Corporate News Releases and Equity Vesting

Alex Edmans ∗

London Business School, Wharton, NBER, CEPR, and ECGI

Luis Goncalves-Pinto †

National University of Singapore

Yanbo Wang ‡

INSEAD

Moqi Xu §

London School of Economics

This Draft: November 18, 2013

Abstract

We show that CEOs strategically time the release of corporate news to coincide with monthsin which their equity vests. These vesting months are determined by equity grants madeseveral years prior and thus unlikely to be driven by the current information environment.We find that, compared to non-vesting months, firms release 12.5% more news during themonths in which CEOs’ restricted pay is pre-scheduled to vest. We also find a reduction innews releases both one month prior to vesting and two months after vesting. News releaseslead to a temporary run-up in stock prices and trading volume, potentially resulting fromincreased investor attention or reduced information asymmetry. This allows the CEO tocash out at a higher price and in a more liquid market.

JEL Classification: G11, G23, G30, G32, G34.

Keywords: Voluntary Disclosure, Equity Vesting, CEO Incentives, Insider Trading.

∗ aedmans@london.edu, London Business School, P-225, Regent’s Park, London NW1 4SA.† lgoncalv@nus.edu.sg, NUS Business School, 15 Kent Ridge Drive, MRB 7-43, Singapore 119245.‡ yanbo.wang@insead.edu, INSEAD, Boulevard de Constance, F-77305 Fontainebleau, France.§ m.xu1@lse.ac.uk, London School of Economics, Room OLD M2.12, UK - WC2A 2AE, London.

1 Introduction

This paper shows that the vesting schedule of the CEO’s compensation contract affects his

timing of corporate news releases. In particular, we find that CEOs release more news in

months in which they have significant vesting equity. Such disclosures lead to an increase

in both the stock price and trading volume, allowing the CEO to cash out at a higher price

and in a more liquid market.

CEOs’ incentives to increase disclosure can stem from two channels. First, it can attract

investor attention, which previous research has shown to temporarily increase stock prices.

For example, Barber and Odean (2008) argue that investors need to browse through thou-

sands of stocks when making a buy decision, but do not face the same search problem when

selling as they tend to sell only stocks they already own. As a result, investors become net

buyers of attention-grabbing stocks, which can in turn positively affect their prices. Second,

increased disclosure can reduce information asymmetry between investors. This in turn en-

courages uninformed investors to buy the stock, also augmenting the stock price. Indeed,

Balakrishnan, Billings, Kelly, and Ljungqvist (2013) find that voluntary disclosures increase

liquidity and thus firm value.

While the consequences of information disclosure have been widely studied, there is

relatively little research on its determinants - in particular, how disclosure depends on the

incentives of the CEO who undertakes it.1 Studying this question is difficult because the

CEO’s incentives to boost the short-term stock price, via increasing disclosure, are likely

endogenous. The CEO’s stock price concerns may stem from a number of channels, but for

each of these channels, there may be reverse causality from disclosure to the incentives, or

omitted variables may jointly affect both the CEO’s incentives and his disclosure decision.

For example, the CEO may care about the current stock price if he intends to issue equity

on behalf of the firm (Stein (1996)) or sell his own shares (Stein (1989)). However, the

decision to sell primary or secondary equity is endogenous and in particular may be driven

by the information environment at the time. For example, it may be that a particular

1 One notable exception is Balakrishnan, Billings, Kelly, and Ljungqvist (2013) who show that an ex-ogenous decrease in public information incentivizes managers to increase disclosure. In this paper, we studyhow the manager’s contract provides him with incentives to release information.

2

month is newsworthy and leads to the CEO undertaking many news releases (even in the

absence of strategic considerations), and the CEO takes advantage of the temporary stock

price increase by opportunistically selling equity. Thus, disclosure causes equity sales rather

than the expectation of equity sales causing disclosure. Alternatively, the CEO’s stock price

concerns may stem from takeover threat (Stein (1988)), which may cause a CEO to disclose

to boost the stock price and alleviate the threat. However, uncertainty about the firm’s

future prospects (an omitted variable) may jointly cause a firm to be a takeover target (as

potential acquirers may have a different view on the firm’s long-run value than the market)

and to voluntarily disclose information (to reduce the uncertainty).

Identification problems are typically addressed by using an exogenous shock to the en-

dogenous variable - for example, unexpected equity sales due to sudden liquidity needs.

However, truly exogenous shocks are unpredictable by the CEO, and thus he is unable to

manage the information environment in advance by increasing disclosure. Thus, identifi-

cation in our setting requires a measure of the CEO’s stock price concerns that are both

predictable and likely to be exogenous - i.e. unaffected by disclosure and unrelated to the

current information environment.

We use the amount of shares and options that is scheduled to vest in a given month.

This amount is driven by the magnitude and vesting period of equity grants made several

years prior2, and thus unlikely to be affected by current disclosures or omitted proxies for

the current information environment. We calculate this amount from 2006 to 2011 using a

new dataset from Equilar, which takes advantage of increasing disclosure requirements as

a result of FAS 123(R), and hand-collect it from proxy statements and SEC Form 4 filings

from 1994 to 2005.

We first find that CEOs sell significant amounts of equity shortly after it vests, consistent

with optimal exercise behavior for a risk-averse agent (e.g., Kahl, Liu, and Longstaff (2003)

and Hall and Murphy (2002)). We observe a CEO’s first trade in 35% of the vesting months

in our sample, and more than 50% within three months of vesting. Thus, scheduled vesting

of equity indeed leads the CEO to be concerned with the short-term stock price.

2 The average vesting horizon in our sample is three years, with a maximum of seven years.

3

We next show that during the month in which significant equity is scheduled to vest,

firms undertake a significantly greater number of news releases, as recorded in the Capital

IQ database. We show that firms release 12.5% more news in vesting months compared

to non-vesting months. These results are robust to controls for determinants of a firm’s

information environment, other components of CEO compensation, as well as firm and year

fixed effects. We then classify news items into discretionary and non-discretionary, under

the rationale that discretionary disclosures are easier to manage by a manager wishing to

boost the stock price in a short period of time. Consistent with this hypothesis, we find

that the increase in news items during the vesting month is concentrated in those that are

discretionary.

We also find that firms significantly reduce the number of corporate news releases both

one month before and two months after the vesting month. These additional results suggest

that the increase in disclosures in the vesting month is not part of a general trend, but the

CEO’s strategic decision to delay news releases until the vesting month, and accelerate such

releases into the vesting month.

Having documented a link between the CEO’s stock price concerns and news releases, we

next study the effect of news releases on stock returns to verify whether disclosure indeed has

the intended effects. Discretionary news released in the vesting month generate a significant

cumulative abnormal return of 30 basis points, for stock and options together, during a

15-day window starting from the release date.

We derive a simple back-of-envelope calculation for the magnitude of the gain a CEO

can extract from undertaking strategic news releases to affect short-term stock returns. In

our sample, the average value of a CEO equity transaction is $5.4 million. According to

the abnormal return estimations provided above for a 15-day window, the implied gains for

an average CEO from the strategic release of discretionary news, amount to $16,200. The

implied gain is modest but is in line with gains in cases of illegal insider trading.

The above calculations measure the CEO’s benefit from increasing disclosure if he has no

price impact and so is only concerned with the level of the stock price. However, if the CEO

expects to sell a significant amount of equity upon vesting, and thus have price impact, he

will also benefit from any increased liquidity that results from higher investor attention or

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lower information asymmetry. The average amount of vesting equity (stock and options) in

a vesting month, as a percent of total shares outstanding, is 2 basis points (with a maximum

of 45 basis points).

We report an abnormal increase in trading volume in the vesting month. On the first day

after a news release, for all equity (stock and options), we find that turnover increases by

0.41% for discretionary news, and 0.71% for non-discretionary news. These values decrease

as we extend the number of days in our event-study windows. Therefore, in addition to the

price increases, CEOs also benefit from the greater liquidity that results from higher trading

volumes surrounding a news release, thus enabling them to trade with a smaller price impact.

The final step is to show that CEOs indeed take advantage of the observed short-term

run-ups in stock price and liquidity. We compute the length of time between the release of

corporate news and the date at which the CEO first sells some of his equity holdings, as re-

ported in the Thomson Financial Insider Trading filings. Focusing on news and transactions

that happen within the vesting month, we find that the median CEO takes 3 days to sell

some of his equity holdings from the date of the news release. In other words, counting from

the last corporate news event recorded on Capital IQ as occurring within a vesting month,

half of the CEOs in our sample sell equity within the period of 3 days. Moreover, 28% of

the CEOs in our sample cash in their shares and options on the same day of the news event.

These results suggest that CEOs can in fact generate trading profits from the short-term

return effect associated with corporate news events. Overall, our results indicate that the

timing of corporate news may be biased by CEOs seeking to affect their firms’ stock price

to benefit their own trading.

