Option listing and information asymmetry ∗Jianfeng Hu †Lee Kong Chian School of Business, Singapore Management University
Date: October 17, 2016
Abstract
Option listing significantly increases both informed and uninformed trading in a sample of 1517
common stocks in the US between 2001 and 2010. Although the average increase of informed
trading reaches 12.4%, uninformed trading increases even more by 23.9%, reducing relative information risk. The causal effects of option listing are established using a control sample of stocks
with similar propensities of listing, are robust in subsamples of stocks, and are confirmed by aquasi-natural experiment using option listing standards. A placebo test shows that options on
exchange-traded-funds increase only uninformed trading. The benefits are more prominent for
stocks with active options trading and opaque stocks. We also find that the reduction of information risk is larger for good news than bad news, and that the stock price response to earnings
surprise weakens after listing. The results suggest that options improve the overall market information environment beyond substitutional effects to stock trading.
JEL Classification: C13, C61, D82, G14.
Keywords: Option listing; information asymmetry; order flow; probability of informed trading;liquidity shocks.
∗ I would like to thank Andrew Ellul (the editor), two anonymous referees, Turan Bali, Efraim Benmelech, Gennaro

Bernile, Ekkehart Boehmer, Zhuo Chen, Tarun Chordia, Bart Danielsen, Sudipto Dasgupta, Jin-Chuan Duan, Jianqing
Fan, Fangjian Fu, Bruce Grundy, Marlene Haas, Bing Han, Harrison Hong, Yongmiao Hong, Armen Hovakimian, Jian
Hua, Raymond Liu, Roger Loh, Chenghu Ma, Roni Michaely, Artem Neklyudov, Phong Ngo, Chayawat Ornthanalai,
Neil Pearson, Lin Peng, Meijun Qian, Robert Schwartz, Elvira Sojli, Avanidhar Subrahmanyam, Liuren Wu, Xinzhong
Xu, Takeshi Yamada, Hong Yan, Rui Yao, Jianfeng Yu, Chu Zhang, Guofu Zhou, Hao Zhou, Xianming Zhou, Qiaoqiao Zhu, and the seminar participants at Australian National University, Baruch College, Fudan University, National
University of Singapore, Sun Yat-Sen University, Singapore Management University, Tsinghua University, the XMUUNCC 2012 International Symposium on Risk Management and Derivatives, the Asian Finance Association 2013
Conference, the 2014 International Conference on Corporate Finance and Capital Market, the 2014 SMU Summer Institute of Finance Conference, the 2014 Australasian Finance and Banking Conference, the 2015 China International
Conference in Finance, and the 23th Conference on the Theories and Practices of Securities and Financial Markets
for comments, and Qianqian Wang for excellent research support. I welcome comments, including references I have
inadvertently overlooked. All remaining errors are mine.
†
Lee Kong Chian School of Business, Singapore Management University, 50 Stamford Road, Room 4073, Singapore 178899. Phone: (+65) 6808-5477, Email: jianfenghu@smu.edu.sg.

11. Introduction
Derivative assets, such as options, are redundant assets in a frictionless market under geometric
Brownian motions as stock price dynamics, documented in Black and Scholes (1973) and Merton(1973). However, in the real market, introducing options can have significant impact on the underlying asset. On the one hand, Ross (1976b) and Hakansson (1982) show that options complete themarket, implying that investors’ trading demand can increase because of hedging purposes. In thepresence of information asymmetry, increased hedging transactions can reduce the probability of
trading against informed traders for uninformed traders. On the other hand, Black (1975) notes that
options can also increase the amount of informed trading because options provide higher leverage
to financially constrained informed traders. Similarly, Cao (1999) shows that investors become
more motivated to acquire private signals if options are available. Figlewski and Webb (1993)
and Johnson and So (2012) argue that options facilitate informed trading by relaxing the short-saleconstraint on stocks. All of these studies suggest that option listing leads to more informed trading,and hence higher information risk for uninformed traders. Finally, Biais and Hillion (1994) showthat the option listing effects condition on exogenous parameters. The key result in their model isthat introducing options can potentially solve the market breakdown problem due to information
asymmetry if uninformed traders value the benefit of hedging more than the risk of trading against
informed traders. As a result, both informed and uninformed trading become more active after
options are introduced. If the incomplete market does not break down at the first place, the benefit
from expanded trading opportunities is less meaningful for uninformed traders. Biais and Hillion(1994) show that under certain parameters, uninformed traders trade less because they can hedge
more precisely using options, thus making the liquidity orders less attractive to informed traders.
In this case, option introductions can reduce the incentive of informed traders and the stock pricebecomes less efficient.
Given contradicting predictions of option listing effects on trading incentives and the levelof information asymmetry, it is better to examine this question empirically. A large body of theliterature examines the lead-lag relation between the stock and options prices and volumes, and2finds mixed evidence regarding options’ contribution to price discovery. 1 However, those studies
do not answer the question whether the same amount of information extracted from the two markets
can be revealed without options. If options only cause traders to migrate across markets, there may
be no impact on the overall information environment that an uninformed investor faces because
options reduce informativeness of the underlying market. In this case, even if options trading
conveys information not in the stock market, that is insufficient to show that options increase thetotal amount of private information revealed to the market. To understand whether options affectthe market information environment beyond substitutional effects, this paper instead focuses on
option listing events.
To gauge overall information asymmetry before and after option listing, we examine the aggregate stock market order flow assuming that options market makers perform full delta hedging.
Under this assumption, the total stock order flow nests the liquidity and information in the options order flow because options market makers transfer options order flow to the stock market.
Therefore, the dynamic of the stock order flow around option listing suffices to describe the overall
trading environment. Admittedly, because of transactions cost and liquidity constraints, full delta
hedging is a strong assumption unlikely to hold in practice despite the fact that delta hedging is
now a standard practice by all options market makers. Nevertheless, the inherent connection between the options order flow and the stock order flow through delta hedging makes the order flow abetter choice to assess the overall market condition than other variables such as the bid-ask spreadand price volatility. To quantify the level of information asymmetry using order flows, we estimate
the sequence trade model of Duarte and Young (2009) (DY hereafter), which extends the famous
Easley and O’Hara (1992) (EO hereafter) model. The order arrival rates from informed (µ) anduninformed traders (ε) are two main parameters of the EO model. The other two parameters are
the probability of information events (α) and the probability of an information event being goodnews (β). Based on empirical observations that the EO model overlooks simultaneous shocks tobuy and sell orders, DY propose to include a symmetric increase (δ) in both uninformed buy and
1 For example, Manaster and Rendleman (1982), Anthony (1988), Easley, O’Hara, and Srinivas (1998), Pan andPoteshman (2006), Bali and Hovakimian (2009), Cremers and Weinbaum (2010), Xing, Zhang, and Zhao (2010),
and Hu (2014) find that the options market leads the stock market in price discovery. Studies showing the opposite
result include, for example, Bhattacharya (1987), Stephan and Whaley (1990), Chan, Chung, and Fong (2002), andMuravyev, Pearson, and Broussard (2013).

3

sell orders in a liquidity shock event with a probability of θ, and achieve better data fits. Moreover,
DY show that the original probability of informed trading (PIN) of EO can be decomposed into
an asymmetry component (APIN) and a symmetric liquidity shock component (PSOS). This paper examines the DY model constructs as well as the model parameters to identify the levels andsources of the listing effects.2
Because options exchanges do not randomly select stocks to list, we follow Mayhew and Mihov (2004) and use the propensity score matching (PSM) method to examine the listing effects
in a sample of 1517 common stocks experiencing option listing in the US between February 11,
2001 and February 28, 2010. Consistent with previous findings by Mayhew and Mihov (2004)
and Danielsen, van Ness, and Warr (2007), we find that options exchanges select stocks basedon the firm size, trading volume, return standard deviation, bid-ask spread, and industry category.
Moreover, the analysis shows that the selected stocks on average have higher institutional ownership, greater buying pressure, and more balanced order flows, consistent with the notion that
options exchanges prefer stocks with large hedging demand, high short selling costs, and low
market-making costs. Each listing stock is then matched to an eligible but non-listing stock withthe closest propensity score of option listing at the same time to construct a control sample.
In the matched sample, the average treatment effect of option listing on the daily volume of
informed trades (µ) is an increase of 12.4% with a t-statistic of 3.07 in the first year after option
listing. The treatment effects on the daily volume of uninformed trades (ε) and symmetric shock
to uninformed order flow (δ) average at 23.9% and 14% with t-statistics of 10.01 and 3.51, respectively. There is no significant change in the probabilities of information events (α) or liquidity
shocks (θ) although the average impact is negative for both types of events. Due to the disproportional increases in informed and uninformed trading, the probability of informed trading (APIN) is
reduced by 8.8% with a t-statistic of −6.71. The probability of a trade from a symmetric liquidity
shock (PSOS) is also reduced by 7.2% with a t-statistic of −4.56. We verify the listing effects
using alternative stock order flows, which considers understated options market activity due to2 Using the EO model instead generates largely the same results regarding the option listing effects on informed

and uninformed trading and the level of information asymmetry. We report these results in Section D of the internet
appendix to the paper.

