Buy on bad news, sell on good news: How insider trading ...wise12... · Likewise, insiders tend to...

27
Buy on bad news, sell on good news: How insider trading analysis can benefit from textual analysis of corporate disclosures Michael Hagenau 1 , Adriana Korczak 2 , Dirk Neumann 1 1 Universität Freiburg, Platz der Alten Synagoge, 79085 Freiburg, Germany {michael.hagenau, dirk.neumann}@is.uni-freiburg.de 2 School of Economics, Finance and Management, University of Bristol, 8 Woodland Road, Bristol BS8 1TN, United Kingdom [email protected] Abstract. We demonstrate how insider trading analysis may benefit from textual analysis. We analyze reported insider trading behavior and explain the association between corporate as well as 3rd-party news announcements on directors’ dealings activity. Previous approaches are extended by adding the sentiment of news to the research setting. We find strong evidence that insiders follow the stock market adage “Buy on bad news, sell on good news”: They tend to buy (sell) securities in those years where their respective companies issue negative (positive) news. Likewise, insiders tend to buy (sell) stocks in years when 3rd- party news coverage is pessimistic (optimistic). The impact of corporate news on insider trading is higher than for 3rd-party news, as corporate news are subject to direct influence by the insiders. We also find that insiders buy when next year’s news improves compared to the current year. Looking more concretely into the language, we also demonstrate that insiders buy when expressing insecurity and uncertainty. Overall, the findings reveal additional insights for insider trading analysis and demonstrate how finance may benefit from textual analysis. Keywords: text mining, sentiment, insider trading, tone of news, corporate news

Transcript of Buy on bad news, sell on good news: How insider trading ...wise12... · Likewise, insiders tend to...

Page 1: Buy on bad news, sell on good news: How insider trading ...wise12... · Likewise, insiders tend to buy (sell) stocks in years when 3rd-party news coverage is pessimistic (optimistic).

Buy on bad news, sell on good news: How insider trading analysis can

benefit from textual analysis of corporate disclosures

Michael Hagenau1, Adriana Korczak2, Dirk Neumann1

1 Universität Freiburg, Platz der Alten Synagoge,

79085 Freiburg, Germany {michael.hagenau, dirk.neumann}@is.uni-freiburg.de

2 School of Economics, Finance and Management, University of Bristol, 8 Woodland Road,

Bristol BS8 1TN, United Kingdom [email protected]

Abstract. We demonstrate how insider trading analysis may benefit from textual

analysis. We analyze reported insider trading behavior and explain the association

between corporate as well as 3rd-party news announcements on directors’

dealings activity. Previous approaches are extended by adding the sentiment of

news to the research setting. We find strong evidence that insiders follow the

stock market adage “Buy on bad news, sell on good news”: They tend to buy

(sell) securities in those years where their respective companies issue negative

(positive) news. Likewise, insiders tend to buy (sell) stocks in years when 3rd-

party news coverage is pessimistic (optimistic). The impact of corporate news on

insider trading is higher than for 3rd-party news, as corporate news are subject to

direct influence by the insiders. We also find that insiders buy when next year’s

news improves compared to the current year. Looking more concretely into the

language, we also demonstrate that insiders buy when expressing insecurity and

uncertainty. Overall, the findings reveal additional insights for insider trading

analysis and demonstrate how finance may benefit from textual analysis.

Keywords: text mining, sentiment, insider trading, tone of news, corporate news

Page 2: Buy on bad news, sell on good news: How insider trading ...wise12... · Likewise, insiders tend to buy (sell) stocks in years when 3rd-party news coverage is pessimistic (optimistic).

1 Introduction

Literature is providing evidence that the way corporate news announcements are phrased

plays an important role in the capital markets. Indisputably, news carries valuable qualitative

information that may influence investor’s expectations about firm’s future performance

(Tetlock et. al, 2008).

Prior financial literature on textual analysis often investigates the relationship between the

tone of news – in this context often dubbed ‘sentiment’ – and its relation to company

performance and capital market reactions such as stock price returns, trading volumes and

volatility (e.g. Tetlock et. al. 2008, Loughran & MacDonald 2011). Tetlock et al. find that

negative words from a psychosocial dictionary in corporate news can predict company’s

earnings and stock returns. Loughran & MacDonald extend these findings by tailoring their

word lists to financial news and also linking the news measure to trading volume, return

volatility and fraud.

These news announcements are formulated within the company by executives or outside the

company by third parties such as analysts. By law, executives are banned from trading based

on unpublished superior material information about the company that may significantly

impact the stock prices. However, there are neither clear definitions of material information

nor clear regulations of how long in advance before a piece of news is revealed for which

directors are not permitted to trade in their shares1. Regardless of whether the news about a

company comes from within or from outside a company, there are no strict or binding rules

and guidance on the linguistic context with which news need to be written. Each piece of

corporate news reveals an important fact about a company and the mix of words used

determines the strength and the tone of news. Thus, reader’s perception and view of the

company’s current position and future prospects are influenced. In companies where

executives are shareholders, executives may strategically use their discretion in formulating

news in order to influence the stock prices and subsequently exploit these stock price

movements by own trading activities. When news about a company is announced by a third

party, it impacts the perception of all investors including executives about the company’s

current position and future prospects. Accordingly, directors lose the insider advantage as

news is novel to all investors equally.

1 There are explicit 60 (30) days blackout periods before preliminary annual or semi-annual (quarterly) earnings

announcement defined by law in the UK, but not before any other news types (see Korczak et al. 2010). In the US, some companies chose to introduce blackout periods and restrict directors trading before earnings announcement

Page 3: Buy on bad news, sell on good news: How insider trading ...wise12... · Likewise, insiders tend to buy (sell) stocks in years when 3rd-party news coverage is pessimistic (optimistic).

From the past literature, it is known that executives trade around different corporate news

announcements such as takeover bids (Seyhun, 1990), seasoned equity offerings (Karpoff &

Lee, 1991), bankruptcy filings (Seyhun & Bradley, 1997) or earnings announcements (Ke,

Huddart & Petron, 2003). In this way, insiders manage to outperform the market by earning

abnormal returns from their trades. Ke et al. (2003), Piotroski & Roulstone (2005) and

Huddart & Ke (2007) show that executives trade based on their superior knowledge about

company’s prospects long before the news is announced. Ke et al. (2003) document that

executives trade based on their superior knowledge about companies’ future accounting

performance up to two years ahead of public announcement of that news. Likewise, Piotroski

& Roulstone (2005) show that insider trading patterns can be explained by executives’

foreknowledge about next year’s results. Korczak, Korczak & Lasfer (2010) also show that

insiders time their trades around different types of news to benefit from their superior

knowledge about the company. At the same time, however, insiders are cautious to avoid

potential litigation problems and a loss of reputation.

Other studies find evidence that actively trading executives not only benefit from their

superior knowledge, but also actively influence corporate disclosures. Aboody & Kasznik,

(2000) find that executives tend to release bad news before stock option grants to fix lower

strike prices. Vice versa, executives release very positive news before stock option exercises

to obtain higher sales prices (Brockman, Martin & Puckett 2010). Similarly, executives who

intend to buy shares for their own account tend to release bad news in the period just before

their insider purchases (Cheng & Lo 2006). This insider trading pattern could appear to be

another example of contrarian trading, but it also could represent active manipulation of stock

market prices.

Our paper extends literature in four ways: First, while ‘good’ and ‘bad’ news have been used

as explanatory variable for insider trading, ‘good’ or ‘bad’ was always measured by the sign

of abnormal returns around the news. However, the actual textual and qualitative content of

the news has never been part of the analysis. Since the capabilities of textual analysis and

news sentiment are not limited to stock price prediction, we use it to measure whether news is

‘good’ or ‘bad’. That is, we use news sentiment as an explanatory variable to analyze how the

tone of news shapes insider trading behavior. This way of measuring whether news is ‘good’

or ‘bad’ also provides a decisive advantage: While the company’s situation is a given fact,

executives do have flexibility in the scoping and wording of disclosures (Cheng & Lo 2006).

Thereby, the tone of news is a more direct measure of executives’ intention than the abnormal

stock price reaction around the news. The stock price reaction is also subject to the influence

Page 4: Buy on bad news, sell on good news: How insider trading ...wise12... · Likewise, insiders tend to buy (sell) stocks in years when 3rd-party news coverage is pessimistic (optimistic).

of other factors such as market’s expectations on the company and the context of the news.

Second, we do not only consider news published shortly before or after insider trades, but

also examine intertemporal relationships to news in the previous and following year.

Consequently, we establish an alternative measurement for management’s expectation about

future firm performance which is currently expressed by future earnings and future stock

returns.

Third, we do not only consider management forecasts (as e.g. Cheng & Lo 2006, Brockman

et al. 2008), but also look at a diverse set of US news announcements archived by Reuters

over 8 years. We also distinguish between corporate news issued by the company and 3rd-

party news published by independent brokers and investment analysts.