Our paper is related to two main literatures: corporate disclosures and equity vesting.

Starting with the former, Balakrishnan, Billings, Kelly, and Ljungqvist (2013) show that

managers increase disclosure by providing more earnings guidance. They do so in response

to a reduction in public information caused by exogenous broker closures or mergers. Ahern

and Sosyura (2013) find that bidders in stock mergers originate significantly more positive

news stories after the start of merger negotiations, but before the public announcement.

They show that such strategy generates a temporary run-up in bidders’ stock prices during

the period when the stock exchange ratio is determined, which can help reduce the cost of the

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takeover. While the decision to undertake a merger or to use stock financing may be driven

by the expectation of imminent positive news releases, we study the incentives to disclose

resulting from equity grants made several years prior. We find that firms tend to delay the

release of news in the month before the vesting month. This result is consistent with the

findings in Chuprinin (2011), which shows that companies tend to ration the delivery of

news and create reserves that allow them to create sustainable price trends and to mitigate

unexpected shortages of public information.

In addition to disclosing information through news releases, firms can do so through

advertising. Indeed, Lou (2013) shows that managers increase firm advertising to attract

investor attention and increase short-term stock returns. Like in our paper, Lou (2013)

connects such activity to insider-trading related benefits. However, advertising expenses are

reported only annually, which makes it harder to isolate causal effects of predicted events

and advertising activity. However, unlike in Lou (2013) and Ahern and Sosyura (2013), we

use equity vesting schedules to predict CEOs’ trading. Such schedules are determined several

years in advance. In addition, our analysis is at a more granular level, studying the number

of news releases within a given month. This granularity reduces the likelihood that, several

years prior, the board chose equity vesting dates to coincide with the precise month in which

it expected the firm to release news.

More generally, our paper is related to the literature on media and its impact on stock

returns and liquidity. On the return dimension, Fang and Peress (2009) find a significant

return premium on stocks with no media coverage, and Huberman and Regev (2001) and

Tetlock (2011) show that individual investors (over)react to stale information. On the trad-

ing dimension, Engelberg and Parsons (2010) show a causal relation between media and

volume, and Brennan and Tamarowski (2000) establish a chain of causation between cor-

porate investor relations activities, the number of stock analysts who follow the firm, and

the liquidity of trading in the firm’s shares. Grullon, Kanatas, and Weston (2004) show

that visibility related to greater advertising expenditures increases ownership breadth and

liquidity. We contribute to this literature by analyzing a compensation-related incentive to

use media as a vehicle to increase stock returns and liquidity.

6

The second literature to which this paper relates studies the relationship between vesting

equity and corporate decisions. While most existing research on CEO contracts study the

level of a CEO’s incentives, some recent papers investigate the horizon of incentives. Gopalan,

Milbourn, Song, and Thakor (2013) are the first to use the Equilar dataset to quantify the

duration of a CEO’s equity incentives, linking it to firms’ earnings management. Edmans,

Fang, and Lewellen (2013) show that newly-vesting equity is associated with declines in

investment in the same year as well as a greater likelihood of the manager meeting or narrowly

beating earnings forecasts. Ladika and Sautner (2013) show that FAS 123(R) led to boards

accelerating the vesting of previously-granted equity to avoid an accounting charge, which

in turn led to a reduction in investment.

This paper is organized as follows. In Section 2, we describe the data and the main

variables used in our study. Section 3 shows that vesting schedules are highly correlated

with actual equity sales. In Section 4, we present the core results of our paper, linking

the timing of news releases with equity vesting schedules. In Section 5, we document the

short-term stock return effects of corporate news events and their relation with the vesting

schedules. Section 6 concludes.

2 Data and Empirical Strategy

This section describes the main variables used in our empirical analysis and our empirical

strategy.

2.1 Equity Vesting, Insider Trading, and Corporate News Data

We obtain data on the vesting schedules of restricted stock and options from the Equilar

dataset. Similar to ExecuComp, Equilar collects their compensation data from the firms’

proxy statements. We obtain details of all stock and option grants to all named executives

of firms in the Russell 3000 index for the period between 2006 and 2011. For each grant,

we have both the size of the grant, the length of the vesting period, and the nature of the

vesting: whether the grant vests equally over the vesting period (graded vesting) or entirely

7

at a specific time (cliff vesting). This data was used in Gopalan, Milbourn, Song, and Thakor

(2013), Edmans, Fang, and Lewellen (2013), and Lou (2013), among others.

For the period before 2006, we hand-collect vesting details available in Form 4 (insider

trading) filings as well as in proxy statements (Cadman, Sunder, and Rusticus (2013)). While

proxy statements contain detailed vesting information for option grants, they typically do not

provide them for restricted stock grants. Form 4 filings provide vesting details for both option

and stock. However, because Form 4 filings are filed by the beneficial owner (the CEO), they

are more prone to errors than proxy statements, which are audited and filed by the firm

on an annual basis. Therefore, we use the information given in proxy statements for option

grants and supplement it with information on stock grants obtained from Form 4 filings.

These are available online starting from 1994. Both sources describe vesting conditions in

footnotes. To limit the work involved in obtaining and coding the footnotes, we restrict

our pre-2006 sample to firms that were part of the S&P 500 within that period. While

information on the grant itself is available in a standardized format in proxy statements and

Form 4 filings, vesting conditions are typically described in footnotes. We present next the

structure of typical footnotes on the proxy statements and Form 4. We selected one for Louis

Gerstner of IBM in 2001, with a grant of 650,000 options with an exercise price of $109.62.

The footnote on the proxy statement reads as follows:

“Mr. Gerstner’s grant becomes exercisable in two equal installments, on March 1, 2001

and March 1, 2002.”

We selected a Form 4 filed by John H. Eyler, Jr., of Toys R Us in 2004. On April 1, 2004,

Mr. Eyler was awarded 20,000 shares of (restricted) common stock. The footnote on Form

4 reads as follows:

“These shares vest 50% on the second anniversary of the award date and 100% on the

third anniversary of the award date.”

We use these footnotes to calculate the number of stocks and options due for vesting on

each date. In the example for Mr. Gerstner, half of the 650,000 options vests on March 1,

2001, and half on March 1, 2002. In the Equilar database, such an example would have been

coded with a vesting period of two years and graded vesting. In the example for Mr. Eyler,

half of the 20,000 shares of common stock vests on April 1, 2006, and half on April 1, 2007.

8

The other category in Equilar is cliff vesting, which means that all of the options will

vest at the end of the vesting period. Equilar does not capture whether graded vesting is

on an annual, quarterly, or monthly basis. Because most graded vesting schedules prior to

2006 are annual, we conduct our base analysis assuming an annual schedule. Our results are

robust if we assume a quarterly schedule for all graded vesting. We also assume that graded

vesting refers to straight-line vesting.

Note that our method of estimating the amount of vesting equity differs slightly from

the method in Edmans, Fang, and Lewellen (2013). That paper studies the actual number

of shares and options that vest in a given year. For example, they calculate how the number

of unvested options with a particular (exercise price, expiration date) combination falls over

the course of the year. By looking at actual vesting ex-post (which is known to the CEO

ex-ante as he observes his contract), they do not require the assumption that graded vesting

refers to straight-line vesting. This measure is only available on an annual basis, consistent

with the fact that Edmans, Fang, and Lewellen (2013) study the link between vesting equity

and investment (which is also available on an annual basis). In contrast, our dependent

variable of interest is the number of news releases in a given month, which requires us to

estimate the number of shares and options that vest in a given month. Thus, rather than

looking at actual vesting ex-post, we follow Gopalan, Milbourn, Song, and Thakor (2013)

by studying predicted vesting ex-ante. Specifically, when new equity is granted, we predict

the number of units of this grant that will vest in a given month by using its grant date,

vesting period, and cliff- versus graded-vesting status. While this requires the assumption

that graded-vesting refers to straight-line vesting, it allows us to estimate vesting equity on

a monthly frequency.

In addition to vesting equity, we also study the dollar value of the actual equity sold. We

obtain this data from the Thomson Financial Insider Trading database, which is compiled

from Form 4 filed with the SEC.

Finally we obtain information on corporate news from Capital IQ. In particular, we

retrieve information on press releases. We then classify all the news into discretionary

and non-discretionary. The discretionary category includes items such as: client announce-

ments, product-related announcements, corporate guidance, company conference presenta-

9

tions, buyback update, strategic alliances, follow-on equity offerings, and shareholder/analyst

calls, among others. The non-discretionary category of news includes items such as: an-

nouncements of earnings, earnings calls, executive/board changes, annual general meeting,

executive changes, regulatory agency inquiries, board meeting, and auditor changes, among

others.

2.2 Control Variables

In addition to the data on corporate news events and on CEO compensation and equity

transactions, we use a number of controls intended to capture firms’ information environment

and other CEO incentives.

