Cross-market Information Transfer and Voluntary Corporate Disclosure:
Evidence from Stock Options Trading
Yangyang Chen
School of Accounting and Finance
The Hong Kong Polytechnic University
yangyang.chen@polyu.edu.hk
Jeffrey Ng
School of Accounting and Finance
The Hong Kong Polytechnic University
tee-yong-jeffrey.ng@polyu.edu.hk
Xin Yang
School of Accounting and Finance
The Hong Kong Polytechnic University
xin.jx.yang@connect.polyu.hk
Abstract
Motivated by the close relation between stock options and the underlying stock and the informed
trading taking place in the options market, we examine the effect of options trading on voluntary
corporate disclosure. We find that options trading is negatively and significantly related to thelikelihood and frequency of management earnings forecasts, suggesting that firms with active
options trading on their stock make fewer voluntary disclosures. This finding suggests that
information spillover from the options market to the stock market can increase stock priceinformativeness, in turn reducing the need for firms to engage in voluntary disclosure to guideinvestor expectations. We further document that the negative relation between options trading andmanagement forecasts is more pronounced for firms with a poorer information environment, afinding that highlights that information transfer from other markets, especially those with informed
trading, is more important for more opaque firms. Consistent with information transfer reducing
the need for voluntary disclosure, we also find that the negative relation is more pronounced forfirms with stock market conditions that facilitate more price discovery. Lastly, we find that options
trading reduces firm information asymmetry. It also reduces the specificity and informativeness ofmanagement forecasts. Our paper offers new insight into how cross-market information transfer
can affect voluntary disclosure.
September 2019
Keywords: Options trading; Cross-market information transfer; Management earnings forecasts;
Voluntary disclosure
JEL classifications: G12; G14; M40; M41

We would like to thank the seminar and conference participants at the Hong Kong Polytechnic University, Nanjing
University, Lingnan University, Deakin University, the WRDS Advanced Research Scholar Program, and the AsianPacific Conference on International Accounting Issues for their helpful comments.

1. Introduction
In this study, we examine how exchange-traded options on a firm’s stock affect the firm’s
voluntary disclosure behavior. According to the Options Clearing Corporation, the total equity
options volume has increased dramatically, from 673 million contracts in 2000 to over 3,689
million in 2017. 1 Stock options’ low cost and high leverage make them an ideal security for
informed investors, who can use them to profit from trading on private information (Black 1975).
Options also alleviate short sale constraints by enabling investors to synthetically short a stock bypurchasing puts and writing calls. Research finds that options trading increases the participation
rate of informed traders (Chakravarty et al. 2004; Hu 2018) and the informational efficiency ofstock prices (Pan and Poteshman 2006; Cremers and Weinbaum 2010). For example, researchshows that options trading reduces the cost of equity capital (Naiker et al. 2013), the stock priceresponse to earnings announcements (Truong and Corrado 2014), and the probability of informed
trading (Hu 2018). The literature on options trading focuses exclusively on its effect on stock
market conditions. However, little is known about whether options trading also affects corporate
decisions. 2 We help fill this gap in the literature by investigating how options trading shapes
managers’ voluntary disclosure behavior.
In this paper, we examine whether an active options trading market on a firm’s stock affects
the firm’s likelihood and frequency of management earnings forecasts. Ex-ante, options trading
could increase or decrease voluntary disclosure. When informed investors trade in the options
market, information could be transferred from that market to the stock market and price discovery
in the stock market could occur (Cremers and Weinbaum 2010; Jin et al. 2012; Johnson and So

1

https://www.theocc.com/webapps/historical-volume-query.
Two papers examine the effect of options trading on corporate decisions. Gao (2010) finds that CEOs in firms withactive options trading (i.e., lower hedging costs) have higher incentive pay. Blanco and Wehrheim (2017) documentthat options trading enhances corporate innovation.
2

1

2012). To the extent that stock price becomes more informative and price discovery reduces
information asymmetry between firm insiders and outside investors, there will be less need for
managers to guide investor expectations via management forecasts. More informative stock prices
and reduced information asymmetry can also result in capital market benefits such as a lower costof capital and improved stock liquidity, which in turn minimize a firm’s need to rely on disclosure
to achieve these benefits. To the extent that information transfer from the options market to thestock market improves stock market conditions, we expect managers to provide less voluntary
disclosure when options trading on the firms’ stock is active.
However, options trading may increase voluntary disclosure. Informed investors go to great
lengths to acquire new and private information and then trade on it (Mayhew et al. 1995; Anthony1988). To the extent that the outcomes of these trades (e.g., spikes in option prices and trading andpossible stock market spillover effects, such as stock price crashes) result in the perception orrevelation that managers have been hiding information, especially information that should havebeen disclosed earlier, the managers might suffer from litigation, reputation loss, or adverse career
effects. Hence, when confronted with the likelihood that options trading will reveal hidden
information, managers’ ability to withhold information from investors becomes more constrained.
From this perspective, we expect managers to be more willing to issue earnings forecasts when
there is active options trading on their firm’s stock.
In sum, there is tension in the hypothesis linking options trading to voluntary disclosure.
Cross-market information transfer, with its effect on reducing expectation misalignment andproviding firms with capital market benefits, predicts that options trading will have a negative
effect on voluntary disclosure. In contrast, if options trading constrains information withholding,the predication would be a positive association between such trading and voluntary disclosure.

2

Hence, the effect of stock options trading on managers’ voluntary disclosure is an empiricalquestion, the answer to which can shed light on cross-market information dynamics. 3
Using a large sample of U.S. public firms with exchange-traded options on their stocks forthe 1996-2016 period, we find that options trading volume is negatively and significantly
associated with the likelihood and frequency of management earnings forecasts, indicating that
options trading reduces voluntary disclosure by management. We conduct several robustness tests
to confirm our baseline results. The baseline results remain consistent when we examine different
types of management forecasts and when we use alternative measures of options trading volume,
alternative samples, alternative specifications such as a logit regression and a change analysis.
An important concern in our analysis is the endogeneity problem. It is possible that both
options trading and management earnings forecasts are correlated with omitted variables that cause
the apparent relation between them. It is also likely that options trading is endogenously
determined by management earnings forecasts. We use two methods to mitigate the potential
endogeneity problem. First, following prior literature we use moneyness and open interest as
instrumental variables of options trading volume (Roll et al. 2009). Our results hold in the twostage least squares (2SLS) regressions with these two variables as the instrument. Second, we
conduct a difference-in-differences (DID) test based on options listings. Options listing decisions
are made by exchanges, so options listings are less likely to be affected by endogenous firm
decisions and are somewhat exogenous to firm characteristics. There are some criteria for options
listing, such as the trading volume and market capitalization of the underlying stock (Mayhew and3

It is also possible that options trading has no effect on voluntary disclosure. A few papers indicate that options trading
does not reveal new information to the stock market. Stephan and Whaley (1990) find that stock trading leads options
trading, and Muravyev et al. (2013) show that the option price does not incorporate valuable information about future
stock prices. We note, however, that the preponderance of the literature suggests that information transfer does occur
from the options market to the stock market.
3

Mihov 2004). We use these criteria to define eligible non-options-listing stocks. For each options
listing firm, we find a matched firm that has the closest propensity score but no options listing inthe same month. Using the matched sample, we find that after an options listing, firms tend not toissue management earnings forecasts, or they issue them less frequently than eligible non-listing
firms. Overall, the results of the instrumental variables and DID test based on options listings
confirm the causal effect of options trading on management earnings forecasts.
To shed more light on the relation between options trading and voluntary disclosure, we
conduct a series of cross-sectional tests. We focus on how firms’ information environment andprice discovery facilitation affect the association between options trading and managers’ voluntary
disclosures. First, options trading reduces the information asymmetry between managers andoutside investors, and an important motivation for managers in issuing voluntary disclosure is to
reduce information asymmetry. The reduction in information asymmetry caused by options trading
is likely to be larger for firms with a poorer information environment, thus leading to a larger
reduction in voluntary disclosure. We use financial reporting quality and readability of 10-K
reports to gauge a firm’s information environment. We find that the negative relation between
options trading and management earnings forecasts is more pronounced for firms with higher
abnormal accruals (i.e., poorer financial reporting quality) and less readable 10-K reports. Second,
price discovery in the stock market due to options trading is also larger for firms with stock market
conditions that facilitate information transfer from the options market to the stock market. The
larger the price discovery in the stock market, the greater the reduction in the need for voluntary
disclosure. We use stock liquidity and transient institutional ownership to capture price discovery
facilitation and find that the negative relation between options trading and management earnings

4

forecasts is more pronounced for firms with higher stock liquidity and larger transient institutional
ownership.
In additional tests, we first examine the effect of options trading on firm information
asymmetry and find evidence that high options trading is associated with less information
asymmetry. Next, we show that firms are more likely to issue more general rather than more
specific forecasts when there is an active options market for their stock, which indicates thatmanagers devote less energy to predicting future earnings as the need for management forecasts is
reduced. We also find that the stock price reaction to an earnings surprise in a management
earnings forecast is lower for firms with active options trading. Because options trading can help
incorporate information into future stock price expectations, the price discovery arising from
options trading reduces the informativeness of management earnings forecasts. Further, weseparately examine the effect of options trading on good news and bad news forecasts. We findthat options trading reduces both types of forecasts, indicating a reduction in the overall level ofvoluntary disclosure. Last, we examine whether options trading also affects other forms ofvoluntary disclosure and find that high options trading is related to a reduced likelihood andfrequency of non-GAAP earnings disclosures.
This study makes two contributions. First, we add to the growing literature on the effect of
options trading from a new perspective, voluntary corporate disclosure. The vast majority ofstudies on options trading focus on the effect of options trading on stock market conditions (Easley
et al. 1998; Kumar et al. 1998; Chakravarty et al. 2004; Johnson and So 2012). As a result, evidenceof how managers respond to the options market in making corporate decisions is limited. There
are two notable exceptions. Gao (2010) finds that CEOs in firms with active options trading (i.e.,lower hedging costs) tend to have higher pay-for-performance sensitivity and higher sensitivity of5CEO wealth to stock return volatility. Blanco and Wehrheim (2017) document that options trading
enhances corporate innovation through increased informational efficiency and increased
monitoring. In this paper, we extend the literature on the real effects of options trading by studying
whether management earnings forecasts are affected by it. A study of this relation is importantbecause management forecasts serve as a timely voluntary disclosure conduit that has a significant
effect on price informativeness (Beyer et al. 2010). 4 In addition, the literature highlights that
options trading and management forecasts can lead to similar equity outcomes, such as increasing
price efficiency and reducing the cost of equity capital.
Second, this paper contributes to the voluntary disclosure literature by showing that pricediscovery originating from the capital markets reduces the need for voluntary disclosure. The
literature documents that characteristics related to CEOs, outside directors, institutional investors,
analysts, product market competition, and employees are important determinants of management
disclosure behavior (Bamber et al. 2010; Ajinkya et al. 2005; Boone and White 2015;
Anantharaman and Zhang 2011; Huang et al. 2017; Bova et al. 2015). There is emerging literature
on how developments in capital markets can generate informational (or feedback) effects that canaffect managerial disclosure choices (e.g., Zuo 2016; Kim et al. 2018; Sethuraman 2019).
Motivated by the presence of sophisticated traders in options markets and the findings of crossmarket information transfer between the options and stock markets (e.g., Merton 1976; Klemkosky
and Resnick 1979; Finucane 1991; Cremers and Weinbaum 2010), we extend this literature by
studying how trades in equity derivatives, specifically stock options, influence voluntary
disclosure.

4

Beyer et al. (2010) estimate that earnings guidance explains the total stock return variance far more than do
mandatory earnings reports and SEC filings.
6

The rest of this paper is organized as follows. Section 2 develops the hypotheses. Section
3 describes the data, sample, and variables. Section 4 presents the baseline regression results andthe results of robustness checks and endogeneity tests. Section 5 reports the results of cross-section
tests, and Section 6 shows the results of additional analyses. Section 7 concludes the paper.