Fourth, we further drill down into drivers of ‘good’ and ‘bad’ news and explain how

confidence and uncertainty expressed in language impacts insider trading behavior.

We find that directors sell in years when their companies issue positive news and buy when

their companies issue negative news. This behavior follows the stock market adage “Buy on

bad news, sell on good news” and is consistent with Cheng & Lo (2006). Analogously, for

3rd-party news, we find that insiders sell in years when news coverage is pleasant and buy

when unpleasant. The impact of corporate news – being influenced by insiders – on insider

trading is higher than for 3rd-party news. We also find that insiders buy when next year’s

news tone improves compared to the current year. Similarly, insiders also buy their

companies’ stock when news tone is worse than in the previous year. Thereby, insider trading

is not only affected by ‘good’ or ‘bad’ news as a whole, but also by insecurity and uncertainty

expressed in the language of the news.

The findings demonstrate how textual analysis can add significant value for applications in

finance when textual data is available. Our findings also indicate that apparent contrarian

trading strategies could as well be a consequence of executives’ active manipulation of firm-

specific information.

The remainder of the paper is organized as follows: Section 2 discusses approaches for news

sentiment analysis and our research design and methodology while Section 3 describes the

data set. Section 4 presents the main empirical findings and its implications. Section 5

summarizes and concludes the paper.

Page 5: Buy on bad news, sell on good news: How insider trading ...wise12... · Likewise, insiders tend to buy (sell) stocks in years when 3rd-party news coverage is pessimistic (optimistic).

2 Data and Research Design

In this section, we introduce the data set and our research design that we use to explain

insider trading behavior. We build on the research design proposed in Piotroski & Roulstone

(2005) by introducing a sentiment variable to account for the tone of news issued by

companies and 3rd-party providers. We start this section by describing the data set and

variables for the regression analysis. Thereby, we first describe our response variable

measuring insider trading. Having defined the essence of sentiment analysis, we elaborate on

how different sentiment measures are included in our analysis. Lastly, we describe the control

variables for our main regression and list descriptive statistics for our key variables.

2.1 Data set

Data for our analysis is collected from Reuters World Archive, Thomson Reuters Insider

Filings, CRSP, Compustat and ExecuComp. Our news sample is based on the Reuters World

Archive. We select all news relating to US companies and split them between corporate news

and 3rd-party news. Corporate news are issued by companies and consist of financial results

and forecasts, changes in management, regulatory issues, dividend announcements, merger

and acquisitions and other stock exchange activities. 3rd-party news is not issued by

companies, but by 3rd-party providers and mainly consists of broker research, press digests

and independent investment analysis. We only select those news messages which are focused

on a single company to avoid confusions with other companies. After filtering, we have

252,700 US corporate news and 48,600 3rd-party news from the years 2003 to 2010. Table 1

lists the content categories included in the news set. For news with multiple labels (e.g.

dividend announcements may be part of financial results reports as illustrated by the headline

“Staples' quarterly profit rises, initiates dividend”), only the main category is listed.

Corporate news mainly (66%) comprises financial results while the majority of 3rd-party

news (71%) stems from broker research.

News relates to 5,032 different US firms which are listed on the NYSE, NASDAQ and

AMEX stock exchanges for which we have information on directors’ trades from Thomson

Reuters Insider Filings. All stock prices and market indices needed to calculate returns are

collected from CRSP database. All accounting data based on financial statements used to

calculate return on assets (ROA) and book-to-market (BM) ratios are sourced from

Page 6: Buy on bad news, sell on good news: How insider trading ...wise12... · Likewise, insiders tend to buy (sell) stocks in years when 3rd-party news coverage is pessimistic (optimistic).

Compustat. Data related to executive compensation variables (i.e. GRANTS,

OPTION_EXERCISE) are gathered from ExecuComp.

Table 1. Overview of news sample

No. of Corporate news 2003 2004 2005 2006 2007 2008 2009 2010 Total

Financial Results 13,514 16,043 16,076 19,391 22,118 25,673 28,469 25,713 166,997

Mergers & Acquisitions 1,063 1,356 1,947 3,337 4,692 3,669 3,426 3,329 22,819

New issues of shares/bonds 2,784 3,033 2,695 2,853 2,450 1,753 2,257 1,965 19,790

Regulatory issues 1,259 1,663 1,608 2,617 2,308 2,265 3,690 3,602 19,012

Management issues 204 1,120 1,389 2,668 2,579 2,515 2,606 1,980 15,061

Dividend 279 399 616 763 701 1,533 39 95 4,425

Company Events 13 197 - - - 227 880 450 1,767

Exchange activities 26 132 124 130 172 178 215 283 1,260

Bankruptcy 86 119 100 53 52 203 445 175 1,233

IPO 30 96 54 45 28 16 26 23 318

Total 19,258 24,158 24,609 31,857 35,100 38,032 42,053 37,615 252,682 No. of 3rd-party news 2003 2004 2005 2006 2007 2008 2009 2010 Total

Broker research 4,519 2,483 5,663 7,633 2,663 1,550 1,957 6,362 34,335

Investment analysis 15 10 19 45 1,410 4,194 3,198 4,799 14,246

Total 4,534 2,493 5,682 7,678 4,073 5,744 5,155 11,161 48,581

2.2 Measurement of Insider-Trading Behavior and Related Characteristics

Following Piotroski & Roulstone (2005), we measure insider-trading behavior using the

firm’s purchase ratio, defined as

PRi,t = BUYi,t / (BUYi,t + SELLi,t)

where BUYi,t (SELLi,t) equals the number of shares bought (sold) by directors of firm i

during fiscal year t. To be comparable with Piotroski & Roulstone (2005) and Rozeff &

Zaman (1998), we only include firm-years in which insiders engaged in open-market

transactions during the fiscal year.

2.3 News Sentiment Analysis

In news sentiment analysis, written texts are associated with a particular numbered value or

category in order to answer related research questions. Most sentiment approaches measure

how market participants perceive and react upon important corporate news. Accordingly, the

Page 7: Buy on bad news, sell on good news: How insider trading ...wise12... · Likewise, insiders tend to buy (sell) stocks in years when 3rd-party news coverage is pessimistic (optimistic).

tone of the news and its relationship to stock price reactions are analyzed (Liebmann et al.

2012; Li 2010a; Tetlock et al. 2008). Based on the stock price reactions following the

announcement, news is classified into either positive or negative.

As written text is a form of unstructured information as compared with e.g. financial

statements, it first has to be translated into a machine readable representation of the text2.

This step includes the identification of those words, which are most relevant and should thus

represent the text. Second, a numerical metric needs to be calculated for each announcement

as an aggregate based on the occurrences of the previously identified words. These two steps

have to be performed for any kind of sentiment analysis. Li (2010a) distinguishes sentiment

analyses into dictionary and statistical approaches.

The dictionary has a fixed set of words and often originates from a certain specific field,

such as the Harvard-IV-4 psychosocial dictionary which is used by Tetlock et al. (2008). For

aggregation into a metric, Tetlock et al. (2008) simply count the negative words and add up

all occurrences of negative words from the dictionary for each message. Other dictionaries

may focus on a combination of psycho-social and economic words. While this approach is

striking for its simplicity, it suffers from many drawbacks (Li 2010a). Few dictionaries exist

that are built for the setting of corporate financial statements and thus may prove

inappropriate for such settings (Loughran & McDonald, 2011). Furthermore, the simple

dictionary-based approach explicitly ignores the context of the sentences. For instance, if a

sentence is about cost, then the word ''increase'' qualifies it as being negative: However, it is

likely to be a positive word if the sentence is about "revenue” (Li 2010a). Lastly, some words

fully change their sentiment depending on the specific domain they are used in. While

“cancer” is a serious disease and labeled negative in the Harvard-IV-4 dictionary, it may be

part of a positive message when a new drug for cancer treatment has been developed.

To mitigate these drawbacks, statistical approaches have been developed to account for

correlations between specific keywords and the text type to classify the documents. This task

is mostly performed by machine learning approaches (Li 2010b; Hagenau et al. 2012).

However, some approaches translate their results into quantifiable metrics which can be used

for competitive comparison to dictionary approaches (Jegadeesh & Wu 2010, Liebmann et al.

2012).

2 In text mining, the step includes three sub-steps: Feature extraction (e.g. retrieving words from a text), Feature Selection

(e.g. determining which of the retrieved words are relevant), and Feature Representation (e.g. selecting the data format how occurrences of words in a text shall be represented). We follow this approach, but simplify the description. In a dictionary approach, there is no feature selection step as the predetermined dictionary itself defines which features to use.

Page 8: Buy on bad news, sell on good news: How insider trading ...wise12... · Likewise, insiders tend to buy (sell) stocks in years when 3rd-party news coverage is pessimistic (optimistic).