Following Gopalan, Milbourn, Song, and Thakor (2013), we compute the average duration

of CEOs’ non-vested compensation. This measure has been shown to be associated with CEO

short-termism and with earnings management. The analysis in our paper focuses however

around the vesting month of CEO restricted pay, and duration incentives are less likely to

be present during that period. We calculate option delta and vega measures, separately for

CEOs’ vested and unvested options, following the method described in Edmans, Fang, and

Lewellen (2013). These measures capture the sensitivity of the price of options to a one

dollar change in the price and a one percent change in volatility of the underlying stock,

respectively. They capture the degree to which CEOs would be willing to influence the mean

level and volatility of the firms’ stock prices. We also measure the moneyness of vesting

options as a weighted average of a dummy variable that equals one for a vesting option that

is in the money, weighted by the Black-Scholes value of the option.

We control for the total compensation of the CEO, measured as the sum of salary and

bonus and the value of the current grants of equity-based compensation. Incentive pay is

the value of stock and options as a fraction of total compensation. These data were obtained

from ExecuComp. We also control for the age of the CEO, to control for life-cycle driven

differences in company policies (e.g., Malmendier and Tate (2005, 2008), Malmendier, Tate,

and Yan (2011), Yim (2013)).

We complement the compensation data with data on stock characteristics from the Center

for Research in Security Prices (CRSP) and financial data from Compustat. In particular,

10

we add to our control variable list some CRSP measures of trading volume, bid-ask spread,

and past stock returns. We also compute a measure of stock illiquidity following Amihud

(2002), which is simply the number of units of absolute stock return per dollar of trading

volume. Regarding company fundamentals, we use the earnings surprise measure (SUE)

available in the I/B/E/S database. We also control for R&D and advertising expenses as

a percentage of total assets, from Compustat. We control for advertising expenses because

it has been shown in Lou (2013) that managers adjust firm advertising to attract investor

attention and benefit from temporarily inflated stock prices.

We add to our list of control variables a measure of mutual fund flow-induced trading

pressure, which serves as a proxy for stock mispricing. We compute this measure following

Edmans, Goldstein, and Jiang (2012). It has been shown that, stock overpricing due to

excess demand pressure from mutual funds is positively associated with new equity issues

(Khan, Kogan, and Serafeim (2012)) and it could also be linked to insider sales.

Finally, we use an indicator variable to capture the effect of firms’ fiscal-year end, which

involves a substantial concentration of disclosure and corporate news activity. In addition,

Oyer (1998) argued that executive bonus plans are commonly based on fiscal-year results,

which can provide incentives for managers to manipulate prices to maximize their own in-

comes rather than their firms’ profits.

2.3 Descriptive Statistics

In Table 1, we present the summary statistics for the main variables used in our study. Our

key dependent variables are corporate news events and insider transactions. A typical firm

in our sample has an average (median) of 3.19 (3) press releases, 2.53 (2) website activities,

and 1.50 (1) transactions in days with observed corporate events as recorded in the Capital

IQ database. Regarding insider transactions, which we obtain from the Thomson Financial

Insider Filing database, the average (median) number of shares sold by CEOs in our sample

is 61,000 (22,000) per transaction, while the average (median) number of options exercised

is 180,000 (83,000). In dollar terms, the average (median) value of shares sold by a CEO is

$1.2 million ($0.48 million) and the average (median) value of options exercised by a CEO

is $4.2 million ($1.8 million).

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Our key independent variables are related to the vesting schedules of CEOs’ restricted

compensation. The average (median) stock grant is worth $1.06 million ($0.70 million), and

the average (median) option grant is worth $0.45 million ($0.23 million). In our sample,

the fraction of option and stock grants that vest entirely at a specific time (cliff vesting) is

37% and 15%, respectively. The remaining grants vest equally over a period of time (graded

vesting).

The equity vesting periods in our sample are on average 3 years long and can become as

long as 7 years (for stock grants). This constitutes one of the most important properties of

the equity vesting schedules. In particular, these schedules are set by equity grants awarded

to the CEO many years in the past and can plausibly be considered exogenous to the current

information environment of the firm.

The average pay duration of the CEOs in our sample is 1.43 years, and the average CEO

is 55 years old. These statistics are consistent with those in Gopalan, Milbourn, Song, and

Thakor (2013).

In the Appendix, we provide a description of the main variables used in our study.

3 Insider Transactions and Equity Vesting

This section studies whether CEOs indeed sell equity soon after it vests. In Table 2, we

compute the average distance between the month in which a CEO’s equity vests and the

month in which for the first time we observe the same CEO executing a transaction. We

provide those statistics separately for restricted stock (Panel A) and stock options (Panel

B), and also separately for cliff and graded vesting. Overall, we observe a CEO’s first stock

trade in 35% of the vesting months in our sample, and more than 50% if we consider not

just the vesting month but also the two subsequent months. Similar values are reported for

the average time between vesting and the first exercise of a stock option grant by a CEO.

These results suggest that managers are likely to sell their stock and exercise their options

upon vesting, which is consistent with optimal behavior of a risk-averse agent (e.g. Hall and

Murphy (2002), Kahl, Liu, and Longstaff (2003)). Therefore, equity vesting can effectively

capture a CEO’s concern with short-term stock prices.

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In Table 3, we provide a multivariate analysis of the determinants of CEO transactions.

In particular, we report the results obtained from the following regression specification:

Transactions,t = α + β ∗3∑

τ=0

V estingMonths,k,t+τ + γ ∗ Controls+ Fixed Effects + εs,t (1)

where the dependent variable Transactions,t is an indicator function which equals one if

the CEO sells his shares or exercises his options in a particular month (t), and equals zero oth-

erwise. Among the independent variables, we consider an indicator function V estingMonths,k,t+τ

for the vesting month (τ = 0), and additional indicators for each of the three months fol-

lowing the vesting month (τ = 1,τ = 2, and τ = 3). The main control variables we use

are described in Section 3.2. In addition to those, we also include indicators for earnings

announcement months (yearly and quarterly), indicators for ex-dividend months, as well as

for annual general meeting (AGM) months to account for news and press activity unrelated

to vesting. Introducing firm and year fixed effects allows us to control for unobserved firm

characteristics and time-varying trends. As a result, our measures are strictly time-varying

differences within the firm.

The results reported in Table 3 corroborate the simple analysis we provided in Table

2. Overall, there is a 19% to 27% higher probability that we observe a transaction by a

CEO during the month in which their stock and option holdings are pre-scheduled to vest.

However, from the second month after vesting and onwards, the likelihood of observing a

CEO transaction decreases significantly.

Consistent with the results in Oyer (1998), we show that firms’ fiscal year ends also

strongly predict CEOs’ transactions. There is a 14% to 20% probability of observing a CEO

trade in the month in which there is an announcement of earnings for the prior fiscal year.

After controlling for yearly earnings announcements, our indicator for quarterly earnings

announcements exhibits a negative and significant coefficient. This is because most of fiscal

year earnings announcements coincide with the last quarter earnings announcements. Ef-

fectively, the indicator function for the fiscal year earnings announcements is an interaction

between yearly and quarterly earnings announcements.

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Note also that the likelihood of a CEO transaction is larger when the vesting month

is also an ex-dividend month or an AGM month. Lastly, not surprisingly, the exercise of

options is positively related to their moneyness.

4 News Releases and Equity Vesting

Table 4 reports the core results of this paper, that CEOs significantly increase disclosures in

months in which they have vesting equity. We run the following regression:

NewsEvents,t = α + β ∗3∑

τ=−1

V estingMonths,k,t+τ + γ ∗ Controls+ Fixed Effects + εs,t (2)

where the dependent variable NewsEvents,t represents a count of the corporate news events

related to a particular firm (s) that were recorded in Capital IQ for a given time period (t).

Compared to specification (1), we add to this regression specification an indicator function

for the month prior (τ = −1) to the vesting month (t). Note that in Table 4 the regression

coefficient associated with the indicator for the month prior to the vesting month is negative

and significant for the full sample (in column 1 we show a coefficient of -0.10 with t = −6.01

for all news events and all securities vesting), and also for the different sub-samples (stock

and option vesting, and discretionary and non-discretionary news). Note that the results are

stronger for the discretionary news (columns 2, 5, and 8). This result suggests that firms are

likely to strategically delay the release of corporate news in the month prior to the vesting

month, which can then create clustering of news in the vesting month. This also suggests

that firms could strategically create reserves of news which they could later use to mitigate

potential shortages of public news (Chuprinin (2011)). To our knowledge, these results are

the first to link the timing of the release of corporate news and the vesting schedules of

CEOs’ restricted compensation.

In the vesting month, we observe 0.05 (t = 3.04) more discretionary news events (column

2), after controlling for other determinants of news releases. Note also that two months

after vesting there appears to be a reversal in the intensity of news releases, stronger also

for discretionary news (column 1), and across the sub-samples (columns 5 and 8).

