed traders (Utama and Cready (1997) and Ali,

Klasa and Li (2008)). Chordia et al. (2011) also find that the more the shares held by institutional

investors, the higher the turnover. To capture the turnover effect of institutional holdings, we adopt

IOR, defined as the average of four quarterly institutional holdings in a given year, where the

institutional holdings is the total number of a firm‟s shares held by institutions in a quarter divided

by its quarter-end total number of shares outstanding.

2.2.3 Different Opinions across Investors

Harrison and Kreps (1978) and Varian (1985) model trading activities arising from investors‟

different opinions. F Likewise, Harris and Raviv (1993) and Kandel and Pearson (1995) suggest that,

even though they share the same public information, investors may interpret it differently and, in

turn, increase trading activities. Following Chordia et al. (2007), we use FDISP and DEBT as

7 See, e.g., Grossman and Stiglitz (1980), Hellwig (1980), Kyle (1985), Admati and Pfleiderer (1988), Grundy and

McNichols (1989), Foster and Viswanathan (1990), Kim and Verrecchia (1991a, 1991b), and Wang (1994).

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proxies for dispersion of opinion. FDISP is analyst forecast dispersion, defined as the monthly

average of the standard deviation of current-year EPS forecasts by two or more analysts in a year

divided by the absolute value of the mean forecast in that year (Banerjee (2011) and Loh and Stulz

(2011)).8 DEBT is defined as the ratio of the book value of long-term debt to the book value of total

assets at the end of each fiscal year. Chordia et al. (2007) note that firms with more forecast

dispersion or with higher leverage will cause larger differences in opinion and lead to more share

turnover.

2.2.4 Investor’s Learning about Fundamental Value and about the Return Generating Process

According to Chordia et al. (2007), stocks with a higher extent of estimation uncertainty will

cause greater learning-induced share turnover. Also, Subrahmanyam (2008) proposes that trading

volume on a stock will increase due to investors‟ learning about the validity of different sources of

information. Following Chordia et al. (2007), we use earnings surprises (ESURP) and earnings

volatility (EVOL) to measure the degree of investor‟s learning about firm value. ESURP is defined

as the absolute value of the difference between the earnings deflated by total assets at the end of

each fiscal year and the average asset-deflated earnings over the past four years. Similarly, EVOL is

defined as the standard deviation of the asset-deflated earnings over the most recent five years (with

a minimum of three years) (Dichev and Tang (2009)).

We further employ residual risk (SIGMA), firm beta (BETA), and portfolio beta (PBETA) to

examine the investors‟ learning about the return generating process. SIGMA and BETA are

8 We set FDISP to zero if data is missing

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respectively defined as the residual standard deviation (root mean square error) and the slope

coefficient generated by the market model regression of a stock‟s weekly excess returns on the

value-weighted market weekly excess returns in each calendar year. To avoid the measurement

error concern, we also follow Chordia et al. (2007) to use portfolio beta (PBETA) as another proxy

for investors‟ learning effect, where PBETA is generated based on the procedure suggested by Fama

and French (1992). As Coles and Loewenstein (1988) point out, stocks with low information or

with high estimation uncertainty tend to exhibit relatively high betas. Furthermore, Chordia et al.

(2007) suggest that low-information stocks will cause investors to make greater estimation errors

and that higher estimation uncertainty will result in greater error modifications, which lead to

higher trading volume. Lo and Wang (2000) also document that turnover is related to both

systematic risk and residual risk. Thus, we expect that SIGMA, BETA, and PBETA all have positive

effects on share turnover.

2.2.5 Trading Cost

Michaely and Vila (1996), Lo and Wang (2000), and Chordia, Roll and Subrahmanyam (2000,

2001, and 2011) have suggested that trading cost is an important determinant of share turnover. We

use Hasbrouck‟s (2009) Gibbs trading cost (CGibbs

) to capture the effect of transaction costs on

trading volume. Since high trading costs tend to deter trading, we expect turnover to be inversely

related to CGibbs

.

In addition, Lo and Wang (2000) and Chordia et al. (2007) find a positive effect of stock price

on trading volume and argue that this positive relation is due to the tendency of high-price stocks to

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experience low transaction costs in terms of brokerage commissions.9 Thus, we include PRC in our

analysis, where PRC is defined as the natural logarithm of year-end share price in each calendar

year.

2.2.6 Dividend-Capture Trades

A number of studies have explored the role dividends play in trading volume (see, e.g.,

Lakonishok and Smidt (1986), Karpoff and Walkling (1990), Michaely and Vila (1996), and Lo and

Wang (2000)). In particular, Michaely and Vila (1996) suggest that trading volume is an increasing

function of dividend yield around the distribution of cash dividends. However, using weekly data,

Lo and Wang (2000) find a negative relation between dividend yield and turnover, which is

inconsistent with the prediction that dividend capture trading affects share turnover. Nonetheless,

we employ DIVDY, defined as the ratio of annual cash dividend to year-end price per share, to

control for dividend capture trading.

2.2.7 Industry Characteristics

As Table 1 shows, turnover varies across industries. Chordia et al. (2007) also find that the

high-tech sector tends to attract more trading because of its uncertainty about fundamental value or

differences of opinion. To control for the industry effect, we use INDTURN, defined as the median

of the detrended annual turnovers of firms in a given industry for each year, where the industry is

identified by Fama and French‟s 48-industry classification.


9 See Brennan and Hughes (1991).

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Table 2 presents the descriptive statistics for the aforementioned variables. In general,

NYSE/AMEX stocks tend to exhibit a higher book-to-market ratio, share price, firm size, firm age,

S&P 500 membership, analyst coverage, leverage, dividend yield, and institutional holdings than do

NASDAQ stocks.

We also present the correlations among the variables in Table 3. The results for the

NYSE/AMEX (NASDAQ) sample are shown in the upper (lower) triangle of this table. Consistent

with common beliefs, there exists a high correlation between firm size and share price (67% in

NYSE/AMEX vs. 60% in NASDAQ); firm size also has strong positive correlations with

institutional holdings (65% in NYSE/AMEX vs. 68% in NASDAQ) and with analyst coverage

(76% in NYSE/AMEX vs. 68% in NASDAQ), but a strong negative correlation with trading cost

(-55% in NYSE/AMEX vs. -64% in NASDAQ).


3. Understanding the Evolution of Cross-Sectional Trading Volume

In this section, we employ the approach proposed by Lemmon, Roberts and Zender (2008) to

study the evolution of trading volume in the cross-section of stocks, and to provide some insight

into liquidity preference of investors in U.S. markets. The framework is composed of the following

steps:

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(1) For each calendar year, we sort stocks into quartiles (quartile 1 to 4), based on their annualized

share turnover to form four portfolios (denoted as Highest, High, Low, and Lowest). The

portfolio formation year is defined as event year 0.

(2) Keeping the portfolio compositions fixed, we compute the average share turnover for each of

the four portfolios over the subsequent 20 years.

(3) We replicate the above two steps for every year in our sample period and then obtain 45 (27)

sets of event-time averages for the NYSE/AMEX (NASDAQ) sample.10

(4) Finally, we compute the average share turnover across the 45 (27) sets for each portfolio in each

event year.

< Figure 1 is inserted about here>

Based on above procedure, Figure 1 depicts the average share turnover for each of the four

portfolios over event years 1 through 20. We first focus on raw turnover in Panel A of Figure 1,

which shows the average raw annualized turnover (TURN) of the four portfolios in event time for

NYSE/AMEX and NASDAQ stocks. The first interesting feature we find is the remarkably wide

spread of cross-sectional turnover in the first event year. The difference of the average raw turnover

for the Highest and the Lowest portfolio is 1.244 for the NYSE/AMEX sample and 2.563 for the

NASDAQ sample. These cross-sectional deviations are large and far from the prediction of the

two-fund separation hypothesis proposed by Lo and Wang (2000). Next, the cross-sectional

turnover exhibits a gradual convergence over time for both the NYSE/AMEX and the NASDAQ

10 This is due to the fact that our NYSE/AMEX sample covers the period 1965 to 2009, while our NASDAQ sample

covers the period 1983 to 2009.

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samples. Third, in spite of the convergence property, the ranking of raw turnover remains fairly

stable over time for the four portfolios. In other words, stocks with relatively high (low) turnover

tend to maintain relatively high (low) turnover over the subsequent 20 years.

Fourth, it is clear that the overall turnover increases over the event years, despite the

differences within the four portfolios showing convergence and persistence. This event-time upward

trend suggests that turnovers of NYSE/AMEX and NASDAQ stocks rise over time (summary

statistics in Table 1 also show the uptrend). This upward trend is also documented by Chordia et al.

(2007), French (2008), and Chordia et al. (2011). Thus, filtering out the time-trend component in

the time-series data is necessary to better analyze the cross-sectional dynamics. Following Chordia

et al. (2007) and Cremers and Mei (2007), we eliminate this nonstationarity contained in the

annualized turnover series by using the Gallant, Rossi, and Tauchen (1992) linear transformation

procedure.11

F We refer to this as the detrended turnover (TURNGRT

). Panel B of Figure 1 presents the

average detrended turnover for the four portfolios in event years. As one can see, after removing

time-trend component, the properties of convergence and persistence displayed in Panel A of Figure

1 are still present.

Another interesting question about the interpretation of Panels A and B in Figure 1 is that the

sorting of stocks based on raw or detrended turnover might merely describe cross-sectional

variation in fundamental characteristics related to cross-sectional variation in turnover. For instance,

since larger-capitalization stocks tend to experience higher trading activities, the composition of the

11 Our computer program on linear transformation to detrend the volume data is obtained from website of George

Tauchen, whom we sincerely thank.

19

Highest (Lowest) turnover portfolio might simply correspond to large-capitalization

(small-capitalization) stocks.12

To address this issue, we alter the sorting procedure, and form four

portfolios based on ranking stocks by unexplained detrended turnover (TURNU), in order to trace

the average detrended turnover of each portfolio in the following 20 years. Unexplained detrended

turnover (TURNU) is defined as the residuals from a yearly cross-sectional regression of TURN

GRT

on one-year lagged RETPos

, RETNeg

, BM, SIZE, AGE, DEBT, SIGMA, BETA, CGibbs

, PRC, DIVDY,

and INDTURN. The definitions of these regressors are summarized in Appendix 1.

Panel C of Figure 1 charts the unexplained detrended turnover (TURNU) portfolios, which has

a pattern approximately analogous to those in Panels A and B. Specifically, a considerable

dissimilarity in turnover between the Highest and the Lowest portfolio remains in the first event

year (0.946 for the NYSE/AMEX sample and 1.737 for the NASDAQ sample). Further, although

some convergence occurs, there remain significant endurable differences in turnover across stocks

over time. In particular, the Highest turnover portfolio stands out. The spread in average detrended

annualized turnover between the Highest and the Lowest portfolio in event year 20 is still large,

with an average spread of 0.341 (0.995) for the NYSE/AMEX (NASDAQ) sample. In Panel D we

report the results of the four portfolios formed based on TURNU ranking in each formation year, in

order to trace the average unexplained detrended annualized turnover (TURNU) of each portfolio.