2. Hypothesis Development
Black and Scholes (1973) argue that under the assumption of a perfect market, options are
redundant assets and their payoffs can be replicated by taking positions in stocks and bonds. Hull(2003) similarly contends that the price of stock options is just a function of the underlying stock’s
price. In contrast, Ross (1976) and Figlewski (1989) note that options are not redundant because
in the absence of a perfect market, it is impossible to perfectly replicate them. Because of their low
cost and high leverage, options are ideal securities for informed investors who have private
information (Black 1975). There is evidence that options traders make an extensive effort toacquire firm-specific information and trade on it to profit from uninformed investors (Mayhew etal. 1995; Anthony 1988; Cremers and Weinbaum 2010; Jin et al. 2012; Johnson and So 2012).
Trading by options traders reveals private information to the stock market, which facilitates thatmarket’s price discovery and enhances the informational efficiency of stock prices (Pan andPoteshman 2006; Chakravarty et al. 2004; Ge et al. 2016). The literature (Avellaneda and Lipkin
2003; Ni et al. 2005; Golez and Jackwerth 2012) has examined the phenomenon of stock pinning,which refers to the tendency of an underlying security’s market price to close at or very near to thestrike price of heavily traded options (in the same security) as the expiration time nears. Options
trading can also serve to correct stock overvaluations and enhance the price discovery process by7

alleviating short sale constraints on the underlying stock (Diamond and Verrecchia 1987;
Figlewski and Webb 1993)
The price discovery arising from options trading reduces information asymmetry between
firm insiders and outside investors, resulting in market consequences that are favorable to firms.
For example, Kumar et al. (1998) suggest that options listings have a beneficial effect on thequality of the market for the underlying stock in terms of higher liquidity, lower information
asymmetry, and greater pricing efficiency. Roll et al. (2009) show that options trading improves
the market valuation of the underlying stock, and Naiker et al. (2013) document that firms withexchange-traded options have a lower implied cost of capital compared to firms without options.
Blanco and Wehrheim (2017) find that options trading enhances corporate innovation throughimproving the efficiency of corporate resource allocation.
Similar to options trading, management forecasts also have an informational role.
Management earnings forecasts allow managers to voluntarily communicate their expectations
about future firm earnings. Such forecasts are an effective means by which managers can disclose
private information to capital market participants (Trueman 1986), thereby aligning investor
expectations with management beliefs about future earnings (Ajinkya and Gift 1984; Matsumoto
2002; Cotter et al. 2006). There is evidence that managers use earnings forecasts to reduce
information asymmetry between firm insiders and outside investors (Coller and Yohn 1997;
Verrecchia 2001; Healy and Palepu 2001; Beyer et al. 2010), resulting in higher stock liquidity(Diamond and Verrecchia 1991; Kim and Verrecchia 1994; Schoenfeld 2017) and a lower cost ofcapital (Cao et al. 2017).
Nevertheless, there are costs associated with voluntary disclosure. For example, disclosing
forward-looking information may attract lawsuits (Johnson et al. 2001). Voluntary disclosure may

8also increase proprietary costs, which damage firms’ competitive positions (Verrecchia 1983;
Huang et al. 2017). Therefore, there is a tradeoff to voluntarily disclosing more information.
Because options trading facilitates information transfer from options traders to the stock
market, it enhances price discovery in the stock market and hence improves the informational
efficiency of stock prices. With reduced information asymmetry between firm insiders and outside
investors, there is less need for managers to issue earnings forecasts to align investor expectations
with management beliefs about future earnings. More informative stock prices and reduced
information asymmetry can also result in capital market benefits, such as lower cost of capital andimproved stock liquidity. This reduces the need for the firm to rely on disclosure to achieve thesebenefits. Consistent with this argument, Sethuraman (2019) shows that managers issue fewer
earnings forecasts when their firms’ bond ratings are more credible, suggesting a substitution
between voluntary disclosure and the credibility of bond rating information. As a result, managers
may reduce earnings forecasts due to less need for them. From this perspective, we expect
managers to be less willing to issue earnings forecasts when there is active options trading on theirfirm’s stock.
Nevertheless, it is likely that options trading increases firms’ voluntary disclosure. Options
traders make extensive efforts to discover new information about firms to trade against uninformed
investors. Options trading also alleviates short sale constraints, making it easier to trade on
negative information. If managers do not disclose important information (especially negative
information) in a timely manner, options traders are likely to detect such information and trade on
it, which may result in negative consequences to managers, such as litigation, reputation loss, oradverse career effects. Skinner (1994) suggests that managers issue earnings forecasts topreemptively disclose bad news to reduce litigation risk. As such, managers might be more willing9

to issue earnings forecasts when options trading is high. Overall, there is a tension about how
options trading affects management earnings forecasts. However, we predict that on balance, thenegative effect of options trading on management earnings forecasts dominates. Therefore, we
formulate the first hypothesis as follows.
H1: The likelihood that and the frequency with which managers issue earnings forecasts arelower when there is an active options trading market for their firm’s stock.
Based on our previous discussion, options trading reduces the information asymmetry
between firm insiders and outside investors by facilitating price discovery and enhancing theinformational efficiency of stock prices. However, the effect of options trading may vary amongfirms, depending on their information environment. Healy and Wahlen (1999) note that managers
engage in earnings management to mislead other stakeholders about the firm’s performance or to
influence contractual outcomes linked to reported accounting numbers. Because earnings
management distorts the information in financial reports, it renders firm financial disclosure
opaque, giving rise to information risk to outside investors. Therefore, firms with high earnings
management have lower quality financial reporting and hence a poorer information environment.
In addition, the market may have delayed responses to information contained in complex financial
reports because it is more difficult to process such information (Bloomfield 2002). Biddle et al.
(2009) argue that less readable financial reports make it harder for investors to infer the future cash
flow implications of accounting information. Therefore, less readable financial reports indicate apoorer firm information environment.
When firms have a poor information environment, as indicated by high earnings
management or less readable financial reports, there is more private information for options traders
to discover, which could result in options trading playing a more significant role. As such, options

10

trading may have a greater substitution effect on management earnings forecasts. Therefore, we
formulate the second hypothesis as follows.
H2: The negative relation between options trading and management earnings forecasts ismore pronounced for firms with a worse information environment (i.e., higher earnings
management or less readable financial reports).
We argue that options trading reduces firm information asymmetry by transferring
information from options traders to the stock market. To incorporate information from options
traders more efficiently, firms need stock market conditions that facilitate stronger price discovery.
Prior literature (e.g., Holmström and Tirole 1993; Edmans 2009) suggests that the marginal valueof information acquisition increases with stock liquidity because high liquidity enables informed
traders to make profits by trading against liquidity traders. As such, high liquidity encourages
trading by informed traders, which facilitates greater information transfer from the options market
to the stock market. Furthermore, transient institutional investors are characterized as having highportfolio turnover, highly diversified portfolio holdings, and a strong interest in short-term trading
profits (Bushee 1998, 2001). Bushee (2001) and Bushee and Goodman (2007) suggest thattransient institutional investors have strong incentives to acquire private information and trade on
it. Trading by these investors accelerates the incorporation of firm-specific information into stock
prices (Piotroski and Roulstone 2004; Ke and Ramalingegowda 2005; Sias et al. 2006). Firms withmore transient institutional investors are expected to have more information transfer from theoptions market, which helps price discovery in the stock market.
With strong price discovery facilitation, such as higher stock liquidity or greater transientinstitutional ownership, more information is transferred from the options market and isincorporated into stock prices. This makes options trading more effective in reducing the11information symmetry between firm insiders and outside investors. As such, managers’ need toengage in voluntary disclosure is further reduced, which results in options trading having a greater
effect on management earnings forecasts. Therefore, we formulate the third hypothesis as follows.
H3: The negative relation between options trading and management earnings forecasts ismore pronounced for firms with stock market conditions that facilitate more price discovery (i.e.,higher stock liquidity or greater transient institutional ownership).

3. Data and Variables
3.1. Sample
We obtain data on management earnings forecasts from I/B/E/S, which covers earnings
forecasts made by managers prior to the official release of reported earnings. The management
forecast data start from 1992 and include detailed information about management forecasts withregard to earnings per share and other data items, such as cash flow per share and revenue. Our
options trading data come from OptionMetrics and contain information about all of the exchangetraded options of U.S. listed stocks since 1996. The dataset contains information about eachindividual put and call option on a daily basis, such as the number of contracts traded, the closing
bid and ask prices, and implied volatility. We collect firm financial information from Compustat
and stock information from CRSP. Analyst coverage, equity issuance, and institutional ownershipdata are from I/B/E/S, Thomson One, and Thomson Reuters Institutional Holdings (13f) databases,
respectively. We obtain institutional investor classification data from Brian Bushee’s website. 5
Since the OptionMetrics data start in 1996, we restrict our sample period to 1996-2016.

5

http://acct.wharton.upenn.edu/faculty/bushee/IIclass.html.
12Our initial sample consists of all firms in the Compustat database. We drop firms withmissing total assets. We further drop firms with no options trading because such firms havedifferent fundamentals than firms with options trading (Mayhew and Mihov 2004). In addition,
Roll et al. (2009) suggest that the informational efficiency associated with options trading depends
on the volume of options traded rather than the options trading itself. Last, we drop observations
with missing values for any of the control variables. We winsorize all of the continuous variablesat the 1st and 99th percentiles to mitigate the effect of outliers. Our final sample consists of 38,493
firm-year observations for 5,022 unique firms during the 1996-2016 period.
Table 1 provides the industry and year distributions of our sample. Panel A shows the
distribution of observations among the industries in the Fama-French 12 industry classificationand the number and percentage of observations with an earnings forecast. The panel shows thatthe business equipment industry has the largest number of observations (7,148) in our sample,
while the telephone and television transmission industry has the smallest (899). It further showsthat the utilities industry has the highest percentage of observations with earnings forecasts(67.89%), and the oil, gas, and coal extraction and products industry has the lowest (16.01%).
Panel B presents the year distribution of the observations and the annual number andpercentage of observations with an earnings forecast. The panel shows that the number ofobservations increases gradually, from 990 in 1996 to 2,461 in 2016. The percentage of
observations with earnings forecasts increases from 24.34% in 1996 to 45.23% in 1999. It jumps
to 64.66% in 2000, likely due to the passage of Regulation Fair Disclosure (Reg FD) that year.
After that, the percentage increases slightly before decreasing gradually to 34.62% in 2016.
[Insert Table 1 about here]
3.2. Variable Construction13

We measure voluntary disclosure using management earnings forecasts that include bothannual and quarterly forecasts. Forecast likelihood (DumMF) is a dummy variable that equals one
if the firm issues any earnings forecasts during a fiscal year and zero otherwise. Forecast frequency(FreqMF) is the natural logarithm of one plus the number of earnings forecasts issued by the firmin a fiscal year. We measure options trading using the dollar options trading volume (LnOptvol).
Options trading volume measures how rich the information environment is and the ease with which
informed trading can be facilitated (Roll et al. 2009). Easley et al. (1998) suggest that informed
traders are motivated to trade in the options market when liquidity is high. Following Roll et al.
(2009), we aggregate the dollar trading volume of all options contracts for each firm during eachfiscal year. Specifically, we multiply the daily trading volume with the midpoint of the end-of-day
bid-ask spread for each options contract on a stock. Then, we aggregate all listed options contracts
on a stock across all trading days during a fiscal year. Last, we take the natural logarithm of one
plus the aggregate dollar trading volume (in millions of U.S. dollars).
Following the literature (Kim et al. 2018; Bourveau et al. 2018), we include the following
variables as controls in the regression. Firm size (Size) is the natural logarithm of the market valueof equity at the fiscal year-end. Larger firms usually have a higher analyst rating for corporate
disclosure (Lang and Lundholm 1993). Kasznik and Lev (1995) also suggest that the likelihood of
disclosure is positively associated with firm size. Firm leverage (Lev) is total liabilities divided bytotal assets at fiscal year-end. The book-to-market ratio (BM) is the ratio of the book value ofequity to its market value at fiscal year-end. Miller (2002) suggests that firm performance
positively affects firm disclosure practices. Return on assets (ROA) is income before extraordinary
items divided by total assets. Operating loss (Loss) is a dummy variable equal to one if income
before extraordinary items for a fiscal year is negative and zero otherwise. Stock return (Ret) is the14

buy-and-hold size-adjusted return for a fiscal year. Earnings volatility (EarnVol) is the standard
deviation of the annual return on assets over the past 10 years with at least five non-missing
observations. Firms with less volatile earnings tend to issue earnings forecasts more frequently(Waymire 1985). Institutional ownership (IO) is the percentage of shares held by institutional
investors in a fiscal year. Bird and Karolyi (2016) suggest that institutional investors demand
public information. As a result, managers disclose more and better quality information when
institutional ownership is high. Analyst following (Analyst) is the natural logarithm of one plus
the number of analysts covering the firm in a fiscal year. Anantharaman and Zhang (2011) showthat managers tend to disclose more information to attract analysts. Litigation risk (Litigation) is
the ex-ante class action litigation risk, calculated using the coefficients in Kim and Skinner (2012).
Altman Z-score (Mid_Zscore) is a dummy variable that equals one if the firm’s Altman Z-scorefalls within the middle quintile of the sample distribution in a given year and zero otherwise. Equity
issuance (Issue) is a dummy variable that equals one if the firm issues equity during the fiscal year
and zero otherwise. Firms are more likely to make an earnings forecast if they need access to thecapital market (Frankel et al. 1995). Detailed variable definitions are available in Appendix A.
3.3 Descriptive Statistics
Table 2 provides the summary statistics of the main variables in the analysis. The table showsthat the mean value of DumMF is 0.463, indicating that 46.3% of firm-year observations in our
sample have at least one earnings forecast. FreqMF has a mean value of 0.803, which reflects thatfirms issue an average of 1.232 earnings forecasts during a year. The mean value of LnOptvol is
2.34 and its median value is 1.862. Further, the mean values of Size, Lev, and BM are 7.367, 0.223,
and 0.562, respectively. The mean values of ROA and Loss are 0.013 and 0.227, respectively,
indicating that most firms in our sample are profitable. The sample firms also have a mean IO of

15

0.657, suggesting that 65.7% of shares are held by institutional investors. The mean value of
Analyst is 2.037, which suggests that an average of 6.667 analysts follow a firm. The mean valuesof Ret, EarnVol, Litigation, and Mid_Zscore are 0.028, 0.111, 0.172, and 0.2, respectively. The
mean value of Issue is 0.137, which suggests that 13.7% of observations have an equity issuance.
[Insert Table 2 about here]

4. Empirical Results
4.1. Options Trading and Management Earnings Forecasts
To investigate the effect of options trading on management earnings forecasts, we performthe following regression:
𝐷𝑢𝑚𝑀𝐹 ,

/𝐹𝑟𝑒𝑞𝑀𝐹 ,= 𝛼 + 𝛼 𝐿𝑛𝑂𝑝𝑡𝑣𝑜𝑙 , + 𝛼 𝑆𝑖𝑧𝑒 , + 𝛼 𝐿𝑒𝑣 , + 𝛼 𝐵𝑀 , + 𝛼 𝑅𝑂𝐴 ,+ 𝛼 𝐿𝑜𝑠𝑠 , + 𝛼 𝐼𝑂 , + 𝛼 𝐴𝑛𝑎𝑙𝑦𝑠𝑡 , + 𝛼 𝑅𝑒𝑡 , + 𝛼 𝐸𝑎𝑟𝑛𝑉𝑜𝑙 ,+ 𝛼 𝐿𝑖𝑡𝑖𝑔𝑎𝑡𝑖𝑜𝑛 , + 𝛼 𝑀𝑖𝑑_𝑍𝑠𝑐𝑜𝑟𝑒,+ 𝛼 𝐼𝑠𝑠𝑢𝑒 , + 𝐹𝑖𝑟𝑚 𝐹𝐸 + 𝑌𝑒𝑎𝑟 𝐹𝐸+𝜀(1)
in which i denotes the firm, t denotes the year, and 𝜀 is the error term. The dependent variables are
forecast likelihood (DumMF) and forecast frequency (FreqMF). The independent variable ofinterest is options trading volume (LnOptvol). We also include a firm fixed effect to control fortime-invariant firm-specific characteristics and a year fixed effect to control for the time trend ofmanagement earnings forecasts. We use an ordinary least squares (OLS) model for both forecast