14

Note that the indicator functions for earnings announcements absorb a significant portion

of the effects related to corporate news releases. We argue that the news items we classified

as discretionary are more voluntary, while the items under the non-discretionary category

are more likely mandated by market regulations. Therefore, managers are more likely to use

discretionary news to affect market perceptions. Our results are consistent with this idea,

as they appear to be statistically and economically stronger for discretionary news.

In Table 5 we focus our analysis on the amount of equity vesting, instead of simply an

indicator for whether a month is a vesting month or not. We use the following regression

specification:

NewsEvents,t = α + ρ ∗ AmountV esting/SOs,k,t +

+ β ∗∑

τ∈{−1,1,2,3}

V estingMonths,k,t+τ +

+ γ ∗ Controls+ Fixed Effects + εs,t (3)

where the variable AmountV esting/SOs,k,t is the amount of equity vesting divided by the

number of shares outstanding in the vesting month. The other variables are as in specification

(2), except that the indicator V estingMonths,k,t+τ does not include the vesting month (τ =

0). The results we report in Table 5 are very similar to those presented in Table 4. There

appears to be evidence of strategic delay of news in the month before vesting, and a positive

relation between the amount of equity vesting and the amount of news releases, in particular

for discretionary news (columns 2, 5 and 8). In Table 5 we do not observe the reversal of

the intensity of news two months after vesting, like in Table 4.

In Table 6 we report the breakdown of the news events by type. These events are ex-

tracted from Capital IQ. In particular, they are extracted from press releases in the Capital

IQ database. We present the top 30 events, which account for 91.44% of our sample. Note,

for instance, that earnings calls account for 9.10% of the news observations in our sample.

The most common news event in our sample related to client announcements (13.14% of all

events). We also present the breakdown of the events observed during the vesting months.

Note that the most frequent news event observed in the vesting months is earnings calls, rep-

resenting 12.77% of all events observed in the vesting months. The second most likely events

15

in the vesting months are product-related announcements (11.96%) followed immediately by

client announcements (11.38%).

5 Returns and Liquidity in the Vesting Month

The results we report in Tables 4 and 5 are consistent with the idea that firms have an

incentive to synchronize the release of corporate news with the vesting schedules of CEOs’

restricted equity. We argue that such timing strategy can help firms attract investors’ at-

tention or reduce information asymmetry among investors during the equity vesting month

from which CEOs can benefit by trading in their own account. We argue that firms have a

preference to cluster in the vesting month the release of news that are discretionary. Such

category of news may carry less fundamental content. This could in turn lead investors with

limited attention and limited processing capacity to take such abnormal increase in corpo-

rate news at face value and become net buyers of the firm’s stock, overreacting and creating

short-lived run-ups in its price (Barber and Odean (2008)). Such short-term run-ups in

firms’ stock price could then be taken advantage of by CEOs who would opportunistically

sell their equity in the firm to explore the stock mispricing.

In Table 7, we study whether the strategy used by firms to cluster the release of corporate

news in the equity vesting months does indeed generate the intended stock price reactions.

We conduct an event study of the impact of the release of corporate news in the vesting

month. We report averages of the cumulative abnormal return (CAR), abnormal bid-ask

spread, and abnormal trading volume (normalized by the amount of shares outstanding

in the vesting month) of the firms’ stock, for the release of both discretionary and non-

discretionary news. We create three time windows that cover one, fifteen, and thirty days

after the news release date within a given vesting month. The results in Table 7 show that

discretionary news released in the vesting month generate a cumulative abnormal return of

30 basis points (t = 5.20) within 15 days from the release date. Using this figure, we can

perform a back-of-envelope calculation of the implied gains that a CEO can extract from

strategically releasing news according to his equity vesting schedules. As discussed in section

2.3., the average value of a CEO transaction is $5.4 million (stock and options together).

16

Therefore, 30 basis points of cumulative abnormal return associated with the release of

discretionary news in the vesting month implies an expected average gain of $16,200 within

15 days of the news release date. This appears to be a modest gain, but it is in line with

gains in cases of illegal insider trading.

Discretionary news released in the vesting month generate a cumulative abnormal return

of 37 basis points (t = 4.76) for restricted stock, and 36 basis points (t = 5.35) for stock

options, during a 15-day window starting from the release date. This return effect does not

appear to vanish after we extend the event window to 30 days from the news release date.

Note that the return effects can be stronger for non-discretionary news. For example, a

cumulative abnormal returns of 53 basis points (t = 3.89) is generated over 15 days for stock

options.

In Figure 1, we present two event-study plots that document the cumulative abnormal

returns (in basis points) that can be generated around the news event dates in the vesting

month, when considering all the types of news (Panel A), and when considering only one

type of discretionary news: corporate guidance (Panel B). We report separately the effects

associated with corporate guidance as this was the type of voluntary discloses that Balakr-

ishnan, Billings, Kelly, and Ljungqvist (2013) focus on. Note that in both Panels A and B

the plots exhibit a significant run-up on the news event date. The run-up appears stronger

for corporate guidance (Panel B), but the reversal is stronger when considering all the news

events (Panel A).

However, the stock return figures presented above do not fully account for the benefits

CEOs can extract from influencing market perceptions regarding their firms’ stock. In par-

ticular, they do not account for the adverse price impact that CEOs can prevent with the

additional trading volume that can be generated by the strategic release of corporate news,

as well as its effect on the stock’s bid-ask price spread.

In Table 7, we also report a positive and significant abnormal trading volume associated

with both discretionary and non-discretionary news releases. We define abnormal turnover

as the ratio of trading volume to the amount of shares outstanding, adjusted by the average

turnover of the 40 days prior to the news event date. Note that, as we gradually extend the

news event window from 1 to 30 days starting from the event date, we observe a progressive

17

decline in abnormal turnover values and also their statistical significance, from 41 basis points

(t = 37.05) to 6 basis point (t = 17.45) for discretionary news and vesting of all equity. Such

pattern remains after we split the sample into stock vesting and option vesting, and also for

non-discretionary news.

In Figure 2 we plot the ratio of the abnormal volume to shares outstanding from 10

days before the news event date until 30 days after the news event date. Note that, when

considering all the types of news events (Panel A), the abnormal turnover of the firm rises

to about 41 basis points on the event date, while for corporate guidance (Panel B) it rises

to about 140 basis points on the event date.

These results suggest that CEOs can also benefit from the increased turnover of their

firms’ stock upon vesting of their equity grants. In particular, the abnormal trading volume

generated by the release of corporate news can help absorb the potentially adverse price

impact of CEOs’ trades.

Lastly, in Table 7, we report the effects of news releases on the abnormal bid-ask price

spread. It appears that the tendency is for the spread to widen immediately after earnings

related news releases, which corrects immediately after. Like in the measurement of stock

turnover, the abnormal bid-ask spread is calculated in excess of the average bid-ask spread

for the 40 days prior to the news event date.

Are CEOs really taking advantage of the observed short-term run-ups in stock price and

liquidity? In order to answer this question, we compute the distance between the date of

the release of corporate news and the date of the actual equity transactions that CEOs are

required to report with the SEC and which can be obtained from Thomson Financial Insider

Filing database. In Table 8, we report only the cases in which both the news event and the

transaction occur with the vesting month.

The median CEO takes 3 days to sell some of his vested equity holdings, counting from

the last observed news event in the Capital IQ database. In other words, counting from

the last corporate news event recorded on Capital IQ during the vesting month, half of the

CEOs in our sample trade equity in the period of 3 days. Moreover, 28% of the CEOs in

our sample cash in their shares and options on the same date as the news event date. These

18

results suggest that CEOs can in fact generate trading profits from the short-term return

and liquidity effects associated with corporate news events.

Overall, our results indicate that the timing of corporate news may be biased by CEOs

seeking to attract investor attention or reduce information asymmetry among their investors

to affect their firms’ stock price to benefit their own trading. Such strategy can accomplish

two related goals: it allows the CEOs to cash in their restricted pay at higher prices and at

reduced price impact.

6 Conclusion

This paper contributes to the literature on managers’ incentives to manipulate short-term

stock prices for their own benefit. We provide evidence that managers synchronize the

release of corporate news with the vesting schedules of their equity grants. In addition, they

strategically delay the release of news in the month prior to the vesting month, in particular

discretionary news. We show that such strategy can effectively increase the short-term price

and liquidity of the firm’s stock, which the CEO can then exploit profitably. Consistent with

the hypothesis that managers actively manage investor attention according to their equity

vesting schedules, we show that the intensity of corporate news releases reverts back to its

normal level two months after vesting, and the stock price exhibits a significant correction

within one month after the run-up created by the abnormal level of news released in the

vesting month.

We then examine whether managers do exploit the temporary return effect of their op-

portunistic news release policy. From the date of the last observed corporate news release,

half of the managers in our sample exercise options and sell shares within three days. There-

fore, they should be able to catch the price and liquidity run-ups before their experience a

correction.