4

discrete delta hedging. Therefore, our results hold even if the full delta hedging assumption isviolated. The listing effects are robust to different measures of order flow using the number of
trades or number of shares traded, and are consistent across stock and options exchanges involvedin the listing event. Regression analysis of the treatment effects on an option listing dummy andcontrol variables shows that option listing effects on the market information environment cannot
be explained by firm characteristics related to the probability of informed trading as suggested byAslan, Easley, Hvidkjaer, and O’Hara (2008).
We perform three additional tests to make sure that the findings are indeed driven by option
listing events. Firstly, concerning that the structural breaks may not occur around the listing date,
we investigate the dynamics of the DY model parameters and the estimated APIN at the quarterly frequency. The results show that the most significant treatment effects on both informed anduninformed trading occur in the first quarter after option listing. Secondly, the baseline analysis
is repeated in a sample of 56 listing stocks with prices just above the minimum price requiredfor option listing. The control stocks are the non-listing stocks that have prices just below theminimum price, but share similar characteristics to the listing stocks. This test is based on thenotion that the mandated minimum stock price splits otherwise identical firms into the treatment
and control groups and creates a quasi-natural experiment to identify the listing effects. All resultsfrom the baseline analysis hold qualitatively the same in this alternative sample, except that theoption listing effect on PSOS loses statistical significance. Finally, we conduct a placebo test on
exchange-traded-fund (ETF) options. Like common stocks, ETFs can also be listed on options exchanges subject to the same regulatory requirement. However, private information is less importantfor ETFs because such information is usually about specific firms and should not have significant
price impact on diversified stock portfolios such as ETFs. Therefore, the option listing impact on
ETFs can be different from that on common stocks. Indeed, we find that in a sample of 85 ETFs
that experience option listing during the same period, there is no significant change in informed
trading, but only in uninformed trading.
The option listing effects exhibit significant cross-sectional heterogeneity. In particular, the reduction of APIN is larger for stocks with high options volumes and large options-to-stock volume5

ratios, consistent with the notion that an active options market reveals more private information.
The reduction of APIN is also greater for stocks with large bid-ask spreads, low market capitalization, large earnings surprise, little voluntary disclosure, low institutional holding, and low accrual
quality. Since these stocks are associated with weak information environment, the option listing
effect is stronger when other information channels are less effective.
We also find that the listing effect is asymmetric to the probability of informed trading based ongood news (APIN Good) and the probability of informed trading based on bad news (APIN Bad).
Although both probabilities are reduced by option listing, the reduction in APIN Good is muchlarger and statistically more significant than APIN Bad. This is true because getting around shortsale constraints can be an important motivation of options trading for informed traders. As a result,the increase in informed selling exceeds the increase in informed buying, and the excess increaseof informed selling curtails the reduction in APIN Bad. Analysis on the probability of information being good (β) shows that β becomes significantly lower after option listing, suggesting that
options make it easier for informed traders to take advantage of bad news.
The decrease in the overall level of information asymmetry may cause an inference problem for
uninformed traders and reduce price efficiency (Stein (1987)). Although more private informationis revealed, the information is clouded by increased noise trading, hence more difficult to learn. For
price efficiency to improve in this case, uninformed investors must be sophisticated enough to filter
out the increased noise. We find that the price response to earnings surprise becomes weaker after
option listing, suggesting that uninformed investors are able to learn more private information fromthe capital markets even though the information is diluted by noise trading. Therefore, introductionof options also improves price efficiency. Finally, to avoid relying exclusively on the DY model,
we examine alternative asymmetry measures including the bid-ask spread, order imbalance, return
standard deviation, realized volatility, and V PIN of Easley, Lopez de Prado, and O’Hara (2012).
Our results indicate that option listing significantly reduces these alternative asymmetry measures
as well except the return standard deviation.
This paper’s contribution to the literature is mainly four folds. First, our analysis of order flow
is able to capture the trading activity on both stock and options markets through the link of delta6

hedging. Therefore, the inference about information asymmetry pertains to the overall market.
Such conclusion cannot be obtained by existing studies that focus only on the stock market. Second, more importantly, we identify the underlying mechanism of this risk reduction by examining
different types of traders’ activities estimated from the DY model. Although there is much lessattention on these model parameters than the probability of informed trading, these parameter estimates have important economic meanings to study the dynamics around the listing event. Kumar,
Sarin, and Shastri (1998) show that information risk measured by the adverse selection componentof the stock bid-ask spread reduces after option listing. However, they could not pinpoint the sourceof the effect by examining the spread, a reduced-form efficiency measure. Our analysis shows thatboth informed and uninformed traders become significantly more active after option listing but theincrease of uninformed trading dominates the increase of informed trading. Meanwhile, option
listing does not significantly change the probability of information events. Therefore, uninformed
traders are less likely to trade against informed traders as a result of imbalanced growth in trading demands. This result is consistent with the theoretical prediction in Biais and Hillion (1994).
To the best of our knowledge, the direct empirical evidence of increased informed trading intensity after option listing is not documented in the literature. Third, our cross-sectional analysis onheterogenous option listing effects is new to the literature. We find both options market liquidityand firm information environment significantly affect the role of options in reducing information
asymmetry. Finally, our results can be used to address an empirical puzzle regarding information
asymmetry and price efficiency after option listing. Despite lower information risk after listing
documented by Kumar, Sarin, and Shastri (1998), price efficiency improves as shown by Skinner(1990), Ho (1993), and Kumar, Sarin, and Shastri (1998). This is puzzling because less informed
trading should make the price less efficient. We show that the amount of private information revealed to the market is actually higher after option listing through more active informed trading,leading to greater efficiency. But an even larger increase in uninformed trading dilutes the informed
trades and makes information risk lower. Therefore, lower information risk can be reconciled withimproved efficiency. In addition, the analysis identifies institutional ownership and investor orderimbalance as important determinants of option listing decisions. These findings are helpful for
empirical investigation into other option listing effects not considered in this paper and more gen-7erally the introduction effects of other derivative securities such as the credit default swap (CDS)
and single stock futures (SSF). Finally, the numerical methodology proposed in Section B of theinternet appendix can be used to address the data overflow problem in model estimation in otherapplications.
The remainder of the paper is organized as follows. Section 2 discusses the related literatureand develops testable hypotheses. Section 3 describes the sample selection and matching procedure. Section 4 reports the empirical results of option listing effects on the information environment. And Section 5 concludes.