This shows again that turnover rates (high or low) persist for many years into the future.

12 For example, Merton‟s (1987) investor recognition hypothesis predicts that investors tend to hold only the securities

they know. This implies that stocks with larger capitalization are likely to exhibit more diverse ownership and facilitate

more trading.

20

The evidence presented in Panels C and D suggests that, after we control for the well-known

turnover determinants, the cross-sectional distinctions across these four portfolios stay sharply

ingrained over time. Such findings imply that there exists a long-run persistent or even

time-invariant component in trading volume, which cannot be explained by the well-known

determinants of trading volume identified in previous studies.

Nevertheless, our results reveal that investors have a persistent preference for trading high

turnover stocks. As we will show in Section 6, high turnover stocks also have lower trading costs

and lower liquidity risk for many years to come. Thus, our results are consistent with liquidity

preference theory, which posits that investors prefer stocks with high liquidity. The persistent

preference for trading high turnover stocks also suggests that Amihud and Mendelson‟s (1986)

clientele effect is persistent in the U.S. markets, i.e., short-term investors persistently trade liquidity

stocks, while long-term investors persistently hold illiquid stocks.

4. Further Evidence of Long-Run Cross-sectional Persistence in Turnover

Figure 1 illustrates that stocks with high (low) past turnover tend to maintain high (low)

turnovers for many years to come. This section further investigates this long-run cross-sectional

turnover persistence using three sets of analysis. First, to show that investor trading preference on

individual stocks is largely formed very early on when they became publicly traded, we use initial

turnover to predict future turnover, and compare initial turnover‟s explanatory power to that of the

previously identified determinants in predicting future turnover. Second, to address the concern that

21

the turnover persistence could be due to the persistent effects of the known determinants of turnover,

we further examine the impact of initial turnover while controlling for alternative lag-lengths of the

known determinants using Fama-MacBeth regression analysis. Third, using a firm-fixed effect

specification, we decompose the cross-sectional variation in turnover to quantify the explanatory

power of each turnover determinant.

4.1 The Impact of Initial Turnover on Future Turnover

To quantify the impact of initial turnover on future turnover, we adopt the following

Fama-MacBeth regression model:

, , 1 ,

GRT Initial

i t i i t i tTURN TURN X (1)

where ,

GRT

i tTURN is Gallant et al.‟s (1992) detrended turnover in year t for stocks i; Initial

iTURN is

the initial turnover defined as the first nonmissing value of the detrended annualized turnover series

for stock i; , 1i t X contains a set of one-year lagged control variables, which are defined in

Appendix 1. We run several variants of the model for each year over the sample period from 1965

(1983) through 2009 for the NYSE/AMEX (NASDAQ) sample, and report the results in Table 4.

To address the concern that the autocorrelation of turnover may cause serial dependence in the

coefficients, we report heteroskedasticity and autocorrelation-consistent (HAC) t-statistics based on

Newey and West (1987). For our purpose, we are most interested in the coefficient that

measures the impact of initial turnover on future turnover. Also, by reporting the standardized

coefficients on each regressor, we can use the coefficients to directly compare the importance of

initial turnover relative to the known determinants identified in the literature.

22

< Table 4 is inserted about here>

Table 4 summarizes the regression results for the NYSE/AMEX sample in Panel A and for the

NASDAQ sample in Panel B. Model (1) contains Initial

iTURN as the sole independent variable and

shows that a one-standard deviation change in a stock‟s initial turnover corresponds to a change of

0.449 (t=9.67) standard deviations in future turnover for the NYSE/AMEX sample. The

corresponding figure is 0.671 (t=29.19) for the NASDAQ sample. These findings support the

persistence property of turnover shown in Figure 1 and demonstrate the importance of initial

turnover in determining future turnover.

To compare the explanatory power of initial turnover with that of the known determinants

identified by previous studies, Model (2) replaces Initial

iTURN by a set of determinants suggested by

Lo and Wang (2000). Specifically, the set consists of one-year lagged αCAPM

, SIZE, SP500, SIGMA,

BETA, CGibbs

, PRC, and DIVDY. The coefficients on the regressors are all statistically significant

and consistent with those findings by Lo and Wang (2000). However, comparing the adjusted R2 in

Model (1) and in Model (2) reveals that initial turnover alone explains more of the cross-sectional

variation in turnover than the set of determinants suggested by Lo and Wang (2000) for both the

NYSE/AMEX sample (0.246 vs. 0.198) and the NASDAQ sample (0.446 vs. 0.286). Further,

Model (3) combines initial turnover with all the regressors in Model (2). It shows that although a

reduction occurs in the coefficient of initial turnover from 0.449 (t=9.67) to 0.382 (t=8.29) for the

NYSE/AMEX sample and from 0.671 (t=29.19) to 0.547 (t=15.69) for the NASDAQ sample, the

initial turnover has the largest standardized coefficient among all the explanatory variables. This

23

suggests that initial turnover bears the most important relation with future turnover, compared to the

determinants suggested by Lo and Wang (2000).

In Model (4), we entertain a set of the determinants proposed by Chordia et al. (2007) as the

regressors, which include RETPos

, RETNeg

, BM, SIZE, AGE, ACOV, FDISP, DEBT, ESURP, EVOL,

PBETA and PRC.13

The regression results in Model (4) are similar to the findings of Chordia et al.

(2007). Again, for both NYSE/AMEX stocks and NASDAQ stocks, the adjusted R2 in Model (1) is

greater than that in Model (4),14

implying that initial turnover alone exhibits more explanatory

power than the set of the turnover determinants proposed by Chordia et al. (2007). In addition to the

turnover determinants proposed by Chordia et al. (2007), Model (5) adds initial turnover as one of

the independent variables. From the results of Model (5), one can see that initial turnover still

exhibits the largest impact on future turnover.

Finally, Model (6) combines all the determinants from Models (3) and (5).15

The results

indicate that the standardized coefficient of initial turnover--0.353 (t=6.95) for the NYSE/AMEX

sample and 0.484 (t=8.86) for the NASDAQ sample--is still large and significant. Again, initial

turnover dominates all the known predictors in determining future turnover.

4.2 Controlling for Long-Run Effects of the Known Determinants

To deal with the concern that the long-run persistence in turnover may simply reflect the

long-term effects of the known determinants of turnover, this subsection further examines the

13 Because data on analyst earning forecasts reported in I/B/E/S covers the period beginning in 1976, we lose many

observations in Model (4). 14 Note that, in Table 4, the sample period for Model (1) is 1965-2009, and it is 1976-2009 for Model (4). The adjusted

R2 of Model (1) is still higher than that of Model (4) in the same time period of 1976-2009. 15 Since RETPos and RETNeg are highly correlated to αCAPM and BETA and PBETA are highly correlated as well, to

alleviate the issue of multicollinearity, we exclude RETPos, RETNeg, and BETA from Model (6).

24

impact of initial turnover on future turnover by controlling for alternative lag-lengths of the

turnover determinants identified in the literature. Specifically, we estimate the following

Fama-MacBeth regression model:

, , ,

1

LGRT Initial

i t i s i t s i t

s

TURN TURN

= X (2)

where L represents the lag order of each regressor included in the vector X, which is comprised of

all control variables employed in Model (6) of Table 4. To determine the applicable lag lengths, we

adopt the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC). Both

of these two procedures suggest that the appropriate lag lengths are 10 for the NYSE/AMEX

sample and 8 for the NASDAQ sample. Table 5 reports the standardized coefficients of initial

turnover ( Initial

iTURN ), taking into account the lag lengths of the control variables from 2 to 10 lags

for the NYSE/AMEX sample in panel A and from 2 to 8 lags for the NASDAQ sample in Panel B.

For the NYSE/AMEX sample, the standardized coefficient on Initial

iTURN decreases slowly

from 0.314 (t=6.36) with 2 lags to 0.215 (t=4.92) with 10 lags; and from 0.436 (t=7.62) with 2 lags

to 0.208 (t=5.32) with 8 lags for the NASDAQ sample. The results suggest that while increasing lag

lengths of the known turnover determinants reduces the impact of initial turnover, future turnover is

still significantly related to initial turnover. And, initial turnover still dominates the known

determinants and their lags in determining future turnover.


Overall, the results reported in Tables 4 and 5 are consistent with the feature of strong turnover

persistence displayed in Figure 1, and show that initial turnover plays a very important role in

25

explaining future turnover, even after controlling for the short-run and long-run effects of the

known turnover determinants identified in the literature. The evidence indicates that turnover

possesses a time-invariant component, which is ignored by previous studies. The time-invariant

component is essentially the firm-fixed effect. We next employ the analysis of variance

decomposition to better quantify the importance of this firm-fixed effect in determining future

turnover.

4.3 Decomposing the Variation in Turnover

To measure the relative importance of the firm-fixed effect and the known determinants in

capturing turnover variation, we use the following pooled OLS regression model:

, , 1 ,

GRT

i t i t i t i tTURN

X (3)

where i is the firm-fixed effect for firm i; t is the time-fixed effect in year t; and X contains

the known determinants used in Table 4. To provide normalized and comparable results, we divide

the Type III partial sum of squares for each regressor by the aggregate partial sum of squares across

all regressors for each model specification. Thus, each value in the table is the proportion of the

model sum of squares attributable to particular determinant which, in turn, forces each column to

sum to one hundred percent (Scheffe (1959) and Lemmon et al. (2008)). Moreover, we report the

adjusted 2R to illustrate the explanatory power for each model specification that includes different

variables.


26

Table 6 reports the variance decomposition of the detrended turnover for the NYSE/AMEX

sample from 1965 to 2009 and the NASDAQ sample from 1983 to 2009. The results demonstrate

that the firm-fixed effects are more important than the time-fixed effects in capturing the

cross-sectional variation in turnover. In particular, as shown in Model (1), about 54.2% (65.2%) of

the variation in turnover is explained by the firm-fixed effects for the NYSE/AMEX (NASDAQ)

sample, whereas, as in Model (2), the time-fixed effects only capture 6.3% (5.5%) of the variation.

Model (3) considers both the firm-fixed effects and the time-fixed effects, and confirms our earlier

inference that a large portion of the total variation in turnover is driven by the time-invariant

components of individual firms.