For the statistical approach, all words in the document corpus have to be extracted first.

Some of these words will be occurring in almost all text messages (i.e. stopwords such as

“the”, “it”, “like”, “or”). Thus, they are of low informative value and need to be excluded

from the analysis. Additionally, assuming that words with the same word stem convey the

same or similar meaning, we employ the Porter Stemmer, as in Porter (1980), which reduces

inflected words to their stem. Subsequently, the most informative words for sentiment

analysis out of the remaining words have to be selected3. For our explanation of insider

trading activity, we focus on two dictionary based approaches and one statistical approach.

Most informative refers in particular to those words which help to discriminate between

positive and negative news messages. If, for example, a word frequently occurs in both,

positive and negative news messages, it is considered to contain less information. However, if

a word occurs less frequently in the overall data set, but concentrates in either positive or

negative news messages, it is considered to be informative. To determine whether a news

message is positive or negative in an objective manner, abnormal stock price returns on the

day the news was disclosed can be used to label the messages. Actual market returns are used

to identify the most informative words. The market feedback however reflects the average

interpretation of all acting market participants which significantly differ from using a

predetermined dictionary as all words are selected that play a discriminating role in the given

text corpus and thus do not miss out on relevant terms that the creator of a predetermined

dictionary has not thought of.

In our empirical analysis, we use three different proxies for news sentiment, Tetlock-neg

(Tetlock et al. (2008)), Net-optimism (Loughran and MacDonald (2011)) and Tonality

Liebmann et al. (2012):

1. Tetlock-neg (H4): Tetlock et al. (2008) define a sentiment measure based on the

negative wordlist of the Harvard-IV dictionary. The authors take the “standardized

fraction of negative words” in each composite news story. Standardization is performed

by subtracting the prior year’s mean and dividing by the prior year’s standard deviation

of the fraction of negative words. The larger the sentiment value, the more negative the

interpretation might be. Consequently, there is a reciprocal relation between Tetlock-

3 This step is generally referred to as ‘Feature Selection’

Page 9: Buy on bad news, sell on good news: How insider trading ...wise12... · Likewise, insiders tend to buy (sell) stocks in years when 3rd-party news coverage is pessimistic (optimistic).

neg and stock price returns. As we expect more insider purchases in times of negative

news, we expect a positive correlation between Tetlock-neg and insider purchases.

2. Net-optimism: Loughran and MacDonald (2011) designed their wordlists for a

particular finance application based on the analysis of a comprehensive set of 10K

reports. They calculate a measure based on the difference between positive and

negative words and divide by “the total words in the text article”. Consequently, results

range between -1 and 1. This sentiment factor differs from Tetlock-neg as it also takes

positive words into account. Thus, it is named Net-optimism. The larger the Net-

optimism the more positive the news message is supposed to be. Hence, there is a

positive relation between Net-optimism and stock price returns and we expect a

negative relation to insider purchases.

3. Tonality: Liebmann et al. (2012) designed their own sentiment factor as a proxy for

announcement interpretation. Tonality differs significantly from the two approaches

above as it is specifically designed for the given data set and does not rely on any

general predetermined dictionary wordlist. Based on the Tonality word list, each

informative word in an announcement receives a weighting factor. The factor has a

positive sign for positive words and a negative sign for negative. The sum of weighting

factors over a whole announcement is standardized by the total number of words. The

larger the Tonality, the more positive the news message is supposed to be. Hence, there

is a positive relation between Tonality and stock price. As Tonality is a statistical

approach, tonalities for each word need to be calibrated on a different, but very similar

news sample with corresponding stock returns. It is important to note that Tonality is

always designed and calibrated out-of-sample. We make use of an out-of-sample

calibration on corporate disclosures of similar news content. For a detailed description

of the calculation of the Tonality, we refer to Liebmann et al. (2012) where motivation

and calculation of the Tonality is described in more detail.

Sentiment metrics as described in the previous subsections calculate a sentiment value for

each news message. As many different news messages are issued throughout the year,

sentiment values have to be aggregated to form an annual sentiment value. We choose an

annual timeframe because of the availability of control variables. Some of our control

variables which are based on a company’s financial reporting are only available on a

Page 10: Buy on bad news, sell on good news: How insider trading ...wise12... · Likewise, insiders tend to buy (sell) stocks in years when 3rd-party news coverage is pessimistic (optimistic).

quarterly basis, for some companies only on an annual basis. Additionally, choosing a

quarterly timeframe would not only reduce the number of companies in the sample, but also

decrease the number of available news per data point to one fourth.

For creating an aggregated annual sentiment, we average sentiment values of news

messages for each firm and fiscal year. Each of the news in our sample meets certain

requirements that we impose to eliminate irrelevant stories. We require that each story

contains at least 20 words in total and at least 3 words from the respective wordlist of the

employed sentiment approach (c.f. Tetlock et al. (2008)). We impose these word count filters

to eliminate news that contain only tables or lists with quantitative information, and to limit

the influence of outliers on our sentiment measures.

For creating a meaningful average, we also require a minimum of four news messages per

fiscal year and firm. A higher number of news reduces noise and variability of the sentiment

measure and thus generates results of higher confidence. This fact is further investigated in

section 3.2 where we demonstrate the impact of different minimum values on the significance

of results.

2.4 Measurement of Future Firm Performance and Contrarian Behaviour

For each fiscal year where insiders were trading, we measure the firm’s future performance in

the following fiscal year (i.e. year t+1). Following Piotroski & Roulstone (2005), we use two

measures for next year’s performance: the firm’s future market-adjusted stock return and

annual earnings innovation.

The firm’s future market-adjusted stock return (MARETt+1) is measured over the 12-moth

period in fiscal year t+1: This metric measures the director’s potential profit from trading in

the firm’s stock compared to the buy-and-hold return of the market portfolio. If directors

have superior knowledge about information influencing future stock returns, director

purchases will be positively related to return performance in t+1.

As neither insiders ex ante nor researchers ex post can perfectly predict or explain how

market expectations and thus future returns will evolve (Piotroski & Roulstone, 2005),

alternative measures of insiders’ information is needed. One measure of future firm

performance is the earnings of the following year. As changes in a firm’s earnings

performance influence stock prices, insiders have an incentive to trade based on superior

information about future earnings. Accordingly, Piotroski & Roulstone (2005) expect insider

Page 11: Buy on bad news, sell on good news: How insider trading ...wise12... · Likewise, insiders tend to buy (sell) stocks in years when 3rd-party news coverage is pessimistic (optimistic).

trading to reflect the sign of next year’s earnings change. Their primary measure of future

earnings news is year t+l’s annual earnings innovation, defined as

∆ROAt+1 = ROAt+1 - ROAt

where ROAt equals net income before extraordinary items standardized by average total

assets which is assumed to represent market expectations about future earnings performance.

Consequently, any changes in annual earnings represent private information of directors. In a

corresponding manner, we also use this year’s annual earnings innovation (∆ROAt) as control

variable.

Following Rozeff & Zaman (1998), we test whether insiders follow contrarian strategies

using the firm’s 12-month market-adjusted buy–and-hold stock return (MARETt) and the

firm’s book-to-market ratio (BMt) in year t. All annual variables are calculated over the fiscal

year. Companies are ranked based on MARETt and BMt and classified into return treciles

(i.e. HRET_t and MRET_t) and book-to-market quintiles respectively. By choosing the same

controls, we facilitate comparability with the results of Piotroski & Roulstone (2005) and

Rozeff & Zaman (1998).

2.5 Controlling for other factors affecting insider trading

Insiders’ trading behavior is influenced by changes in their holdings due to the granting of

stocks and options and the exercising of stock options (Ofek & Yermack 2000; Piotroski &

Roulstone 2005). We use two variables measuring compensation-related changes in insider

holdings: number of shares of restricted stock and stock options granted (GRANTSt) and

number of stock options exercised (OPTN_EXRCt) in year t. Following Piotroski &

Roulstone (2005), due to the skewness of the data, we measure each variable as the log of one

plus the ratio of number of shares granted or options exercised during the fiscal year,

respectively, scaled by total shares outstanding at year-end.

We also control for firm size (i.e. logarithm of total assets TAt) for each firm-year.

2.6 Descriptive Statistics of Key Variables

In this subsection, we provide descriptive statistics on the sample we used in this study. Table

2 presents descriptive statistics for both our sentiment metrics as well as other control

variables.

Page 12: Buy on bad news, sell on good news: How insider trading ...wise12... · Likewise, insiders tend to buy (sell) stocks in years when 3rd-party news coverage is pessimistic (optimistic).

Given the current executive compensation schemes in which stock options account for a

large part of variable compensation, the majority of our insider trading data consists of sell

transactions. 70.3% of firm-years exhibit net sell transactions while 36.0% represent sell-only

transactions. This distribution is also consistent with Piotroski & Roulstone (2005).