Our results have implications for the literature about media effects on financial markets

and the incentives of executive compensation. First, we use an ex-ante measure of insider

trading to show that executives are able to manipulate investor attention deliberately for

their own benefit. Second, because the trigger is determined ex-ante, we are able to pin

19

down the effect of manipulated news releases on liquidity and stock returns. This means

that investors have limited ability to disentangle manipulated from real news. Third, we show

that restricted compensation has a real effect on executives’ behavior - albeit not necessarily

the intended. Such forms of compensation have become commonplace in the recent years

(Bettis, Bizjak, Coles, and Kalpathy (2010)).

20

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22

Appendix: Variable Definitions

Variable DefinitionsVariable Definition

Accrualsis the amount of discretionary accruals reported by thefirm.

Age is the logarithm of the CEO age.

AGM Month is an indicator function that equals one if a particularmonth coincides with the firm’s annual general meeting,and equals zero otherwise.

Advertisement Expense / Total Assets is the ratio of the advertising expenditures (data45 inCompustat) of the current year and the total assets ofthe previous year (data6 in Compustat).

Cliff denotes a sample split that focuses on CEO equity grantsthat vest entirely at a specific time.

Duration is the measure of pay duration proposed in Gopalan, Mil-bourn, Song, and Thakor (2013), and it is calculatedas the sum of the product of the vesting periods of allthe components of the CEO compensation (including re-stricted stock, stock options, salary, and bonus) and thepresent value of all those components, divided by the sumof the present values of all such components.

Earn Ann Month Quarterly is an indicator function that equals one if a particularmonth coincides with the firm’s announcement of quar-terly earnings, and equals zero otherwise.

Earn Ann Month Yearly is an indicator function that equals one if a particularmonth coincides with the firm’s fiscal year end, and equalszero otherwise.

Earnings denotes a sample split that focuses on corporate newsevents that are related to earnings.

Earnings Surprise is the earnings surprise measure (SUE) from the I/B/E/Sdatabase.

Ex-Dividend Month is an indicator function that equals one if a particularmonth coincides with the ex-dividend month, and equalszero otherwise.

Graded denotes a sample split that isolates CEO equity grantsthat vest gradually over a period of time.

Incentive Pay is the fraction of the total compensation of theCEO excluding salary and bonus, and it is calculatedas (Tdc2-Salary-Bonus)/Tdc2 using the variables fromExecuComp.

Moneyness is the fraction of vesting options that is in-the-money.

Variable DefinitionsMutual Fund Flow is a proxy for the upward price pressure induced by mu-tual fund inflows, it follows the definition suggested inEdmans, Goldstein, and Jiang (2012) and is calculated

as∑M

j=1(Fj,tHi,j,t−1)/(V OLi,t), where the variable Fj,t

denotes the total inflow experienced by fund j in quartert, the variable Hi,j,t−1 is the dollar holding of stock i byfund j in quarter t − 1, and the variable V OLi,t is thedollar trading volume of stock i in quarter t.

Non-Earnings denotes a sample split that focuses on corporate newsevents that are not related to earnings but instead withmany other aspects of a firm’s operations.

Option Delta is the (Black-Scholes) sensitivity of non-vested options toa 1 dollar increase in the underlying stock price.

Option Vega is the (Black-Scholes) sensitivity of non-vested options toa 1% change in the underlying stock volatility.

R&D Expense / Total Assets is the ratio of the R&D expenses of this year and the totalassets of the previous year.

Total Compensation is the logarithm of the total compensation of the CEO(Tdc2 in ExecuComp).

Vesting Month is the calendar month in which stock and option grants arepre-scheduled to vest according to the Equilar databaseand manual identification.

Table

1:

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6-3

.54

00

7R

&D

Exp

ense

/T

ota

lA

sset

47,0

00

0.1

20.0

60.2

5.3

754.1

70

0.0

20.1

40.9

1A

dver

tisi

ng

Exp

ense

/T

ota

lA

sset

37,3

00

0.0

30.0

10.0

64.1

627.5

60

00.0

40.3

1A

ccru

als

100,4

17

-1.2

1-0

.04

48.8

2-7

5.0

96,7

86.3

3-3

.44

-0.1

10

0.4

4

Table 2: Distance from Vesting Month to Month of First CEO Transaction

This table reports the distance between the month of equity vesting and the month of the first ob-served transaction for the CEO. The data on equity vesting is in part extracted from Equilar (years2006-2011, Russell 3000 firms) and in part hand-collected from 10-K forms (years 1994-2005, S&P500firms). The data on CEO trading is extracted from Thomson Financial Insider Trading filings (SECForm 4). In Panel A we present the results we obtain for the stock grants, and in Panel B we presentthe results for the option grants. For both stock and options grants, we also split the sample be-tween grants that vest entirely at one time (cliff vesting), and grants that vest gradually over a pe-riod of time (graded vesting). We show the frequency of observations and their cumulative percentages.

PANEL A: Stock Grants All Stocks Stocks Graded Stocks Cliff

# Months Freq. Cum. Perc. Freq. Cum. Perc. Freq. Cum. Perc.

0 1,745 35.3 1,502 34.66 300 40.541 474 44.88 432 44.62 54 47.842 286 50.67 238 50.12 54 55.143 222 55.16 202 54.78 27 58.784 151 58.21 132 57.82 21 61.625 115 60.54 106 60.27 9 62.846 158 63.73 143 63.57 19 65.417 142 66.61 130 66.57 17 67.78 144 69.52 126 69.47 20 70.419 169 72.94 150 72.93 24 73.6510 170 76.38 134 76.03 38 78.7811 255 81.53 227 81.26 33 83.2412 224 86.06 198 85.83 34 87.84>12 689 100 614 100 90 100

PANEL B: Option Grants All Options Options Graded Options Cliff

# Months Freq. Cum. Perc. Freq. Cum. Perc. Freq. Cum. Perc.

0 2,619 37.78 2,460 37.39 189 45.111 612 46.61 586 46.29 35 53.462 340 51.51 316 51.09 29 60.383 308 55.96 290 55.5 21 65.394 195 58.77 186 58.33 10 67.785 189 61.5 180 61.06 11 70.416 238 64.93 227 64.51 11 73.037 209 67.95 203 67.6 6 74.468 195 70.76 189 70.47 8 76.379 291 74.96 277 74.68 14 79.7110 250 78.56 244 78.39 7 81.3811 374 83.96 357 83.81 20 86.1612 295 88.21 285 88.15 12 89.02>12 817 100 780 100 46 100

Table 3: Vesting Month and Actual CEO Transactions

In this table, we report the estimates of a model designed to study whether CEOs are more likely to exercise their stockoptions or sell their restricted shares during the month in which they had been pre-scheduled to vest. We use the followingregression specification: Transactions,t = α + β ∗

∑3τ=0 V estingMonths,k,t+τ + γ ∗ Controls + Fixed Effects + εs,t, where

Transactions,t is an indicator function that equals one if the CEO sells stock or exercises options of firm s during month t,and equals zero otherwise. The variable V estingMonths,k,t+τ is an indicator function that equals one if there is a type kgrant vesting in month t for the underlying firm s, where k ∈ {stock, options} × {all, graded, cliff}. Note that, when τ = 0,the function V estingMonths,k,t+τ indicates the vesting month t, while if τ ∈ {1, 2, 3} the function indicates each of thethree months subsequent to the vesting month. We include in our list of controls, variables that are intended to capturethe information environment of the firm, other components of the CEO compensation structure, as well as indicators forwhether the month of equity vesting coincides with other important periodic events not related to vesting. For instance,whether the vesting month is also the fiscal-year end of the firm. We also control for firm and year fixed effects. A detaileddescription of each control variable is included in the Appendix. Our sample covers the period between 2002 and 2012. Wereport t-statistics in parenthesis, and ∗, ∗∗, and ∗ ∗ ∗ represent significance at the 10%, 5%, and 1% levels, respectively.