Model (4) contains the time-fixed effects and the set of determinants introduced by Lo and

Wang (2000). Its adjusted R2 is 21.2% (30.4%) for the NYSE/AMEX (NASDAQ) sample, and,

among the set of the variables, SIZE, SIGMA, BETA, CGibbs, and DIVDY each contribute more than

10% to the model‟s adjusted R2 in explaining the variation in turnover. However, when we add the

firm-fixed effects into the model, as shown in Model (5), the adjusted R2 for the NYSE/AMEX

(NASDAQ) sample increases from 21.2% to 61.4% (30.4% to 69.9%). The firm-fixed effects

account for more than 90% of Model 5‟s adjusted R2, while the contributions of SIZE, SIGMA,

BETA, CGibbs, and DIVDY to the model‟s adjusted R2 all decrease to below 1%. The results illuminate

the meaningful contribution of the firm-fixed effects in explaining the cross section of turnover.

We obtain similar results in Models (6) and (7), where we compare the firm-fixed effects and

the turnover determinants proposed by Chordia et al. (2007). Essentially, adding the firm-fixed

27

effects increase the adjusted R2 from 26.5% to 64.9% for NYSE/AMEX stocks and from 30.9% to

70.9% for NASDAQ stocks.

Model (8) combines all the determinants used in Models (4) and (6),16

along with the

time-fixed effect and institutional holding (IOR). In Model (9), we incorporate the firm-fixed effects,

while keeping all the variables in Model (8). Again, comparing Model (8) with Model (9), one can

see that the firm-fixed effects capture most of the cross-sectional variation in turnover, and that the

determinants identified by previous studies show dismal contributions.

In sum, by decomposing the variation in turnover attributable to observed firm characteristics

and the firm-fixed factor, we find that the majority of cross-sectional variation in turnover is

accounted for by the time-invariant firm-fixed factor. By and large, as Tables 4 and 5 show, initial

turnover provides a good proxy for the time-invariant firm-fixed factor in future turnover. The

results substantiate the view that cross-sectional long-run persistence exists in turnover, and the

turnover persistence starts very early on after IPOs.

5. Robustness Analysis

5.1 Evidence from an IPO Sample

The essence of our findings in this paper is that although the convergence occurs in the first

few years following the portfolio formation year, the cross section of turnover exhibits long-lived

stability. In this section, we provide a robustness check using an IPO sample. What we attempt to

16 We exclude RETPos, RETNeg, and BETA from model (8) to avoid the multicollinearity problem, as explained in

footnote 15.

28

verify is whether stocks that begin their public lives with a high (low) trading volume remain as

such over the long run in event time.

Our sample of IPOs covering the 1975-2009 period are obtained from Jay Ritter‟s website.17

The initial sample contains 9,036 IPO firms each with identifiable CRSP Permno and offer date.

After merging with share turnover obtained from CRSP over the 12-month period following the

IPO month, we are left with 8,132 IPO firms. Requiring RETPos

, RETNeg

, BM, SIZE, DEBT, SIGMA,

BETA, CGibbs, PRC, DIVDY, and INDTURN for generating unexplained IPO turnover (IPOTURNU)

further reduces the sample size to 5,127 IPO firms (including 1,208 NYSE/AMEX firms and 3,919

NASDAQ firms).

To describe the cross-sectional behavior of turnover for IPO firms, we adjust the procedures

used in Section 3. First, since some IPO stocks may not have the entire 12 monthly observations in

the IPO year, we proxy raw IPO turnover in the IPO year by summing the first 12 monthly

turnovers following the IPO month. We then generate the detrended annualized turnover series for

each IPO stock, based on the linear transformation procedure suggested by Gallant et al. (1992).

Detrended IPO turnover (IPOTURN) is defined as the first nonmissing value for the detrended

annualized turnover in the IPO year. Second, in Panel A of Figure 2, for each IPO year, we sort IPO

stocks into four portfolios based on their ranking of IPOTURN and then trace the average detrended

turnover for each of the four portfolios over 20 years following the IPO year. In Panel B of Figure 2,

for each IPO year, we sort IPO stocks into four portfolios based on their ranking of unexplained

17 IPO data source: http://bear.warrington.ufl.edu/ritter/ipodata.htm.

29

IPO turnover (IPOTURNU) and then trace the average detrended turnover for each of the four

portfolios over 20 years following the IPO year. Unexplained IPO turnover (IPOTURNU) is defined

as the residuals from a cross-sectional regression of detrended IPO initial turnover on the initial

values of RETPos

, RETNeg

, BM, SIZE, DEBT, SIGMA, BETA, CGibbs, PRC, DIVDY, and INDTURN.

Also contained in the regression is the IPO year-fixed effect. Detrended IPO initial turnover and

initial values for each regressor are calculated as the average over years 0, 1, and 2, where year 0 is

the IPO year. Third, after sorting and averaging for each year over the periods from 1975 to 2009

for the NYSE/AMEX sample and from 1983 to 2009 for the NASDAQ sample, the average

detrended annualized turnover across event time for these four portfolios are plotted in Figure 2.


Figure 2 has the properties of convergence and persistence similar to those of Figure 1. In

particular, Figure 2 shows that, compared to IPO stocks with low turnover, IPO stocks with high

turnover tend to continue to have high turnover as they age. The pattern confirms that stocks tend to

maintain their relative rankings of initial turnover for many years. This implies that the cross

section of future share turnovers, years after the IPOs, seem largely determined around the time of

the IPOs. Our findings illustrate the importance of establishing high liquidity at the beginning

because it has long-lasting impacts on future liquidity.

5.2 Evidence from Time-Honored Stocks

We conduct another robustness check using time-honored stocks, where the time-honored

stocks are defined as those stocks that have at least 20 years of nonmissing observations on share

30

turnover in our sample period. For long-term studies, survivorship bias is a concern. We used

time-honored stocks to mitigate this concern.

Our analysis on time-honored stocks listed on the NYSE/AMEX from 1965 to 2009 and on the

NASDAQ from 1983 to 2009 reveals very similar results, showing cross-sectional long-term

persistence in share turnover. Specifically, as Figure 3 shows, it is evident that stocks that have high

(low) turnover in the past year, which is the portfolio formation year, tend to continue to have high

(low) turnover for many years to come. Thus, the cross-sectional turnover persistence is unlikely to

be due to survivorship bias.


6. Historical Turnover and Future Liquidity and Liquidity Risk

While turnover reflects stock liquidity, there are alternative measures of liquidity. For example,

Amihud‟s (2002) illiquidity (ILLIQ) measure and Hasbrouck‟s (2009) Gibbs trading cost (CGibbs

)

measure are widely used liquidity proxies, and are often related to the liquidity premium in stock

returns. Also related to liquidity premium is liquidity risk. In this section, we use Pastor and

Stambaugh‟s (2003) liquidity risk (LIQBETA), which captures the extent of co-movements between

returns on a stock and market-wide liquidity innovations.

Given that historical turnover is a good predictor of future turnover, it is natural to ask whether

historical turnover is also a good predictor of future liquidity and liquidity risk. The linkage is

important for two reasons. First, if the cross section of future turnover reflects traders‟ liquidity

31

preference, those stocks with higher turnover should have higher liquidity and lower liquidity risk.

Second, by relating it to liquidity and liquidity risk, we can show that historical turnover has

long-term effects on the liquidity premium.

6.1 Future Liquidity

Figure 4 presents the averages of Amihud‟s (2002) ILLIQ measure and Hasbrouck‟s (2009)

CGibbs

measure across the four detrended turnover portfolios in event time for NYSE/AMEX stocks

from 1965 to 2009 and NASDAQ stocks from 1983 to 2009. Like Figure 1, Figure 4 is constructed

with the following procedure: (1) For each calendar year, we sort stocks into four portfolios

(denoted as Highest, High, Low and Lowest) based on their detrended annualized turnover

(TURNGRT

); (2) keeping the portfolio components fixed, we trace the average ILLIQ and the

average CGibbs

for each of the four portfolios in the subsequent 20 years; (3) we replicate step (1)

and (2) by sorting and averaging for every year in our sample period; and (4) after averaging across

the years in the sample period, we plot the average ILLIQ and the average CGibbs

in event time for

the four portfolios in Panel A and in Panel B of the figure, respectively.


Panel A of Figure 4 shows that stocks with high (low) turnover in one year tend to have lower

(higher) future price impact of trades, as measured by Amihud‟s ILLIQ, for the subsequent 20 years.

Similarly, Panel B shows that high (low) turnover stocks tend to have lower trading costs, as

measured by Hasbrouck‟s CGibbs

, for many years into the future.

32

Table 7 reports the differences in ILLIQ and in CGibbs

between the highest turnover stocks and

the lowest turnover stocks in years 1, 5, 10, 15, and 20 after the portfolio formation year and tests

whether the differences are statistically significant. The results show that, on average, the highest

turnover stocks have significantly lower price impact of trades and lower trading costs than the

lowest turnover stocks for the next 20 years. Therefore, the results in Figure 4 and Table 7 are

consistent with the notion that the cross section of historical turnover of U.S. stocks contains useful

information about the cross section of their long-term liquidity.


6.2 Future Liquidity Risk

Figure 5 presents the average of Pastor and Stambaugh‟s (2003) liquidity risk (LIQBETA)

across the four detrended turnover portfolios in event time for NYSE/AMEX stocks from 1965 to

2009.18

The construction of the four detrended turnover portfolios is the same as that for Figure 4.

Liquidity risk (LIQBETA) is measured as the coefficient on Pastor and Stambaugh‟s (2003) liquidity

factor estimated from a regression model that includes the traditional four risk factors (Fama and

French (1993) and Carhart (1997)) by using all data available over a period of 60 months (if at least

36 months are available) prior to the end of each year.


Figure 5 illustrates that stocks with higher historical turnover tend to have lower future

liquidity risk for many years to come. Table 7 shows that the differences in liquidity risk between

18 We estimate liquidity risk only for our NYSE/AMEX sample because Pastor and Stambaugh (2003) construct their

liquidity factor using only NYSE/AMEX stocks.

33

the highest turnover stocks and the lowest turnover stocks are statistically significant from one year

to 20 years after the portfolio formation year. Interestingly, while both ILLIQ and CGibbs

show

downward trends, suggesting improvements in liquidity of NYSE/AMEX stocks over time,

LIQBETA exhibits an upward trend, implying that the effect of liquidity risk on stock returns

increases over time. Nevertheless, the relative rankings of liquidity and liquidity risk remain stable

across the four portfolios formed by historical turnover. Again, the results imply that the cross

section of historical turnover of U.S. stocks contains useful information about the cross section of

long-term liquidity risk.

7. Conclusion

By examining the cross-section of turnovers for NYSE/AMEX and NASADQ stocks over time,

this paper documents new evidence that trading volume possesses strong long-run cross-sectional

persistence. In other words, stocks with relatively high (low) turnover in a given year tend to

maintain relatively high (low) turnover for the subsequent 20 years. Higher turnover stocks also

tend to have lower trading costs and lower liquidity risk for many years to come. The evidence

suggests that investors in the U.S. markets have a persistent preference for trading high turnover

stocks. Consequently, there is a long-run component or even a time-invariant component in trading

volume. We use initial turnover as a proxy for the time-invariant component, and find that, for

explaining future turnover, initial turnover dominates all the well-known determinants of trading

volume identified in previous studies.