As a minimum number of news is required per firm-year observations to calculate a

sentiment value, the availability of news determines the number of observations in our

empirical analysis. As there is less 3rd-party news in the sample, we have fewer observations

available for sentiment metrics based on 3rd-party news. For corporate news based metrics,

we obtain 13,881 firm-year observations. For 3rd-party news based metrics, we obtain 2,613

observations. Despite the different sample sizes, the distribution of these two subsets of news

is very similar.

The Tonality sentiment measure based on corporate news shows more positive than

negative years with a median Tonality of 0.484. This can be attributed to companies reporting

more positive than negative news. Tonality based on 3rd party news also measures more

positive than negative news. However, with a median at 0.374 3rd party news messages are

assigned a less positive meaning than corporate announcements. At a first glance, Tetlock-

neg’s shows a different behavior with the median being closer to zero. However, Tetlock-neg

only measures the negative content in news and is standardized on annual basis (Tetlock et al.

2008). Similar to Tetlock-neg, Net-optimism is not skewed to the positive side. This is not

astonishing as the stemmed wordlist behind Net-optimism contains 85% negative words and

is thus much more sensitive to negative content (Loughran & MacDonald 2011). Comparing

corporate news and 3rd party news, all three sentiment measures attribute more positive

meanings to corporate news than to 3rd-party news.

Looking at control variables, future market-adjusted returns yield a different view than

future earnings innovations. While 60.2% of firm-years are associated with a negative future

return performance, only 47.2% exhibit a negative earnings innovation. The median of

earnings innovations (∆ROAt) is only slightly above zero demonstrating that about an equal

number of firm-years have positive and negative earnings changes. The wide spread in firm

sizes measured by total assets (ln(TAt)) expresses the large variety of firms in the sample.

Data availability for option exercises and grants is lower than for other control variables.

Specifically, only 2,606 data points have values for Tonality (Corporate news) and grants. For

3rd-party news, the number decreases to 985 data points. Thus, we do not show option grants

and exercises in our regression analyses, but use them for robustness checks only.

All continuous variables are winsorized at the 1% level.

Page 13: Buy on bad news, sell on good news: How insider trading ...wise12... · Likewise, insiders tend to buy (sell) stocks in years when 3rd-party news coverage is pessimistic (optimistic).

Table 2 - Descriptive statistics This table presents descriptive statistics for sentiment metrics as well as other control variables. The number of observations ‘N’ reflects the maximum number of observations in the data sample with a valid entry for the specified variable. For each of the variables, only a fraction of the sample has available and valid entries. The number of observations in the regressions will be lower as only those data points can be used for regression which have a valid entry in all employed variables, i.e. for the univariate regression at least ‘PR’ and the independent variable is needed.

N Mean Stdev 5th Pctl 25th Pctl Median 75th Pctl 95th Pctl

PR 20,062 0.297 0.407 0.000 0.000 0.020 0.678 1.000

Tonality (Corp. news)

13,881 0.463 0.587 -0.533 0.074 0.484 0.884 1.400

Tonality (3rd-party news)

2,613 0.363 0.434 -0.358 0.092 0.374 0.643 1.065

Tetlock-neg (Corp. news)

13,854 -0.103 0.524 -0.927 -0.453 -0.128 0.211 0.798

Tetlock-neg (3rd-party news)

2,642 0.019 0.650 -0.976 -0.412 -0.006 0.452 1.052

Net-optimism (Corp. news)

11,158 -0.001 0.014 -0.023 -0.009 0.000 0.008 0.021

Net-optimism (3rd-party news)

2,642 -0.006 0.014 -0.030 -0.014 -0.005 0.003 0.016

MARETt+1 23,989 0.124 0.841 -0.560 -0.229 -0.004 0.273 1.104

∆ROAt+1 21,228 -0.002 0.278 -0.194 -0.021 0.000 0.022 0.196

∆ROAt 23,987 0.005 0.267 -0.197 -0.022 0.001 0.023 0.210

ln(TAt) 25,003 6.365 2.161 2.904 4.857 6.332 7.748 10.096

BM t 24,999 0.699 0.778 0.120 0.318 0.530 0.829 1.762

OPTN_EXRCt 11,268 0.004 0.145 0.000 0.000 0.001 0.003 0.012

GRANTSt 4,800 0.006 0.009 0.000 0.001 0.004 0.008 0.020

3. Results

In this section, we demonstrate how news sentiment of both corporate news and 3rd-party

news contributes to the explanation of insider trading behavior. After showing the results for

contemporaneous sentiment (i.e. this year’s sentiment), we examine drivers for significance

and also look into associations of intertemporal sentiment changes (i.e. changes between

previous, current and next year’s sentiment) and insider purchasing behavior. Lastly, we

present the impact of confidence and uncertainty in language on insider trading activity.

Page 14: Buy on bad news, sell on good news: How insider trading ...wise12... · Likewise, insiders tend to buy (sell) stocks in years when 3rd-party news coverage is pessimistic (optimistic).

3.1 Contemporary results

To analyze the association between contemporary sentiment and insider purchasing behavior,

we use the three sentiment measures described in the previous section. Table 3 presents basic

univariate relations between news sentiment measures and insider trading (i.e. purchase ratio

PR).

Table 3 – Univariate Regression: Prediction of Purchase Ratio (PR) of different sentiment approaches

Factors Cons Coeff t-stat p-value R² N Tonality (Corp. news)

0.2862 -0.0878 -14.77 0.000 1.93% 11,080

Tonality (3rd-party news)

0.1673 -0.0749 -6.72 0.000 1.48% 3,001

Tetlock-neg (Corp. news)

0. 2510 0. 0388 5.65 0.000 0.09% 11,054

Tetlock-neg (3rd-party news)

0.1306 -0.0175 -1.84 0.071 0.16% 2,061

Net-optimism (Corp. news)

0.2175 -0.9895 -3.58 0.000 0.14% 9,060

Net-optimism (3rd-party news)

0.1170 -2.010 -4.53 0.000 0.99% 2,061

Univariate regressions document that indeed sentiment measures are correlated to insider

trading activity. For corporate news, coefficients are negative for Tonality and Net-optimism:

This indicates that insiders sell in years when their companies issue positive news and

insiders buy when their companies issue negative news. Analogously, for 3rd-party news, the

negative coefficient indicates that insiders sell in years when news coverage is pleasant and

buy when unpleasant. The correlation coefficient is significantly higher for corporate news.

This indicates that the impact of corporate news (which may be influenced by insiders

themselves) on insider trading is higher than for 3rd-party news. Also Tetlock-neg for

corporate news confirms the indications above. As Tetlock-neg expresses the negativity of

news, the sign of the coefficient needs to be positive to confirm results. For 3rd-party news,

the sign of Tetlock-neg’s coefficient is not as expected. However, the correlation is not very

strong. Comparing sentiment approaches, the regression also shows that Tonality based on

both news types has the highest explanatory power.

The number of observations in the regression varies with the news type. As a minimum

number of news is required per year and firm for calculating a sentiment value, the

availability of news limits the number of observations. There is less 3rd-party news in the

Page 15: Buy on bad news, sell on good news: How insider trading ...wise12... · Likewise, insiders tend to buy (sell) stocks in years when 3rd-party news coverage is pessimistic (optimistic).

sample, thus, fewer observations are available for sentiment metrics based on 3rd-party news.

The intersection of both news types will lead to the lowest number of observations.

These univariate tests, however, fail to separate out the effects of trading against

misvaluation and trading with superior future information (Piotroski & Roulstone 2005). To

test whether the relations between insider purchase ratios and tone of the news are

incremental to variables capturing contrarian trading and future firm performance, we utilize

the methodology in Piotroski & Roulstone (2005) as our benchmark. In particular, we

estimate coefficients from the following cross-sectional model:

PRi;t = α + β1 * sentiment_metrics + β2 * MARETi;t + β3 * ∆ROAi;t+1 + β4 * ∆ROAi;t

+ β5 * HRETi;t + β6 * MRETi;t + β7 * BM1 i;t+ β8 * BM2 i;t + β9 * BM3i;t + β10 *BM4 i;t

+ β11 * ln(TA t)

where sentiment_metrics use different sentiment measures (Tonality, Tetlock-neg, Net-

optimism) interchangeably in our regression equation. HRETt and MRETt are indicator

variables equal to one if the firm’s 12-month-market-adjusted return (MARETt) is in the top

and middle third of all sample firms that year, and zero otherwise. The indicator variables

BMl t, BM2t, BM3t, and BM4t are equal to one if the firm’s BM ratio ranks in the bottom,

second, third and forth quintiles, respectively, of annual BM ratios, and zero otherwise

(Piotroski & Roulstone 2005).