16likelihood and frequency.6 We also adjust the standard errors for heteroscedasticity and clustering
at the firm level.
The regression results are presented in Table 3. Column (1) presents the results for forecast
likelihood and shows that the coefficient on options trading volume (LnOptvol) is negative andstatistically significant, suggesting that a larger options trading volume is associated with a lower
likelihood of a management forecast. In terms of economic significance, increasing the options
trading volume by one standard deviation (1.986) reduces the probability of an earnings forecast
by 1.986 × 0.016 = 0.032. Given that the mean probability is 0.463, this constitutes a 6.91%
reduction compared to the mean. Therefore, the effect of options trading on the forecast likelihood
is not only statistically but also economically significant.
Column (2) presents the results for forecast frequency. The coefficient on options trading
volume (LnOptvol) is negative and statistically significant, which indicates that options trading
volume also reduces the frequency of management earnings forecasts. In terms of economic
significance, increasing the options trading volume by one standard deviation (1.986) reduces theforecast frequency by 1.986 × 0.036 = 0.071. This magnitude is comparable to other key
determinants. For example, a one-standard-deviation increase in the return on assets (ROA) is
associated with an increase in the forecast frequency of 0.033 (0.141 × 0.231 = 0.033). Therefore,
the effect of options trading on the frequency of earnings forecasts is also economically significant.
[Insert Table 3 about here]6

We use an OLS regression for DumMF in the main regression because of the inclusion of firm fixed effects. Whenthe number of fixed effects is large, a logit regression is inconsistent and suffers from quasi-separation issues (Albertand Anderson 1984). Also, the inclusion of firm fixed effects in a logit model typically results in a significant drop in
the number of observations. Nevertheless, our results hold when we use a logit regression in one of the robustness
checks in Section 4.2.
17

The coefficients on the control variables are generally consistent with the literature. The
coefficient on Size is positive and significant, indicating that larger firms are more likely to issue
earnings forecasts (Kasznik and Lev 1995; Lang and Lundholm 1993). The coefficient on ROA is
positive and significant, while the coefficient on Loss is negative and significant. This is consistent
with Miller’s (2002) evidence that profitable firms are more likely to issue earnings forecasts. In
addition, the coefficient on Analyst is positive and significant. Graham et al. (2005) document that
financial analysts use information disclosed by managers in predicting earnings, which results ina greater demand for management forecasts when analyst following is high. The coefficient on Ret
is negative and significant, suggesting that firms with high stock returns are less likely to makeearnings forecasts. The coefficients on the other control variables are mainly insignificant.
Collectively, the results in Table 3 document that high options trading volume is associatedwith a lower likelihood and frequency of management earnings forecasts. This is consistent with
Hypothesis 1 that options trading improves informational efficiency and price discovery in thestock market, which reduces information asymmetry between firm insiders and outside investors.
With reduced capital market benefits, managers’ incentives to issue earnings forecasts are lower,
resulting in a negative relation between options trading and management earnings forecasts.
4.2. Robustness Tests
In this section, we perform a series of robustness tests to validate the findings in our main
test. We report the results of the robustness tests in Table 4. For brevity, we only report thecoefficient on the variable of interest, options trading volume. In Panel A of Table 4, we examine
different types of management forecasts. In the main analysis, we use both annual and quarterly
earnings forecasts in measuring the likelihood and frequency of management forecasts. As arobustness check, we examine the annual and quarterly earnings forecasts separately. The results

18

are reported in columns (1)-(4), which show that the coefficient on LnOptvol remains negative andstatistically significant for both annual and quarterly earnings forecasts. In addition, we consider
non-earnings management forecasts. Forecasts of other items, such as sales and capital expenditure,
can also provide useful information to investors and hence reduce information asymmetry. In
columns (5) and (6), the coefficient on LnOptvol is insignificant for forecast likelihood but
negative and statistically significant for forecast frequency. The results are consistent with our
main findings in general.
[Insert Table 4 about here]
Panel B reports the results using the alternative measures for options trading volume. First,
we examine whether the trading volumes of call (LnCallOptvol) and put (LnPutOptvol) options
have differing effects on management earnings forecasts. The results in columns (1) and (2) showthat the coefficients on both LnCallOptvol and LnPutOptvol are negative and statisticallysignificant, suggesting that the effects of the two types of options on management forecasts are
similar. Second, we aggregate the number of options contracts for each firm during each fiscal
year and use the natural logarithm of one plus the total number of contracts (Lnsoptvol) as themeasure of options trading volume. Third, we follow Roll et al. (2010) and define the option-tostock ratio (LnO/S) as the natural logarithm of the ratio of the annual dollar options trading volume
to the dollar trading volume of the underlying stock.7 Columns (3)-(6) show that the coefficientson both alternative measures of options trading volume are negative and statistically significant,consistent with our baseline results.
In Panel C, we present the results using alternative samples. In the main analysis, we exclude
firms with no exchange-traded options. As a robustness check, we include these firms and set their

7

Our results hold when we define LnO/S as the natural logarithm of the ratio of the annual options trading volume inshares to the stock trading volume in shares.
19

options trading volume to zero following Roll et al. (2009). Next, we exclude the dot-com bubble(i.e., 2000-2001) and financial crisis (i.e., 2007-2008) periods. Because the health of financial
institutions deteriorated during these periods, managers’ incentive to issue earnings forecasts toseek capital may have seen a decline (Lo 2014). Last, we limit our analysis to the post Reg FD
period (i.e., 2000 onward). Because Reg FD prohibits public firms from disclosing information tocertain parties privately, management forecast behavior may have changed after its
implementation (Bailey et al. 2003; Heflin et al. 2003; Heflin et al. 2016). The panel shows thatthe coefficient on LnOptvol remains negative and statistically significant, suggesting that ourfindings are not driven by a particular sample.
Panel D reports the results using alternative regression specifications. In the main analysis,
we use OLS in the regression for forecast likelihood. As a robustness check, we use the logit modelin the regression and report the results in column (1). Even if the sample size is reduced
significantly, the coefficient on LnOptvol is still negative and statistically significant. Next, we use
a change analysis to mitigate the possibility that time-invariant firm-specific characteristics may
drive our main findings. The results are presented in columns (2) and (3), which show that thecoefficient on the change in options trading volume (∆LnOptvol) is negatively associated with thechanges in forecast likelihood (∆DumMF) and forecast frequency (∆FreqMF). The results confirm
the robustness of our findings to alternative regression specifications.
Last, we perform tests that control for CDS trading and report the results in Panel E. Kim etal. (2018) find that CDS trading is positively related to the likelihood and frequency ofmanagement earnings forecasts. They argue that CDS trading weakens lenders’ incentives tomonitor firms, which raises shareholders’ demand for management disclosure. While it is unclear
whether the monitoring incentives of lenders affect information transfer from the options market

20

to the stock market, we add CDS trading as an additional control variable. Following Kim et al.
(2018), we define PostCDS as a dummy variable that equals one for years after CDS initiation andzero otherwise, and CDSTraded as a dummy variable that equals one if the firm ever has CDS
trading during our sample period and zero otherwise. Due to the availability of CDS trading data,
our sample period for this test is from 1997 to 2015. We do not include a firm fixed effect because
CDSTraded does not have any within-firm variation and will be subsumed if a firm fixed effect isincluded. We find that the coefficient on LnOptvol is still negative and statistically significant
when controlling for CDS trading.8
4.3. Endogeneity
A concern about our analysis is that options trading and management earnings forecasts
could be endogenously determined. They may both be correlated with variables omitted from theregression, which would result in an apparent relation between them. It is also likely that thecausality extends from management earnings forecasts to options trading. For example, firms that
constantly issue earnings forecasts may have less private information for options traders to tradeon, resulting in a low options trading volume. We use two methods to mitigate the potential
endogeneity problems.
In the first method, we follow Roll et al. (2009) and use option moneyness and open interest
as instrumental variables of options trading. Option moneyness (Moneyness) is the annual averageabsolute difference between a stock’s market price and an option’s strike price. Informed traders
are attracted by out-of-the-money (OTM) options because they offer the greatest leverage, while
uninformed traders are interested in in-the-money (ITM) options to avoid risky positions (Pan and8

Our result for PostCDS is different from Kim et al. (2018), mainly because we use a different sample and different
regression specifications. When we follow their methods exactly, we obtain results similar to theirs (i.e., a negativeand statistically significant coefficient on PostCDS).
21

Poteshman 2006). In addition, volatility traders are likely to dodge deep ITM or OTM options
because their vega is close to zero (Roll et al. 2009). Therefore, option moneyness is closely related
to options trading volume. Option open interest (OpenInterest) is the annual average number ofoutstanding options contracts that have not been settled across all options on a stock. Because open
interest indicates unsettled options contracts, it is closely related to options trading volume.
However, there is no economic intuition that option moneyness and open interest are related to
management earnings forecasts. As a result, these two instruments meet the exclusion restrictions(Larcker and Rusticus 2010).
[Insert Table 5 about here]
We follow the standard two-stage least squares (2SLS) estimation using option moneyness
and open interest as the instrumental variables. In the first stage, we regress options trading volume
against option moneyness, open interest, and the set of control variables in Equation (1). In thesecond stage, we regress forecast likelihood and frequency against the predicted value of options
trading volume from the first-stage regression and the same set of control variables. Table 5
presents the results of the 2SLS regression. Column (1) shows that the coefficients on option
moneyness and open interest are both positive and statistically significant, consistent with our
expectation. In columns (2) and (3), the coefficient on predicted options trading volume remains
negative and statistically significant in both regressions. Overall, the results of the 2SLS regression
are consistent with the baseline results, which helps mitigate the endogeneity concerns.
In the second method, we perform a DID analysis based on options listing events (i.e., thefirst time the firm has exchange-traded options). Because options listing decisions are made bystock exchanges, they are largely exogenous to firm policies, especially firm disclosure practices.
We identify options listing events using the OptionMetrics database, and we define the options

22

listing date as the first day the firm appears in the database. 9 In total, there are 1,198 eligible
options listing events in our sample.
For each options listing firm during the month prior to the options listing date, we find amatched non-listing firm from a pool of firms that meet the selection criterion in Mayhew andMihov (2004) and Hu (2018). The criteria are as follows: (1) the stock should be listed on the
NYSE, Amex, or Nasdaq; (2) the stock should issue at least seven million publicly held shares; (3)the stock should reach the minimum price;10 and (4) the stock should have no options trading
history and been traded for at least 252 trading days in the CRSP database. The matched nonlisting firm should have the closest propensity score (i.e., the probability of an options listing),
calculated based on firm size, trading volume, stock return volatility, and bid-ask spread (Mayhew
and Mihov 2004; Hu 2018). The regression results in calculating the propensity score are shownin Panel A of Appendix B. Further, Panel B of Appendix B shows that there is almost no significant
difference between options listing firms and matched non-listing firms, which suggests that ourmatching is successful.
We adopt the DID approach and examine whether options listing firms experience a
reduction in management earnings forecasts after options listing compared to matched non-listing
firms. The regression specification is as follows:
𝐷𝑢𝑚𝑀𝐹 ,

/𝐹𝑟𝑒𝑞𝑀𝐹 ,= 𝛼 + 𝛼 𝑃𝑜𝑠𝑡 , + 𝛼 𝑆𝑖𝑧𝑒 , + 𝛼 𝐿𝑒𝑣 , + 𝛼 𝐵𝑀 , + 𝛼 𝑅𝑂𝐴 , + 𝛼 𝐿𝑜𝑠𝑠 ,+ 𝛼 𝐼𝑂 , + 𝛼 𝐴𝑛𝑎𝑙𝑦𝑠𝑡 , + 𝛼 𝑅𝑒𝑡 , + 𝛼 𝐸𝑎𝑟𝑛𝑉𝑜𝑙 , + 𝛼 𝐿𝑖𝑡𝑖𝑔𝑎𝑡𝑖𝑜𝑛 ,+ 𝛼 𝑀𝑖𝑑_𝑍𝑠𝑐𝑜𝑟𝑒,+ 𝛼 𝐼𝑠𝑠𝑢𝑒 , + 𝐹𝑖𝑟𝑚 𝐹𝐸 + 𝑌𝑒𝑎𝑟 𝐹𝐸 + 𝜀

9OptionMetrics coverage starts in 1996, and we are unable to identify the options listing year for firms that have
options trading information in OptionMetrics in 1996. Therefore, we drop firms that first appear in the OptionMetrics
database in 1996.
10
The minimum price is $7.5 before 2002 and $3 after 2002.
23

(2)in which i denotes the firm, t denotes the year, and 𝜀 denotes the error term. We require firms tohave at least one firm-year observation before and after the options listing. For options listing firms,
Post equals one for years after the options listing and zero for years before it. For matched nonlisting firms, Post equals zero both before and after the options listing.
[Insert Table 6 about here]
The results of the DID analysis are presented in Table 6. Columns (1) and (2) report theresults for one year before and after the options listing, and columns (3) and (4) report the results
for the three years before and after the options listing. In all of the columns, the coefficient on Postis negative and statistically significant, suggesting that compared to matched non-listing firms,
options listing firms are less likely to issue earnings forecasts after an options listing, confirming
the negative relation between options trading and management earnings forecasts. Overall, theresults are consistent with the baseline results, indicating that endogeneity is unlikely to drive ourfindings.

5. Cross-Sectional Tests
5.1 The Effect of Information Environment
After establishing the negative relation between options trading and management earnings
forecasts, we examine the effect of a firm’s information environment on the relation. As illustrated
in Hypothesis 2, if options trading leads to a reduced need for managers’ voluntary disclosure by
mitigating the information asymmetry between firm insiders and outside investors, we would
expect the effect of options trading to be more prominent for firms with a poorer information
environment.