34

The cross-sectional turnover persistence tells us: (1) there is a persistent investor clientele

effect (i.e. short-term investors persistently trade liquidity stocks, while long-term investors

persistently hold illiquid stocks, as Amihud and Medelson (1986) propose); (2) consistent with

Tobin (1958), investors have strong liquidity preference and that, with such investors, it is relatively

easy to find liquidity premium in asset pricing tests; and (3) Booth and Chua‟s (1996)

liquidity-purchasing theory of IPO underpricng is relevant in IPO pricing because initial turnover

has long-lasting impacts on future liquidity.

Thus, cross-sectional turnover persistence is an important phenomenon. It has implications for

asset pricing and for corporate finance. Given that the liquidity premium is pervasive in many

markets (Amihud, Mendelson, and Petersen (2005)), our findings imply that firms that have higher

historical turnover will have a persistently lower cost of equity capital, which might lead the firms

to rely more on equity capital versus debt.

This implication is potentially interesting because Lemmon, Roberts, and Zender (2008)

document a strong cross-sectional persistence in corporate capital structure. So far, there appears to

be no theories that can explain this capital structure phenomenon. We leave for future research the

issue of whether cross-sectional persistence in turnover and cross-sectional persistence in corporate

capital structure are related.

35

Appendix 1. Variable Definitions

Appendix 1 details the definitions of key variables employed in this paper.

Variable Description (Data Source)

I. Turnover Measure

TURN Time-aggregated raw annualized turnover, measured by

accumulating monthly turnovers across months in each

calendar year, where turnover in a given month is defined as

total trading volume in that month divided by the number of

shares outstanding at the end of the month. (CRSP)

TURNGRT

Detrended annualized turnover, generated by following the

detrended procedure of Gallant, Rossi, and Tauchen (1992).

(CRSP)

TURNInitial

Initial turnover, defined as the first nonmissing value of

detrended annualized turnover series. (CRSP)

TURNU Unexplained turnover, defined as the residuals from a yearly

cross-sectional regression of TURNGRT

on one-year lagged

RETPos

, RETNeg

, BM, SIZE, AGE, DEBT, SIGMA, BETA,

CGibbs

, PRC, DIVDY, and INDTURN. (CRSP, Compustat,

French‟s website, and Hasbrouck‟s website)

IPOTURN Detrended IPO turnover, defined as follows. First, we generate

raw annualized turnover series by summing monthly turnovers

over each year following the IPO year for each IPO stock. To

mitigate the concern that some IPO stocks are likely to not

have all 12 monthly observations in the IPO year, we proxy

raw annualized IPO turnover by summing 12 monthly raw

monthly turnovers following the IPO month. We then generate

the detrended annualized turnover series for each IPO stock

based on Gallant et al.‟s (1992) procedure. IPOTURN is the

detrended annualized turnover in the IPO year. (CRSP and

Ritter‟s website)

IPOTURNU Unexplained IPO turnover, defined as the residuals from a

cross-sectional regression of the initial detrended turnover on

the initial values of RETPos

, RETNeg

, BM, SIZE, DEBT, SIGMA,

BETA, CGibbs

, PRC, DIVDY, and INDTURN. The initial

detrended turnover and initial values of regressors are

calculated as the average over year 0, 1, and 2, where year 0 is

the IPO year. (CRSP, Compustat, French‟s website,

Hasbrouck‟s website and Ritter‟s website)

36

II. Turnover Determinants

A. Liquidity Trading: Portfolio

Rebalancing Needs and Stock

Visibility

RETPos

The annualized return of an individual stock if it is positive,

and zero otherwise. (CRSP)

RETNeg

The annualized return of an individual stock if it is negative,

and zero otherwise. (CRSP)

αCAPM

Intercept from the CAPM regression of individual stock‟s

weekly excess returns on the value-weighted market‟s weekly

excess returns in each calendar year. Weekly return is

generated by following that of Lo and MacKinlay (1988)

based on CRSP daily data. (CRSP and WRDS)

BM Book value of equity at the end of each fiscal year divided by

the year-end market capitalization (share price multiplied by

total number of shares outstanding). (Compustat)

SIZE Natural logarithm of year-end market capitalization in each

calendar year. (CRSP)

AGE Value of ln(1+A), where A represents firm age that is defined

as the number of years since a stock listed on an exchange

reported in the CRSP. (CRSP)

SP500 Dummy variable equals to one if the firm-year is a member of

S&P 500 index, and zero otherwise. (Compustat)

B. Mass of Informed Trades

ACOV Analyst coverage, defined as the maximum number of analysts

that make annual earnings forecasts in any month over a year.

We set the observations to zero if data unavailable. Data on

analyst coverage started in 1976. (I/B/E/S)

IOR Institutional holdings, defined as the quarterly average in a

given year of the total number of a firm‟s shares held by

institutions in a quarter divided by its quarter-end total number

of shares outstanding. We set the observations to zero if data

unavailable. Data on institutional holdings started in 1980.

(Thomson Financial 13F Data) C. Different Opinion across

Investors

FDISP Analyst forecast dispersion, defined as the monthly average of

standard deviation of current-year EPS forecasts by two or

more analysts in a year divided by the absolute value of the

mean forecast for the year. We set the observations to zero if

data unavailable. (I/B/E/S)

37

DEBT Debt ratio, defined as the ratio of book value of long-term debt

to book value of total assets at the end of each fiscal year.

(Compustat)

D. Investor’s Learning about

Fundamental Value and

about the Return Generating

Process

ESURP Earnings surprise, defined as the absolute value of the

difference between the earnings deflated by total assets at the

end of each fiscal year and the average asset-deflated earnings

over the past four years. (Compustat)

EVOL Earnings volatility, defined as the standard deviation of the

asset-deflated earnings over the most recent five years (with a

minimum of three years). (Compustat)

SIGMA Residual standard deviation (root mean square error) from the

market model regression of individual stock‟s weekly excess

returns on the value-weighted market‟s weekly excess returns

for each calendar year. (CRSP)

BETA Slope coefficient from the market model regression of

individual stock‟s weekly excess returns on the value-weighted

market‟s weekly excess returns for each calendar year. (CRSP)

PBETA Portfolio beta, generated by following that of Fama and French

(1992). (CRSP)

E. Trading Cost

CGibbs

Gibbs trading cost, proposed by Hasbrouck (2009) as an

improved effective bid-ask spread measure based on Roll‟s

spread. (Hasbrouck‟s website)

PRC Natural logarithm of year-end share price in each calendar

year. (CRSP)

F. Dividend-Capture Trades

DIVDY Dividend yield, defined as the ratio of annual cash dividend to

year-end price per share. (Compustat)

G. Industry Characteristic

INDTURN Median of detrended annual turnover in a given industry for

each year, where the industry is identified by Fama and

French‟s 48-industry classification. (CRSP and French‟s

website)

38

III. Other Key Variables

ILLIQ Amihud‟s (2002) illiquidity measure, defined as the annual

average ratio of the daily absolute return to the dollar trading

volume on that day. (CRSP)

LIQBETA Liquidity beta, the coefficient on Pastor and Stambaugh‟s

(2003) liquidity factor from the regression model that includes

the traditional four factors (Fama and French (1993) and

Carhart (1997)) with all data available over a period of 60

months (if at least 36 months are available) prior to the end of

each year. (CRSP and WRDS)

39

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45

Table 1. Descriptive Statistics of Turnover

This table summarizes the description of annualized turnover for NYSE/AMEX stocks from 1965 to 2009 and NASDAQ

stocks from 1983 to 2009. Annualized turnover is calculated by accumulating monthly turnovers in each calendar year,

where a monthly turnover is defined as total trading volume in the month divided by the number of shares outstanding at

the end of the month. Data on monthly turnovers are generated from CRSP data. The table reports mean, median,

standard deviation (STD), and firm-year observations (N). Panel A shows the results by subperiods, and Panel B reports

the top ten and the bottom ten industries in turnover (sorting by the median level), where industry classification is based

on Fama and French‟s 48-industry groups. We first calculate the mean, median, and STD of turnover cross-sectionally

year by year and then obtain the time-series means for each subperiod and report them in Panel A, and similarly for each

industry and report them in Panel B.

Panel A: Turnover by Calendar Year

NYSE/AMEX NASDAQ

Year N Mean Median STD Year N Mean Median STD

1965-1970 11,812 0.438 0.286 0.451 - - - - -

1971-1975 11,652 0.285 0.216 0.235 - - - - -

1976-1980 10,999 0.364 0.278 0.285 - - - - -

1981-1985 9,984 0.508 0.421 0.358 1983-1985 7,962 0.557 0.394 0.512

1986-1990 9,098 0.603 0.492 0.457 1986-1990 17,198 0.676 0.455 0.680

1991-1995 9,746 0.622 0.491 0.509 1991-1995 17,693 1.085 0.696 1.159

1996-2000 10,494 0.829 0.668 0.652 1996-2000 19,999 1.697 1.063 1.839

2001-2005 9,257 1.271 0.993 1.085 2001-2005 14,676 1.685 0.951 2.027

2006-2009 6,652 2.494 2.017 2.053 2006-2009 10,004 1.980 1.327 2.074

1965-2009 89,694 0.778 0.613 0.641 1983-2009 87,532 1.307 0.826 1.421

Panel B: Turnover by Industry Classification

NYSE/AMEX NASDAQ

Industry N Mean Median STD Industry N Mean Median STD

Highest 10: Highest 10:

Coal (29) 274 1.197 1.170 0.631 Defense (26) 91 2.370 2.406 1.901

Precious Metals (27) 443 1.177 1.079 0.794 Coal (29) 71 1.728 1.666 1.532

Construction (18) 1,288 1.134 0.935 0.843 Electronic Eq. (36) 4,939 2.060 1.543 1.750

Healthcare (11) 1,025 0.958 0.915 0.611 Computers (35) 3,410 2.064 1.509 1.845

Steel Works (19) 2,474 1.017 0.887 0.706 Pharmaceutical (13) 4,330 1.792 1.394 1.439 Petroleum and Natural Gas (30) 4,590 0.959 0.849 0.669 Business Services (34) 11,170 1.693 1.248 1.519

Computers (35) 1,745 1.019 0.837 0.703 Communication (32) 2,361 1.623 1.211 1.486

Retail (42) 5,616 0.948 0.832 0.651 Healthcare (11) 1,872 1.589 1.197 1.344

Transportation (40) 2,342 1.078 0.832 0.845 Precious Metals (27) 436 1.335 1.137 0.835

Electronic Eq. (36) 3,435 0.993 0.827 0.759 Electrical Eq. (22) 2,967 1.629 1.134 1.494

Lowest 10: Lowest 10:

Agriculture (01) 247 0.476 0.359 0.374 Real Estate (46) 705 0.489 0.302 0.564

Real Estate (46) 1,164 0.534 0.395 0.446 Utilities (31) 789 0.637 0.347 0.824

Candy and Soda (03) 547 0.518 0.455 0.343 Banking (44) 9,849 0.642 0.408 0.733

Beer and Liquor (04) 416 0.517 0.459 0.470 Insurance (45) 2,199 0.671 0.413 0.786

Rubber and Plastic (15) 985 0.583 0.473 0.434 Trading (47) 7,322 0.770 0.426 0.935

Textiles (16) 1,448 0.634 0.499 0.524 Shipping Containers

(39)

199 0.776 0.522 0.762

Food Products (02) 2,154 0.576 0.505 0.453 Agriculture (01) 280 0.859 0.559 0.886

Utilities (31) 5,958 0.570 0.507 0.319 Beer and Liquor (04) 270 0.869 0.563 0.895

Trading (47) 4,244 0.689 0.534 0.613 Food (02) 1,028 0.908 0.572 0.966

Business Supplies (38) 1,381 0.6224 0.540 0.423 Textiles (16) 339 0.708 0.596 0.576

46

Table 2. Summary Statistics of Known Turnover Determinants

This table summarizes descriptive statistics of the known turnover determinants identified in previous studies for

NYSE/AMEX stocks from 1965 to 2009 and NASDAQ stocks from 1983 to 2009. Definitions of the variables and their

data sources are detailed in Appendix 1. The table reports mean, median, standard deviation (STD), and firm-year

observations (N). The values of the statistics (Mean, Median, and STD) are first calculated cross-sectionally year by year

and then the time-series means of those values are presented. All variables are winsorized at the upper and the lower

1-percentiles. Data on analyst coverage and analyst forecast dispersion reported in I/B/E/S runs from 1976 to 2009.

Institutional holding data reported in Thomson Financial 13F covers the period from 1980 to 2009.

NYSE/AMEX NASDAQ

Turnover Determinants N Mean Median STD N Mean Median STD



Visibility

RETPos 89,694 0.270 0.138 0.375 87,532 0.303 0.097 0.470

RETNeg 89,694 -0.113 -0.051 0.154 87,532 -0.173 -0.072 0.219

αCAPM (%) 89,684 0.105 0.078 0.762 87,532 0.120 0.098 1.039

BM 71,898 0.820 0.729 0.628 71,116 0.695 0.572 0.640

SIZE 89,694 18.953 18.980 1.985 87,532 17.889 17.814 1.646

AGE 86,232 2.736 2.742 0.824 87,532 2.111 2.092 0.609

SP500 89,694 0.281 0.000 0.449 87,532 0.038 0.000 0.190

B. Mass of Informed Trading

ACOV 66,230 7.717 4.971 8.159 87,532 3.316 1.630 4.719

IOR 57,330 0.411 0.425 0.241 87,532 0.261 0.204 0.223

C. Different Opinions across

Investors

FDISP 66,230 0.101 0.029 0.250 87,532 0.080 0.008 0.234

DEBT 73,600 0.208 0.186 0.169 70,840 0.133 0.059 0.174

D. Investors’ Learning about

Fundamental Value and about the

Return Generating Process

ESURP 71,331 0.051 0.022 0.088 61,137 0.122 0.044 0.210

EVOL 71,331 0.048 0.024 0.076 61,317 0.119 0.053 0.185

SIGMA (%) 89,684 5.445 4.673 2.975 87,532 7.879 7.028 4.180

BETA 89,684 0.982 0.930 0.632 87,532 0.821 0.722 0.803

PBETA 89,694 0.991 1.002 0.230 87,532 0.834 0.803 0.266

E. Trading Cost

CGibbs (×100) 88,001 0.781 0.466 0.874 73,347 1.606 1.230 1.246

PRC 89,694 2.208 2.372 1.109 87,532 1.848 1.964 1.147

F. Dividend-Capture Trade

DIVDY 72,788 0.022 0.017 0.023 70,717 0.007 0.000 0.016


INDTURN 89,694 0.630 0.601 0.229 87,532 0.879 0.917 0.364

47

Table 3. Correlations among the Turnover Determinants

This table reports the Pearson correlation coefficients (in percentage) among the turnover determinants listed in Table 2. The sample consists of NYSE/AMEX stocks from 1965 to

2009 and NASDAQ stocks from 1983 to 2009 on CRSP with share codes 10 or 11. The results for NYSE/AMEX (NASDAQ) sample are shown in upper (lower) triangle of this

table. Definitions of variables and data sources are detailed in Appendix 1. All variables are winsorized at the upper and the lower 1-percentiles.

RETPos RETNeg αCAPM BM SIZE AGE SP500 ACOV IOR FDISP DEBT ESURP EVOL SIGMA BETA PBETA CGibbs PRC DIVDY INDTURN



Visibility

RETPos 100.00 28.94 64.72 0.25 0.18 -6.04 -3.50 -7.24 -4.31 -0.52 -0.54 0.67 1.57 18.26 10.16 3.38 1.07 6.34 0.25 3.20

RETNeg 25.37 100.00 60.86 0.23 27.75 14.82 12.33 9.91 8.00 -0.90 -3.46 -10.75 -8.12 -34.15 -6.97 -5.03 -26.57 31.36 0.22 -8.67

αCAPM 58.71 64.65 100.00 0.51 5.35 -0.92 -0.36 -6.32 -1.11 0.25 -0.39 -1.28 1.10 24.48 0.81 1.22 3.03 10.50 0.51 2.05

BM -0.68 0.30 0.22 100.00 0.66 0.32 0.53 0.71 0.54 -0.01 -0.29 -0.15 -0.10 0.03 -0.23 -0.68 -0.21 0.57 9.94 -0.08

SIZE 12.26 32.53 17.14 -1.19 100.00 38.94 52.42 76.25 65.48 2.52 -1.13 -7.68 -6.59 -42.21 3.79 8.45 -55.15 67.10 0.68 28.86

AGE -0.31 12.03 5.33 0.50 17.91 100.00 33.83 28.72 18.22 1.01 -0.88 -4.50 -4.84 -24.23 -9.24 -3.88 -18.76 9.98 0.31 3.46

SP500 1.71 6.45 2.03 -0.53 33.83 10.47 100.00 59.58 33.80 1.82 -0.40 -5.33 -5.16 -24.23 4.96 7.28 -24.26 6.50 0.53 0.32

B. Mass of Informed Trade

ACOV -0.80 2.88 -3.28 -1.23 68.29 10.86 47.30 100.00 46.99 1.99 -1.73 -4.21 -4.35 -27.76 11.35 19.37 -32.85 31.93 0.74 7.73

IOR -0.21 7.66 -0.51 -0.94 67.66 20.22 21.50 62.31 100.00 1.39 0.60 -2.30 -2.44 -24.46 20.80 34.16 -38.38 39.63 0.56 33.03


Investors

FDISP -0.37 -1.28 -0.32 -0.01 2.67 1.37 4.49 3.79 2.30 100.00 0.06 -0.08 -0.09 -0.31 0.20 0.01 0.54 0.63 -0.01 3.85

DEBT -1.46 -4.63 -3.17 -0.01 -2.99 -0.23 -2.03 -0.64 1.55 -0.07 100.00 24.80 11.00 5.67 -0.38 -0.90 7.58 -4.31 -0.27 -0.52

D. Investors‟ Learning about



Process

ESURP 0.70 -9.88 -1.38 -1.01 -6.07 -5.14 -1.69 -2.38 -4.22 -0.06 5.67 100.00 79.19 19.73 2.41 5.28 16.58 -9.90 -0.05 7.32

EVOL 1.66 -9.05 -0.11 -0.83 -5.73 -7.39 -2.02 -3.13 -4.86 -0.11 3.81 83.09 100.00 18.66 3.50 4.99 12.43 -8.80 -0.05 8.02

SIGMA 17.91 -35.29 26.74 -1.42 -32.08 -12.53 -8.87 -14.01 -19.24 -0.41 1.90 13.09 14.39 100.00 14.38 13.44 59.58 -38.01 0.06 15.34

BETA 7.17 -6.38 3.59 -1.08 29.26 -3.01 10.07 28.83 27.06 0.26 -1.51 3.56 4.34 16.45 100.00 44.96 -5.87 11.60 -0.22 4.79

PBETA 8.56 -10.32 9.38 -1.32 27.57 -6.36 13.56 24.45 20.95 0.71 -1.96 3.41 3.46 11.56 37.71 100.00 -21.43 29.22 -0.68 9.54

E. Trading Cost

CGibbs -3.94 -19.48 3.30 0.66 -63.69 -7.55 -12.35 -35.94 -43.00 0.83 2.75 3.45 3.19 40.91 -21.60 -48.78 100.00 -47.92 -0.19 -6.65

PRC 13.00 41.23 20.75 -0.12 59.60 22.14 11.71 36.55 45.88 1.17 -2.70 -8.83 -8.62 -39.62 1.64 12.20 -51.77 100.00 0.56 14.22


DIVDY -0.73 1.89 -0.23 0.14 0.05 3.12 0.39 -0.85 -1.06 -0.06 -0.14 -0.40 -0.36 -4.03 -2.40 -0.22 -0.88 1.26 100.00 -0.05


INDTURN 6.99 -14.92 3.84 -1.17 29.98 8.99 6.23 26.97 40.44 2.29 2.00 5.48 7.78 25.02 28.14 26.20 -16.47 7.89 -4.66 100.00

48

Table 4. The Impact of Initial Turnover on Future Turnover

This table reports the standardized coefficients estimated from Fama-MacBeth regressions of

detrended annualized turnover (TURNGRT

) on initial turnover (TURNInitial

) and the known

determinants identified in previous studies for NYSE/AMEX stocks in Panel A and for NASDAQ

stocks in Panel B. Definitions of the variables and data sources are detailed in Appendix 1. All

variables are winsorized at the upper and the lower 1-percentiles. The model specification is as

follows:

, , 1 ,

GRT Initial

i t i i t i tTURN TURN

X

where ,

GRT

i tTURN is Gallant et al.‟s (1992) detrended turnover in year t for stock i; Initial

iTURN is the

initial turnover, defined as the first nonmissing value of the detrended annualized turnover series for

stock i; Xi, t-1 contains a set of one-year lagged known turnover determinants as the control variables.

The standardized coefficients in this table represent the change in terms of standard deviation in

TURNGRT

that results from a change of one standard deviation in each of independent variables.

Numbers in parentheses are heteroskedasticity and autocorrelation-consistent (HAC) t-statistics

based on Newey and West (1987). Data on analyst coverage (ACOV) and analyst forecast dispersion

(FDSIP) reported in I/B/E/S runs from 1976 to 2009. Institutional holdings (IOR) reported in

Thomson Financial 13F covers the period from 1980 to 2009

49

Table 4. The Impact of Initial Turnover (cont.)