Furthermore, we tested for multicolinearity to ensure that no relevant abnormality

confounds the experiments’ results and could not find any biasing influences. We perform

nine separate regression models: Six regressions - one model for each sentiment approach

and each news type, one empty control model as benchmark, one model for Tonality

sentiment for corporate news and 3rd-party news, and one full combined model to compare

the interactions between sentiment approaches and news types. For each of the estimations,

we perform a set of ordinary least square (OLS) regressions to compare the sentiment

approaches against one another. Table 4 presents the results of all nine regression models for

the prediction of purchase ratio PRt during fiscal year t.

We estimate all models with firm fixed effects allowing us to control for unobservable firm

characteristics that may drive differences in trading behavior between firms (e.g. firm-

specific insider trading policies or restrictions). The standard errors of the coefficients in all

regressions are adjusted for clustering at the firm level to control for non-independence of

observations within a firm.

Page 16: Buy on bad news, sell on good news: How insider trading ...wise12... · Likewise, insiders tend to buy (sell) stocks in years when 3rd-party news coverage is pessimistic (optimistic).

Table 4. Multivariate Regression: Predictor Models for Purchase Ratio (PR)

Table 4

This table presents estimated coefficients of the OLS regressions with firm fixed effects to explain insider purchasing activity. t-statistics are listed in parentheses. Sentiment metrics are based on all corporate news, 3rd-party news and on a combination of both, respectively. Standard errors of the coefficients are adjusted for clustering at the firm level. All variables are as defined in section 2. Significance is denoted at the 0.01 (***), 0.05 (**) and 0.1 (*) level, respectively.

(1) (2) (3) (4) (5) (6) (7) (8) (9)

News Focus - All Corp. All Corp. All Corp. 3rd-party 3rd-party 3rd-party All Corp & 3rd-party

All Corp & 3rd-party

Tonality (Corp. news)

-0.061*** (-7.41)

-0.096*** (-4.56)

-0.094*** (-3.68)

Tetlock-neg (Corp. news)

0.028*** (3.16)

0.069*** (3.01)

Net-optimism (Corp. news)

0.243 (0.71)

2.229** (2.46)

Tonality (3rd-party news)

-0.076*** (-4.51)

-0.053*** (-3.00)

-0.044* (-1.83)

Tetlock-neg (3rd-party news)

-0.008 (-0.81)

-0.022** (-1.99)

Net-optimism (3rd-party news)

-1.785*** (-2.85)

-0.783 (-0.90)

MARETt+1 0.0735** (7.47)

0.075*** (7.66)

0.074*** (7.50)

0.073*** (7.45)

0.054** (2.19)

0.061** (2.23)

0.062** (2.28)

0.055** (2.12)

0.058** (2.21)

∆ROAt+1 0.224***

(5.97) 0.201***

(5.32) 0.211***

(5.60) 0.226***

(5.98) 0.211**

(2.30) 0.246**

(2.45) 0.231**

(2.34) 0.188* (1.89)

0.172* (1.76)

∆ROAt 0.011 (0.30)

0.006 (0.16)

0.005 (0.12)

0.012 (0.33)

-0.039 (-0.44)

-0.072 (-0.83)

-0.08 (-0.92)

-0.086 (-0.90)

-0.106 (-1.11)

HRETt -0.042**

(-4.40) -0.035***

(-3.62) -0.042***

(-4.25) -0.044***

(-4.43) -0.041**

(-1.98) -0.058***

(-2.73) -0.043**

(-2.05) -0.036* (-1.67)

-0.036* (-1.68)

MRETt -0.032***

(-3.40) -0.026***

(-2.72) -0.031***

(-3.2) -0.033***

(-3.41) -0.041**

(-2.09) -0.051**

(-2.56) -0.044**

(-2.21) -0.04** (-2.03)

-0.038* (-1.89)

BM1t -0.165***

(-6.05) -0.141***

(-5.16) -0.16***

(-5.87) -0.166***

(-6.09) -0.214***

(-2.92) -0.246***

(-3.09) -0.236***

(-2.98) -0.193**

(-2.43) -0.188**

(-2.39)

BM2t -0.141***

(-6.12) -0.121***

(-5.23) -0.137***

(-5.95) -0.143***

(-6.19) -0.199***

(-3.04) -0.235***

(-3.30) -0.227***

(-3.21) -0.19***

(-2.67) -0.187***

(-2.65)

BM3t -0.097***

(-4.80) -0.084***

(-4.12) -0.094***

(-4.62) -0.098***

(-4.84) -0.156***

(-2.64) -0.18***

(-2.80) -0.173***

(-2.71) -0.147**

(-2.29) -0.149**

(-2.31)

BM4t -0.047**

(-2.56) -0.037**

(-2.02) -0.044**

(-2.40) -0.047***

(-2.57) -0.057 (-1.11)

-0.068 (-1.22)

-0.064 (-1.14)

-0.053 (-0.94)

-0.055 (-0.99)

ln(TAt) 0.000 (0.01)

0.008 (0.59)

-0.002 (-0.17)

-0.001 (-0.08)

0.031 (1.17)

0.017 (0.60)

0.032 (1.09)

0.023 (0.79)

0.019 (0.62)

cons 0.358***

(3.61) 0.311***

(3.17) 0.375***

(3.77) 0.368***

(3.69) 0.076 (0.32)

0.199 (0.77)

0.043 (0.16)

0.151 (0.56)

0.189*** (0.68)

R² 6.5% 7.4% 6.7% 6.5% 10.4% 8.7% 9.5% 12.3% 13.5%

N 10,012 10,012 9,986 9,986 2,137 1,956 1,956 1,931 1,915

Estimation (1) is our empty control model, which serves as benchmark confirming the

previously documented relations. Consistent with Piotroski & Roulstone (2005), we illustrate

Page 17: Buy on bad news, sell on good news: How insider trading ...wise12... · Likewise, insiders tend to buy (sell) stocks in years when 3rd-party news coverage is pessimistic (optimistic).

that executives are more likely to purchase securities during periods of falling stock prices.

Table 4 documents the significant and negative coefficient of HRETt and MRETt. Insider

purchases are positively related to the firm’s book-to-market ranking (with BM1 being the

bottom quintile). We also confirm that insider purchase decisions are significantly positively

associated with future firm performance, measured by both future earnings innovation

(∆ROAt+1) and future market returns (MARETt+1). The control for firm sizes is insignificant

for all of our estimations.

Priotroski & Roulstone (2005) also show an inverse relation, albeit substantially weaker,

between current earnings performance (∆ROA_t) and the firm’s purchase ratio. This relation

would indicate that insiders sell their securities during periods of stronger firm performance.

However, we cannot confirm this finding.

Estimations (2), (3), and (4) examine the association between sentiment of corporate news

and insider purchasing behavior. Consistent with the unconditional results in Table 3, there is

a strong relation between a negative sentiment and a high insider purchasing activity and vice

versa. This highlights again that insiders tend to sell in years when their company issues

positive news and buy when the company issues negative news. The findings confirm

previous research showing that executives publish bad news before purchasing shares (Cheng

& Lo 2006). While Tonality and Tetlock-neg exhibit strong significance, Net-optimism is not

significant at all.

Similarly, estimations (5), (6), and (7) examine the association between sentiment of 3rd-

party news and insider purchasing behavior. Consistent with the results in Table 3 and the

estimation models based on corporate news sentiment, there is a relation between negative

sentiment of 3rd-party news coverage and insider purchasing activity and vice versa. This

demonstrates our hypothesis that insiders are also influenced by 3rd-party news in a way that

they sell in years when news coverage is advantageous and buy when unpleasant. If

sentiment metrics based on 3rd-party news are included into the regression, the current firm

performance (∆ROA_t) exhibits its expected behavior, i.e. the coefficient turns negative.

Comparing different sentiment metrics, the Tonality metric is most significant. As for the

univariate regression Tetlock-neg based on 3rd-party news is not significant and carries a

different sign than expected. Comparing the different news types, corporate news lead to

more significant sentiment metrics. In contrast to corporate news in estimation (4), Net-

optimism is significantly related to purchase ratio. The respective word list seems to be better

tailored to 3rd-party news than to the language used in corporate news.

Page 18: Buy on bad news, sell on good news: How insider trading ...wise12... · Likewise, insiders tend to buy (sell) stocks in years when 3rd-party news coverage is pessimistic (optimistic).

To even better compare the different news types, estimation (8) examines the association

between Tonality sentiment on both corporate news and 3rd-party news and insiders

purchasing behavior. Essentially, Tonality based on corporate news is more significant than

Tonality based on 3rd-party news indicating that corporate news exhibit a higher influence on

insider trading behavior than 3rd-party news. As corporate news is subject to direct influence

by the insiders, this also suggests that corporate news may be used to actively manipulate

stock prices. Having both sentiment metrics significant demonstrates that the combination of

both news types adds additional explanatory power. Thus, both news types each have an

influence on insider trading behavior.