24

We use two types of measures to capture a firm’s information environment. The first is basedon abnormal accruals and is estimated following Dechow and Dichev (2002) and McNichols(2002). A higher value for abnormal accruals indicates more earnings management and hence
lower quality financial reporting. We define accruals (Accruals) as a dummy variable that equals
one if abnormal accruals are above the sample median and zero otherwise. The second is thereadability of the firm’s 10-K reports. We use the modified readability measures proposed by Kim
et al. (2019). Specifically, we define modified fog (Modfog) as a dummy variable that equals one
if the modified fog score is above the sample median and zero otherwise. We also define modified
flesch (Modflesch) as a dummy variable that equals one if the modified Flesch score is above thesample median and zero otherwise, and modified kincaid (Modkincaid) as a dummy variable that
equals one if the modified Kincaid score is above the sample median and zero otherwise. Higher
Modfog, Modflesch, and Modkincaid indicate less readable financial reports. We interact the four
variables with options trading volume (LnOptvol) and include the interaction term in the regression
specification in Equation (1). The results are reported in Table 7.
[Insert Table 7 about here]
Columns (1) and (2) show that the coefficient on LnOptvol×Accruals is negative andstatistically significant in both regressions, suggesting that the effect of options trading on
management forecasts is more pronounced for firms with higher abnormal accruals (i.e., lower
quality financial reporting). In columns (3) to (8), the coefficients on LnOptvol×Modfog,
LnOptvol×Modflesch, and LnOptvol×Modkincaid are all negative and statistically significant,indicating that the effect of options trading on management forecasts is more pronounced for firms
whose annual reports are less readable. Overall, the results show that when a firm’s information
environment is poor, options trading is more effective in reducing information asymmetry, which25results in a larger reduction in the demand for voluntary firm disclosure. The findings provide
supporting evidence to our Hypothesis 2.
5.2. The Effect of Price Discovery Facilitation
In this section, we further examine whether the relation between options trading andmanagement earnings forecasts is stronger for firms with stock market conditions that facilitate
more price discovery. Hypothesis 3 states that if the stock market facilitates more price discovery,
there will be greater information transfer from the options market to the stock market. As such,
options trading will be more effective in curtailing information asymmetry between firm insiders
and outside investors, which results in even less need for voluntary disclosure.
We measure price discovery facilitation using stock liquidity and transient institutional
ownership. With high stock liquidity, it is easier for informed traders to trade in the stock market,
which facilitates more information transfer from the options market and hence greater pricediscovery in the stock market. We follow Amihud (2002) and calculate Amihud stock illiquidity
as the average ratio of the daily absolute return to the dollar trading volume across the year.
Because higher values indicate greater illiquidity of the underlying stock, we define stock liquidity(Liquidity) as a dummy variable that equals one if the Amihud stock illiquidity measure is below
the sample median and zero otherwise. Further, Bushee (1998, 2001) classifies institutional
investors into transient, quasi-index, and dedicated institutions. Transient institutional investors
are characterized as having high portfolio turnover and highly diversified portfolio holdings aswell as focusing on short-term trading profits. Transient institutional investors have strong
incentives to acquire private information. Their trading facilitates more information transfer fromthe options market, which helps price discovery in the stock market. We define transientinstitutional ownership (TraIO) as a dummy variable that equals one if the proportion of the firm’s

26

shares held by transient institutional investors is above the sample median and zero otherwise. We
also control for quasi-index (QixIO) and dedicated (DedIO) institutional ownership, defined as adummy variable that equals one if the proportion of the firm’s shares held by quasi-index or
dedicated institutional investors is above the sample median and zero otherwise. We interact stock
liquidity (Liquidity) and institutional investor variables (TraIO, QixIO, and DedIO) with options
trading volume (LnOptvol) and include the interaction terms in the regression specification in
Equation (1). The results are reported in Table 8.
[Insert Table 8 about here]
Columns (1) and (2) show that the coefficient on LnOptvol×Liquidity is negative andstatistically significant in both regressions, indicating that the effect of options trading on
management forecasts is greater for firms with high stock liquidity. In columns (3) and (4), thecoefficient on LnOptvol×TraIO is negative and statistically significant, which indicates that theeffect of options trading on management forecasts is more pronounced for firms with high transientinstitutional ownership. The coefficients on LnOptvol×QixIO and LnOptvol×DedIO are bothinsignificant. Collectively, the results lend support to Hypothesis 3 by showing that options trading
is more effective in reducing information asymmetry when stock market conditions facilitate
greater information transfer from the options market. This results in greater price discovery in thestock market and hence a larger reduction in voluntary disclosure.

6. Additional Analyses
6.1 The Effect of Options Trading on Information Asymmetry
According to our story, options trading improves informational efficiency and pricediscovery in the stock market, which reduces information asymmetry between firm insiders and27

outside investors. This results in less need for managers to guide investor expectations via
management forecasts. In this section, we try to validate our story by examining whether options
trading reduces firm information asymmetry. We adopt two information asymmetry measures.
Bid-ask spread (BASPREAD) is defined as 100 times the annual average daily difference between
the ask price and the bid price divided by the midpoint of the ask and bid prices. A higher bid-ask
spread value indicates greater information risk (Glosten and Milgrom 1985). Probability of
informed trading (PIN) measures the probability that trades are conducted by informed investors
and hence captures the information asymmetry among investors in the stock market (Easley andOhara 1987; Brown and Hillegeist 2007). We obtain the probability of informed trading data from
Brown and Hillegeist (2007).11[Insert Table 9 about here]
We regress bid-ask spread and probability of informed trading against options trading(LnOptvol) and the same set of control variables in Equation (1). The results are reported in Table
9. In column (1), the coefficient on LnOptvol is negative and statistically significant, suggestingthat options trading reduces the bid-ask spread of the underlying stock. In column (2), thecoefficient on LnOptvol is also negative and statistically significant, indicating that options trading
also reduces the probability of informed trading. Overall, our results suggest that options trading
does reduce the information asymmetry of the underlying stock, which provides further support
for our argument.
6.2 Management Forecast Characteristics
In this section, we extend our analysis to management forecast characteristics. It is likely that
options trading affects not only the likelihood and frequency of management earnings forecasts

11

Because the probability of informed trading data is only available for the period 1993-2010, we restrict our sample
in this test to the period 1996-2010.
28

but also their characteristics. We document in the main analysis that options trading reduces firm
information asymmetry and hence the need for managers to issue earnings forecasts. If this is thecase, managers who issue earnings forecasts for various other reasons (e.g., maintaining the
tradition of making earnings forecasts) may devote less energy to preparing them due to theirdecreasing value. We test this prediction by investigating whether high options trading is
associated with more general management earnings forecasts.
Forecast width (Width) is calculated as the difference between the upper and lower end
estimates, divided by stock price. We assign a width of zero for point estimates and take theaverage value of the forecast width among the forecasts if the firm makes multiple forecasts during
the year. For ease of interpretation, we multiply the forecast width by -100. Forecast range (Range)
is defined as an ordinal variable equal to one for forecasts with an open range estimate, two for
forecasts with a closed range estimate, and three for forecasts with a point estimate. Following
Bova et al. (2015), we take the natural logarithm of the sum of the values across all forecasts if thefirm makes multiple forecasts during the year. For both measures, a higher value indicates that theforecasts are more general. We restrict the analysis to the sample of firms that make at least one
forecast during the year in the test. We regress forecast width and range against options trading(LnOptvol) and the same set of control variables in Equation (1). The results are reported in Table
10.
[Insert Table 10 about here]
Column (1) shows that the coefficient on LnOptvol is negative and statistically significant
when forecast width is the dependent variable, suggesting that managers tend to issue more general
forecasts when options trading is high. The results in column (2) are similar: they show that thecoefficient on LnOptvol is still negative and statistically significant when forecast range is the29dependent variable. In sum, our results suggest that there is less need for managers to issue specificearnings forecasts when options trading is high. The findings are consistent with our argument thatwith active options trading on their firm’s stock, managers who issue earnings forecasts devote
less energy to preparing those forecasts due to the decrease in their value.
6.3. Stock Market Reaction to Management Earnings Forecasts
The literature (Truong and Corrado 2014) demonstrates that the stock price response toearnings announcements is reduced when there is active options trading on the firm’s stock
because some of the private information is partially revealed to the public by options traders prior
to earnings announcements. There is also evidence that the prices of non-optioned stocks takesignificantly longer to adjust to earnings announcements (Jennings and Starks 1986), takeover
announcements (Cao et al. 2005), and stock-split announcements (Chern et al. 2008). If options
trading enables information transfer from the options market, which facilitates more pricediscovery in the stock market, some of the information in management earnings forecasts may berevealed by options trading before the forecasts’ release. Therefore, we expect the market response
to management forecast information to be lower for firms with high options trading. To test thisprediction, we perform the following regression.
𝐶𝐴𝑅 ,= 𝛼 + 𝛼 𝑀𝐹𝑆𝑢𝑟𝑝 ,+ 𝛼 𝑀𝐹𝑆𝑢𝑟𝑝 ,× 𝐻𝑖𝑔ℎ𝑂𝑝𝑡 , +𝛼 𝑀𝐹𝑆𝑢𝑟𝑝 ,× 𝑆𝑖𝑧𝑒 ,+ 𝛼 𝑀𝐹𝑆𝑢𝑟𝑝 ,+ 𝛼 𝑀𝐹𝑆𝑢𝑟𝑝 ,× 𝐿𝑜𝑠𝑠 ,+ 𝛼 𝐻𝑖𝑔ℎ𝑂𝑝𝑡 , + 𝛼 𝑆𝑖𝑧𝑒 ,+ 𝛼 𝐵𝑀 ,+𝛼 𝑀𝐹𝑆𝑢𝑟𝑝 ,× 𝐵𝑀 ,

× 𝐴𝑛𝑎𝑙𝑦𝑠𝑡 ,+ 𝛼 𝐿𝑜𝑠𝑠 ,+ 𝛼 𝐴𝑛𝑎𝑙𝑦𝑠𝑡 ,+ 𝐹𝑖𝑟𝑚 𝐹𝐸 + 𝑌𝑒𝑎𝑟 𝐹𝐸 + 𝜀(3)30

in which i denotes the firm, t denotes the year, and 𝜀 is the error term. We restrict the analysis tothe sample of firms that make at least one earnings forecast during a year. If there are multiple
earnings forecasts during a year, we use the last annual earnings forecast (Ajinkya et al. 2005).
Market reaction to a forecast (CAR) is the three-day value-weighted market-adjusted cumulative
abnormal returns around the release of the forecast. Management forecast surprise (MFSurp) is
calculated as the difference between the management forecast and the consensus analyst forecast
divided by stock price. High options trading (HighOpt) is a dummy variable that equals one if
options trading (LnOptvol) is above the sample median and zero otherwise. The results arepresented in Table 11.
[Insert Table 11 about here]
Column (1) presents the results of the original model without the interaction term between
MFSurp and HighOpt. The columns show that the coefficient on MFSurp is positive andstatistically significant, suggesting that the market has a stronger reaction to management earnings
forecasts when forecast surprises are higher. In column (2), we include in the model an interaction
term between MFSurp and HighOpt. The coefficient on MFSurp×HighOpt is negative andstatistically significant, which indicates that the market reaction to forecast surprises is mitigatedfor firms with active options trading on their stock.
Overall, our results indicate that options trading can help incorporate information about
future earnings into stock prices earlier, which reduces the informativeness of management
earnings forecasts. The findings lend further support to our argument that options trading reduces
the need for managers to make earnings forecasts because options trading reveals some earnings
information.
6.4 Good News versus Bad News Forecasts

31

We show in the main analysis that options trading reduces the likelihood and frequency ofmanagement earnings forecasts. Nevertheless, it is likely that options trading affects good news
and bad news forecasts differently. Our competing hypothesis suggests that options trading could
encourage voluntary disclosures because managers are subject to negative consequences such as
litigation, reputation loss, and adverse career effects if undisclosed information is detected andrevealed by options traders. Because such effects would be more severe for bad news (Skinner1994), managers may issue more forecasts that preemptively disclose bad news when the firm has
active options trading on its stock.
To test this possibility, we classify management earnings forecasts into good news and badnews forecasts based on management forecast surprise (MFSurp) and the market reaction to aforecast (CAR), respectively. We define good news forecasts as those with positive MFSurp or
CAR and bad news forecasts as those with negative MFSurp or CAR. Then we define good news
forecast likelihood (DumGN) as a dummy variable that equals one if the firm issues any good news
earnings forecasts during a fiscal year and zero otherwise, and bad news forecast likelihood(DumBN) as a dummy variable that equals one if the firm issues any bad news earnings forecasts
during a fiscal year and zero otherwise. We define good news forecast frequency (FreqGN) as thenatural logarithm of one plus the number of good news earnings forecasts issued by the firm in a
fiscal year, and bad news forecast frequency (FreqBN) as the natural logarithm of one plus thenumber of bad news earnings forecasts issued by the firm in a fiscal year.
[Insert Table 12 about here]
We re-estimate the baseline regression in Equation (1) with good and bad forecast variables
as the dependent variables. The results are reported in Table 12. Panel A presents the results fordefining good and bad news forecasts by forecast surprise. The panel shows that the coefficient on

32

options trading volume (LnOptvol) is negative and statistically significant for the likelihood andfrequency of both good and bad news forecasts. Panel B presents the results for defining good andbad news forecasts by market reaction, with similar findings. Overall, the results suggest that
options trading lowers the likelihood and frequency of both good and bad news forecasts,
consistent with options trading reducing information asymmetry and hence the need for managers
to issue earnings forecasts.
6.4 The Effect of Options Trading on Non-GAAP Earnings
Non-GAAP earnings, earnings excluding components that managers deem to be lessrepresentative of core operations, are one of the most important performance metrics that managers
voluntarily disclose to investors (Graham et al. 2005). Prior research finds that investors perceive
non-GAAP earnings to be more informative than GAAP earnings to forecast future performance(e.g., Bradshaw and Sloan 2002; Bhattacharya et al. 2003; Bradshaw et al. 2018). As options
trading enhances informational efficiency and price discovery in the stock market, we expect that
options trading reduces the need for non-GAAP earnings to provide more informative earnings
information. We obtain the non-GAAP earnings data from Bentley et al. (2018). 12 Non-GAAP
disclosure likelihood (DumNonGAAP) is defined as a dummy variable that equals one if the firmmakes any non-GAAP earnings disclosure during a fiscal year and zero otherwise. The non-GAAP
disclosure frequency (FreqNonGAAP) is the natural logarithm of one plus the number of nonGAAP earnings disclosure made by the firm in a fiscal year.
[Insert Table 13 about here]
We regress the non-GAAP disclosure likelihood and frequency against options trading(LnOptvol) and the same set of control variables in Equation (1). The results are reported in Table

12

Because the non-GAAP disclosure data are only available for the period 2003-2016, we restrict our sample in thistest to the same period.
33

13. Column (1) shows that the coefficient of LnOptvol is negative and statistically significant,suggesting that options trading reduces the likelihood of non-GAAP disclosure. The findings incolumn (2) are similar and show that the frequency of non-GAAP disclosure also decreases when
options trading is high. Overall, the results in this section suggest that with the decrease in
information asymmetry associated with options trading, firms not only reduce the issuance of
earnings forecast but also reduce other forms of voluntary disclosure such as non-GAAP earnings
disclosure.