2. Related literature and hypothesis development
The investigation of option listing effects has a long history in the finance literature. The modernderivative pricing models starting from Black and Scholes (1973) and Merton (1973) typicallyassume a frictionless market and geometric Brownian motions as stock price dynamics. In thisframework, options can be perfectly replicated by the underlying asset and a cash bond. Therefore,
introducing options has no effect on the underlying asset prices or trading volumes. However, these
two assumptions imply market completeness, which does not hold in practice. If the market isincomplete, it is possible that introducing options can make the market more complete and increase
investors’ welfare as shown by Ross (1976b) and Hakansson (1982). The improved opportunity
set also implies more trading for hedging purposes. Since the optimizers’ hedging trades do notdependent on private information, they can be viewed as liquidity or uninformed trades. Therefore,
we have the following hypothesis.
Hypothesis 1: Options increase the hedging demand by completing the market. The treatment
effect of option listing on the intensity of uninformed trading is positive.
If options only increase uninformed trading, the information risk will become lower after option listing. However, options can also become informed traders’ target. The informational roleof options is first noted by Black (1975) as he argues that informed traders can use options to8

achieve higher leverage. Figlewski and Webb (1993) find an increase in the short interest after
option listing. Since short selling is more likely to be information based (Diamond and Verrecchia(1987)), relaxing shot-sale constraints can lead to more informed trading. Options can also increase traders’ incentive to collect costly information, resulting in more informed trading as in thepartially rational expectation framework of Cao (1999). Therefore, we have the second hypothesis
below.
Hypothesis 2: Options benefit informed traders by relaxing capital requirement or short-saleconstraint. The treatment effect of option listing on the intensity of informed trading is positive.
Given possible option listing effects on both informed and uninformed trading, the equilibrium
effect on the level of information asymmetry becomes ambiguous. Conceptually, the option listing
effect on the overall level of asymmetry will depend on the magnitude of listing effects on bothtypes of trades. This problem becomes even more complicated when liquidity and informed trades
are interdependent and the informed traders mimic the liquidity traders as in Biais and Hillion(1994). In addition to the classic no trade problem under information asymmetry as in Milgrom
and Stokey (1982), Biais and Hillion (1994) show that in an incomplete market with a single
tradable security of stock, the market can break down even without the presence of information
asymmetry. If the benefit of increased hedging opportunity by using options exceeds the potential
loss of trading against informed traders, introducing options solves the no trade problem. As aresult of more liquidity trading, the informed traders also trade more, and more private informationis revealed to the market. However, if there is no breakdown in the incomplete market, option
introduction can reduce the incentive and the profit of the informed traders because the liquidity
traders structure less extreme trades using options to hedge their endowment shocks, making theliquidity orders less attractive to the informed traders. In line with the argument of Stein (1987)
about information externality, Huang and Wang (1997) reach similar conclusions on information
efficiency by noting that option introduction increases the informational trades but also changes
the information content of the existing allocational trades. Given the contradicting predictions, wehave the following hypothesis.
Hypothesis 3: If option listing affects both informed and uninformed trading, the treatment9effect on the level of information asymmetry depends on the dominating effect between informed
and uninformed trading.
The empirical test strategy for the above hypotheses is nontrivial because neither informed nor
uninformed trading is observable in the market and the information risk is hard to quantify. We
rely on the sequence trade models developed by EO and DY to measure the trading intensity andinformation asymmetry. The order arrival rates from informed and uninformed traders are the twomain model parameters that can be estimated using daily order flow data. 3 One important resultof this model is that the probability of informed trading (PIN) can be calculated from estimatedmodel parameters. Unlike price-based measures of information asymmetry such as the bid-ask
spread, PIN focuses on information in order flow and is a quantity-based measure of information
asymmetry. Compared to the price-based measures, PIN has two distinguished advantages. First,
the stock market PIN is able to nest the information in options order flow if options market makers
perform full delta hedging. Delta hedging eventually transmits all stock exposure and stock priceinformation in the options market to the stock market. Therefore, the total stock order flow suffices
to represent the activities in the two markets under this assumption. This feature is particularly
important when studying the option listing effect on the overall market conditions without utilizing
the options transaction data. Second, the EO model estimates enable empirical tests on all of thethree hypotheses discussed earlier while the reduced-form information measures are silent on the
trading intensity of different types of traders. While there is a long list of studies that examine
PIN as a measure of information asymmetry, investigation into the model parameters is largely
absent.4 The empirical evidence regarding the performance of PIN is mixed. While supporting
evidence of PIN as an asymmetry measure is provided by many studies, e.g., Brown, Hillegeistb,
and Lo (2004), Vega (2006), Ellul and Pagano (2006), Chen, Goldstein, and Jiang (2007), andEllul and Panayides (2016), several studies raise questions on the effectiveness of PIN before
major corporate events, e.g., Benos and Jochec (2007), Aktas, de Bodt, Declerck, and van Oppens(2007), and Collin-Dufresne and Fos (2015). There is also mixed evidence regarding the pricing
3 Interested readers can refer to the internet appendix for more details about the EO and DY models. The internet

appendix also contains a new maximum likelihood estimation method to circumvent the data overflow problem.
4 One exception is Duarte, Hu, and Young (2015), who compare the estimated probability of information events (α)
from several models.

10effect of PIN, e.g., Easley, Hvidkjaer, and O’Hara (2002), Lambert, Leuz, and Verrecchia (2007),
Duarte and Young (2009), Mohanram and Rajgopal (2009), and Lai, Ng, and Zhang (2014). The
major weakness of the PIN model is pinpointed by DY as that the EO model restricts the buy andsell orders from simultaneous increases. Motivated by the observed positive correlation between
buy and sell orders, DY introduce a symmetric liquidity shock to the EO model and decompose theoriginal PIN into an adjusted probability of informed trading, APIN, and a probability of a tradefrom symmetric liquidity shocks, PSOS. Since the DY model fits the historical data better thanthe EO model, we adopt the DY model in the paper. Nevertheless, we also use the EO model asan alternative and find consistent results. Those reports are available in Section D of the internet
appendix of the paper. One limitation of using the EO and DY models is that informed traders canonly use market orders to trade with a market maker in this framework. Theoretically, informed
traders can also choose limit orders if the competition among informed traders is not intense as
shown by Kaniel and Liu (2006). There is also supporting evidence for the information content
of limit orders from laboratory settings (Bloomfield, O’Hara, and Saar (2005) and Bloomfield,
O’Hara, and Saar (2015)) and empirical studies (Barclay, Hendershott, and McCormick (2003),
Collin-Dufresne and Fos (2015), and Baruch, Panayides, and Venkataraman (2015)). Given thedata limitation, we are unable to directly investigate the asymmetry measures based on the limit
order book around option listing. To avoid relying exclusively on the trade sequence models, we
instead examine additional information asymmetry measures to validate our main result.
Most empirical studies on option introductions focus on the underlying asset price or volatility.5 In sparse empirical analysis of the option listing effects on stock market quality, Kumar, Sarin,
and Shastri (1998) find that the stock market bid-ask spread narrows and the quote depth increases
after option listing, indicating that information asymmetry reduces. Danielsen, van Ness, and Warr(2007) show that the bid-ask spread starts decreasing even before option listing. Both studies,
however, are subject to a criticism of Mayhew and Mihov (2004), that option listing decisions areendogenous. In a matched sample, Mayhew and Mihov (2004) find that option listing has the same
5 For example, studies on option listing impact on the underlying price include Branch and Finnerty (1981), Conrad(1989), Detemple and Jorion (1990), Sorescu (2000), and Danielsen and Sorescu (2001). Studies on option listing
impact on the underlying volatility include Conrad (1989), Skinner (1989), Fedenia and Grammatikos (1992), Kumar,
Sarin, and Shastri (1998), Bollen (1998), and Mayhew and Mihov (2004).