To corroborate the preceding results, control for omitted performance variables and

compare the interactions between sentiment approaches and news types, we estimate a full

model (estimation (9)) that includes all sentiment metrics for both news types. Tonality again

proves to be the most significant sentiment metric with corporate news being more significant

than 3rd-party news. The full model carries the highest explanatory power at R² = 9.9%.

Our analysis builds on the assumption that insiders trade after the news is published.

Although the research design does not control for the exact timing of insider transactions,

trades before publication of major news are less likely due to regulatory reasons. Black-out

periods before the announcement of scheduled news (such as quarterly or annual financial

reports) prevent insiders from trading (see Footnote1 and Korczak et al. 2010) while insiders

trading on major unpublished news are subject to prosecution. Furthermore, previous

research controlled for the timing of disclosure and came to similar results (Cheng & Lo

2006).

Our results are robust to the inclusion of stock option grants and stock option exercises, i.e.

sign of coefficients stay the same and key tonality variables remain on similar significance

levels. Still, they are not included as they drastically reduce the number of data points in our

regressions due to lower data availability.

The sentiment metrics in this section were produced requiring a minimum of 4 news

messages per firm year (i.e. medium confidence as denoted in section 3.2). The following

section will analyze robustness and significance for different minima.

3.2 Increasing significance of results

In this section, we analyze two drivers for significance: First, we vary the number of news

which we require as minimum per firm and year to calculate a Tonality average. If there is

Page 19: Buy on bad news, sell on good news: How insider trading ...wise12... · Likewise, insiders tend to buy (sell) stocks in years when 3rd-party news coverage is pessimistic (optimistic).

less news in a firm-year, no Tonality average is calculated and the data point is omitted from

the regression. We denote the different thresholds as different confidence levels (i.e. low,

medium, and high). If more news messages contribute to the Tonality average per firm and

year, the average should reduce variability and the impact of outliers. Second, we analyze the

effect of different news types within the set of corporate news. In addition to metrics based

on all corporate news, we also base our sentiment metrics on financial results only, which

make up two-thirds of our corporate news data set. Other news categories may not be

analyzed separately as number of observations would become too low.

We perform nine separate regression models: One regression for each of the three

confidence levels and each of the three news categories (i.e. all corporate news, corporate

financial results, 3rd-party news). Table 5 presents the results of all nine regression models for

the prediction of purchase ratio PRt during fiscal year t.

Table 5 – Predictor Models for Purchase Ratio (PR) for different confidence levels and news categories

This table presents estimated coefficients of the OLS regressions with firm fixed effects to explain insider purchasing activity. t-statistics are listed in parentheses. Sentiment metrics are based on all corporate news, corporate financial results (a subset of the previous one), and 3rd-party news, respectively. Standard errors of the coefficients are adjusted for clustering at the firm level. Tonality is calculated for different confidence levels, i.e. low confidence requires at least one news message per firm-year, medium confidence requires a minimum of 4 messages while high requires 7 messages. For 3rd-party news, high confidence only requires 5 news messages due to lower data availability. All other variables are as defined in section 2. Significance is denoted at the 0.01 (***), 0.05 (**) and 0.1 (*) level, respectively.

(1) (2) (3) (4) (5) (6) (7) (8) (9)

News Focus All Corp. All Corp. All Corp. Fin.

Results Fin.

Results Fin.

Results 3rd-party 3rd-party 3rd-party

Tonality (low confidence)

-0.019*** (-3.51)

-0.029***

(-5.46)

-0.031*** (-4.26)

Tonality (med. confidence)

-0.061***

(-7.41)

-0.068*** (-8.32)

-0.076***

(-4.51)

Tonality (high confidence)

-0.089***

(-8.42)

-0.101*** (-9.83)

-0.067***

(-2.89)

MARETt+1 0.07***

(9.02) 0.075***

(7.66) 0.085***

(6.75) 0.070***

(8.72) 0.073***

(7.05) 0.088***

(6.25) 0.053***

(4.03) 0.054**

(2.19) 0.049 (1.55)

∆ROAt+1 0.17***

(5.89) 0.201***

(5.32) 0.208***

(4.05) 0.194***

(6.26) 0.200***

(4.66) 0.123**

(2.15) 0.184***

(3.49) 0.211**

(2.30) 0.168 (1.51)

∆ROAt 0.018 (0.61)

0.006 (0.16)

0.026 (0.57)

0.020 (0.64)

-0.022 (-0.57)

-0.003 (-0.06)

-0.115** (-2.17)

-0.039 (-0.44)

-0.075 (-0.67)

HRETt -0.04***

(-4.90) -0.035***

(-3.62) -0.035***

(-3.09) -0.038***

(-4.55) -0.033***

(-3.23) -0.029**

(-2.47) -0.042***

(-3.59) -0.041**

(-1.98) -0.024 (-0.97)

MRETt -0.028***

(-3.49) -0.026***

(-2.72) -0.028***

(-2.56) -0.027***

(-3.37) -0.024**

(-2.39) -0.023**

(-2.03) -0.04***

(-3.38) -0.041**

(-2.09) -0.026 (-1.1)

BM1t -0.181***

(-8.40) -0.141***

(-5.16) -0.135***

(-4.11) -0.172***

(-7.64) -0.141***

(-4.83) -0.112***

(-3.19) -0.219***

(-6.52) -0.214***

(-2.92) -0.232***

(-2.59)

BM2t -0.15***

(-8.43) -0.121***

(-5.23) -0.116***

(-4.08) -0.144***

(-7.69) -0.113***

(-4.53) -0.101***

(-3.25) -0.177***

(-5.96) -0.199***

(-3.04) -0.214***

(-2.69)

BM3t -0.1*** (-6.53)

-0.084*** (-4.12)

-0.081*** (-3.17)

-0.092*** (-5.74)

-0.079*** (-3.61)

-0.08*** (-2.91)

-0.127*** (-4.78)

-0.156*** (-2.64)

-0.159** (-2.19)

Page 20: Buy on bad news, sell on good news: How insider trading ...wise12... · Likewise, insiders tend to buy (sell) stocks in years when 3rd-party news coverage is pessimistic (optimistic).

BM4t -0.057***

(-4.19) -0.037**

(-2.02) -0.041* (-1.77)

-0.049*** (-3.44)

-0.026 (-1.33)

-0.035 (-1.37)

-0.082*** (-3.41)

-0.057 (-1.11)

-0.048 (-0.76)

ln(TAt) 0.004 (0.39)

0.008 (0.59)

0.015 (0.96)

-0.003 (-0.26)

0.000 (0.03)

0.01 (0.59)

0.009 (0.60)

0.031 (1.17)

-0.001 (-0.02)

cons 0.365***

(4.92) 0.311***

(3.17) 0.216* (1.71)

0.404*** (5.02)

0.353*** (3.36)

0.224 (1.59)

0.273** (2.22)

0.076 (0.32)

0.345 (1.02)

R² 6.0% 7.4% 9.0% 6.6% 8.2% 9.8% 7.6% 9.7% 8.0%

N 14,517 10,012 6,715 13,451 9,009 5,594 6,198 2,137 1,555

For all corporate news, significance of associations between purchasing activity and Tonality

increases with the confidence level (estimations (1), (2), and (3)). This suggests that

increasing confidence reduces noise and limits the impact of outliers. Still, lowest confidence

(i.e. only one news message per firm-year) already exhibits significant relation (estimation

(1)) showing the robustness of results. Significance of relations between purchasing activity

and sentiment further increases when Tonality is based on financial results only instead of the

full set of corporate news (estimations (4), (5), and (6)). Financial results seem to transport

more relevant content explaining insider trading than the average news. This is consistent

with Tetlock et al. (2008) who find that stories about fundamentals predict earnings and

returns more effectively than other stories. However, another possible explanation might also

be that it is just easier to influence and adjust the tone in financial results than in other news.

It is important to note that the number of observations significantly increases with lower

required confidence – we witness twice as many data points for low confidence compared to

high confidence.

Tonality based on 3rd-party news also benefits from medium confidence compared to low

confidence (estimations (7) and (8)). However, Tonality based on 3rd-party news does not

benefit from high confidence (estimation (9)) which could be attributed to the lower number

of news in the 3rd-party sample.

3.3 Intertemporal results

While the previous sections only considered contemporaneous Tonality, this section looks at

tone changes between the previous, current, and following year. In the same manner as future

firm performance is measured by MARETt+1 and ∆ROAt+1, the future tone of news is also of

importance. As MARET (market adjusted return) and ∆ROA (delta return on assets) are

relative measures for the change between two subsequent years, Tonality is also calculated as

Page 21: Buy on bad news, sell on good news: How insider trading ...wise12... · Likewise, insiders tend to buy (sell) stocks in years when 3rd-party news coverage is pessimistic (optimistic).

the delta between two subsequent years. ∆Tonalityt+1 is calculated as the difference between

the next and the current year while ∆Tonalityt is the difference between the current and the

previous year. For this analysis, we focus on the Tonality metric only as this sentiment metric

showed the strongest relation between tone of news and insider purchasing activity in

previous regressions of this paper.