7. Conclusion
This study examines the effect of options trading on voluntary disclosure. Options traders
are informed investors who have private information about a firm. These traders’ trading
disseminates their private information into options prices, and the resulting information transferfrom the options market to the stock market can increase informational efficiency and pricediscovery in the latter. Due to the reduced information asymmetry and capital market benefits that
options trading provides, there is less need for managers to issue earnings forecasts, which results
in a negative relation between options trading and earnings forecasts. Nevertheless, it is also likelythat when confronted with the likelihood of options trading revealing hidden information,
managers’ ability to withhold information from investors becomes more constrained. As a result,managers are more willing to issue earnings forecasts when there is active options trading on thefirm’s stock.
Using a large sample of U.S. public firms with exchange-traded options on their stocks, wefind that options trading volume is significantly and negatively associated with the likelihood andfrequency of management earnings forecasts. Our findings are robust to a battery of robustness34

tests. We use instrumental variables and DID tests based on options listings to alleviate potential
endogeneity problems. In the cross-sectional tests, we find that the negative relation between
options trading and management earnings forecasts is more pronounced for firms with a poorer
information environment and for those with stronger price discovery facilitation. Further, we showthat options trading is associated with a decrease in firm information asymmetry. We also find thatfirms are more likely to issue a more general earnings forecast when there is active options tradingon its stock. Additionally, options trading reduces the informativeness of management earnings
forecasts because such trading helps incorporate information into stock prices. We also separately
examine the effect options trading has on good and bad news forecasts and find that it reduces thelikelihood and frequency of both types of forecasts. Last, we find that options trading also reduces
the likelihood and frequency of non-GAAP earnings disclosure.
Our study contributes to the growing literature on the effect of options trading from theperspective of voluntary disclosure. The vast majority of studies in options trading concentrate on
its effect on stock market conditions. We extend the literature on the real effects of options trading
by studying whether management earnings forecasts are affected by it. Our study also contributes
to the voluntary disclosure literature by showing that price discovery originating from the capital
markets reduces the need for voluntary disclosure. In this sense, our paper is in line with the
emerging literature on how capital market developments can generate informational (or feedback)effects that can affect managerial disclosure choices.

35

References
Ajinkya, B., S. Bhojraj, and P. Sengupta. 2005. The association between outside directors,
institutional investors and the properties of management earnings forecasts. Journal of
Accounting Research 43 (3):343-376.
Ajinkya, B. B., and M. J. Gift. 1984. Corporate managers’ earnings forecasts and symmetricaladjustments of market expectations. Journal of Accounting Research 22 (2):425-444.
Albert, A., and J. A. Anderson. 1984. On the existence of maximum likelihood estimates in logistic
regression models. Biometrika 71 (1):1-10.
Amihud, Y. 2002. Illiquidity and stock returns: Cross-section and time-series effects. Journal of
financial markets 5 (1):31-56.
Anantharaman, D., and Y. Zhang. 2011. Cover me: Managers’ responses to changes in analyst
coverage in the post-regulation FD period. The Accounting Review 86 (6):1851-1885.
Anthony, J. H. 1988. The interrelation of stock and options market trading-volume data. The
Journal of Finance 43 (4):949-964.
Avellaneda, M., and M. D. Lipkin. 2003. A market-induced mechanism for stock pinning.
Quantitative Finance 3 (6):417-425.
Bailey, W., L. I. Haitao, C. X. Mao, and R. U. I. Zhong. 2003. Regulation fair disclosure andearnings information: Market, analyst, and corporate responses. The Journal of Finance 58(6):2487-2514.
Bamber, L. S., J. John, and W. Isabel Yanyan. 2010. What’s my style? The influence of top
managers on voluntary corporate financial disclosure. The Accounting Review 85 (4):11311162.
Beyer, A., D. A. Cohen, T. Z. Lys, and B. R. Walther. 2010. The financial reporting environment:
Review of the recent literature. Journal of Accounting and Economics 50 (2-3):296-343.
Bhattacharya, N., E. Black, T. Christensen, and C. Larson. 2003. Assessing the relative
informativeness and permanence of pro forma earnings and GAAP operating earnings.
Journal of Accounting and Economics 36(1-3): 285–319.
Biddle, G. C., G. Hilary, and R. S. Verdi. 2009. How does financial reporting quality relate toinvestment efficiency? Journal of Accounting and Economics 48(2-3): 112-131.
Bird, A., and S. A. Karolyi. 2016. Do institutional investors demand public disclosure? Review of
Financial Studies 29 (12):3245-3277.
Black, F. 1975. Fact and fantasy in the use of options. Financial Analysts Journal 31 (4):36-41.
Black, F., and M. Scholes. 1973. The pricing of options and corporate liabilities. Journal of
Political Economy 81 (3):637-654.
Blanco, I., and D. Wehrheim. 2017. The bright side of financial derivatives: Options trading andfirm innovation. Journal of Financial Economics 125 (1):99-119.
Bloomfield, R. J. 2002. The “incomplete revelation hypothesis” and financial reporting.
Accounting Horizons 16 (3): 233–243.
Boone, A. L., and J. T. White. 2015. The effect of institutional ownership on firm transparency
and information production. Journal of Financial Economics 117 (3):508-533.
Bourveau, T., Y. Lou, and R. Wang. 2018. Shareholder litigation and corporate disclosure:
Evidence from derivative lawsuits. Journal of Accounting Research 56 (3):797-842.
Bova, F., Y. Dou, and O.-K. Hope. 2015. Employee ownership and firm disclosure. Contemporary
Accounting Research 32 (2):639-673.

36

Bradshaw, M.T., T.E. Christensen, K.H. Gee, and B.C. Whipple. 2018. Analysts’ GAAP earnings
forecasts and their implications for accounting research. Journal of Accounting andEconomics 66(1): 46-66.
Bradshaw, M., and R. Sloan. 2002. GAAP versus the street: An empirical assessment of twoalternative definitions of earnings. Journal of Accounting Research 40(1): 41-66.
Brown, S., and S. Hillegeist. 2007. How disclosure quality affects the level of information
asymmetry. Review of Accounting Studies 12 (2/3):443-477.
Bushee, B. J. 1998. The influence of institutional investors on myopic R&D investment behavior.
The Accounting Review 73 (3):305-333.
———. 2001. Do institutional investors prefer near-term earnings over long-run value?
Contemporary Accounting Research 18 (2):207-246.
Bushee, B. J., and T. H. Goodman. 2007. Which institutional investors trade based on private
information about earnings and returns? Journal of Accounting Research 45 (2):289-321.
Cao, C., Z. Chen, and J. M. Griffin. 2005. Informational content of option volume prior totakeovers. The Journal of Business 78 (3):1073-1109.
Cao, Y., L. A. Myers, A. Tsang, and Y. G. Yang. 2017. Management forecasts and the cost ofequity capital: International evidence. Review of Accounting Studies 22 (2):791-838.
Chakravarty, S., H. Gulen, and S. Mayhew. 2004. Informed trading in stock and option markets.
The Journal of Finance 59 (3):1235-1257.
Chern, K.-Y., K. Tandon, S. Yu, and G. Webb. 2008. The information content of stock split
announcements: Do options matter? Journal of Banking & Finance 32 (6):930-946.
Coller, M., and T. L. Yohn. 1997. Management forecasts and information asymmetry: An
examination of bid-ask spreads. Journal of Accounting Research 35 (2):181-191.
Cotter, J., I. Tuna, and P. D. Wysocki. 2006. Expectations management and beatable targets: How
do analysts react to explicit earnings guidance? Contemporary Accounting Research 23(3):593-624.
Cremers, M., and D. Weinbaum. 2010. Deviations from put-call parity and stock return
predictability. Journal of Financial and Quantitative Analysis 45 (2):335-367.
Diamond, D. W., and R. E. Verrecchia. 1987. Constraints on short-selling and asset priceadjustment to private information. Journal of Financial Economics 18 (2):277-311.
Diamond, D. W., and R. E. Verrecchia. 1991. Disclosure, liquidity, and the cost of capital. The
Journal of Finance 46 (4):1325-1359.
Easley, D., M. O’hara, and P. S. Srinivas. 1998. Option volume and stock prices: Evidence on
where informed traders trade. The Journal of Finance 53 (2):431-465.
Easley, D., and M. O’hara. 1987. Price, trade size, and information in securities markets. Journalof Financial Economics 19 (1):69-90.
Edmans, A. 2009. Blockholder trading, market efficiency, and managerial myopia. The Journal of
Finance 64 (6):2481-2513.
Figlewski, S. 1989. Options arbitrage in imperfect markets. The Journal of Finance 44 (5):12891311.
Figlewski, S., and G. P. Webb. 1993. Options, short sales, and market completeness. The Journalof Finance 48 (2):761-777.
Finucane, T. J. 1991. Put-call parity and expected returns. Journal of Financial and Quantitative
Analysis 26 (4):445-457.
Frankel, R., M. McNichols, and G. P. Wilson. 1995. Discretionary disclosure and externalfinancing. The Accounting Review 70 (1):135-150.
37

Gao, H. S. 2010. Optimal compensation contracts when managers can hedge. Journal of Financial
Economics 97 (2):218-238.
Ge, L., T.-C. Lin, and N. D. Pearson. 2016. Why does the option to stock volume ratio predict
stock returns? Journal of Financial Economics 120 (3):601-622.
Glosten, L. R., and P. R. Milgrom. 1985. Bid, ask and transaction prices in a specialist market withheterogeneously informed iraders. Journal of Financial Economics 14 (1):71-100.
Golez, B., and J. C. Jackwerth. 2012. Pinning in the S&P 500 futures. Journal of Financial
Economics 106 (3):566-585.
Graham, J. R., C. R. Harvey, and S. Rajgopal. 2005. The economic implications of corporate
financial reporting. Journal of Accounting and Economics 40 (1-3):3-73.
Healy, P. M., and K. G. Palepu. 2001. Information asymmetry, corporate disclosure, and the capital
markets: A review of the empirical disclosure literature. Journal of Accounting andEconomics 31 (1-3):405-440.
Healy, P. M., and J. M. Wahlen. 1999. A review of the earnings management literature and its
implications for standard setting. Accounting Horizons 13 (4):365-383.
Heflin, F., W. J. Kross, and S. Inho. 2016. Asymmetric effects of regulation FD on management
earnings forecasts. The Accounting Review 91 (1):119-152.
Heflin, F., K. R. Subramanyam, and Y. Zhang. 2003. Regulation FD and the financial information
environment: Early evidence. The Accounting Review 78 (1):1-37.
Holmström, B., and J. Tirole. 1993. Market liquidity and performance monitoring. Journal of
Political Economy 101 (4):678-709.
Hu, J. 2018. Option listing and information asymmetry. Review of Finance 22 (3):1153-1194.
Huang, Y., R. Jennings, and Y. Yu. 2017. Product market competition and managerial disclosureof earnings forecasts: Evidence from import tariff rate reductions. The Accounting Review
92 (3):185-207.
Hull, J. C. 2003. Options futures and other derivatives. Pearson Education: India.
Jennings, R., and L. Starks. 1986. Earnings announcements, stock price adjustment, and theexistence of option markets. The Journal of Finance 41 (1):107-125.
Jin, W., J. Livnat, and Y. Zhang. 2012. Option prices leading equity prices: Do option traders havean information advantage? Journal of Accounting Research 50 (2):401-432.
Johnson, M. F., K. Ron, and K. K. Nelson. 2001. The impact of securities litigation reform on thedisclosure of forward-looking information by high technology firms. Journal of
Accounting Research 39 (2):297-327.
Johnson, T. L., and E. C. So. 2012. The option to stock volume ratio and future returns. Journal of
Financial Economics 106 (2):262-286.
Kasznik, R., and B. Lev. 1995. To warn or not to warn: Management disclosures in the face of anearnings surprise. The Accounting Review 70 (1):113-134.
Ke, B., and S. Ramalingegowda. 2005. Do institutional investors exploit the post-earnings
announcement drift? Journal of Accounting and Economics 39 (1):25-53.
Kim, I., and D. J. Skinner. 2012. Measuring securities litigation risk. Journal of Accounting andEconomics 53 (1-2):290-310.
Kim, J. B., P. Shroff, D. Vyas, and R. Wittenberg‐Moerman. 2018. Credit default swaps andmanagers’ voluntary disclosure. Journal of Accounting Research 56 (3):953-988.
Kim, O., and R. E. Verrecchia. 1994. Market liquidity and volume around earnings announcements.
Journal of Accounting and Economics 17 (1-2):41-67.