Panel A: NYSE/AMEX

(1) (2) (3) (4) (5) (6)

TURNInitial 0.449 0.382 0.381 0.353

(9.67) (8.29) (7.67) (6.95)



Visibility

RETPos 0.145 0.105

(9.48) (9.62)

RETNeg -0.058 -0.026

(-4.63) (-3.15)

αCAPM 0.033 0.046 0.057

(2.52) (4.57) (6.19)

BM -0.021 -0.018 0.010

(-2.19) (-1.78) (1.52)

SIZE 0.211 0.160 0.073 0.116 0.035

(10.27) (7.46) (2.38) (2.96) (1.89)

AGE -0.207 -0.069 -0.105

(-3.56) (-1.97) (-3.50)

SP500 0.063 0.093 0.078

(2.00) (5.46) (5.04)

B. Mass of Informed Trade ACOV 0.225 0.138 0.109 (6.32) (8.86) (8.35) IOR 0.186 (10.27) C. Different Opinions across Investors

FDISP 0.089 0.060 0.049 (4.81) (3.92) (3.22) DEBT 0.015 0.017 0.021 (2.25) (2.19) (2.72) D. Investor‟s Learning about



ESURP 0.039 0.026 0.016 (2.37) (2.18) (2.27)

EVOL 0.036 0.027 0.024 (2.47) (2.21) (2.01) SIGMA 0.171 0.085 0.124 (9.76) (10.40) (11.28) BETA 0.177 0.110 (18.62) (11.39)

PBETA 0.123 0.069 0.045 (4.10) (4.93) (4.33) E. Trading Cost

CGibbs -0.127 -0.071 -0.090

(-5.91) (-3.94) (-3.79)

PRC 0.056 0.058 0.087 0.079 0.077

(2.05) (2.21) (2.04) (1.99) (2.10)

F. Dividend-Capture Trade DIVDY -0.133 -0.062 -0.050

(-7.12) (-6.29) (-3.24)

G. Industry Characteristic INDTURN 0.024

(4.91)

Sample Period 1965-2009 1965-2009 1965-2009 1976-2009 1976-2009 1980-2009

Adj. R2 0.246 0.198 0.357 0.225 0.362 0.424

N 67,309 67,309 67,309 48,570 48,570 41,292

50

Table 4. The Impact of Initial Turnover (cont.)

Panel B: NASDAQ

(1) (2) (3) (4) (5) (6)

TURNInitial 0.671 0.547 0.529 0.484

(29.19) (15.69) (10.04) (8.86)



Visibility

RETPos 0.148 0.116 (4.32) (6.76) RETNeg -0.039 -0.020 (-2.01) (-2.13) αCAPM

0.063 0.057 0.089 (4.05) (3.04) (5.59)

BM -0.083 -0.046 -0.016 (-3.49) (-4.87) (-2.52) SIZE 0.272 0.191 0.086 0.048 0.016 (6.83) (6.62) (2.78) (2.96) (1.72) AGE -0.223 -0.078 -0.072 (-6.27) (-3.64) (-3.25)

SP500 0.056 0.033 0.018 (3.78) (4.39) (3.12) B. Mass of Informed Trade ACOV 0.274 0.161 0.125 (8.04) (8.94) (8.49) IOR 0.086

(5.53) C. Different Opinions across Investors FDISP 0.027 0.012 0.005 (4.62) (2.99) (1.08) DEBT -0.024 -0.013 -0.012 (-2.98) (-2.79) (-2.40)




ESURP 0.022 0.020 0.019 (2.11) (2.22) (2.10) EVOL 0.112 0.059 0.046 (6.54) (4.55) (4.15) SIGMA 0.241 0.115 0.083 (8.32) (8.26) (6.14)

BETA 0.171 0.087 (9.84) (8.60) PBETA 0.091 0.055 0.048 (5.84) (5.78) (5.38) E. Trading Cost CGibbs -0.133 -0.066 -0.072

(-2.60) (-2.19) (-2.34) PRC 0.061 0.026 0.097 0.041 0.028 (2.18) (1.77) (4.46) (2.24) (1.97) F. Dividend-Capture Trade DIVDY -0.180 -0.082 -0.042 (-9.52) (-9.33) (-4.99)

G. Industry Characteristic INDTURN 0.072

(6.70)

Sample Period 1983-2009 1983-2009 1983-2009 1983-2009 1983-2009 1983-2009

Adj. R2 0.446 0.286 0.501 0.311 0.533 0.543

N 54,384 54,384 54,384 46,516 46,516 46,516

51

Table 5. The Impact of Initial Turnover on Future Turnover Controlling for Alternative

Lag-lengths of the Known Turnover Determinants

This table reports the standardized coefficients on initial turnover (TURNinitial

) from Fama-MacBeth

regressions of the detrended turnover (TURNGRT

) taking into account alternative lag-lengths of the

control variables in Model (6) of Table 4. Panel A reports the results for the NYSE/AMEX sample

during 1980-2009, since one of the control variables, IOR, reported in Thomson Financial 13F started

in 1980. Panel B reports the results for the NASDAQ sample during 1983-2009. The model

specification is as follows:

, , ,

1

LGRT Initial

i t i s i t s i t

s

TURN TURN

= X

where ,

GRT

i tTURN is Gallant et al.‟s (1992) detrended turnover in year t for stock i; Initial

iTURN is the

initial turnover defined as the first nonmissing value of detrended annualized turnover series for stock

i; L represents the lag order of each control variable included in the vector X which is comprised of all

control variables in Model (6) of Table 4. Definitions of variables and data sources are detailed in

Appendix 1. All variables are winsorized at the upper and the lower 1-percentiles. We use the Akaike

Information Criterion (AIC) and the Bayesian Information Criterion (BIC) to determine the

appropriate lag lengths, which are 10 periods for the NYSE/AMEX sample and 8 periods for the

NASDAQ sample. Thus, Panel A reports the standardized coefficients on TURNInitial

after considering

each control variable with lag-lengths from 2 lags to 10 lags, while Panel B reports the standardized

coefficients on TURNInitial

after considering each control variable with lag-lengths from 2 to 8 lags.

The standardized coefficients in this table represent the change in terms of standard deviation in

TURNGRT

that results from a change of one standard deviation in TURNInitial

. Numbers in parentheses

are heteroskedasticity and autocorrelation-consistent (HAC) t-statistics based on Newey and West

(1987). Firm-year observations (N) vary from one specification to another due to data availability.

Panel A: NYSE/AMEX

2 lags 3 lags 4 lags 5 lags 6 lags 7 lags 8 lags 9 lags 10 lags

TURNInitial

0.314 0.286 0.260 0.248 0.245 0.241 0.239 0.230 0.215

(6.36) (6.17) (5.66) (5.58) (5.11) (5.27) (5.17) (5.07) (4.92)

Sample Period 1980-2009 1980-2009 1980-2009 1980-2009 1980-2009 1980-2009 1980-2009 1980-2009 1980-2009

Adj. R2 0.436 0.457 0.479 0.499 0.513 0.560 0.572 0.598 0.647

N 36,402 32,134 28,348 25,035 22,137 19,576 17,317 15,299 13,499

Panel B: NASDAQ

2 lags 3 lags 4 lags 5 lags 6 lags 7 lags 8 lags

TURNInitial

0.436 0.401 0.351 0.316 0.245 0.231 0.208

(7.62) (7.37) (6.87) (6.92) (6.88) (6.77) (5.32)

Sample Period 1983-2009 1983-2009 1983-2009 1983-2009 1983-2009 1983-2009 1983-2009

Adj. R2 0.541 0.537 0.562 0.572 0.611 0.619 0.652

N 38,332 31,526 25,921 21,298 17,394 14,071 11,421

52

Table 6. Decomposing the Variation in Turnover

This table reports the variance decomposition of detrended annualized turnover (TURNGRT

) for the

NYSE/AMEX sample from 1965 to 2009 in Panel A and for the NASDAQ sample from 1983 to

2009 in Panel B. The sample consists of NYSE/AMEX- and NASDAQ-listed stocks with CRSP

share codes 10 and 11. We delete the first firm-year observation without all 12 monthly turnover

data for each stock. The pooled OLS regression model is specified as follow:

, , 1 ,

GRT

i t i t i t i tTURN

X

where is a firm-fixed effect; is a time-fixed effect; and other variables are identical to Table 4.

Definitions of the variables and data sources are detailed in Appendix 1. All variables are

winsorized at the upper and the lower 1-percentiles. To provide the normalized and comparable

results, we divide the Type III partial sum of squares for each regressor by the aggregate partial sum

of squares across all regressors for each model specification. Thus, each value in the table is the

proportion of the model sum of squares attributable to particular determinant which, in turn, forces

columns to sum to one hundred percent. For instance, the interpretation of the value, 93.28, in

Model (3) of Panel A is that 93.28% of the explained sum of squares captured can be attributed to

the firm-fixed effects.

53

Table 6. Decomposing the Variation in Turnover (cont.)

Panel A: NYSE/AMEX

(1) (2) (3) (4) (5) (6) (7) (8) (9)

Firm-Fixed Effect 100.00 93.28 93.69 94.54 95.11

Time-Fixed Effect 100.00 6.72 0.60 5.47 0.41 4.16 0.30 4.01


Rebalancing Needs and

Stock Visibility

RETPos 18.98 0.73

RETNeg 2.53 0.01

αCAPM 2.64 0.62 3.25 0.17

BM 0.10 0.02 0.08 0.00

SIZE 29.11 0.05 8.73 0.11 0.02 0.04

AGE 28.34 0.13 23.56 0.03

SP500 2.20 0.11 0.25 0.05


ACOV 12.98 0.00 5.43 0.02

IOR 34.21 0.22


Investors

FDISP 8.02 0.02 2.45 0.03

DEBT 1.22 0.00 0.43 0.01




Process

ESURP 1.20 0.12 0.23 0.07

EVOL 1.03 0.14 0.44 0.08

SIGMA 14.50 0.02 10.76 0.09

BETA 17.30 0.04

PBETA 9.03 0.00 4.21 0.01

E. Trading Cost

CGibbs 10.19 0.01 3.95 0.00

PRC 7.15 0.00 7.43 0.02 4.21 0.02


DIVDY 16.30 0.00 2.39 0.01


INDTURN 3.83 0.03 Sample Period 1965-2009 1965-2009 1965-2009 1965-2009 1965-2009 1976-2009

1976-2009 1980-2009 1980-2009

Adj. R2 0.542 0.063 0.605 0.212 0.614 0.265 0.649 0.308 0.655

N 67,309 67,309 67,309 67,309 67,309 48,570 48,570 41,292 41,292

54

Table 6. Decomposing the Variation in Turnover (cont.)