Stock Vesting Option Vesting

All Graded Cliff All Graded Cliff(1) (2) (3) (4) (5) (6)

Vesting Month 0.205 0.198 0.207 0.267 0.263 0.189(33.46)*** (30.21)*** (14.14)*** (41.93)*** (40.55)*** (6.657)***

1 Month After Vesting 0.0121 0.0134 -0.00237 0.0117 0.0122 -0.00295(2.884)*** (2.997)*** (-0.240) (3.067)*** (3.155)*** (-0.183)

2 Month After Vesting -0.0116 -0.011 -0.0157 -0.0183 -0.0186 -0.00417(-3.328)*** (-2.957)*** (-1.981)** (-6.068)*** (-6.086)*** (-0.277)

3 Month After Vesting -0.00888 -0.00537 -0.032 0.00314 0.00428 -0.00761(-2.651)*** (-1.501) (-4.208)*** (1.02) (1.38) (-0.487)

Earn Ann Month Yearly 0.156 0.165 0.191 0.135 0.139 0.203(25.18)*** (26.27)*** (30.14)*** (19.51)*** (19.93)*** (26.70)***

Earn Ann Month Quarterly -0.00936 -0.00966 -0.0129 -0.00449 -0.00435 -0.00979(-4.988)*** (-5.125)*** (-6.782)*** (-2.053)** (-1.987)** (-4.263)***

Earnings Surprise -0.00063 -0.00075 -0.00043 -0.00195 -0.00209 -0.0019(-0.841) (-0.997) (-0.568) (-2.300)** (-2.454)** (-2.128)**

Ex-Dividend Month, t 0.0191 0.0201 0.0206 0.00865 0.00928 0.014(6.241)*** (6.495)*** (6.612)*** (2.546)** (2.711)*** (3.865)***

AGM Month, t 0.0152 0.0158 0.0209 0.0164 0.0166 0.0288(3.848)*** (4.037)*** (5.304)*** (3.396)*** (3.441)*** (5.844)***

Duration 0.00 (0.00) 0.01 0.01 0.01 0.01(0.72) (-0.283) (4.452)*** (3.899)*** (3.668)*** (6.847)***

Moneyness 7.52 12.70 71.00(1.792)* (2.832)*** (7.961)***

Option Delta (0.00) (0.00) (0.00)(-0.953) (-0.838) (-0.670)

Option Vega 0.00 0.00 0.00(1.64) (1.42) (2.445)**

Total Compensation 0.02 0.02 0.02 0.01 0.01 0.01(6.893)*** (6.808)*** (6.698)*** (2.504)** (2.494)** (2.484)**

Incentive Compensation -0.017 -0.0167 -0.0198 0.0284 0.0272 0.0253(-1.593) (-1.564) (-1.830)* (2.077)** (1.992)** (1.793)*

Institutional Ownership 0.00578 0.00627 0.00849 0.0126 0.0119 0.00902(0.79) (0.86) (1.25) (1.894)* (1.828)* (1.38)

Constant -0.132 -0.127 -0.146 -0.236 -0.232 -0.227(-5.341)*** (-5.075)*** (-5.841)*** (-5.466)*** (-5.372)*** (-5.405)***

Firm Fixed Effect Yes Yes Yes Yes Yes YesYear Fixed Effect Yes Yes Yes Yes Yes YesObservations 59,684 59,684 59,684 45,136 45,136 45,136R-squared 0.235 0.226 0.199 0.229 0.225 0.135

Table

4:

Tim

ing

of

New

sE

vents

an

dV

est

ing

Month

Inth

ista

ble

,w

ere

port

the

esti

mate

sof

an

OL

Sre

gre

ssio

nd

esig

ned

tost

ud

yw

het

her

CE

Os

rele

ase

more

corp

ora

ten

ews

du

rin

gth

eves

tin

gm

onth

of

thei

rre

stri

cted

equ

ity.

We

use

the

foll

ow

ing

spec

ifica

tion

:NewsEvent s,t

=α

+β∗∑ 3 τ=

−1VestingMonths,k,t+τ

+γ∗Controls

+F

ixed

Eff

ects

+ε s,t

wh

ereNewsEvent s,t

isa

vari

ab

leth

at

cou

nts

the

nu

mb

erof

corp

ora

ten

ews

even

tsre

lease

dby

firm

sd

uri

ng

montht,

an

deq

uals

zero

oth

erw

ise.

Th

evari

ab

leVestingMonths,k,t+τ

isan

ind

icato

rfu

nct

ion

that

equ

als

on

eif

ther

eis

an

ews

even

tof

typ

ek

inm

ontht

for

the

un

der

lyin

gfi

rms,

wh

erek∈{s

tock,o

pti

on

s}×{a

ll,d

iscr

etio

nary,n

on

-dis

cret

ion

ary}.

Note

that,

wh

enτ

=0,

the

fun

ctio

nVestingMonths,k,t+τ

ind

icate

sth

eves

tin

gm

ontht,

wh

ile

ifτ

=−

1th

efu

nct

ion

ind

icate

sth

em

onth

bef

ore

ves

tin

g,an

dfo

rτ∈{1,2,3}

itin

dic

ate

sth

eth

ree

month

ssu

bse

qu

ent

toth

eves

tin

gm

onth

.W

ein

clu

de

inou

rlist

of

contr

ols

,vari

ab

les

that

are

inte

nd

edto

captu

reth

ein

form

ati

on

envir

on

men

tof

the

firm

,as

wel

las

ind

icato

rsfo

rw

het

her

the

month

of

equ

ity

ves

tin

gco

inci

des

wit

hoth

erim

port

ant

per

iod

icev

ents

not

rela

ted

toves

tin

g.

Ad

etailed

des

crip

tion

of

each

contr

ol

vari

ab

leis

incl

ud

edin

the

Ap

pen

dix

.O

ur

sam

ple

cover

sth

ep

erio

db

etw

een

2002

an

d2012.

We

rep

ort

t-st

ati

stic

sin

pare

nth

esis

,an

d∗,∗∗

,an

d∗∗∗

rep

rese

nt

sign

ifica

nce

at

the

10%

,5%

,an

d1%

level

s,re

spec

tivel

y.

All

Ves

tin

gS

tock

Ves

tin

gO

pti

on

Ves

tin

g

All

Dis

cret

.N

on

-Dis

cret

.A

llD

iscr

et.

Non

-Dis

cret

.A

llD

iscr

et.

Non

-Dis

cret

.(1

)(2

)(3

)(4

)(5

)(6

)(7

)(8

)(9

)

1M

onth

Bef

ore

Ves

tin

g-0

.101

-0.0

837

-0.0

171

-0.1

22

-0.0

997

-0.0

227

-0.1

12

-0.0

969

-0.0

153

(-6.0

06)*

**

(-5.5

91)*

**

(-3.1

35)*

**

(-5.7

03)*

**

(-5.2

23)*

**

(-3.2

31)*

**

(-5.6

20)*

**

(-5.3

97)*

**

(-2.4

19)*

*V

esti

ng

Month

0.0

679

0.0

48

0.0

199

0.0

46

0.0

318

0.0

142

0.0

795

0.0

578

0.0

217

(3.8

03)*

**

(3.0

41)*

**

(3.2

80)*

**

(2.0

27)*

*(1

.58)

(1.8

28)*

(3.7

66)*

**

(3.0

93)*

**

(3.0

80)*

**

1M

onth

Aft

erV

esti

ng

-0.0

338

-0.0

192

-1.4

5E

-02

-0.0

264

-0.0

0935

-0.0

171

-0.0

216

-0.0

0694

-0.0

146

(-1.9

45)*

(-1.2

32)

(-2.5

99)*

**

(-1.2

04)

(-0.4

76)

(-2.3

54)*

*(-

1.0

48)

(-0.3

73)

(-2.2

68)*

*2

Month

Aft

erV

esti

ng

-0.0

36

-0.0

35

-0.0

0105

-0.0

511

-0.0

406

-0.0

105

-0.0

404

-0.0

415

0.0

0108

(-1.9

28)*

(-2.1

26)*

*(-

0.1

65)

(-2.0

40)*

*(-

1.8

45)*

(-1.2

22)

(-1.8

67)*

(-2.1

86)*

*(0

.14)

3M

onth

Aft

erV

esti

ng

0.0

793

0.0

47

0.0

323

0.0

873

0.0

612

0.0

261

0.0

996

0.0

572

0.0

424

(4.4

40)*

**

(2.9

98)*

**

(5.2

51)*

**

(3.7

12)*

**

(2.9

93)*

**

(3.1

65)*

**

(4.7

94)*

**

(3.1

48)*

**

(5.8

47)*

**

Earn

An

nM

onth

Yea

rly

0.1

53

0.1

12

0.0

404

0.1

59

0.1

17

0.0

421

0.1

50.1

10.0

4(6

.006)*

**

(5.0

17)*

**

(4.5

00)*

**

(6.3

36)*

**

(5.3

06)*

**

(4.7

09)*

**

(5.9

48)*

**

(4.9

54)*

**

(4.5

02)*

**

Earn

An

nM

onth

Qu

art

erly

1.0

26

0.4

75

0.5

51

1.0

26

0.4

75

0.5

52

1.0

28

0.4

76

0.5

51

(68.2

5)*

**

(38.4

2)*

**

(90.2

9)*

**

(68.3

3)*

**

(38.4

4)*

**

(90.4

8)*

**

(68.4

1)*

**

(38.5

7)*

**

(90.3

7)*

**

Earn

ing

Su

rpri

se0.0

201

0.0

123

0.0

0781

0.0

204

0.0

125

0.0

079

0.0

201

0.0

123

0.0

078

(3.1

25)*

**

(2.1

59)*

*(3

.687)*

**

(3.1

63)*

**

(2.1

88)*

*(3

.727)*

**

(3.1

15)*

**

(2.1

50)*

*(3

.681)*

**

Ex-D

ivid

end

Month

,t

0.2

36

0.1

96

0.0

40.2

38

0.1

97

0.0

404

0.2

37

0.1

97

0.0

403

(10.8

6)*

**

(10.0

1)*

**

(5.9

18)*

**

(10.9

3)*

**

(10.0

7)*

**

(5.9

73)*

**

(10.9

0)*

**

(10.0

4)*

**

(5.9

54)*

**

AG

MM

onth

,t

1.0

95

0.3

50.7

45

1.1

05

0.3

54

0.7

51

1.0

96

0.3

51

0.7

45

(37.4

3)*

**

(14.6

2)*

**

(60.6

3)*

**

(37.8

6)*

**

(14.8

6)*

**

(61.1

2)*

**

(37.7

3)*

**

(14.7

9)*

**

(60.7

9)*

**

Ad

Exp

ense

s/T

ota

lA

sset

s0.2

14

0.2

55

-0.0

416

0.2

16

0.2

58

-0.0

427

0.2

19

0.2

59

-0.0

399

-0.4

15

-0.5

7(-

0.2

26)