38

Kim, C., K. Wang, and L. D. Zhang. 2019. Readability of 10‐K reports and stock price crash risk.
Contemporary Accounting Research 36(2): 1184-1216.
Klemkosky, R. C., and B. G. Resnick. 1979. Put-call parity and market efficiency. Journal of
Finance 34 (5):1141-1155.
Kumar, R., A. Sarin, and K. Shastri. 1998. The impact of options trading on the market quality of
the underlying security: An empirical analysis. The Journal of Finance 53 (2):717-732.
Lang, M., and R. Lundholm. 1993. Cross-sectional determinants of analyst ratings of corporate
disclosures. Journal of Accounting Research 31 (2):246-271.
Larcker, D. F., and T. O. Rusticus. 2010. On the use of instrumental variables in accounting
research. Journal of Accounting and Economics 49 (3):186-205.
Lo, A. K. 2014. Do declines in bank health affect borrowers’ voluntary disclosures? Evidence frominternational propagation of banking shocks. Journal of Accounting Research 52 (2):541581.
Matsumoto, D. A. 2002. Management’s incentives to avoid negative earnings surprises. The
Accounting Review 77 (3):483-514.
Mayhew, S., and V. Mihov. 2004. How do exchanges select stocks for option listing? The Journalof Finance 59 (1):447-471.
Mayhew, S., A. Sarin, and K. Shastri. 1995. The allocation of informed trading across related
markets: An analysis of the impact of changes in equity-option margin requirements. The
Journal of Finance 50 (5):1635-1653.
Merton, R. C. 1976. Option pricing when underlying stock returns are discontinuous. Journal of
Financial Economics 3 (1-2):125-144.
Miller, G. S. 2002. Earnings performance and discretionary disclosure. Journal of Accounting
Research 40 (1):173-204.
Naiker, V., F. Navissi, and C. Truong. 2013. Options trading and the cost of equity capital. The
Accounting Review 88 (1):261-295.
Ni, S. X. Y., N. D. Pearson, and A. M. Poteshman. 2005. Stock price clustering on option
expiration dates. Journal of Financial Economics 78 (1):49-87.
Pan, J., and A. M. Poteshman. 2006. The information in option volume for future stock prices.
Review of Financial Studies 19 (3):871-908.
Piotroski, J. D., and D. T. Roulstone. 2004. The influence of analysts, institutional investors, andinsiders on the incorporation of market, industry, and firm-specific information into stock
prices. The Accounting Review 79 (4):1119-1151.
Roll, R., E. Schwartz, and A. Subrahmanyam. 2009. Options trading activity and firm valuation.
Journal of Financial Economics 94 (3):345-360.
———. 2010. O/S: The relative trading activity in options and stock. Journal of Financial
Economics 96 (1):1-17.
Ross, S. A. 1976. Options and efficiency. Quarterly Journal of Economics 90 (1):75-89.
Schoenfeld, J. 2017. The effect of voluntary disclosure on stock liquidity: New evidence fromindex funds. Journal of Accounting and Economics 63 (1):51-74.
Sethuraman, M. 2019. The effect of reputation shocks to rating agencies on corporate disclosures.
The Accounting Review 94 (1):299-326.
Sias, R. W., L. T. Starks, and S. Titman. 2006. Changes in institutional ownership and stock returns:
Assessment and methodology. The Journal of Business 79 (6):2869-2910.
Skinner, D. J. 1994. Why firms voluntarily disclose bad news. Journal of Accounting Research 32(1):38-60.
39

Trueman, B. 1986. Why do managers voluntarily release earnings forecasts? Journal of
Accounting and Economics 8 (1):53-71.
Truong, C., and C. Corrado. 2014. Options trading volume and stock price response to earnings
announcements. Review of Accounting Studies 19 (1):161-209.
Verrecchia, R. E. 1983. Discretionary disclosure. Journal of Accounting and Economics 5 (3):179194.
Verrecchia, R. E. 2001. Essays on disclosure. Journal of Accounting and Economics 32 (1-3):97180.
Waymire, G. 1985. Earnings volatility and voluntary management forecast disclosure. Journal of
Accounting Research 23 (1):268-295.
Zuo, L. 2016. The informational feedback effect of stock prices on management forecasts. Journalof Accounting and Economics 61 (2-3):391-413.

40

Appendix A: Variable Definitions
Variable
Definition
Variables in the Baseline Analysis
Dummy variable that equals one if the firm issues at least one earnings forecast
DumMF
during the fiscal year, zero otherwise.
Natural logarithm of one plus the number of earnings forecasts issued by the firm in
FreqMF
a fiscal year.
Natural logarithm of one plus the total annual dollar options volume (in millions of
LnOptvolU.S. dollars) in a fiscal year.
Size
Natural logarithm of the market value of equity at the fiscal year-end.
Lev
Total debt divided by total assets at the fiscal year-end.
BM
Book value of equity divided by the market value of equity at the fiscal year-end.
ROA
Income before extraordinary items divided by total assets.
Dummy variable equal to one if income before extraordinary items at the fiscal yearLoss
end is negative, zero otherwise.
Percentage of total shares outstanding held by institutional investors at the fiscal
IO
year-end.
Natural logarithm of one plus the number of analysts covering the firm in a fiscal
Analystyear.
Ret
Buy-and-hold size-adjusted return in a fiscal year.
Standard deviation of the annual return on assets over the past 10 years with, at
EarnVolminimum, five non-missing observations.
Ex-ante class action litigation risk, calculated using the coefficients from Model (3)Litigationin Kim and Skinner (2012).
Dummy variable that takes a value of one if the firms’ Altman Z-score falls within
the middle quintile of the sample distribution at the fiscal year-end, zero otherwise.
Mid_Zscore
Z-score = 1.2 × (current assets minus current liabilities/total assets) + 1.4 × (retained
earnings/total assets) + 3.3 × (earnings before interest and taxes/total assets) + 0.6 ×(market value of equity/total debt) + 0.999 × (sales/total assets).
Dummy variable that takes a value of one if the firm issues equity during the fiscal
Issue
year, zero otherwise.
Additional Variables in Table 4
Natural logarithm of one plus the total annual dollar call options volume (in millions
LnCallOptvolof U.S. dollars) in a fiscal year.
Natural logarithm of one plus the total annual dollar put options volume (in millions
LnPutOptvolof U.S. dollars) in a fiscal year.
Natural logarithm of one plus the total annual number of options contracts in a fiscal
Lnsoptvol
year.
LnO/S
Natural logarithm of the ratio of dollar options volume over dollar stock volume.
Additional Variables in Table 5
Annual average absolute difference between a stock’s market price and the option’s
Moneyness
strike price.
OpenInterest
Average open interest across all options on a stock throughout the fiscal year.
Additional Variables in Table 6
Dummy variable that equals one for options listing firms after the options listing year
Post
and zero for options listing firms before the options listing year and for matched
control firms throughout all years.
Additional Variables in Table 7

41

Dummy variable that equals one if abnormal accruals are greater than the sample
median, zero otherwise. Abnormal accruals are calculated following Dechow andDichev (2002) and McNichols (2002).
Dummy variable that equals one if the modified fog score is greater than the sample
median, zero otherwise. To capture readability in the financial context, Kim et al.
(2019) identify a list of words that exceed three syllables but are not difficult toModfogunderstand in the financial context and classify them as simple words. The modified
fog index is calculated as (words per sentence + percentage of complex words) × 0.4.
A higher modified fog score indicates a less readable report.
Dummy variable that equals one if the modified Flesch score is greater than thesample median, zero otherwise. Using words classified as simple in the financial
context by Kim et al. (2019), the modified Flesch score is calculated as 206.835 –Modflesch(1.015 × words per sentence) – (84.6 × syllables per word). A higher modified Fleschscore indicates a more readable report. For ease of interpretation, we multiply theindex by -1 so that higher values indicate a less readable report.
Dummy variable that equals one if the modified Kincaid score is greater than thesample median, zero otherwise. Using the words classified as simple in the financial
Modkincaid
context by Kim et al. (2019), the modified Kincaid score is calculated as (11.8 ×syllables per word) + (0.39 × words per sentence) – 15.59. A higher modified Kincaidscore indicates a less readable report.
Additional Variables in Table 8
Dummy variable that equals one if Amihud stock illiquidity is less than the sample
Liquidity
median, zero otherwise. Amihud stock illiquidity is calculated following Amihud(2002).
Dummy variable that equals one if transient institutional ownership is greater than
the sample median, zero otherwise. Transient institutional ownership is the
TraIO
percentage of total shares outstanding held by transient institutional investors for a
fiscal year.
Dummy variable that equals one if quasi-indexer institutional ownership is greaterthan the sample median, zero otherwise. Quasi-indexer institutional ownership is the
QixIO
percentage of total shares outstanding held by quasi-indexer institutional investors
for a fiscal year.
Dummy variable that equals one if dedicated institutional ownership is greater than
the sample median, zero otherwise. Dedicated institutional ownership is the
DedIO
percentage of total shares outstanding held by dedicated institutional investors for a
fiscal year.
Additional Variables in Table 9
100 times the average daily difference between the ask and bid prices divided by the
BASPREAD
midpoint of the bid price and ask price in a fiscal year.
PIN
Probability of informed trading measured following Brown and Hillegeist (2007).
Additional Variables in Table 10-100 times the average difference between the upper and lower end estimates, divided
Width
by price. Point estimates are assigned a value of zero.
Natural logarithm of one plus the forecast range. We assign a value of one to aforecast with an open range estimate, a value of two to a forecast with a closed range
Range
estimate, and a value of three to a forecast with a point estimate. We sum the values
across all of a firm’s earnings forecasts in a fiscal year.
Additional Variables in Table 11
Three-day value-weighted market-adjusted cumulative abnormal returns around the
CAR
release of management earnings forecasts.
Accruals42Dummy variable that equals one if the options volume (LnOptvol) is greater than thesample median, zero otherwise.
Difference between a management forecast and the consensus analyst forecast,
MFSurpdivided by stock price.
Additional Variables in Table 12
Dummy variable that equals one if the firm issues at least one good news forecast in
DumGN
a fiscal year, zero otherwise. Good news forecasts are those with a positive MFSurp
or CAR.
Dummy variable that equals one if the firm issues at least one bad news forecast in a
DumBN
fiscal year, zero otherwise. Bad news forecasts are those with a negative MFSurp or
CAR.
Natural logarithm of one plus the number of good news forecasts issued by the firm
FreqGNin a fiscal year. Good news forecasts are those with a positive MFSurp or CAR.
Natural logarithm of one plus the number of bad news forecasts issued by the firm in
FreqBN
a fiscal year. Bad news forecasts are those with negative MFSurp or CAR.
Additional Variables in Table 13
Dummy variable that equals one if the firm reports at least one quarterly non-GAAP
DumNonGAAP
earnings disclosure in a fiscal year, zero otherwise.
Natural logarithm of one plus the number of quarterly non-GAAP earnings
FreqNonGAAP
disclosures by the firm in a fiscal year.
HighOpt

43

Appendix B: Options Listing
Panel A of this table shows the logit regression of the probability of an options listing. Following theselection criterion in Mayhew and Mihov (2004) and Hu (2018), we define stocks as eligible for an options
listing in the next month if they meet the following requirements: (1) they are listed on the NYSE, Amex,
or Nasdaq; (2) they issue at least seven million publicly held shares; (3) they reach the minimum price ($7.5
before 2002 and $3 after 2002); and (4) they have no options trading history and have stock trading history
for at least 252 trading days. Options listing (Optlist) is a dummy variable that equals one for firm-months
when the options of a stock are first traded, zero otherwise. The control variables include the natural
logarithm of the market value at the end of last month (Sizet-1); the natural logarithm of the average dailyvolume in the past 12 months (Volumet-1,t-12), in the last month (Volumet-1), and in month t-12 (Volumet-12);
the natural logarithm of the standard deviation of daily returns in the past year (STDt-1,t-12), in the last month(STDt-1), and in month t-12 (STDt-12); the average daily percentage bid-ask spread at market close in the past
year (Spreadt-1,t-12), in the last month (Spreadt-1), and in month t-12 (Spreadt-12); and industry fixed effects
based on the two-digit SIC code and year fixed effects. The z-statistics in parentheses are adjusted forheteroscedasticity and clustered by firm. Panel B of this table shows a comparison of the independent
variables in Panel A between the options listing firms and control firms. ***, **, and * denote significanceat the 1%, 5%, and 10% levels, respectively.
Panel A: Logit Regression of the Probability of an Options Listing
Optlistt
Variables(1)
Sizet-1-0.1832***(-6.11)
Volumet-1,t-12-0.8116***(-12.48)
Volumet-1
1.1563***(28.59)Volumet-12-0.4942***(-11.90)
STDt-1,t-12
0.4563***(4.81)
STDt-10.2649***(5.80)
STDt-120.5648***(10.23)
Spreadt-1,t-12-0.3840***(-5.80)Spreadt-1-0.6002***(-9.30)Spreadt-12-0.0446(-1.16)
Constant
6.7793***(14.55)
Industry FE
Yes
Year FE
Yes
N
320,684
Pseudo R2
0.149

44

Panel B: Difference in Pre-Listing Firm Characteristics
Variables
Options Listing Firms
Control Firms
Sizet-1
12.90012.930Volumet-1,t-12
11.69011.710Volumet-1
12.07012.060Volumet-12
11.17011.210
STDt-1,t-12-3.571-3.558STDt-1-3.599-3.586
STDt-12-3.710-3.687
Spreadt-1,t-12
0.956
0.961
Spreadt-1
0.681
0.720
Spreadt-12
1.198
1.181

45

Difference
0.028
0.017
-0.001
0.039
0.013
0.013
0.023
0.006
0.040**-0.017

Table 1: Sample Distribution
This table shows the industry and year distribution of the observations. The industry distribution is basedon the Fama-French 12 industry classification.
Panel A: Industry Distribution
Industry
Consumer Non-Durables
Consumer Durables
Manufacturing
Oil, Gas, and Coal Extraction and Products
Chemicals and Allied Products
Business Equipment
Telephone and Television Transmission
Utilities
Wholesale, Retail, and Some Services
Healthcare, Medical Equipment, and Drugs
Finance
Other
Total
Panel B: Year Distribution
Year
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
Total

Total no.
1,854
954
3,904
1,980
1,042
7,148
899
1,445
3,784
4,214
6,541
4,728
38,493