11

impact on the volatilities of both listing and control stocks and the treatment effect is negligible.
One limitation of the existing empirical studies is that the samples often end before 2000 when theoptions market in the United States was not yet a consolidated national market (Battalio, Hatch, andJennings (2004)). The option listing effects can be greatly undermined by lack of price competition
and illiquidity in the options market at that time. The options market has developed significantly asa result of more stringent regulations and technology innovations since then. Therefore, investigating a recent sample period can provide timely and more relevant information about the underlying
effects of option listing. In this paper, we follow Mayhew and Mihov’s method to create a matchedsample of option listing after 2000. We then investigate the treatment effect of option listing on
the DY model parameters as well as the model constructs, APIN and PSOS. Although predictions
about option listing effects on the probability of information events (α), the probability of liquidity
events (θ), the liquidity shock order flow (δ), and the probability of liquidity shock trading (PSOS)
are not contained in the theoretical models discussed in the paper, empirical evidence of additional
option listing effects can be important for future research. For example, several recent studies explore liquidity dynamics and the asset pricing implication (see Bali, Peng, Shen, and Tang (2014)
and Chordia, Hu, Tong, and Subrahmanyam (2015)). The option listing effect on θ, δ, and PSOS
may provide insight on the sources of liquidity shocks.
The existing literature lacks discussion on the heterogenous option listing effects in the crosssection. From a social planner’s standpoint, it is important to understand what kind of stocks benefit more from options listing. We posit that the option listing effects on information asymmetry
depend on at least two types of firm characteristics, namely the options market liquidity and firm
information environment. Consider options market liquidity first. Admati and Pfleiderer (1988)
point out that options are not beneficial to informed traders without sufficient liquidity in the market. Empirically, Holowczak, Simaan, and Wu (2006) and Hu (2014) show that the informationalrole of options strengthens when the options market is active. Roll, Schwartz, and Subrahmanyam(2009) argue that stocks with high options volumes have more efficient prices and enjoy higher
valuation. Roll, Schwartz, and Subrahmanyam (2010) and Johnson and So (2012) find that theoptions to stock volumes ratio conveys stock price information. All these studies highlight the importance of options volumes in the feedback effect on the underlying market. Therefore, we have12the following hypothesis.
Hypothesis 4: The option listing effect on the adjusted probability of informed trading, APIN,
is larger for stocks with more liquid options.
Next, we turn to the firm information environment. In addition to security trading, private
information can be disseminated through several other channels. For example, the role of stock
analysts in mitigating information asymmetry is well documented in the literature (e.g., see Holden
and Subrahmanyam (1992), Hong, Lim, and Stein (2000), Barth and Hutton (2004), and Ellul andPanayides (2016)). Firms can also provide voluntary disclosure to reduce information asymmetry.
Empirically, Balakrishnan, Billings, Kelly, and Ljungqvist (2014) show that firms use earnings
guidance as an substitute to analyst coverage. Institutions with significant holding of the stock
can also be motivated to collect more private information as shown by Bushee and Noe (2000),
Ajinkya, Bhojraj, and Sengupta (2005), and Amihud and Li (2006). All these information channels
can be substitutional to each other regarding a firm’s fundamental information. Therefore, we havethe following hypothesis.
Hypothesis 5: The option listing effect on the adjusted probability of informed trading, APIN,
is stronger for less transparent stocks before listing.
The calculation of APIN is not affected by the probability of news being good or bad. However, the option listing effect can be asymmetric for the two types of information due to shortsale constraints. Options relax such constraints but the benefit is relevant only for informed
traders to sell before bad news. Therefore, the likelihood of informed selling should increase
more than informed buying. Ceteris paribus, the reduction in the adjusted probability of informed trading should be larger for good news than for bad news. Following Brennan, Huh,and Subrahmanyam (2015), we decompose APIN into a probability of informed trading basedon good news (APIN Good = β ∗ APIN) and a probability of informed trading based on bad news
(APIN Bad = (1 − β) ∗ APIN). And we propose the following testable hypothesis.
Hypothesis 6: The option listing effect on the adjusted probability of informed trading conditioning on good news, APIN Good, is more negative than the adjusted probability of informed

13

trading conditioning on bad news, APIN Bad.
Finally, we concern the impact of option listing on price efficiency of the underlying stock.
The effect is not straight-forward given potential offsetting effects from informed and uninformed
trading. On the one hand, more trading activity from informed traders after option listing canbring more information to the market and potentially make the stock price more efficient. On theother hand, increased uninformed trading and expanded trading set of informed traders can makethe private information more difficult to learn as argued by Stein (1987), Grossman (1988), andBack (1993). Therefore, the overall effect is ambiguous. In an experimental setting, de Jong,
Koedijk, and Schnitzlein (2006) find that the introduction of options leads to more aggressive
insider trading and improve price efficiency. The empirical literature typically addresses the issue
by examining the price behavior after public information shocks such as earnings announcements.
If options trading makes the stock price more efficient by revealing private information before
announcements, price reaction to announcement surprise should become less significant. Early
studies such as Skinner (1990) and Ho (1993) support this hypothesis. However, Mendenhall
and Fehrs (1999) find that after 1986, stocks with options exhibit slightly larger price reaction toearnings surprise than stocks without options. The results in these studies are also clouded by theearly sample period examined and lack of matching based on the probability of receiving treatment.
Using a listing-probability matched sample from a recent period, we test the following hypothesis.
Hypothesis 7: Options trading improves stock price efficiency. Therefore, the price response toearnings surprise becomes weaker after option listing.

3. Data, sample selection, and matching
3.1. Option listing stocks
Information on option listing events is acquired from the Options Clearing Corporation (OCC)
between February 11, 2001 and February 28, 2010. Included in the analysis are only new listing
stocks that have no options traded at any options exchange in the US at the time of listing. The
14

main analysis excludes options on indices and ETFs and focuses on single name equity options(CRSP code 10 and 11) because information asymmetry considered in this study is less relevant
for diversified portfolios. To construct explanatory variables for listing decisions, a listing firmmust have valid price information in the CRSP database and valid transaction data in the NYSE
TAQ database for at least 252 trading days prior to the option listing date. To study the treatment
effect, the options must continue trading for at least 252 trading days after listing. Options canalso be delisted and relisted later. If a stock has more than one qualified listing event during thesample period, only the first record is used. The final sample includes 1517 observations. Table 1
shows the number of new listings every year during the sample period. The number of new listings
increases gradually in the first half of the sample period and peaks at 246 in 2004. It decreases
slightly afterward and remains 143 in 2009. The sample covers only the first two months in 2010
and there are 27 option listing events in those two months. The time series pattern shows that there
are active option listings in the entire sample period.

[Table 1 about here]

3.2. Eligible non-listing stocks
We follow Mayhew and Mihov (2004) and constructs a sample of control stocks that are eligible, but not selected, for option listing. Mayhew and Mihov (2004) document detailed regulation
changes in option listing standards imposed by the SEC before 1997. Comparing the current requirements with the requirements at the end of their sample period, several differences are noted:(i) the requirement of minimum trading volume has been removed; (ii) a stock can have options
five days after its initial public offering (IPO) now, while previously it had to be traded for at least
twelve months after IPO; (iii) the minimum security price is reduced from $7.50 to $3.00. The
other three requirements remain unchanged: (1) the stock must be listed on a national exchange;(2) the stock must have at least seven million publicly held shares; (3) there must be at least two
thousand shareholders. This paper defines eligible stocks for option listing in the next month as15

those meeting requirements (1) and (2) at the end of each month with the price above $3.00, 6 but
having no options trading history in the previous year, and having at least 252 trading days in the
CRSP database. Mayhew and Mihov (2004) argue that it is practically impossible to filter according to requirement (3) because many shareholders hold shares in street names, and this omissionis unlikely to misclassify qualified stocks. Therefore, this listing criterion is also deactivated inthis study. We adopt more stringent criteria about the trading history due to the needs of constructing explanatory variables for listing decisions. Insider holdings data is extracted from theThomson Reuter’s insider database and daily stock price and volume data from the CRSP. The
filtering generates 140,277 eligible firm-month observations during the entire sample period. The
annual breakdown is also reported in Table 1. As equity options become more common over time,
the pool of eligible stocks for listing shrinks from 18,117 observations in 2001 (1,647 stocks permonth) to 9,792 observations in 2009 (816 stocks per month).

3.3. Determinants of the option listing decision
Options exchanges have the incentive of listing stocks that are likely to generate large options
volumes. In practice, new listing proposals are initiated by options market makers affiliated withthe exchanges. These market makers are typically reputable brokerage houses or hedge funds thatare able to collect and process large amounts of data from both public and private sources. Previous
studies on determinants of option listings find significant effects of the firm size, trading volume,
return standard deviation, industry classification (Mayhew and Mihov (2004)) and the percentage
bid-ask spread (Danielsen, van Ness, and Warr (2007)). In addition to these easily observablevariables, we investigate the role of three other firm characteristics in the listing decisions:
1. Volume order imbalance (VOI). Stock order imbalance can reflect the difficulty of shortselling. Pessimistic investors are less likely to participate in trading in the presence of short-saleconstraints and stock order imbalance can become positively biased. As an alternative to short
6 The minimum price requirement was relaxed at the beginning of the sample period of this study. For example,

Hecla Mining Co. (ticker: HL) was traded at $4.27 when its options were listed on January 22, 2003, and Golden Star
Resources, Ltd (ticker: GSS) was traded at $3.77 when its options were listed on October 6, 2003. Therefore, setting
the minimum price to $3.00 is unlikely to bias the sample toward penny stocks in this recent sample period.