(0.4

2)

(0.5

8)

(-0.2

32)

(0.4

3)

(0.5

8)

(-0.2

16)

Log(A

naly

sts+

1)

0.0

442

0.0

466

-0.0

0233

0.0

442

4.6

6E

-02

-0.0

024

0.0

443

0.0

466

-0.0

0231

(11.1

3)*

**

(12.5

3)*

**

(-2.1

05)*

*(1

1.1

1)*

**

(12.5

2)*

**

(-2.1

69)*

*(1

1.1

4)*

**

(12.5

4)*

**

(-2.0

83)*

*In

stit

uti

on

al

Ow

ner

ship

0.0

382

0.0

599

-0.0

218

0.0

396

0.0

609

-0.0

213

0.0

379

0.0

597

-0.0

218

-1.1

24

(1.6

55)*

(-1.6

87)*

(1.1

7)

(1.6

84)*

(-1.6

42)

(1.1

2)

(1.6

50)*

(-1.6

94)*

Con

stant

-1.0

56

-0.9

56

-0.0

996

-1.0

58

-0.9

63

-0.0

95

-1.0

61

-0.9

59

-0.1

02

(-2.9

63)*

**

(-2.7

62)*

**

(-1.2

25)

(-2.9

66)*

**

(-2.7

83)*

**

(-1.1

62)

(-2.9

78)*

**

(-2.7

71)*

**

(-1.2

50)

Fir

mF

ixed

Eff

ect

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yea

rF

ixed

Eff

ect

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Ob

serv

ati

on

s95,9

57

95,9

57

95,9

57

95,9

57

95,9

57

95,9

57

95,9

57

95,9

57

95,9

57

R-s

qu

are

d0.6

58

0.6

42

0.4

55

0.6

58

0.6

42

0.4

55

0.6

58

0.6

42

0.4

55

Table

5:

Am

ou

nt

of

Equ

ity

Vest

ing

an

dT

imin

gof

New

sE

vents

Inth

ista

ble

,w

ere

port

the

esti

mate

sof

an

OL

Sre

gre

ssio

nd

esig

ned

tost

ud

yw

het

her

CE

Os

rele

ase

more

corp

ora

ten

ews

du

rin

gth

eves

tin

gm

onth

of

thei

rre

stri

cted

eq-

uit

y.W

eu

seth

efo

llow

ing

spec

ifica

tion

:NewsEvent s,t

=α

+ρ∗AmountVesting/SOs,k,t

+β∗∑ τ∈{−

1,1,2}VestingMonths,k,t+τ

+γ∗Controls

+F

ixed

Eff

ects

+ε s,t

wh

ereNewsEvent s,t

isa

vari

ab

leth

at

cou

nts

the

nu

mb

erof

corp

ora

ten

ews

even

tsre

lease

dby

firm

sd

uri

ng

month

t,an

deq

uals

zero

oth

erw

ise.

Th

evari

ab

leAmountVesting/SOs,k,t

isth

eam

ou

nt

of

equ

ity

ves

tin

gd

ivid

edby

the

nu

mb

erof

share

sou

tsta

nd

ing

inth

eves

tin

gm

onth

.T

he

vari

ab

leEquityVestings,k,t+τ

isan

in-

dic

ato

rfu

nct

ion

.F

orτ

=−

1,

itin

dic

ate

sth

em

onth

bef

ore

ves

tin

g,

an

dfo

rτ∈{1,2,3}

itin

dic

ate

sth

eth

ree

month

ssu

bse

qu

ent

toth

eves

tin

gm

onth

.U

nlike

inT

ab

le4,

inth

ista

ble

this

ind

icato

rfu

nct

ion

does

not

incl

ud

eth

eves

tin

gm

onth

(τ=

0).

We

rep

ort

the

resu

lts

for

all

the

new

sev

ents

,an

dals

ose

para

tely

for

dis

-cr

etio

nary

an

dfo

rn

on

-dis

cret

ion

ary

new

s.M

ore

over

,w

ere

port

the

resu

lts

for

all

the

equ

ity

ves

tin

g,

an

dals

ose

para

tely

for

ves

tin

gst

ock

an

dves

tin

gop

tion

s.W

ein

clu

de

inou

rli

stof

contr

ols

,vari

ab

les

that

are

inte

nd

edto

cap

ture

the

info

rmati

on

envir

on

men

tof

the

firm

,as

wel

las

ind

icato

rsfo

rw

het

her

the

month

of

equ

ity

ves

tin

gco

inci

des

wit

hoth

erim

port

ant

per

iod

icev

ents

not

rela

ted

toves

tin

g.

Ad

etailed

des

crip

tion

of

each

contr

ol

vari

ab

leis

incl

ud

edin

the

Ap

pen

dix

.O

ur

sam

ple

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ep

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db

etw

een

2002

an

d2012.

We

rep

ort

t-st

ati

stic

sin

pare

nth

esis

,an

d∗,∗∗

,an

d∗∗∗

rep

rese

nt

sign

ifica

nce

at

the

10%

,5%

,an

d1%

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s,re

spec

tivel

y.

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tin

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tock

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tin

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cret

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cret

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et.

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cret

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et.

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cret

.(1

)(2

)(3

)(4

)(5

)(6

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)(8

)(9

)

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onth

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ore

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of

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ity

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tin

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66)*

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55)

(-2.9

68)*

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(-2.7

89)*

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46)

Fir

mF

ixed

Eff

ect

Yes

Yes

Yes

Yes

Yes

Yes

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Yes

Yes

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rF

ixed

Eff

ect

Yes

Yes

Yes

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Yes

Yes

Yes

Yes

Yes

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serv

ati

on

s95,9

57

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57

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57

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57

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57

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qu

are

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42

0.4

55

0.6

58

0.6

42

0.4

55

0.6

58

0.6

42

0.4

55

Table 6: Distribution of Corporate News Events

In this table, we report the distribution of corporate news events by type. We only report the top 30news events, which account for 91.44% of all the events in our sample, and account for 93.70% of all theevents that happen in the vesting month. We extracted these news events from the Capital IQ database.

Event Type Percentage of All Events

All Months Vesting Months

Client Announcements 13.14% 11.38%Product-Related Announcements 11.72% 11.96%Announcements of Earnings 9.93% 5.77%Earnings Calls 9.10% 12.77%Executive/Board Changes - Other 8.27% 6.20%Company Conference Presentations 6.52% 9.36%Dividend Affirmations 5.29% 4.82%Announcements of Earnings; Corporate Guidance - New/Confirmed 4.46% 5.07%Earnings Release Date 3.24% 9.11%Business Expansions 2.92% 2.01%Strategic Alliances 1.86% 1.70%Debt Financing Related 1.84% 1.14%Lawsuits & Legal Issues 1.77% 1.10%Dividend Increases 1.34% 1.23%Corporate Guidance - New/Confirmed 1.03% 0.72%Shareholder/Analyst Calls 0.87% 0.79%Announcements of Sales/Trading Statement 0.76% 0.48%Annual General Meeting 0.72% 0.70%Executive/Board Changes - Other; Executive Changes - CFO 0.70% 0.42%Corporate Guidance - Raised; Announcements of Earnings 0.64% 0.34%Dividend Affirmations; Preferred Dividend 0.63% 0.51%Announcements of Earnings; Dividend Affirmations 0.61% 0.34%Executive/Board Changes - Other; Executive Changes - CEO 0.59% 0.35%Announcements of Earnings; Impairments/Write Offs 0.56% 0.73%Special Calls 0.54% 0.41%Conferences 0.54% 2.83%Preferred Dividend 0.52% 0.42%Seeking Acquisitions/Investments 0.48% 0.37%Board Meeting 0.46% 0.47%Corporate Guidance - Raised/New/Confirmed; Announcements of Earnings 0.38% 0.20%

Total 91.44% 93.70%

Table

7:

Reacti

on

sto

New

sE

vents

inth

eV

est

ing

Month

Inth

ista

ble

,w

ere

port

the

resu

lts

of

an

even

tst

ud

yof

the

react

ion

of

stock

retu

rns,

bid

-ask

spre

ad

s,an

dtr

ad

ing

volu

me,

toth

ere

lease

of

corp

ora

ten

ews

du

rin

gth

eves

tin

gm

onth

.In

part

icu

lar,

we

rep

ort

the

aver

age

cum

ula

tive

ab

norm

al

valu

eof

those

thre

evari

ab

les

for

1,

15,

an

d30

days

sub

sequ

ent

toth

en

ews

rele

ase

date

.T

he

cum

ula

tive

ab

norm

al

retu

rnis

the

raw

bu

y-a

nd

-hold

retu

rnad

just

edu

sin

gth

ees

tim

ate

db

eta

from

the

mark

etm

od

el.