Total no.
990
1,178
1,341
1,406
1,344
1,296
1,433
1,535
1,666
1,765
1,867
1,909
1,977
2,090
2,168
2,222
2,418
2,456
2,483
2,488
2,461
38,493

No. with forecast
1,188
485
2,058
317
627
4,403
203
981
2,412
1,821
1,428
1,898
17,821

No. with forecast
241
411
482
636
869
842
927
1,003
1,006
1,034
1,030
966
867
906
942
950
1,007
982
941
927
852
17,82146% with forecast
64.08%
50.84%
52.72%
16.01%
60.17%
61.60%
22.58%
67.89%
63.74%
43.21%
21.83%
40.14%
46.30%% with forecast
24.34%
34.89%
35.94%
45.23%
64.66%
64.97%
64.69%
65.34%
60.38%
58.58%
55.17%
50.60%
43.85%
43.35%
43.45%
42.75%
41.65%
39.98%
37.90%
37.26%
34.62%
46.30%

Table 2: Summary Statistics
This table presents the summary statistics of the variables in the analysis. Variable definitions are presented
in Appendix A.
Variable
DumMFt+1
FreqMFt+1
LnOptvolt
Sizet
Levt
BMt
ROAt
Losst
IOt
Analystt
Rett
EarnVolt
Litigationt
Mid_ZscoretIssuetN38,49338,49338,49338,49338,49338,49338,49338,49338,49338,49338,49338,49338,49338,49338,493

Mean
0.463
0.803
2.340
7.367
0.223
0.562
0.013
0.227
0.657
2.037
0.028
0.111
0.172
0.200
0.137

S.D.
0.499
0.955
1.986
1.678
0.192
0.437
0.141
0.419
0.264
0.820
0.452
0.208
0.138
0.400
0.344

Median
0.000
0.000
1.862
7.295
0.198
0.459
0.035
0.000
0.713
2.079
-0.019
0.049
0.145
0.000
0.000

47

P5
0.000
0.000
0.053
4.705
0.000
0.107
-0.241
0.000
0.082
0.693
-0.595
0.005
0.002
0.000
0.000

P25
0.000
0.000
0.627
6.198
0.046
0.276
0.005
0.000
0.504
1.609
-0.238
0.022
0.041
0.000
0.000

P75
1.000
1.609
3.668
8.450
0.350
0.714
0.075
0.000
0.864
2.639
0.211
0.108
0.303
0.000
0.000

P95
1.000
2.485
6.238
10.331
0.585
1.350
0.158
1.000
1.000
3.219
0.822
0.396
0.395
1.000
1.000

Table 3: Options Trading and Management Earnings Forecasts
This table presents the effect of options trading on the likelihood and frequency of management earnings
forecasts. The regressions are performed using OLS. The t-statistics in parentheses are adjusted forheteroscedasticity and clustered by firm. ***, **, and * denote significance at the 1%, 5%, and 10% levels,
respectively. Variable definitions are presented in Appendix A.
Variables
LnOptvolt
Sizet
Levt
BMt
ROAt
Losst
IOt
Analystt
Rett
EarnVolt
Litigationt
Mid_ZscoretIssuetFirm FE
Year FE
N
Adjusted R2(1)
DumMFt+1-0.016***(-4.915)
0.063***(8.309)0.053*(1.770)0.015(1.368)0.145***(4.876)-0.045***(-5.861)
0.009(0.429)0.055***(7.622)-0.018***(-3.654)-0.048(-1.333)
0.013(0.801)0.011*(1.759)-0.004(-0.720)
Yes
Yes
38,493
0.595

48

(2)
FreqMFt+1-0.036***(-5.565)
0.141***(9.285)0.096(1.632)0.030(1.516)0.231***(4.256)-0.106***(-7.826)
0.002(0.053)0.094***(6.866)-0.031***(-3.598)-0.046(-0.623)0.041(1.372)0.021*(1.715)-0.018*(-1.694)
Yes
Yes
38,4930.669

Table 4: Robustness Tests
This table shows the results of the robustness tests. For brevity, all of the control variables are included but
not reported. The regressions are performed using OLS. The t/z-statistics in parentheses are adjusted forheteroscedasticity and clustered by firm. ***, **, and * denote significance at the 1%, 5%, and 10% levels,
respectively. Variable definitions are presented in Appendix A.
Panel A: Different Types of Management Forecasts
Annual earnings
Quarterly earnings
forecasts
forecasts
(1)
(2)
(3)
(4)
Variables
DumMFt+1
FreqMFt+1
DumMFt+1
FreqMFt+1
LnOptvolt-0.018***-0.025***-0.011***-0.025***(-5.745)(-4.651)(-3.219)(-4.987)
Controls
Yes
Yes
Yes
Yes
Firm FE
Yes
Yes
Yes
Yes
Year FE
Yes
Yes
Yes
Yes
N
38,49338,49338,49338,493Adjusted R2
0.593
0.655
0.504
0.595
Panel B: Alternative Options Trading Measures
(1)
(2)
(3)
Variables
DumMFt+1 FreqMFt+1 DumMFt+1
LnCallOptvolt-0.010**-0.027***(-2.201)(-3.046)
LnPutOptvolt-0.010**-0.016*(-2.017)(-1.794)
Lnsoptvolt-0.010***(-4.524)
LnO/StControls
Firm FE
Year FE
N
Adjusted R2

Yes
Yes
Yes
75,086
0.569

Yes
Yes
Yes
75,086
0.649

Yes
Yes
Yes
38,4930.595(4)FreqMFt+1

Non-earnings
forecasts(5)(6)
DumMFt+1
FreqMFt+1
0.001-0.016**(0.364)(-2.505)
Yes
Yes
Yes
Yes
Yes
Yes
38,49338,493
0.663
0.729
(5)
DumMFt+1(6)
FreqMFt+1-0.010***(-4.320)
Yes
Yes
Yes
38,3060.595-0.022***(-4.880)
Yes
Yes
Yes
38,3060.669-0.020***(-5.168)
Yes
Yes
Yes
38,493
0.669

Panel C: Alternative Samples
Including firms without
options trading
Variables
LnOptvolt
Controls
Firm FE
Year FE
N
Adjusted R2(1)
DumMFt+1-0.010***(-4.162)
Yes
Yes
Yes
75,0860.569(2)FreqMFt+1-0.010**(-2.164)
Yes
Yes
Yes
75,086
0.649

Excluding the dot-com
bubble and financial crisis
periods(3)(4)
DumMFt+1
FreqMFt+1-0.016***-0.031***(-4.235)(-4.268)
Yes
Yes
Yes
Yes
Yes
Yes
31,96731,967
0.600
0.678

49

Post Reg FD period(5)
DumMFt+1-0.016***(-5.109)
Yes
Yes
Yes
32,2340.680(6)FreqMFt+1-0.039***(-6.180)
Yes
Yes
Yes
32,2340.738

Panel D: Alternative Regression Specifications
Logit regression(1)
Variables
DumMFt+1
LnOptvolt-0.143***(-4.694)∆LnOptvolt
Controls
Firm FE
Year FE
N
Pseudo/Adjusted R2

Change analysis

Yes
Yes
Yes
22,2400.098(2)∆DumMFt+1(3)∆FreqMFt+1-0.009***(-2.775)
Yes
Yes
Yes
32,6190.022-0.021***(-4.450)
Yes
Yes
Yes
32,6190.046

Panel E: Controlling for CDS Trading
Variables
LnOptvolt
PostCDSt
CDSTradedt
Controls
Year FE
N
Adjusted R2(1)
DumMFt+1-0.016***(-4.137)
0.005(0.284)0.064***(3.215)
Yes
Yes
35,042
0.170

50

(2)
FreqMFt+1-0.029***(-3.631)0.036(0.893)0.103**(2.543)
Yes
Yes
35,0420.195

Table 5: Instrumental Variable ApproachThis table shows the results of a two-stage least squares (2SLS) regression with moneyness and option
interest as the instrumental variables. The t-statistics in parentheses are adjusted for heteroscedasticity andclustered by firm. ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively. Variable
definitions are presented in Appendix A.

Variables
LnOptvolt
Sizet
Levt
BMt
ROAt
Losst
IOt
Analystt
Rett
EarnVolt
Litigationt
Mid_ZscoretIssuetMoneynesst
OpenInterestt
Firm FE
Year FE
N
Adjusted R2

First stage(1)
LnOptvolt

Second stage

0.892***(38.452)
0.466***(5.809)0.184***(5.854)0.337***(3.628)0.171***(8.782)0.263***(4.154)0.145***(7.364)-0.239***(-17.681)
0.284***(2.947)0.457***(10.352)-0.038**(-2.217)0.110***(6.861)0.818***(11.425)0.001***(17.266)
Yes
Yes
37,919
0.855

51

(2)
DumMFt+1-0.056***(-5.359)
0.099***(8.590)0.075**(2.470)0.027**(2.461)0.151***(5.094)-0.036***(-4.532)0.015(0.733)0.060***(8.215)-0.027***(-5.051)-0.033(-0.912)0.030*(1.760)0.009(1.373)0.001(0.085)(3)
FreqMFt+1-0.106***(-4.966)
0.204***(8.794)0.135**(2.256)
0.052**(2.553)0.243***(4.500)-0.090***(-6.451)
0.013(0.340)0.104***(7.418)-0.048***(-4.915)-0.019(-0.259)0.070**(2.271)0.017(1.364)-0.009(-0.858)

Yes
Yes
37,919
0.591

Yes
Yes
37,9190.667

Table 6: Difference-in-Differences Analysis Based on Options ListingThis table shows the results of the DID analysis based on options listing. The regressions are performed
using OLS. The t-statistics in parentheses are adjusted for heteroscedasticity and clustered by firm. ***, **,and * denote significance at the 1%, 5%, and 10% levels, respectively. Variable definitions are presented
in Appendix A.

Variables
Postt
Sizet
Levt
BMt
ROAt
Losst
IOt
Analystt
Rett
EarnVolt
Litigationt
Mid_ZscoretIssuetFirm FE
Year FE
N
Adjusted R2

One year before and after(1)(2)
DumMFt+1
FreqMFt+1-0.035**-0.053**(-2.281)(-2.238)0.034*
0.051*(1.896)(1.745)0.167**
0.101(2.056)(0.674)-0.010-0.014(-0.416)(-0.402)0.201***
0.387***(2.784)(2.830)-0.058**-0.085**(-2.542)(-2.491)
0.056
0.078
(1.093)(0.930)0.063***
0.099***(3.710)(3.604)-0.009-0.001(-0.687)(-0.052)-0.021-0.035(-0.293)(-0.297)
0.044
0.070
(0.831)(0.835)0.027
0.043(1.385)(1.316)0.004
0.003(0.234)(0.107)
Yes
Yes
Yes
Yes
5,236
5,236
0.600
0.686

52

Three years before and after(3)(4)
DumMFt+1
FreqMFt+1-0.029**-0.043*(-2.059)(-1.787)
0.038***
0.065***(3.341)(3.458)0.139***
0.133(2.807)(1.537)0.010
0.021(0.772)(1.100)0.147***
0.262***(3.487)(3.487)-0.050***-0.091***(-4.107)(-4.536)0.069*
0.135*(1.808)(1.779)0.063***
0.095***(6.293)(5.938)-0.004
0.009(-0.499)(0.820)-0.038-0.024(-1.045)(-0.385)
0.045
0.069
(1.472)(1.270)0.014
0.025(1.187)(1.269)-0.004-0.008(-0.372)(-0.502)
Yes
Yes
Yes
Yes
13,332
13,3320.600
0.679

Table 7: The Effect of the Firm Information Environment
This table presents the effect of the firm information environment on the relation between options trading and management earnings forecasts. The
regressions are performed using OLS. The t-statistics in parentheses are adjusted for heteroscedasticity and clustered by firm. ***, **, and * denotesignificance at the 1%, 5%, and 10% levels, respectively. Variable definitions are presented in Appendix A.
Variables
LnOptvolt×Accrualst
Accrualst(1)
DumMFt+1-0.004**(-2.012)0.013**(1.992)(2)
FreqMFt+1-0.006*(-1.742)0.021*(1.828)

LnOptvolt×Modfogt
Modfogt(3)
DumMFt+1(4)
FreqMFt+1-0.006**(-2.101)
0.007(0.792)-0.009*(-1.733)
0.005(0.292)

LnOptvolt×Modflescht
Modflescht(5)
DumMFt+1(6)
FreqMFt+1-0.008**(-2.498)
0.022**(2.269)-0.013**(-2.202)
0.033*(1.916)

LnOptvolt×Modkincaidt
Modkincaidt
LnOptvolt
Sizet
Levt
BMt
ROAt
Losst
IOt

-0.012***(-3.001)
0.061***(7.216)0.013(0.416)0.020(1.611)0.120***(3.787)-0.042***(-4.826)
0.017-0.033***(-4.340)0.152***(8.747)0.059(0.900)0.040*(1.755)0.174***(2.962)-0.107***(-6.873)-0.002-0.016***(-4.075)
0.077***(8.534)0.050(1.453)0.015(1.156)0.158***(4.689)-0.047***(-5.241)-0.003-0.040***(-5.246)
0.177***(9.767)0.080(1.166)0.025(1.074)0.256***(4.202)-0.116***(-7.412)-0.031-0.015***(-3.711)0.077***(8.539)0.051(1.477)0.015(1.149)0.158***(4.667)-0.048***(-5.290)-0.004-0.038***(-4.835)
0.177***(9.776)0.081(1.185)0.025(1.068)0.255***(4.176)-0.117***(-7.467)-0.032(7)
DumMFt+1(8)
FreqMFt+1-0.007**(-2.528)0.010(1.178)-0.015***(-3.896)0.077***(8.551)0.050(1.459)0.015(1.155)0.158***(4.678)-0.047***(-5.251)-0.004-0.011*(-1.950)
0.011(0.714)-0.039***(-5.091)
0.177***(9.781)0.080(1.167)0.025(1.069)0.255***(4.189)-0.116***(-7.429)-0.032