16

selling, options on such stocks can generate high trading volumes, and options exchanges should
prefer to list these stocks to attract short sellers. Therefore, a positive relation is expected between
order imbalance and the likelihood of option listing. In this paper, the order imbalance is definedas (B − S)/(B + S), where B and S denote daily buyer-initiated and seller-initiated dollar trading
volumes, respectively. The Lee and Ready (1991) algorithm is employed to determine the trade
direction. Unlike Lee and Ready, however, we do not apply a five-second delay in matching quotes
and trades because this reporting lag is found absent after 1998 as shown by Madhavan, Porter,and Weaver (2005) and Chordia, Roll, and Subrahmanyam (2005). Specifically, if a transaction
is executed at a price above (below) the midpoint of the contemporaneous National Best Bid andOffer (NBBO) prices, it is classified as buyer-initiated (seller-initiated). For trades falling on themid quote price, they are classified as buyer-initiated (seller-initiated) if the last price change ispositive (negative).
2. Absolute volume order imbalance (AVOI). Intuitively, this variable measures the magnitudeof the unsigned order imbalance that relates to liquidity and market making costs. 7 It is morechallenging for options market makers to perform delta hedging in an imbalanced stock market
because options market makers usually do not make market for the underlying stock and may needto pay the bid-ask spread when performing delta hedging. 8 When order flow is imbalanced, options
market makers need to compete with other liquidity demanders to hedge the options positions,increasing the uncertainty of delta hedging costs. Therefore, options exchanges should prefer tolist stocks with small absolute volume order imbalance. Using AV OI as a potential determinant
for option listing also mitigates the concern that the option listing decision might be based on
the dynamics of information asymmetry. Easley, Engle, O’Hara, and Wu (2008) show that theprobability of informed trading (PIN) in the original EO model is approximately equal to the|B−S|

expectation of the absolute order imbalance E[ B+S ]. Therefore, including this variable in theselection model controls for the pre-listing dynamic of the information environment itself.
7 The absolute imbalance has the same information as the variance of order imbalance in the cross section. Chordia,

Hu, Tong, and Subrahmanyam (2015) provide both theoretical reasoning and empirical evidence that the variance oforder imbalance captures the liquidity dynamic in the presence of informed traders.
8 Options market makers can also use limit orders in the stock market, which reduces the trading costs but increases
the likelihood of failure in hedging if the limit order is not executed.

17

3. Institutional ownership. Institutional ownership has contradicting effects on the likelihood of
option listing. On the one hand, institutional investors are more likely to trade options if they do nothave contractual or legal restrictions from using equity options for hedging purposes. 9 Therefore,
options exchanges should prefer stocks with large institutional ownership to cater to their hedging
demand. On the other hand, institutional ownership is negatively related to the stock borrowing
cost for short sellers because institutional investors provide the most supply in the security lending
market. Therefore, options exchanges may select stocks with low institutional ownership to attract
short sellers targeting those stocks. The overall effect of institution holdings is thus unclear.
Both Mayhew and Mihov (2004) and Danielsen, van Ness, and Warr (2007) use a long-term
average version and a short-term abnormal version of each independent variable to predict option
listings. To improve the prediction accuracy, this study considers the innovations in the independent variables as an additional type of determinant. This is achieved by adding further lagged
variables to the model. At the end of each month, for each of the explanatory variables except
return standard deviation and institutional ownership, we calculate the average daily values in theprevious year (t − 1, t − 12), the previous month (t − 1), and twelve months ago (t − 12). Return
standard deviation (ST D) is calculated using daily stock returns in the three measurement windows. Institutional ownership is updated at quarterly frequency. Constructing three variables fromscarce observations can lead to serious multicollinearity. Therefore, we use only the most recent
value (t − 1) and the one-year lagged value (t − 12) of institutional ownership. To investigate determinants of option listings, we estimate the following model using logistic regressions in the fullsample:Listingt =β0 + β1 Sizet−1 + β2Volumet−1,t−12 + β3Volumet−1 + β4Volumet−12 + β5 ST Dt−1,t−12+ β6 ST Dt−1 + β7 ST Dt−12 + β8 Spreadt−1,t−12 + β9 Spreadt−1 + β10 Spreadt−12+ β11V OIt−1,t−12 + β12V OIt−1 + β13V OIt−12 + β14 AV OIt−1,t−12 + β15 AV OIt−1+ β16 AV OIt−12 + β17 Institutiont−1 + β18 Institutiont−12 + ωIndustry + θYear + ε,(1)
9 A recent study of Natter, Rohleder, Schulte, and Wilkens (2014) shows that over 87% of actively managed US

equity mutual funds with N-SAR filings are permitted to use equity options.

18

where the dependent variable, Listingt is a dummy variable equal to one for listing stock-monthobservations and zero otherwise, Sizet−1 is the natural logarithm of the market capitalization at
the end of month t − 1, all Volume and ST D variables are in natural logarithm for standardization,
percentage spread (Spread) is calculated as 2(ask-bid)/(ask+bid) at the market close, Industry isa vector of 71 industry dummy variables based on the two-digit SIC code, and Year is a vector ofyear dummies to control for time fixed effects. Firm subscription is omitted for all variables in theequation for brevity.
The estimation results are reported in Table 2. To establish a benchmark, the first model uses
only independent variables that have been used in previous studies. All variables have predictive
ability with significant coefficient estimates at the 1% level. Specifically, large stocks and stocks
with high volatility and low bid-ask spread are more likely to be listed, consistent with Mayhew andMihov (2004) and Danielsen, van Ness, and Warr (2007). However, the effect of trading volume
is not clear. The long-term trading volume has a negative coefficient estimate, but the short-term
volume has a positive one, both significant at the 1% level. The second model includes oneyear lagged trading volume, volatility, and spread. All lag variables have significant coefficientestimates while the original predictors retain the predictive ability. The results reveal important
information about the dynamics of trading volume, volatility, and spread of the listing stocks. At
one year before the listing date, these stocks have lower trading volume, higher volatility, and larger
percentage spread than non-listing stocks. Approaching the listing date, however, these stocks
experience an increase in trading volume and a decrease in percentage spread, while the volatility
remains high. The findings are consistent with the pre-listing spread dynamic as in Danielsen, vanNess, and Warr (2007).

[Table 2 about here]

The last column in Table 2 reports the full specification test results of Equation (1). With
additional explanatory variables, firm size becomes an insignificant predictor for option listing decisions. Stock trading volume is still a significant predictor at all lags with similar magnitude ofcoefficient estimates. The volatility and spread effects slightly weaken. The new variables, namely
19the volume order imbalance, absolute volume order imbalance, and institutional ownership areimportant determinants of option listing because all of these variables have statistically significant
coefficient estimates. The recent and one-year average buy pressures measured by the volume orderimbalances (V OI) positively and significantly predict option listings, while the one-year lagged order imbalance has an insignificant coefficient estimate. This result is consistent with the predictionthat options exchanges prefer stocks with higher short-selling costs because short-sale constraints
are likely to sideline sellers and make V OI more positive. Introducing options on such stocks
can potentially attract high trading volumes from short sellers. Both of the recent and one-year
absolute volume order imbalances (AV OI) negatively predict option listings and the coefficient estimate is positive for the lagged absolute imbalance, suggesting that the listing stock’s order flow
becomes more balanced over time and is more balanced than unselected stocks at the time of listing. This result is consistent with the conjecture that options exchanges prefer stocks with low
market-making costs. The dynamic of the absolute order imbalance also suggests that information
asymmetry can play a role in the option listing decision. Growth in institutional ownership also
predicts option listings. At one year before the listing date, the listing stocks have significantly
lower institution holdings than unselected stocks. At the time of listing, however, the listing stocks
have significantly higher institutional ownership, suggesting that the hedging demand of institutions outweighs the consideration of catering to excess short interests. The full model generates
the highest McFadden’s pseudo-R squared out of the three models in Table 2 and has the highest
accuracy ratio. Therefore, we use the full-model prediction to match stocks later. 10
As a summary of the logistic regression results, we find that when selecting stocks for listing,options exchanges consider both the concurrent level and the dynamic of several stock characteristics. The probability of option listing is higher for stocks experiencing recent increases in the
trading volume, volatility, volume order imbalance, and institutional ownership, and decreases inthe bid-ask spread and absolute order imbalance. The uncovered pre-listing dynamics of liquidityrelated variables such as the bid-ask spread, trading volume, and order imbalance are particularly important for analysis on listing effects because these trends may continue after listing, and
10 Matched samples based on the other models in Table 2 generate largely the same results, which are available uponrequest.