We perform nine separate regression models: Three for each news categories (i.e. all

corporate news, corporate financial results, 3rd-party news). Within each of the three news

categories, we run the current delta-Tonality, the future delta-Tonality and the combination of

both. Table 6 presents the results of all nine regression models for the prediction of purchase

ratio PRt during fiscal year t.

Table 6 – Predictor Models for Purchase Ratio (PR) for intertemporal sentiment measures

This table presents estimated coefficients of the OLS regressions with firm fixed effects to explain insider purchasing activity. t-statistics are listed in parentheses. Sentiment metrics are based on all corporate news, corporate financial results (a subset of the previous one), and 3rd-party news, respectively. Standard errors of the coefficients are adjusted for clustering at the firm level. Intertemporal changes in Tonality are calculated as follows: ∆Tonality_t = Tonality_t – Tonality_t-1

∆Tonality_t+1 = Tonality_t+1 – Tonality_t

All other variables are as defined in section 2. Significance is denoted at the 0.01 (***), 0.05 (**) and 0.1 (*) level, respectively.

(1) (2) (3) (4) (5) (6) (7) (8) (9)

News Focus All Corp. All Corp. All Corp. Fin.

Results Fin.

Results Fin.

Results 3rd-party 3rd-party 3rd-party

∆Tonalityt -0.03***

(-4.32)

-0.028*** (-3.42)

-0.031*** (-4.55)

-0.029***

(-3.64) -0.008 (-0.55)

0.035 (1.53)

∆Tonalityt+1 0.023***

(3.58) 0.008 (0.91)

0.021***

(3.34) 0.01

(1.17)

0.022 (1.44)

0.074** (2.34)

MARETt+1 0.086***

(6.44) 0.072***

(6.76) 0.088***

(6.53) 0.093***

(6.57) 0.071***

(6.32) 0.095***

(6.48) 0.036 (0.98)

0.044 (1.18)

0.004 (0.08)

∆ROAt+1 0.231***

(5.06) 0.226***

(5.45) 0.235***

(4.94) 0.237***

(4.48) 0.253***

(5.60) 0.226***

(4.09) 0.208 (1.53)

0.309* (1.88)

0.396* (1.76)

∆ROAt -0.029 (-0.61)

-0.001 (-0.03)

-0.006 (-0.13)

-0.016 (-0.29)

0.004 (0.09)

-0.021 (-0.37)

-0.025 (-0.21)

-0.076 (-0.53)

-0.038 (-0.24)

HRETt -0.022* (-1.83)

-0.043*** (-4.31)

-0.025** (-2.00)

-0.026** (-2.05)

-0.044*** (-4.12)

-0.027** (-2.08)

-0.04 (-1.03)

-0.025 (-0.78)

-0.003 (-0.06)

MRETt -0.017 (-1.42)

-0.036*** (-3.60)

-0.023* (-1.79)

-0.024* (-1.87)

-0.035*** (-3.24)

-0.025* (-1.87)

-0.043 (-1.37)

-0.022 (-0.78)

0.004 (0.09)

BM1t -0.144***

(-4.32) -0.159***

(-5.45) -0.133***

(-3.95) -0.129***

(-3.55) -0.16***

(-5.23) -0.114***

(-3.11) -0.384***

(-2.71) -0.4*** (-3.10)

-0.514*** (-2.90)

BM2t -0.124***

(-4.27) -0.137***

(-5.55) -0.118***

(-4.03) -0.114***

(-3.58) -0.135***

(-5.17) -0.102***

(-3.16) -0.343***

(-2.63) -0.336***

(-2.99) -0.444***

(-2.66)

BM3t -0.077***

(-2.94) -0.091***

(-4.20) -0.072***

(-2.71) -0.074***

(-2.61) -0.096***

(-4.17) -0.067**

(-2.31) -0.207* (-1.89)

-0.246** (-2.47)

-0.259* (-1.95)

BM4t -0.054**

(-2.29) -0.04** (-2.04)

-0.053** (-2.20)

-0.048* (-1.84)

-0.039* (-1.86)

-0.047* (-1.75)

-0.106 (-1.09)

-0.091 (-1.24)

-0.121 (-1.13)

ln(TAt) 0.008 (0.44)

0.007 (0.53)

0.006 (0.30)

-0.016 (-0.79)

-0.003 (-0.21)

-0.021 (-0.93)

-0.015 (-0.33)

0.01 (0.26)

-0.007 (-0.14)

Page 22: Buy on bad news, sell on good news: How insider trading ...wise12... · Likewise, insiders tend to buy (sell) stocks in years when 3rd-party news coverage is pessimistic (optimistic).

cons 0.246* (1.66)

0.292*** (2.67)

0.257 (1.61)

0.418*** (2.57)

0.362*** (3.05)

0.445** (2.47)

0.556 (1.23)

0.335 (0.88)

0.56 (1.01)

R² 7.1% 6.9% 7.4% 7.7% 7.3% 7.9% 10.8% 13.3% 17.4%

N 6,810 9,024 6,410 5,893 8,031 5,492 899 1,033 522

Estimations (1), (2), and (3) examine the association between Tonality changes based on all

corporate news and insider purchasing behavior. ∆Tonalityt is negatively related while

∆Tonalityt+1 is positively associated with purchasing activity. This demonstrates that insiders

buy on both worsening news between last and current year and improving news between the

current and next year. The observation reconfirms that insiders trade on superior knowledge

about future earnings innovations and future stock returns (Piotroski & Roulstone 2005). The

relation of purchasing activity to ∆Tonalityt is stronger than to ∆Tonalityt+1 (estimation (3))

showing that impact of existing news is stronger than expected future news. While insiders

profit from their superior knowledge about future earnings and returns, existing news – with

direct impact on stock prices – still seem to be the stronger driver for insider purchasing

behavior than future news.

Estimations (4), (5), and (6) examine association between Tonality changes based on

financial results only and insider purchasing behavior. They confirm the results based on all

corporate results, but exhibit slightly stronger relationships. Again, our sentiment metrics

based on financial results seem to be more relevant.

The analysis is repeated for 3rd-party news (estimations (7), (8), and (9)). Thereby, ∆Tonalityt

is still negatively related (positively related for ∆Tonalityt+1), but not significant any more. In

particular, the combination of both time horizons in estimation (9) shows that ∆Tonalityt+1 is

more strongly associated to insider purchasing activity than for ∆Tonalityt. This indicates that

insiders also buy on improving 3rd-party news coverage. In contrast to corporate news emitted

by the company, the improving news coverage is more important than the change to the

previous year. A possible explanation may be that 3rd-party news cannot be directly

influenced by executives and mirror actual development and condition of the company in a

reflected manner.

3.4. Relating insider trading to confidence and uncertainty in news

Literature does not only provide dictionaries for the tone of news, but also for other forms of

expression (Loughran & MacDonald 2011). By means of these dictionaries, not only low or

Page 23: Buy on bad news, sell on good news: How insider trading ...wise12... · Likewise, insiders tend to buy (sell) stocks in years when 3rd-party news coverage is pessimistic (optimistic).

high confidence (i.e. weak or strong modality) in language could be measured, but also

uncertainty. Examples of strong modal words are words such as always, must, highest, and

will . Examples of weak modal words are depending, could, might, and possibly (Loughran &

MacDonald 2011). Examples of words expressing uncertainty are risk, uncertain, unknown,

and volatile. The respective metrics count occurrences of these wordlists and divide by the

total number of words per message.

We perform three separate regression models – one for each word category. Table 7 presents

the results of the regression models for the prediction of purchase ratio PRt during fiscal year

t.

Table 7 – Predictor Models for Purchase Ratio (PR) for different word categories

This table presents estimated coefficients of the OLS regressions with firm fixed effects to explain insider purchasing activity. t-statistics are listed in parentheses. Sentiment metrics are based on all corporate news. Standard errors of the coefficients are adjusted for clustering at the firm level. Uncertainty, Strong_Modal, and Weak_Modal are frequencies of the respective word lists (c.f. Loughran & MacDonald 2011) divided by the total number of words per message. All other variables are as defined in section 2. Significance is denoted at the 0.01 (***), 0.05 (**) and 0.1 (*) level, respectively.

(1) (2) (3)

News Focus All Corp. All Corp. All Corp.