Analystt
Rett
EarnVolt
Litigationt
Mid_ZscoretIssuetFirm FE
Year FE
N
Adjusted R2(0.771)
0.062***(7.292)-0.017***(-3.097)-0.033(-0.849)
0.028(1.492)0.012*(1.706)-0.004(-0.620)
Yes
Yes
29,2250.607(-0.042)0.113***(6.794)-0.034***(-3.422)-0.031(-0.379)
0.072**(2.139)0.019(1.441)-0.020(-1.505)
Yes
Yes
29,2250.684(-0.135)
0.052***(6.132)-0.025***(-4.558)-0.030(-0.755)
0.014(0.780)0.022***(3.058)-0.007(-0.984)
Yes
Yes
30,5210.579

54(-0.727)0.089***(5.630)-0.050***(-5.066)-0.006(-0.076)0.047(1.442)0.038***(2.682)-0.018(-1.458)
Yes
Yes
30,521
0.663(-0.152)
0.052***(6.148)-0.025***(-4.546)-0.029(-0.742)0.014(0.775)0.022***(3.057)-0.007(-0.962)
Yes
Yes
30,521
0.579(-0.739)0.089***(5.647)-0.050***(-5.055)-0.005(-0.059)
0.047(1.439)0.038***(2.682)-0.018(-1.436)
Yes
Yes
30,521
0.663(-0.161)
0.052***(6.129)-0.025***(-4.560)-0.030(-0.759)0.014(0.807)0.022***(3.053)-0.007(-0.979)
Yes
Yes
30,521
0.579(-0.748)0.089***(5.630)-0.051***(-5.069)-0.007(-0.080)
0.048(1.464)0.038***(2.678)-0.018(-1.452)
Yes
Yes
30,521
0.663

Table 8: The Effect of Information Transfer Facilitation
This table presents the effect of information transfer facilitation on the relation between options trading andmanagement earnings forecasts. The regressions are performed using OLS. The t-statistics in parentheses
are adjusted for heteroscedasticity and clustered by firm. ***, **, and * denote significance at the 1%, 5%,
and 10% levels, respectively. Variable definitions are presented in Appendix A.
Variables
LnOptvolt×Liquidityt
Liquidityt(1)
DumMFt+1-0.012**(-2.507)0.058***(4.186)(2)
FreqMFt+1-0.017*(-1.920)0.115***(4.370)

LnOptvolt×TraIOt
TraIOt
LnOptvolt×QixIOt
QixIOt
LnOptvolt×DedIOt
DedIOt
LnOptvolt
Sizet
Levt
BMt
ROAt
Losst
IOt
Analystt
Rett
EarnVolt
Litigationt
Mid_ZscoretIssuetFirm FE-0.021***(-5.588)0.057***(7.419)0.053*(1.746)0.014(1.293)0.146***(4.915)-0.045***(-5.793)0.000(0.017)
0.053***(7.319)-0.016***(-3.219)-0.049(-1.355)
0.016(0.955)0.011*(1.790)-0.005(-0.752)
Yes-0.045***(-5.831)0.127***(8.217)0.094(1.611)0.027(1.403)0.235***(4.313)-0.104***(-7.730)-0.014(-0.372)
0.091***(6.600)-0.026***(-2.940)-0.047(-0.636)0.046(1.553)0.022*(1.753)-0.018*(-1.698)
Yes(3)
DumMFt+1(4)
FreqMFt+1-0.008***(-2.603)
0.014(1.525)-0.005(-1.361)
0.025**(2.018)0.001(0.410)-0.023**(-2.516)-0.009**(-2.249)
0.062***(8.148)0.054*(1.782)0.014(1.320)0.144***(4.847)-0.045***(-5.796)-0.010*(-1.873)
0.024(1.466)-0.007(-0.981)0.047**(2.115)0.005(0.830)-0.049***(-2.861)-0.028***(-3.479)
0.138***(9.135)0.098*(1.663)0.029(1.483)0.230***(4.220)-0.104***(-7.727)0.054***(7.528)-0.017***(-3.511)-0.049(-1.353)0.014(0.871)0.011*(1.800)-0.004(-0.696)
Yes

0.091***(6.687)-0.030***(-3.435)-0.047(-0.639)
0.042(1.412)0.022*(1.753)-0.018*(-1.655)
Yes

Year FE
N
Adjusted R2

Yes
38,4930.595

Yes38,493
0.670

56

Yes
38,4930.595

Yes38,4930.670

Table 9 The Effect of Options Trading on Information Asymmetry
This table shows the effect of options trading on information asymmetry. The regressions are performed
using OLS. The t-statistics in parentheses are adjusted for heteroscedasticity and clustered by firm. ***, **,and * denote significance at the 1%, 5%, and 10% levels, respectively. Variable definitions are presented
in Appendix A.
Variables
LnOptvolt
Sizet
Levt
BMt
ROAt
Losst
IOt
Analystt
Rett
EarnVolt
Litigationt
Mid_ZscoretIssuetFirm FE
Year FE
N
Adjusted R2(1)
BASPREADt-0.023***(-5.820)-0.135***(-13.207)0.187***(4.959)0.121***(7.466)-0.239***(-5.861)-0.015*(-1.725)-0.303***(-11.868)-0.046***(-5.290)0.056***(9.106)-0.074*(-1.942)0.043**(2.296)-0.012(-1.605)-0.026***(-4.171)
Yes
Yes
38,465
0.789

57

(2)
PINt
-0.009***(-24.593)-0.017***(-18.607)
0.008**(2.486)0.002(1.586)0.001(0.172)-0.001(-0.874)-0.026***(-7.828)-0.006***(-7.001)
0.008***(14.597)-0.014***(-3.338)0.010***(5.380)-0.001(-0.954)-0.001(-1.195)
Yes
Yes
23,0670.712

Table 10: Forecast Characteristics
This table shows the effect of options trading on the characteristics of management earnings forecasts. The
regressions are performed using OLS. The t-statistics in parentheses are adjusted for heteroscedasticity andclustered by firm. ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively. Variable
definitions are presented in Appendix A.
Variables
LnOptvolt
Sizet
Levt
BMt
ROAt
Losst
IOt
Analystt
Rett
EarnVolt
Litigationt
Mid_ZscoretIssuetFirm FE
Year FE
N
Adjusted R2(1)
Widtht+1-0.021***(-4.107)0.119***(7.906)-0.127***(-2.996)-0.260***(-8.274)
0.273***(2.731)-0.073***(-4.061)
0.077**(2.139)0.005(0.393)0.026***(2.867)0.036(0.665)-0.000(-0.002)0.010(1.083)0.002(0.167)
Yes
Yes
17,1470.668(2)Ranget+1-0.032***(-4.426)0.129***(7.297)0.076(1.074)-0.062*(-1.921)0.154*(1.754)-0.094***(-4.949)
0.037(0.820)0.024(1.436)-0.014(-1.090)0.024(0.318)0.034(0.846)
0.026*(1.824)0.004(0.268)
Yes
Yes
17,821
0.494

58

Table 11: Stock Market Reactions to Management Earnings Forecasts
The table shows the results of the effect of options trading on stock market reactions to management
earnings forecasts. The regressions are performed using OLS. The t-statistics in parentheses are adjusted
for heteroscedasticity and clustered by firm. ***, **, and * denote significance at the 1%, 5%, and 10%
levels, respectively. Variable definitions are presented in Appendix A.
Variables
MFSurpt+1(1)
CARt+14.012**(2.500)

MFSurpt+1×HighOptt
MFSurpt+1×Sizet+1
MFSurpt+1×BMt+1
MFSurpt+1×Losst+1
MFSurpt+1×Analystt+1-0.253(-1.220)-0.471(-1.117)-1.233***(-2.945)0.521(1.590)HighOptt
Sizet+1
BMt+1
Losst+1
Analystt+1
Firm FE
Year FE
N
Adjusted R2

0.014***(4.034)-0.020**(-2.488)-0.002(-0.424)-0.017***(-4.412)
Yes
Yes
9,921
0207

59

(2)
CARt+1
3.381**(2.034)-0.918**(-2.154)-0.176(-0.829)-0.398(-0.943)-1.103***(-2.626)
0.771**(2.147)-0.012***(-3.509)
0.016***(4.380)-0.018**(-2.314)-0.001(-0.255)-0.015***(-3.976)
Yes
Yes
9,921
0.210

Table 12: Good News versus Bad News Forecasts
This table shows the effect of options trading on the occurrence and frequency of good news and bad news
disclosures. In Panel A, we measure good (bad) news based on management forecast surprise. Good (bad)news forecasts are those with a positive (negative) forecast surprise. In Panel B, we measure good (bad)news based on the three-day value-weighted market-adjusted cumulative abnormal returns around therelease of a management forecast. Good (bad) news forecasts are those with a positive (negative) three-day
value-weighted market-adjusted cumulative abnormal returns. The regressions are performed using OLS.
The t-statistics in parentheses are adjusted for heteroscedasticity and clustered by firm. The symbols ***,**, and * denote significance at the 1%, 5%, and 10% levels, respectively. Variable definitions are presented
in Appendix A.
Panel A: Good and Bad News Forecasts Defined by Forecast Surprise
(1)
(2)
(3)
Variables
DumGNt+1
DumBNt+1
FreqGNt+1
LnOptvolt-0.019***-0.013***-0.023***(-5.878)(-3.932)(-5.794)
Sizet
0.021***
0.082***
0.020**(2.837)(10.632)(2.048)Levt
0.037
0.075**
0.029(1.257)(2.529)(0.750)BMt
0.002
0.026***-0.006(0.177)(2.638)(-0.520)
ROAt
0.051**
0.134***
0.048(1.976)(4.662)(1.607)Losst-0.045***-0.048***-0.050***(-6.002)(-6.431)(-5.466)
IOt
0.005-0.0180.007(0.262)(-0.902)(0.290)Analystt
0.034***
0.039***
0.035***(5.192)(5.576)(4.273)Rett
0.015***-0.035***0.022***(3.024)(-6.686)(3.718)
EarnVolt-0.019-0.014-0.030(-0.588)(-0.432)(-0.698)
Litigationt
0.008
0.013
0.020
(0.444)(0.762)(0.975)
Mid_Zscoret
0.010
0.007
0.019**(1.556)(1.102)(2.256)
Issuet-0.016**0.000-0.021***(-2.520)(0.018)(-2.870)
Firm FE
Yes
Yes
Yes
Year FE
Yes
Yes
Yes
N
38,49338,49338,493Adjusted R2
0.435
0.495
0.434

60

(4)
FreqBNt+1-0.018***(-3.840)0.142***(12.817)0.126***(3.066)
0.048***(3.532)0.161***(4.309)-0.074***(-7.565)-0.017(-0.616)0.050***(5.082)-0.058***(-8.478)-0.022(-0.454)0.018(0.798)0.007(0.701)-0.007(-0.900)
Yes
Yes
38,4930.522

Panel B: Good and Bad News Forecasts Defined by Market Reaction
(1)
(2)
(3)
Variables
DumGNt+1
DumBNt+1
FreqGNt+1
LnOptvolt-0.018***-0.011***-0.031***(-5.472)(-3.409)(-6.143)
Sizet
0.036***
0.081***
0.054***(4.707)(10.732)(4.664)Levt
0.020
0.057*
0.027(0.641)(1.922)(0.585)
BMt
0.002
0.014
0.002
(0.223)(1.395)(0.116)
ROAt
0.097***
0.119***
0.107***(3.548)(4.060)(2.840)Losst-0.042***-0.045***-0.067***(-5.643)(-5.866)(-6.275)
IOt
0.027-0.0030.023(1.362)(-0.152)(0.777)
Analystt
0.045***
0.041***
0.061***(6.375)(5.876)(5.780)Rett-0.003-0.024***-0.000(-0.580)(-4.530)(-0.019)
EarnVolt-0.019-0.0370.004(-0.561)(-1.034)(0.064)
Litigationt
0.027*
0.012
0.049**(1.666)(0.690)(2.015)
Mid_Zscoret0.017***
0.000
0.023**(2.650)(0.011)(2.247)
Issuet-0.006-0.005-0.017*(-1.027)(-0.756)(-1.855)
Firm FE
Yes
Yes
Yes
Year FE
Yes
Yes
Yes
N
38,49338,49338,493Adjusted R2
0.547
0.517
0.568

61

(4)
FreqBNt+1-0.016***(-3.302)0.143***(12.374)
0.095**(2.096)0.039***(2.657)0.167***(3.801)-0.076***(-7.001)-0.017(-0.585)0.064***(6.168)-0.035***(-4.684)-0.048(-0.920)0.005(0.182)0.000(0.017)-0.012(-1.299)
Yes
Yes
38,4930.527

Table 13: The Effect of Options Trading on Non-GAAP Earnings Disclosures
The table shows the results of the effect of options trading on the likelihood and frequency of non-GAAP
earnings disclosures. The regressions are performed using OLS. The t-statistics in parentheses are adjusted
for heteroscedasticity and clustered by firm. ***, **, and * denote significance at the 1%, 5%, and 10%
levels, respectively. Variable definitions are presented in Appendix A.
Variables
LnOptvolt
Sizet
Levt
BMt
ROAt
Losst
IOt
Analystt
Rett
EarnVolt
Litigationt
Mid_ZscoretIssuetFirm FE
Year FE
N
Adjusted R2(1)
DumNonGAAPt+1-0.008**(-2.236)
0.036***(3.845)0.177***(4.681)0.050***(3.924)0.000(0.009)-0.012(-1.238)
0.009(0.354)0.040***(4.871)-0.006(-0.921)0.007(0.229)-0.027(-1.312)0.002(0.201)0.006(0.723)
Yes
Yes
27,984
0.466

62

(2)
FreqNonGAAPt+1-0.009**(-2.006)
0.053***(4.579)0.216***(4.734)0.075***(4.977)-0.010(-0.258)-0.019*(-1.670)-0.000(-0.006)
0.041***(4.076)-0.010(-1.355)
0.036(0.995)-0.034(-1.412)0.008(0.746)0.007(0.815)
Yes
Yes
27,9840.540