We

ob

tainβ̂

from

the

regre

ssio

nRs,d

=αs,τ

+βs,τ∗Rm,d

+ε s,τ

for

daysd∈{τ−

200,τ},

wh

ereRs,d

isth

ere

turn

of

firm

son

dayd,

an

dRm,d

isth

em

ark

etre

turn

on

dayd.

Giv

enth

ees

tim

ate

db

eta,

the

cum

ula

tive

ab

norm

al

retu

rnfo

rp

erio

d[h,H

]is

giv

enby:AbRets,τ

h,H

=[ ∏ τ

+H

j=τ+h

(1+Rs,j

)] −1−β̂s,τ

([ ∏τ+H

j=τ+h

(1+Rm,j

)] −1) it

isca

lcu

late

dfr

om

the

close

of

dayτ−

1to

the

close

on

trad

ing

dayτ

+H

.W

ere

port

the

case

sin

wh

ichh

=0

an

dH∈{1,1

5,3

0}.

Th

eab

norm

al

valu

esof

the

bid

-ask

spre

ad

an

dof

the

stock

turn

over

are

ad

just

edby

sub

tract

ing

thei

raver

age

valu

efr

om

the

40

days

pri

or

toth

en

ews

even

td

ate

.W

ere

port

the

resu

lts

sep

ara

tely

for

dis

cret

ion

ary

an

dn

on

-dis

cret

ion

ary

new

s,an

dals

ose

para

tely

for

stock

an

dop

tion

ves

tin

g.

We

rep

ort

t-st

ati

stic

sin

pare

nth

esis

,an

d∗,∗∗

,an

d∗∗∗

rep

rese

nt

sign

ifica

nce

at

the

10%

,5%

,an

d1%

level

s,re

spec

tivel

y.

Ab

norm

al

Ret

urn

sA

bn

orm

al

Bid

-Ask

Sp

read

Ab

norm

al

Tu

rnover

[0,1

][0

,15]

[0,3

0]

[0,1

][0

,15]

[0,3

0]

[0,1

][0

,15]

[0,3

0]

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

Discretionary

News

All

Ves

tin

g0.2

64%

0.3

02%

0.2

49%

0.0

06%

-0.0

05%

-0.0

06%

0.4

10%

0.1

09%

0.0

60%

(8.8

33)*

**

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00)*

**

(3.2

49)*

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49)*

*(-

4.9

30)*

**

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55)*

**

(37.0

5)*

**

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0)*

**

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5)*

**

Sto

ckV

esti

ng

0.2

70%

0.3

66%

0.3

54%

0.0

11%

-0.0

03%

-0.0

04%

0.4

08%

0.1

07%

0.0

53%

(6.9

83)*

**

(4.7

64)*

**

(3.5

75)*

**

(2.4

57)*

*(-

3.1

07)*

**

(-4.5

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**

(28.1

7)*

**

(19.7

9)*

**

(11.2

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**

Op

tion

Ves

tin

g0.2

81%

0.3

57%

0.3

10%

0.0

02%

-0.0

05%

-0.0

07%

0.4

12%

0.1

10%

0.0

63%

(8.0

17)*

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(5.3

54)*

**

(3.4

79)*

**

(1.2

5)

(-4.6

21)*

**

(-5.6

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(31.4

9)*

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Non-D

iscretionary

News

All

Ves

tin

g0.2

74%

0.3

26%

0.1

54%

0.0

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0.0

00%

-0.0

05%

0.7

13%

0.2

11%

0.1

30%

(4.1

18)*

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(2.6

64)*

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67

(4.1

88)*

**

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(-1.8

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7)*

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9)*

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Sto

ckV

esti

ng

0.2

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0.2

09%

-0.0

34%

0.0

31%

0.0

00%

-0.0

04%

0.7

55%

0.2

27%

0.1

44%

(2.5

38)*

*(1

.20)

(-0.1

49)

(2.7

03)*

**

(-0.1

38)

(-1.4

53)

(30.2

6)*

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(22.1

6)*

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(16.0

5)*

**

Op

tion

Ves

tin

g0.3

71%

0.5

31%

0.3

35%

0.0

25%

0.0

02%

-0.0

03%

0.7

15%

0.2

11%

0.1

26%

(4.7

92)*

**

(3.8

90)*

**

(1.8

88)*

(5.0

38)*

**

(0.4

6)

(-0.9

13)

(34.4

2)*

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(25.3

6)*

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(18.0

2)*

**

Table 8: Time from News Event Date to CEO Transaction Date

In this table, we report the distribution of the distance (in calendar days) between the dateof the release of corporate news and the first observed transaction of the CEO, only forthe cases in which both news and transaction occur in the vesting month. We reportthe results of this calculation separately for stock and options, and also separately for corpo-rate news in the Capital IQ database that we classify as discretionary and non-discretionary.

Obs Mean Median STD Skew Kurt P1 P25 P75 P99

All NewsAll Vesting 2524 5.384 3 6.336 1.427 4.600 0 0 8 26Stock Vesting 1189 5.587 3 6.511 1.325 4.074 0 0 8 26Option Vesting 1799 5.158 3 6.111 1.508 5.056 0 0 7 26Discretionary NewsAll Vesting 2379 6.043 4 6.671 1.318 4.188 0 1 9 27Stock Vesting 1113 6.313 4 6.957 1.236 3.773 0 1 9 27Option Vesting 1701 5.791 4 6.381 1.375 4.515 0 1 8 27Non-Discretionary NewsAll Vesting 944 6.764 5 7.233 1.061 3.378 0 0 11 28Stock Vesting 441 6.950 5 7.389 1.043 3.336 0 0 12 28Option Vesting 674 6.503 5 7.111 1.131 3.576 0 0 10 28

−10 0 10 20 30

−20

0

20

40

60

80

100

Number of Days Relative to the Event Date

Cum

ulat

ive

Abn

orm

al R

etur

ns (

bps)

PANEL A: All Events

−10 0 10 20 30

−20

0

20

40

60

80

100PANEL B: Corporate Guidance

Number of Days Relative to the Event Date

Cum

ulat

ive

Abn

orm

al R

etur

ns (

bps)

Figure 1: Stock Return Reaction to News Releases in the Vesting Month

This figure reports the cumulative abnormal stock return around the date of the release of news inthe vesting month. Panel A shows the return reaction to any news event in either the discretionaryor the non-discretionary categories. Panel B shows the return reaction to the release of one specifictype of discretionary news: corporate guidance, which is the main type of voluntary disclosuresstudied in Balakrishnan, Billings, Kelly, and Ljungqvist (2013). In both graphs, the event-studywindow spans from 10 days before the news release date until 30 days after. Similarly to theprocedure adopted in Table 7, in this figure the cumulative abnormal return is calculated as theraw buy-and-hold return adjusted using the estimated beta from the market model.

−10 0 10 20 30−20

0

20

40

60

80

100

120

140

Number of Days Relative to the Event Date

Rat

io o

f Abn

orm

al V

olum

e to

Sha

res

Out

stan

ding

(bp

s)

PANEL A: All Events

−10 0 10 20 30−20

0

20

40

60

80

100

120

140

PANEL B: Corporate Guidance

Number of Days Relative to the Event Date

Rat

io o

f Abn

orm

al V

olum

e to

Sha

res

Out

stan

ding

(bp

s)

Figure 2: Trading Volume Reaction to News Releases in the Vesting Month

This figure shows the reaction of trading volume to the release of news in the vesting month. Inparticular, it reports the ratio of the abnormal trading volume to the number of shares outstanding.Panel A shows the trading volume reaction to any news event in either the discretionary or thenon-discretionary categories. Panel B shows the return reaction to the release of one specific typeof discretionary news: corporate guidance, which is the main type of voluntary disclosures studiedin Balakrishnan, Billings, Kelly, and Ljungqvist (2013). In both graphs, the event-study windowspans from 10 days before the news release date until 30 days after. Similarly to the procedureadopted in Table 7, in this figure the abnormal trading volume ratio is adjusted by subtracting itsaverage value from the 40 days prior to the news event date.