Uncertaintyt 5.454*** (4.80)

Strong_Modalt

10.672* (1.65)

Weak_Modalt

5.476*** (4.14)

MARETt+1 0.073*** (5.27)

-0.001 (-0.01)

0.085*** (5.21)

∆ROAt+1 0.228*** (4.25)

0.116 (0.57)

0.200*** (3.22)

∆ROAt -0.033 (-0.66)

-0.072 (-0.28)

-0.037 (-0.60)

HRETt -0.045*** (-3.56)

-0.063* (-1.67)

-0.033** (-2.20)

MRETt -0.039*** (-3.26)

-0.056* (-1.79)

-0.032** (-2.16)

BM1t -0.184*** (-5.44)

-0.35*** (-3.03)

-0.186*** (-4.56)

BM2t -0.158*** (-5.45)

-0.295*** (-2.65)

-0.15*** (-4.23)

BM3t -0.123*** (-4.78)

-0.235** (-2.29)

-0.114*** (-3.62)

BM4t -0.069*** (-2.88)

-0.136 (-1.39)

-0.065** (-2.24)

ln(TAt) -0.001 (-0.06)

-0.044 (-0.99)

0.014 (0.72)

Page 24: Buy on bad news, sell on good news: How insider trading ...wise12... · Likewise, insiders tend to buy (sell) stocks in years when 3rd-party news coverage is pessimistic (optimistic).

cons 0.278**

(2.02) 0.792* (1.79)

0.136 (0.81)

R² 7.8% 8.4% 7.3%

N 5,905 926 4,294

Estimation (1) examines the association between uncertainty and insider purchasing

behavior. There is a strong positive relation between uncertainty and a high insider

purchasing activity. We also observe a positive relation between weak_modal and purchasing

activity. This indicates that insiders buy when expressing insecurity and uncertainty.

Insecurity and uncertainty is also a form of ‘bad’ news driving down stock prices so that

insiders’ purchases are more likely to be profitable.

Strong_modal is also positively associated to purchasing behavior (estimation (2)). However,

the relation is weaker than for uncertainty and weak_modal. Despite the weaker relation, it

indicates that insiders buy their firms’ stocks in those years when they influenced the tone of

published news to appear confidently. This finding is related to research of Larcker &

Zakolyukina (2012) who demonstrate that CEOs influence company publications (such as

analyst conference calls) for their own purpose by using “fewer anxiety words”. The actual

number of observations is low as the word list is short and contained words that occur less

frequently.

4. Conclusion

We examine reported insider trading behavior and explain the association between corporate

as well as 3rd-party news announcements on directors’ dealings activity. Our main

contribution is to extend previous approaches by adding the sentiment of news to the research

setting. Our analysis is based on a comprehensive set of US corporate disclosures and 3rd-

party news from Reuters between 2003 and 2010.

The empirical analysis demonstrates that not only financial forecasting, but also the

explanation of insider trading behavior can be augmented by measuring qualitative

information in financial news. Our augmented approach delivered a more differentiated

explanation of director’s dealings than previous approaches through adding qualitative

information. In detail, analysis reveals that insiders sell in years when their company issues

positive news and buy when the company issues negative news. Further, insiders are also

influenced by 3rd-party news. They sell their securities in years when news coverage about

Page 25: Buy on bad news, sell on good news: How insider trading ...wise12... · Likewise, insiders tend to buy (sell) stocks in years when 3rd-party news coverage is pessimistic (optimistic).

their company is pleasant and buy securities when news coverage is less pleasant. Thereby,

the impact of corporate news, which may be influenced by insiders themselves, on insider

transactions is higher than for 3rd-party news. Insiders seem to care more about the news they

may influence themselves than about independent 3rd-party analysis. We also find that

insiders tend to buy on both worsening news between last and current year and improving

news between the current and next year. By using textual analysis, the finding reconfirms that

insiders trade on superior knowledge about future earnings innovations and future stock

returns. Observations also support that insiders purchase their companies’ stocks when using

language of uncertainty and of distinct high or weak confidence.

By analyzing different news categories, we find that financial results seem to transport more

relevant content explaining insider trading behavior than the average news. It might be also

easier to influence and adjust the tone in financial results than in other news. Our findings

also indicate insiders do not only follow an apparent contrarian trading strategy, but also

could actively manipulate disclosures and firm-specific information.

Implications of this paper are manifold: First, our analysis demonstrates a vivid example how

finance may benefit from textual analysis. Application of textual analysis in finance is not

limited to stock price and volatility predictions, but may also be used in other areas as long as

comprehensive qualitative information is available. Second, our study is also relevant to

financial investment managers. Analysis indicates that investment managers have to either

respond very quickly to news announcements (i.e. using automated news trading) or very

carefully, as they might lose return when trading after news is already factored into the stock

price. It might be necessary to trade the contrarian strategy of insiders: To buy on bad news

and to sell on good news. Our analysis also highlights the need for automated analysis of

news to control for content that is used for manipulation of stock markets. Lastly, the

regulator needs to secure that news announcements are not abused by insiders in companies

to influence the stock market for their own purposes.

REFERENCES

1. Aboody, D., Kasznik, R., 2000. CEO stock option awards and the timing of voluntary

corporate disclosures. Journal of Accounting and Economics 29, 73-100.

2. Brockman, P., Martin, X., Puckett, A., 2010. Voluntary disclosures around CEO stock

option exercises. Journal Corporate Finance 16, 120-136.

Page 26: Buy on bad news, sell on good news: How insider trading ...wise12... · Likewise, insiders tend to buy (sell) stocks in years when 3rd-party news coverage is pessimistic (optimistic).

3. CHENG, Q. and LO, K. (2006), Insider Trading and Voluntary Disclosures. Journal of

Accounting Research, 44: 815–848

4. Hagenau, M., Liebmann, M., Hedwig, M., Neumann, D. 2012. „Automated News

Reading: Automated news reading: Stock Price Prediction based on Financial News

Using Context-Specific Features”, Proceedings of the Hawaii International Conference on

System Sciences, Waleia, Hawaii

5. Huddart and Ke 2007. Information Asymmetry and Cross-sectional Variation in Insider

Trading, Contemporary Accounting Research, 24 (1), pp. 195-232

6. Jegadeesh, N. & Wu, A. D. 2012. “Word Power: A New Approach for Content Analysis”,

Working Paper

7. Karpoff, J. M. and D. S. Lee (1991), 'Insider trading before new issue announcements'.

Financial Management, Vol. 20, pp. 18-26.

8. Ke, Huddart and Petroni, 2003, What Insiders Know about Future Earnings and how

They Use It: Evidence from Insider Trades, Journal of Accounting and Economics (35),

pp.315-346

9. Korczak, A., Korczak, P. 2010. “To Trade or Not to Trade: The Strategic Trading of

Insiders around News Announcements”, Journal of Business Finance and Accounting, 37

(3-4), pp.369-407

10. LARCKER, D. F. and ZAKOLYUKINA, A. A. (2012), Detecting Deceptive Discussions

in Conference Calls. Journal of Accounting Research, 50: 495–540.

11. Li, F. 2008. Annual Report Readability, Current Earnings, and Earnings Persistence.

Journal of Accounting and Economics 45: 221-47.

12. Li, F. 2010a, “Textual Analysis of Corporate Disclosures: A Survey of the Literature”,

Journal of Accounting Literature, Vol. 29, 2010, pp. 143-165

13. Li, F. 2010b. “The Information Content of Forward-Looking Statements in Corporate

Filings - A Naïve Bayesian Machine Learning Approach. Journal of Accounting

Research. Vol. 48 No.5, 49-102.

14. Liebmann, M., Hagenau, M., Häussler, M., Neumann, D. 2012. "Predicting Analyst and

Investor Interpretation Measuring Subjective Tone in Financial News”, Working Paper

15. Loughran, T. & McDonald, B. 2011. “When Is a Liability Not a Liability? Textual

Analysis, Dictionaries, and 10-Ks”, The Journal of Finance, Vol. 66: pp. 35–65.

16. Ofek, E., Yermack, D., 2000. “Taking stock: equity-based compensation and the

evolution of managerial ownership”, The Journal of Finance 55, 1367–1384.

Page 27: Buy on bad news, sell on good news: How insider trading ...wise12... · Likewise, insiders tend to buy (sell) stocks in years when 3rd-party news coverage is pessimistic (optimistic).

17. Piotroski, J.D., Roulstone, D.T. 2005. “Do insider trades reflect both contrarian beliefs

and superior knowledge about future cash flow realizations?”, Journal of Accounting and

Economics 39 (2005) 55–81

18. Porter, M.F. 1980. “An Algorithm for Suffix Stripping”, Program, 14(3): p.130–137

19. Rozeff, M., Zaman, M., 1998. Overreaction and insider trading: evidence from growth

and value portfolios. The Journal of Finance 53, 701–716.

20. Seyhun, H. N. (1990), 'Do bidder managers knowingly pay too much for target firms'.

Journal of Business, Vol. 63, pp. 439-464.

21. Seyhun, H. N. (1997), Corporate Bankruptcy and Insider Trading, Journal of Business,

Vol. 70, pp. 189-216.

22. Tetlock, P. C., Saar-Tsechansky, M. & Macskassy, S, 2008. “More than words:

Quantifying Language to Measure Firms’ Fundamentals”, The Journal of Finance, Vol.

63, Number 3, pp. 1437-1467