20

changes in the informational environment might be caused by the same variables that affect options
exchanges’ decisions (selection effect) rather than the treatment effect of option listing. Therefore,
it is critical to use a control group of stocks with similar characteristics including pre-listing dynamics to disentangle the selection effect and the treatment effect.

3.4. Matching listing stocks to eligible non-listing stocks
After obtaining the propensity score of option listing, we match each listing stock to the eligible
non-listing stock with the closest propensity score in the same calendar month. The matchingis done without replacement and once a non-listing stock is matched, it exits the pool of nonlisting stocks for one year before it becomes available for matching again to avoid overlapping
observations. The firm characteristics of the listing and the matched stocks are reported in Table 3.
The average log size of the listing stocks is 20.23, equivalent to an average market capitalization of
610 million dollars. The matched non-listing stocks have similar size on average but the standard
deviation is larger than that of the listing stocks. The rest of the reported characteristics exhibit thesame pattern that the means of the two groups are well matched but the standard deviation of thecontrol group is larger than that of the listing group. Unreported t-tests on the mean confirm that
there is no systematic difference between the two groups. Finally, the last row in Table 3 presents
the mean and standard deviation of the fitted probability of option listing in the two groups. The
average predicted probability is 6% for the listing group and 5.3% for the control group.
[Table 3 about here]

4. Option listing effect on the information environment
This section performs empirical tests about the effects of option listings on the overall market
information environment. Since the study focuses on the information in investors’ order flow, thestructural break should occur when options become available to investors but not when the listing
decisions are announced. Therefore, the event day (day 0) is defined as the listing date recorded by21

the OCC. The analysis leaves a one-month window around the listing date 11 , and defines the event
year before listing as trading days [−262, −11] and the event year after listing as trading days [11,262].

4.1. Baseline test
We estimate the DY model in the two event windows for all of the listing and control stocks andcalculate the paired difference between the two groups as the treatment effect of option listing.
The results are reported in Table 4. Panel A presents the cross-sectional averages of the estimatedmodel parameters, APIN, and PSOS for the two groups. The probability of information events(α) and the probability of liquidity shocks (θ) are almost unaltered in both groups after the listing
event. The order arrival rates are reported in natural logarithm for cross-sectional standardization.
In both groups, all three of the order arrival rates increase after listing. The average probabilityof informed trading (APIN) of the listing stocks is 15.5% before listing and 13.3% after listing.
The average APIN of the control stocks is 16% before listing and 14.9% after listing. A similar
reduction is observed for the probability of a trade from liquidity shocks (PSOS) in both groups.
Panel B reports the average changes in the model parameters and constructs after option listing,and the treatment effects with robust t-statistics. To account for cross-sectional variation in theparameters, the changes are calculated as log differences to approximate percentage changes. On
average, the listing stocks experience significant increases in the probability of information events(α), arrival rate of informed orders (µ), symmetric liquidity shock order flow (δ), and arrival rateof uninformed orders (ε), but no significant change in the probability of liquidity shocks (θ). The
probability of informed trading (APIN) and the probability of a trade from liquidity shocks (PSOS)
decrease by 15.7% and 16.8%, respectively. As expected, the control stocks exhibit similar changes
in all the variables. To identify the treatment effect, the last column in Panel B reports the averages
11 Not all options become available immediately on the listing date if the appointed market maker is not ready forproviding quotes. There can be several days of delay before the options are quoted and traded on the exchange.
Moreover, the options market makers usually need to establish a reasonable inventory of the underlying stocks before
options trading starts. Their purchase of the underlying stocks can cause uninformative order imbalance before thelisting date and potentially contaminate the measure of information asymmetry.

22

of the paired differences between the listing group and the control group. On the one hand, thelisting effects on the probability of information events (α) and probability of liquidity shocks (θ)
are both negative but statistically insignificant. On the other hand, the listing effects on all threeorder flow variables are positive and statistically significant at the 1% level. Specifically, option
listing on average increases the informed order flow (µ), symmetric liquidity shock order flow (δ),
and uninformed order flow (ε) by 12.4%, 14%, and 23.9%, respectively. The results support both
Hypotheses 1 and 2. The average treatment effects on APIN and PSOS are −8.8% and −7.2%,
respectively. Quantifying the selection effect using the results on the control group in the second
column, it is found that the treatment effects on APIN and PSOS are at the same magnitude as theselection effect.
The above analysis shows that the reduction in information asymmetry could be due to imbalanced growth in the three order flow variables. While the percentage analysis might potentiallyoverstate the change due to a small initial value, we explicitly examine the economic significanceof option listing by focusing on a relative measure of order flows. Note that in the DY model,
APIN is defined as
APIN =ααµ=.
αµ + 2θδ + 2ε α + 2 θδ+ε(2)µ

Given insignificant treatment effect on α, the listing effect on APIN is largely determined by theintensity of uninformed trading (θδ + ε) relative to the intensity of informed trading (µ) in the
denominator of the above equation. We term this relative order flow variable ROF and it is clearthat a larger ROF leads to a lower APIN. We find that the average ROF is 0.86 (0.84) for listing(control) stocks before listing, and 1.061 (0.946) after listing. Although both listing and control
stocks have higher ROF after option listing, the increase is much larger on the listing stocks. The
treatment effect on ROF reaches 0.094 with a t-statistic of 4.76.
The results in this subsection indicate that option listing significantly reduces the probabilityof informed trading in the market. Moreover, this change is not due to change in the probability of
information events but disproportional increases in uninformed and informed trading. Both typesof traders become more active when options are available but the increase in uninformed trading
outweighs the increase in informed trading, reducing the likelihood of trading against an informed
23

trader.

[Table 4 about here]

4.2. Robustness
This subsection conducts robustness tests on the baseline results documented in the previous subsection to warrant the causal inference regarding option listing effects.

4.2.1. Deviation from full delta hedging assumption
The key assumption in our analysis is full delta hedging. Only under this assumption the stockorder flow perfectly represents trading in both stock and options markets. In this subsection, weinvestigate the effect of discrete delta hedging on our inference of option listing effects. The modern option pricing research is generally motivated by the non-arbitrage argument, which implies
full delta hedging by liquidity providers (see, e.g., Merton (1973) and Ross (1976a)). The roleof transactions cost in replication and hedging is also studied by Leland (1985), Boyle and Vorst(1992), and Longstaff (1995). However, without detailed information on options market makers’
positions in all securities, how frequently delta hedging is performed on option positions remains
largely unknown to academics. Indirectly, Easley, O’Hara, and Srinivas (1998), Holowczak, Hu,
and Wu (2014), and Hu (2014) find that options delta order flow strongly affects the contemporaneous stock prices, indicating that delta hedging could occur within the test horizon in thesestudies. While Easley, O’Hara, and Srinivas (1998) and Hu (2014) mainly examine the inter-day
dynamics, Holowczak, Hu, and Wu (2014) show clear contemporaneous price impact from optionsdelta imbalance at all frequencies they examine ranging from five seconds to fifteen minutes.
Although the true delta hedging frequency and total hedging volumes cannot be empirically
identified, our conversations with options market makers yield a consistent remark that the market makers typically go home “flat”. Having little inventory risk overnight is also consistent withthe market making practice in other security markets. Therefore, the imbalance in options delta
24