Panel B: NASDAQ

(1) (2) (3) (4) (5) (6) (7) (8) (9)

Firm-Fixed Effect 100.00 94.82 95.28 95.51 95.65

Time-Fixed Effect 100.00 5.18 1.60 3.93 1.81 3.27 2.17 3.23


Rebalancing Needs and

Stock Visibility

RETPos 23.01 0.77

RETNeg 3.61 0.03

αCAPM 1.30 0.59 5.80 0.56

BM 6.11 0.04 1.20 0.05

SIZE 15.88 0.04 0.04 0.11 0.01 0.09

AGE 26.8 0.03 24.98 0.05

SP500 5.01 0.03 1.07 0.03


ACOV 31.45 0.02 17.02 0.00

IOR 6.82 0.06


Investors

FDISP 2.12 0.02 0.55 0.04

DEBT 0.66 0.01 0.63 0.01




Process

ESURP 0.01 0.00 0.02 0.03

EVOL 0.02 0.01 0.01 0.01

SIGMA 26.53 0.00 16.3 0.01

BETA 16.39 0.00

PBETA 1.26 0.10 0.58 0.06

E. Trading Cost

CGibbs 6.50 0.01 3.42 0.02

PRC 0.13 0.11 3.10 0.08 0.23 0.08


DIVDY 26.67 0.00 4.44 0.00


INDTURN 14.75 0.02 Sample Period 1983-2009 1983-2009 1983-2009 1983-2009 1983-2009 1983-2009

1983-2009 1983-2009 1983-2009

Adj. R2 0.652 0.055 0.679 0.304 0.699 0.309 0.709 0.398 0.741

N 54,384 54,384 54,384 54,384 54,384 46,516 46,516 46,516 46,516

55

Table 7. Liquidity Level and Liquidity Risk Sorted by Historical Turnover

This table reports liquidity level and liquidity risk of portfolios sorted by historical detrended turnover (denoted as Lowest, Low, High, and Highest) for the NYSE/AMEX sample

from 1965 to 2009 in Panel A and for the NASDAQ sample from 1983 to 2009 in Panel B. We report the results in years t+1, t+5, t+10, t+15, and t+20 (where t is portfolio formation

year) based on alternative turnover portfolios sorted by lag 1-year, lag 5-year average, lag 10-year average, lag 15-year average, and lag 20-year average of TURNGRT. Liquidity level

is proxied by Amihud‟s (2002) illiquidity (ILLIQ) and Hasbrouck‟s (2009) Gibbs trading cost (CGibbs), while liquidity risk is measured by Pastor and Stambaugh‟s (2003) liquidity

beta (LIQBETA). In Panel B we do not report the results of LIQBETA for the NASDAQ sample since Pastor and Stambaugh‟s (2003) market liquidity innovation is generated using

NYSE/AMEX stocks. Some missing results for the NASDAQ sample in Panel B are due to data unavailable. The t-statistic tests the null hypothesis that the difference in mean

between the Highest and the Lowest detrended turnover portfolio is equal to zero. Definitions of the variables and data sources are detailed in Appendix 1. All variables are

winsorized at the upper and the lower 1-percentiles.

Panel A: NYSE/AMEX

Event Year

t+1 t+5 t+10 t+15 t+20

Portfolio by TURNGRT Highest Lowest H-L t-value Highest Lowest H-L t-value Highest Lowest H-L t-value Highest Lowest H-L t-value Highest Lowest H-L t-value

A. ILLIQ (in millions)

1-year lag 0.67 3.68 -3.01 -49.83 0.90 3.04 -2.14 -30.19 0.52 2.24 -1.72 -24.94 0.25 1.65 -1.40 -26.66 0.20 1.24 -1.04 -18.58

Mean of 5 lags 0.66 3.59 -2.94 -39.34 0.55 2.32 -1.77 -26.42 0.17 1.58 -1.41 -29.35 0.12 1.34 -1.22 -23.46 0.12 1.06 -0.94 -16.63

Mean of 10 lags 0.33 2.44 -2.10 -33.06 0.14 1.58 -1.44 -31.29 0.08 1.38 -1.30 -25.00 0.06 1.19 -1.13 -19.03 0.04 0.92 -0.89 -13.83

Mean of 15 lags 0.12 1.65 -1.53 -31.77 0.09 1.37 -1.28 -24.38 0.05 1.17 -1.12 -19.07 0.02 0.92 -0.90 -14.44 0.02 0.74 -0.73 -9.59

Mean of 20 lags 0.08 1.41 -1.33 -24.96 0.05 1.17 -1.12 -19.01 0.03 0.89 -0.86 -14.29 0.01 0.77 -0.76 -9.75 0.01 0.52 -0.51 -5.30

B. CGibbs (×100)

1-year lag 0.61 1.02 -0.41 -39.07 0.62 0.91 -0.29 -21.81 0.57 0.79 -0.22 -15.20 0.46 0.76 -0.30 -15.41 0.47 0.69 -0.23 -9.08

Mean of 5 lags 0.62 0.93 -0.31 -27.48 0.58 0.77 -0.20 -16.12 0.51 0.72 -0.21 -14.19 0.47 0.73 -0.26 -13.28 0.46 0.64 -0.19 -9.01

Mean of 10 lags 0.54 0.78 -0.24 -19.80 0.50 0.69 -0.19 -13.46 0.24 0.71 -0.47 -13.05 0.43 0.69 -0.26 -11.71 0.38 0.54 -0.15 -8.59

Mean of 15 lags 0.49 0.71 -0.22 -15.29 0.47 0.70 -0.23 -12.55 0.42 0.68 -0.26 -12.18 0.37 0.53 -0.15 -8.81 0.36 0.46 -0.11 -5.14

Mean of 20 lags 0.47 0.71 -0.24 -12.77 0.42 0.67 -0.25 -11.90 0.38 0.52 -0.14 -7.84 0.36 0.46 -0.10 -4.95 0.30 0.39 -0.09 -3.49

C. LIQBETA

1-year lag -3.32 -0.31 -3.01 -8.29 -2.36 -0.12 -2.24 -6.09 -2.11 0.86 -2.96 -7.17 -1.12 1.18 -2.30 -4.22 -0.39 1.14 -1.53 -2.53

Mean of 5 lags -3.81 0.42 -4.23 -10.67 -2.89 0.52 -3.41 -7.08 -1.57 0.86 -2.42 -4.28 0.08 0.95 -0.87 -2.05 0.37 1.73 -1.36 -2.72

Mean of 10 lags -3.68 0.74 -4.42 -9.14 -2.13 1.86 -3.99 -7.04 0.40 1.70 -1.30 -3.46 1.02 2.08 -1.06 -2.63 1.08 1.90 -0.83 -2.08

Mean of 15 lags -2.37 1.79 -4.16 -7.26 0.66 2.00 -1.34 -3.82 0.93 3.64 -2.70 -3.61 1.09 3.92 -2.82 -3.16 1.28 3.12 -1.84 -2.55

Mean of 20 lags 0.61 1.79 -1.18 -2.45 1.27 3.17 -1.90 -2.59 1.23 3.51 -2.28 -2.89 1.51 3.23 -1.72 -2.07 1.77 3.21 -1.44 -1.81

56

Table 7. Liquidity Level and Liquidity Risk Sorted by Historical Turnover (cont.)

Panel B: NASDAQ

Event Year

t+1 t+5 t+10 t+15 t+20

Portfolio by TURNGRT Highest Lowest H-L t-value Highest Lowest H-L t-value Highest Lowest H-L t-value Highest Lowest H-L t-value Highest Lowest H-L t-value

A. ILLIQ (in millions)

1-year lag 0.90 8.06 -7.15 -47.78 1.57 7.29 -5.72 -29.55 1.52 7.88 -6.36 -16.87 0.95 8.14 -7.19 -10.69 0.90 9.44 -8.54 -8.04

Mean of 5 lags 1.00 8.09 -7.08 -32.86 0.92 7.90 -6.98 -18.71 0.98 8.54 -7.56 -10.97 0.38 13.18 -12.80 -7.92 2.11 19.01 -16.90 -4.45

Mean of 10 lags 0.83 8.22 -7.39 -19.42 0.86 8.66 -7.80 -10.93 0.23 12.05 -11.82 -8.05 0.45 18.74 -18.29 -4.67 - - - -

Mean of 15 lags 0.76 8.84 -8.07 -11.24 0.19 14.28 -14.09 -8.18 0.24 18.62 -18.38 -4.60 - - - - - - - -

Mean of 20 lags 0.22 12.16 -11.94 -8.14 0.41 18.41 -18.00 -4.57 - - - - - - - - - - - -

B. CGibbs (×100)

1-year lag 1.22 2.26 -1.03 -52.60 1.20 1.80 -0.60 -28.72 0.95 1.53 -0.58 -22.27 0.75 1.20 -0.46 -15.74 0.58 1.12 -0.54 -11.51

Mean of 5 lags 1.17 2.06 -0.89 -34.82 0.96 1.75 -0.79 -24.31 0.72 1.41 -0.69 -18.08 0.56 1.20 -0.64 -14.38 0.68 1.86 -1.18 -6.41

Mean of 10 lags 0.91 1.76 -0.85 -26.78 0.69 1.39 -0.70 -18.81 0.55 1.20 -0.65 -14.22 0.60 1.77 -1.18 -6.79 - - - -

Mean of 15 lags 0.67 1.39 -0.72 -19.26 0.55 1.20 -0.64 -13.79 0.56 1.82 -1.26 -6.97 - - - - - - - -

Mean of 20 lags 0.54 1.18 -0.64 -14.08 0.67 1.82 -1.14 -5.94 - - - - - - - - - - - -

57

Panel A: TURN by TURN Portfolios

NYSE/AMEX

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Event Year

Highest High Low Lowest

NASDAQ

0.00

0.50

1.00

1.50

2.00

2.50

3.00

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Event Year


Panel B: TURNGRT

by TURNGRT

Portfolios

NYSE/AMEX

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Event Year


NASDAQ

0.00

0.50

1.00

1.50

2.00

2.50

3.00

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Event Year


Panel C: TURNGRT

by TURNU

Portfolios

NYSE/AMEX

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Event Year


NASDAQ

0.00

0.50

1.00

1.50

2.00

2.50

3.00

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Event Year


Panel D: TURNU

by TURNU

Portfolios

NYSE/AMEX

-0.50

-0.40

-0.30

-0.20

-0.10

0.00

0.10

0.20

0.30

0.40

0.50

0.60

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Event Year


NASDAQ

-0.80

-0.60

-0.40

-0.20

0.00

0.20

0.40

0.60

0.80

1.00

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Event Year


58

Figure 1. Evolution of Turnover across Turnover Portfolios in Event Time

This figure presents the evolution of average annualized turnover across four turnover portfolios in

event time for NYSE/AMEX stocks from 1965 to 2009 and NASDAQ stocks from 1983 to 2009,

where year 1 is the first year following the portfolio formation year. The sample consists of stocks

with CRSP share codes 10 and 11. We delete the first firm-year observation for each stock without

all the 12 monthly turnover data. The figure is constructed in the following procedure: (1) for each

calendar year, we sort stocks into four portfolios (denoted as Highest, High, Low and Lowest) based

on their annualized turnover; (2) keeping the portfolio compositions fixed, we trace the average

turnover for each of the four portfolios in the subsequent 20 years. For instance, we form four

portfolios sorted by annualized turnover in 1985 and then calculate the average turnover for each of

the four portfolios for each year from 1986 to 2005; (3) we replicate step (1) and (2) of sorting and

averaging year by year over our sample period. After operating this sorting and averaging for each

year, the average turnover across event time for these four portfolios are plotted in the figure. Panel

A presents the results based on the ranking of raw turnover (TURN) in each formation year to trace

the average raw turnover of each portfolio. Panel B presents the results based on the ranking of

Gallant et al. (1992) detrended turnover (TURNGRT

) in each formation year to trace the average

Gallant et al. (1992) detrended turnover of each portfolio. Panel C presents the results based on the

ranking of unexplained turnover (TURNU) in each formation year to trace the average Gallant et al.

(1992) detrended turnover of each portfolio, where unexplained turnover (TURNU) is defined as the

residuals from a yearly cross-sectional regression of TURNGRT


, RETNeg

,

BM, SIZE, AGE, DEBT, SIGMA, BETA, CGibbs

, PRC, DIVDY, and INDTURN. Panel D presents the

results based on the ranking of unexplained turnover (TURNU) in each formation year to trace the

average unexplained turnover of each portfolio. Definitions of the variables and data sources are

detailed in Appendix 1. All variables are winsorized at the upper and the lower 1-percentiles.

59

Panel A: Detrended Turnover by IPOTURN Portfolios

NYSE/AMEX

0.00

0.50

1.00

1.50

2.00

2.50

3.00

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Event Year


NASDAQ

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

4.50

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Event Year


Panel B: Detrended Turnover by IPOTURNU

Portfolios

NYSE/AMEX

0.00

0.50

1.00

1.50

2.00

2.50

3.00

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Event Year


NASDAQ

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Event Year


Figure 2. Evolution of Average Detrended Turnover across Turnover Portfolios for IPO Stocks

in Event Time This figure presents the average detrended annualized turnover of four turnover portfolios in event time for IPO stocks

listed on NYSE/AMEX from 1975 to 2009 and on NASDAQ from 1983 to 2009, where year 1 is the first year

following the IPO year. The initial sample of 9,036 IPOs during 1975-2009 is obtained from Jay Ritter‟s website. After

merging share turnover data obtained from CRSP, we leave with 8,132 IPO firms. Since we need data on RETPos,

RETNeg, BM, SIZE, DEBT, SIGMA, BETA, CGibbs, PRC, DIVDY, and INDTURN to generate unexplained IPO turnover

(IPOTURNU), our final sample consists of 5,127 IPO firms (including 1,208 NYSE/AMEX-listed firms and 3,919

NASDAQ-listed firms). The figure is constructed in the following procedure: (1) the raw annualized turnover series is

generated by summing monthly turnovers in each year following the IPO year for each IPO stock. To mitigate the effect

that some IPO stocks are likely to not have all 12 monthly observations in the IPO year, we proxy raw IPO turnover in

the IPO year by summing the first 12 monthly observations of turnover following the IPO month. We then generate the

detrended annualized turnover series for each IPO stock based on the linear transformation procedure suggested by

Gallant et al. (1992). Detrended IPO turnover (IPOTURN) is the first nonmissing value for the detrended annualized

turnover in the IPO year; (2) In Panel A, for each IPO year, we sort IPO stocks into four portfolios based on their

ranking of IPOTURN and then trace the average detrended turnover for each of the four portfolios over 20 years

following the IPO year. In Panel B, for each IPO year, we sort IPO stocks into four portfolios based on their ranking of

unexplained IPO turnover (IPOTURNU) and then trace the average detrended turnover for each of the four portfolios

over 20 years following the IPO year. Unexplained IPO turnover (IPOTURNU) is defined as the residuals from a

cross-sectional regression of detrended IPO initial turnover on the initial values of RETPos, RETNeg, BM, SIZE, DEBT,

SIGMA, BETA, CGibbs, PRC, DIVDY, and INDTURN. Also contained in the regression is IPO year-fixed effect.

Detrended IPO initial turnover and the initial values for each regressor are calculated as the average over year 0, 1, and

2, where year 0 is the IPO year; (3) After operating this sorting and averaging for each year over the period from 1975

to 2009 for the NYSE/AMEX sample and over the period from 1983 to 2009 for the NASDAQ sample, the average

detrended annualized turnover across event time for the four portfolios are plotted in the figure. Definitions of the

variables and data sources are detailed in Appendix 1. All variables are winsorized at the upper and the lower

1-percentiles.

60

Panel A: TURN by TURN Portfolios

NYSE/AMEX

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Event Year


NASDAQ

0.00

0.50

1.00

1.50

2.00

2.50

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Event Year


Panel B: TURNGRT

by TURNGRT

Portfolios

NYSE/AMEX

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Event Year


NASDAQ

0.00

0.50

1.00

1.50

2.00

2.50

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Event Year


Panel C: TURNGRT

by TURNU

Portfolios

NYSE/AMEX

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Event Year


NASDAQ

0.00

0.50

1.00

1.50

2.00

2.50

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Event Year


Panel D: TURNU

by TURNU

Portfolios

NYSE/AMEX

-0.40

-0.30

-0.20

-0.10

0.00

0.10

0.20

0.30

0.40

0.50

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Event Year


NASDAQ

-1.00

-0.80

-0.60

-0.40

-0.20

0.00

0.20

0.40

0.60

0.80

1.00

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Event Year


61

Figure 3. Evolution of Average Turnover across Turnover Portfolios for Time-Honored Stocks

in Event Time

This figure presents the average annualized turnover of four turnover portfolios for time-honored

stocks in event time, where year 1 is the first year following the portfolio formation year. The

sample consists of time-honored stocks listed on NYSE/AMEX from 1965 to 2009 and on

NASDAQ from 1983 to 2009, where the time-honored stocks are defined as those stocks that have

at least 20 years of nonmissing observations on share turnover in our sample period. The figure is

constructed based on the same procedure as described in Figure 1. Panel A presents the results

based on the ranking of raw turnover (TURN) in each formation year to trace the average raw

turnover of each portfolio. Panel B presents the results based on the ranking of Gallant et al. (1992)

detrended turnover (TURNGRT

) in each formation year to trace the average Gallant et al. (1992)

detrended turnover of each portfolio. Panel C presents the results based on the ranking of

unexplained turnover (TURNU) in each formation year to trace the average Gallant et al. (1992)

detrended turnover of each portfolio, where unexplained turnover (TURNU) is defined as the

residuals from a yearly cross-sectional regression of TURNGRT


, RETNeg

,

BM, SIZE, AGE, DEBT, SIGMA, BETA, CGibbs

, PRC, DIVDY, and INDTURN. Panel D presents the

results based on the ranking of unexplained turnover (TURNU) in each formation year to trace the

average unexplained turnover of each portfolio. Definitions of the variables and data sources are

detailed in Appendix 1. All variables are winsorized at the upper and lower 1-percentiles. Since this

subsample of stocks is required to exist for at least 20 years, we can only operate the portfolio

formation procedure from 1965 to 1989 for the NYSE/AMEX sample and from 1983 to 1989 for

the NASDAQ sample.

62

Panel A: Amihud (2002) Illiquidity (ILLIQ) by TURNGRT

Portfolios

NYSE/AMEX

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Event Year


NASDAQ

0.00

2.00

4.00

6.00

8.00

10.00

12.00

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Event Year


Panel B: Gibbs Trading Cost (CGibbs

) by TURNGRT

Portfolios

NYSE/AMEX

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Event Year


NASDAQ

0.00

0.50

1.00

1.50

2.00

2.50

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Event Year


Figure 4. Evolution of Average Amihud (2002) Illiquidity and Hasbrouck’s (2009) Gibbs

Trading Cost across Turnover Portfolios in Event Time

This figure presents the average Amihud (2002) illiquidity (ILLIQ) and Hasbrouck‟s (2009) Gibbs

trading cost (CGibbs

) across four detrended turnover portfolios in event time for NYSE/AMEX

stocks from 1965 to 2009 and NASDAQ stocks from 1983 to 2009, where year 1 is the first year

following the portfolio formation year. The sample consists of stocks with CRSP share codes 10 and

11. We delete the first firm-year observation for each stock without the entire 12 monthly turnover

data. The figure is constructed in the following procedure: (1) For each calendar year, we sort

stocks into four portfolios (denoted as Highest, High, Low and Lowest) based on their detrended

annualized turnover (TURNGRT

); (2) keeping the portfolio compositions fixed, we trace the average

Amihud (2002) illiquidity (ILLIQ) in Panel A and Hasbrouck‟s (2009) Gibbs trading cost (CGibbs

) in

Panel B for each of four portfolios in the subsequent 20 years. (3) We replicate steps (1) and (2) of

sorting and averaging for every year in our sample period. After operating this sorting and

averaging for each year, the average ILLIQ and CGibbs

across event time for the four portfolios are

plotted in the figure. Definitions of the variables and data sources are detailed in Appendix 1. All

variables are winsorized at the upper and the lower 1-percentiles.

63

PS (2003) Liquidity Risk (LIQBETA) by TURNGRT

Portfolios

NYSE/AMEX

-4.00

-3.00

-2.00

-1.00

0.00

1.00

2.00

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Event Year


Figure 5. Evolution of Average Pastor and Stambaugh’s (2003) Liquidity Risk (LIQBETA)

across Turnover Portfolios in Event Time

This figure presents the average of Pastor and Stambaugh‟s (2003) liquidity risk (LIQBETA) across

four detrended turnover portfolios in event time for NYSE/AMEX stocks from 1965 to 2009, where

year 1 is the first year following the portfolio formation year. The sample consists of

NYSE/AMEX-listed stocks with CRSP share codes 10 and 11. We delete the first firm-year

observation for each stock without the entire 12 monthly turnover data. The figure is constructed in

the following procedure: (1) For each calendar year, we sort stocks into four portfolios (denoted as

Highest, High, Low and Lowest) based on their detrended annualized turnover (TURNGRT

); (2)

keeping the portfolio compositions fixed, we trace the average LIQBETA for each of the four

portfolios in the subsequent 20 years. (3) We replicate steps (1) and (2) of sorting and averaging for

every year in our sample period. After operating this sorting and averaging for each year, the

average LIQBETA across event time for the four portfolios are plotted in the figure. Definitions of

the variables and data sources are detailed in Appendix 1. Data on LIQBETA is winsorized at the

upper and the lower 1-percentiles.

Liquidity Preference and Cross-Sectional Turnover Persistence · Their liquidity preference theory...

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