should always be hedged in the underlying stock market at the end of the day regardless of thehedging frequency. If the market maker performs discrete hedging and waits for offsetting trades,
some symmetric positions in long and short delta may not lead to delta hedging, resulting in lower
trading volumes in the underlying stock than full delta hedging. In this case, the stock order flow
underestimates the total trading activities in the two markets. Estimating the DY model with lower
buy and sell volumes are likely to lead to spurious inferences about option listing effects. To understand the potential bias due to the unhedged delta volumes, it is important to note that the gap
between the observed stock order flow and unobservable total order flow is the same for both thebuyer-initiated and seller-initiated volumes because the unhedged volumes should be netted out.
To recover the unobserved total order flow, we set the unhedged volume to be 25%, 50%, 75%,
and 100% of the total options trading volume of the same stock on a day. After adding half the unhedged volume to both the buy and sell stock volumes on the same day, we estimate the DY modelusing these alternative order flow numbers and reexamine the listing effects. Note that since there
is no options trading on the listing stocks before listing or on the control stocks during the entire
event period, we perform this volume adjustment only for the listing stocks after listing. Table 5
reports the results using these alternative volumes. Compared to the main results in Table 4, we
find that the volume adjustment does not significantly change the treatment effects of option listingon any of the DY model parameters or the model constructs. The volume adjustment has similareffects to symmetric liquidity shocks in the DY model. Indeed, we find that the listing effect of
the liquidity shock order flow (δ) increases as the volume adjuster increases from 25% to 100%.
Larger estimates of liquidity trading also lowers APIN and increases PSOS. Therefore, the listing
effects on APIN becomes marginally smaller and the effects on PSOS becomes marginally larger.
Nonetheless, the reduction in APIN remains at 7.5% with a t-statistic of −5.48 even when theunhedged options volume reaches 100%. The analysis in this subsection indicates that our conclusions about the option listing effects do not critically depend on the full delta hedging assumption.

[Table 5 about here]25

4.2.2. Alternative model estimation and subsample analysis
We first estimate the DY model using number of trades instead of trading volumes and report
the difference-in-difference analysis results in Column (1) of Table 6. Comparing the treatment
effects in this column to the main results in Table 4, it is clear that the same pattern exists that bothinformed (µ) and uninformed trading (ε) increase and the effect of the latter dominates, resulting
in a lower probability of informed trading. The magnitude of the estimated listing effects and thestatistical significance are also close to those in Table 4.
[Table 6 about here]
The second robustness test concerns that the listing stocks of the full sample come from all
stock exchanges in the US, but the NASDAQ is traditionally a dealers’ market, making the inference of order flow potentially different from that of a specialists’ market such as the New York
Stock Exchange (NYSE). We then divide the full sample into two subsamples based on the stock
exchange of the listing stock and report the listing effects in Column (2) for NASDAQ stocks and in
Column (3) for NYSE and American Stock Exchange (AMEX) stocks. The pattern of the parameter dynamics in these two columns is consistent with the main findings. Interestingly, the option
listing effects on the order flow variables are greater for NYSE and AMEX stocks but the listing effects on APIN and PSOS are weaker for the same stocks, suggesting that the imbalanced growth ininformed and uninformed trading is more prominent for NASDAQ stocks. NASDAQ stocks, generally with smaller market capitalization, can be less transparent than NYSE and AMEX stocks on
average. Therefore, options trading as an additional information channel is possible to have larger
effects on NASDAQ stocks in reducing information asymmetry. We formally revisit the impact offirm information environment in Subsection 4.3 when we test Hypothesis 5.
There might be concerns that the baseline results are contaminated by the great financial crisis(GFC) in 2008 because of extreme stock market volatilities. Moreover, the short selling ban during
the crisis period can also have significant impact on options trading (see, for example, Grundy,
Lim, and Verwijmeren (2012)). To isolate the effect of GFC, Column (4) excludes the option
listing events in 2008 and 2009 and replicates the baseline analysis. The estimated listing effects
26

are almost identical to the baseline results in Table 4. In an unreported test using only observations
during GFC, no significant results are found on the treatment effects, suggesting that the option
listing effects come from the non-GFC period only.
Next, we examine the option listing effects conditioning on which options exchange is involved.
In our sample, the Chicago Board of Options Exchange (CBOE) is the most active participant inlisting new options. 12 Out of the 1517 events examined in the study, CBOE is involved in 676
listings. Given CBOE’s unique industry position and longest history of trading options, Columns(5) and (6) report the listing effects in the subsamples of CBOE listings and non-CBOE listings,
respectively. While the parameter dynamics generally show the same pattern in the two columns,
it is found that CBOE listings exhibit a smaller increase in informed trading (9.9% versus 14.3%)
but a much larger increase in uninformed trading (32.2% versus 17.7%) than non-CBOE listings.
As a result, the reductions in APIN and PSOS for CBOE listings almost double the reductions
for non-CBOE listings. Increased informed trading increases the cost of delta hedging for options
market makers because the order flow is more likely to be imbalanced. It is possible that as theoldest options exchange, CBOE and its market makers are better at selecting stocks to manage
their hedging costs.

4.2.3. Controlling for firm dynamics
Aslan, Easley, Hvidkjaer, and O’Hara (2008) find that several firm characteristics explain the crosssectional variation in the probability of informed trading (PIN). Specifically, they estimate thefollowing model:

PIN = b0 + b1 Size + b2 Growth + b3Age + b4 Analyst + b5 Turnover + b6 Insider+ b7 Institution + b8Accrual + b9 ROA + b10 ST D + b11 TobinQ + ΩIndustry + η, (3)12 Eight options exchanges operated in the US during the sample period including the American Stock Exchange,

Better Alternative Trading System, Boston Options Exchange, Chicago Board Options Exchange, International Securities Exchange, NASDAQ, New York Stock Exchange ARCA, and Philadelphia Stock Exchange.

27

where Growth is the annual growth rate in sales, Age is the length of history in CRSP, Analyst
is the number of analysts following the company, Turnover is the annual stock market trading
volume scaled by shares outstanding, Insider is the percentage ownership of company insiders,
Accrual is the estimate of the discretionary component of total accruals, ROA is return on asset
calculated as net income after depreciation over total asset, TobinQ is the market value of equity
plus book value of debt over total asset, and Size, ST D, and Industry are the same as definedearlier in Equation (1). Those authors find that all variables are significantly related to PIN except
Accrual. Given the close relation between the EO model and the DY model, it is possible thatthe same firm characteristics also affect APIN and PSOS. Therefore, we estimate the first orderdifference version of Equation (3) using pooled observations of both the listing and control stocks:

dY =b0 Listing + b1dSize + b2 dGrowth + b3dAnalyst + b4 dTurnover + b5 dInsider+ b6 dInstitution + b7dROA + b8 dST D + b9 dTobinQ + η,

(4)

where Listing is a dummy variable equal to one for listing stocks and zero for control stocks, andall the other variables are annual changes after option listing. The dependent variables dY arechanges of all the DY model parameters and constructs examined in previous analysis. All of
the three order flow variables are calculated as the natural logarithm of the DY model estimates.
Accrual is dropped from the model because it is not related to PIN. Also excluded from the model
are Age and Industry because the change in the firm age is identical for all stocks and the industry
classification is unlikely to change.
The sample size decreases slightly after merging the DY model estimates with firm characteristics extracted from several other databases. The balance sheet items are from the COMPUSTAT
database. Analyst coverage is from the I/B/E/S and is assigned zero if no record is found for thefirm. Insider holding data are from the Thomson Reuters. There are 1,256 listing stocks and 1,283
control stocks with all firm characteristics available after data merging. Table 7 reports the OLS regression results. Across the columns, the listing dummy has negative but insignificant coefficientsfor the two probability parameters (α and θ), positive and significant coefficients for all three order28

flow variables (µ, δ, and ε), and negative and significant coefficients for both APIN and PSOS,
again consistent with the main results. Given the pre-listing averages of 15.5% and 31.7% for listing stocks’ APIN and PSOS, the estimated reductions of 1.4% and 3% in APIN and PSOS in thistable translate to about 10% of the original value. The economic significance in this multivariate
test is therefore at the same order of magnitude as the main test on the percentage changes in APIN
and PSOS contained in Table 4.
[Table 7 about here]

4.2.4. Dynamic analysis
Event studies examining long event windows could potentially generate spurious inferences. For
example, if the structural break occurs a few months before the option listing date, comparing
information in the two annual event windows before and after listing may still generate similar
results. To explore the dynamic effects of option listing, we estimate the DY model for four
quarters before and after the option listing date. Event quarter −1 is defined as trading days [−73,−11] relative to the option listing date (day 0) and event quarter 1 is defined as trading days [11,73]. The rest of the event quarters are defined in a similar way so that each event quarter contains 63
trading days.13 We exclude the estimates with corner solutions and examine the quarterly treatment
effects from option listing in Table 8.
The first column shows that the number of paired observations in each event quarter is between
1513 and 1515, indicating that the majority of the sample is free from the corner solution problem
in estimation. The second column shows that there is a marginal increase in the probability of
information events, α in quarter 2, which then quickly reverses in quarter 3. There is no significant
treatment effect on α in the other event quarters. No significant listing effect is observed on theprobability of liquidity shocks (θ) in any event quarter in Column (3). Column (4) shows thatthe treatment effect on the informed order flow, µ, is significant in two event quarters, 1, and 3.
Average µ of the listing stocks increases by 12% over the control stocks (t-statistic = 3) in quarter
13

Further increasing the granularity of the analysis may have insufficient number of observations to reliably estimate
the DY model.

29

1, making it the biggest quarterly change in terms of both magnitude and statistical significanceacross all quarters. Column (5) shows that the liquidity shock order flow, δ, also receives significant
treatment effect in the first quarter after option listing. The increase in δ is 19.7% with a t-statisticof 4.89. A mild reversal in δ is found in quarter 3 (−7.6% with a t-statistic of −1.92). Column (6)shows that the arrival rate of uninformed trading (ε) experiences consistent increase over all event
quarters, indicating that the stock liquidity generally improves around option listing. However, themost significant increase in ε occurs in quarter 1. The treatment effect of 12.5% (t-statistic = 6.1)
in the first quarter is more than double the effect from any other quarter. Column (7) shows thatthe probability of informed trading (APIN) is significantly reduced by more than 5% (t-statistic= −3.36) in the first quarter after option listing. There is further reduction in APIN in the two
following quarters amounting to 3.4% together. However, these changes are no longer statisticallysignificant in individual quarters. Finally, the last column shows that the listing stocks exhibit
some changes in the probability of a trade from liquidity shocks (PSOS) only before listing but not
after. We also calculate the cumulative change relative to the last quarter before listing for all event
quarters after listing. Not surprisingly, the cumulative change is always positive and significant
for µ, δ, and ε, and always negative and significant for APIN. The cumulative change of PSOS
becomes significantly negative in the second quarter after listing. The dynamic analysis results inthis table support the causal effects from option listing on the information environment because
the most significant impact occurs in the first quarter after listing. 14[Table 8 about here]

4.2.5. A quasi-natural experiment on option listing
Causal effects from option listings are at the center of the analysis. However, the inference from
PSM analysis so far can be impeded by missing variables in the selection process if these variables
14 We also conduct an additional placebo test to confirm the timing of structural breaks using randomized event

dates. Specifically, for each pair of listing and control stocks, a pseudo-event date is randomly chosen from last threemonths before the option listing date. Then we replicate the baseline analysis using one quarter’s data before and after
the pseudo-event date. The results show no significant treatment effect from these pseudo events. These results areavailable upon request.

30

affect the listing decisions and the information environment at the same time. To address thisconcern, this subsection examines an alternative matched sample in a quasi-natural experiment of
option listing. The SEC mandates a minimum stock price requirement for option listing, which
creates discontinuity in the probability of option listing. If two stocks share similar characteristics
including stock prices but only one stock marginally meets the listing standard, the listing decision
is then more likely a result from the exogenous listing standard. Such observations are useful in
identifying the causal effect from the treatment, i.e. option listing in this case. To find the listing
stocks on the margin, we include only listing stocks with prices above the mandated minimum
price by less than one dollar at the time of listing. 15 There are 56 such listing events during thesample period. To find the control sample, we first construct a sample of non-listing common
stocks that have prices below the minimum price requirement by less than one dollar, but meet
all the other requirements. Then the same logistic regression is estimated for Equation (1), andthe non-listing stock with the closest propensity of listing at the same time is matched to a listing
stock.
Table 9 replicates the baseline analysis in this alternative sample. Despite relatively small
sample size, similar dynamics in the DY model parameters and constructs are found in this table.
Focusing on the treatment effect in the last column, it is clear that all of the three order flow variables receive significant and positive treatment effects from option listing, but the two probability
variables do not. The magnitude of the increase in the informed order flow (µ) is again dwarfed
by those in the liquidity shock order flow (δ) and uninformed order flow (ε). As a result, APIN
significantly reduces by 14.5% with a t-statistic of −2.36. The treatment effect on liquidity shocks(PSOS), however, is not statistically significant in this sample although the estimated average effectis negative and large (−10.7%, t-statistic = −1.18). The results in this quasi-natural experimentsupport the causal effect from option listing on the information environment and the estimateddynamics are consistent with the main findings.

[Table 9 about here]
15 The minimum price is set to $7.5 until end of 2002 and is set to $3 thereafter because the first low-price listing on

GSS occurs in January 2003.

31

4.2.6. A placebo test on ETF options
The analysis so far excludes ETF options because private information is usually firm-specific andaffects the price of common stocks more than diversified stock portfolios such as ETFs. However,the liquidity effect can exist for ETF options because of increased hedging trades. Therefore,
the overall information risk can still decrease but the mechanism is different. This subsection
examines the option listing effect on ETFs as a placebo test. There are 85 ETFs that experience
option introductions and have enough information to compute the selection variables during thesame sample period. We also estimate the propensity of option listing for ETFs only in a separate
logit regression and construct a control sample of ETFs. The previous difference-in-difference
analysis on the DY model parameters is repeated in this matched ETF sample. Table 10 showsthat for the DY model parameters, there are significant listing effects on the uninformed order flow(ε), but not on the informed order flow (µ, t-statistic = 0.27). The increase in ε reaches 48% witha t-statistic of 4.76. The probability of informed trading (APIN) is also reduced in this sample at
an even larger magnitude (−23%) than the effect in the common stock sample (−8.8%) because
informed trading does not become more active in the ETFs after option listing. The effect on PSOS
is absent. The results of this placebo test suggest that although option listing increases liquidity ingeneral, the effect on informed trading is found only on common stocks, consistent with the notionthat informed trading is more relevant for individual stocks than diversified stock portfolios.
[Table 10 about here]

4.3. Heterogenous listing effects
To test the heterogenous listing effects in the cross section of Hypotheses 4 and 5, we merge themain sample with additional databases. To measure options market liquidity, we extract options
volume data from the Option Metrics database. The daily options volume is the aggregate trading
volume of all option contracts on the same stock. The options to stock volume ratio, O/S, is thenthe aggregate options volume scaled by the stock trading volume on the same day. Figure 1 plots
the average daily O/S in the cross section of listing stocks in the first year after listing. A clear
32

trend of growth in O/S is found. The average O/S is around 2.5% immediately after the listing dateand it gradually increases to around 5% at the end of the first year, comparable to the average O/S
at 5.78% of all stocks with options between 1996 and 2010 reported by Johnson and So (2012).
The growth is rapid in the first three months (before day 63) and large variation in O/S is also
found. We calculate the average daily options volume (OpVol) and average daily O/S ratio in thefirst year after listing for each listing stock to measure the options market activeness.

[Figure 1 about here.]

To measure the listing stock’s information environment before listing, we calculate the following proxies using stock price data from CRSP, financial statements from COMPUSTAT, analyst,
earnings, and voluntary disclosure information from I/B/E/S, and ownership data from Thomson
Reuter’s 13f filings database.
• Spread: Average daily closing bid-ask spread scaled by the midpoint of the bid and ask inthe year before listing. Classic market microstructure theories such as Glosten and Milgrom(1985) and Easley and O’Hara (1992) suggest that information risk widens the dealer’s bidask spread.
• Size: Market capitalization in logarithm at the end of the month before listing. Large firms
tend to be more transparent because more attention from investors are given to them.
• Analyst: Number of analysts following the stock in the year before listing. Greater coverageof analysts generally reduces information asymmetry.
• |SU E|: Average absolute earnings surprise of four quarters before listing, where earnings
surprise is calculated as the actual earnings minus the median analyst forecast scaled by thestock price at the end of the previous month. It is more difficult to forecast earnings for
opaque firms, leading to larger magnitude of earnings surprise. Therefore, firms with weak
information environment usually have large |SU E|.

33• Guidance: A dummy variable that equals one for firms that issue any earnings guidancein the year before listing, and zero otherwise. Managers’ voluntary disclosure is an important information channel as shown by Balakrishnan, Billings, Kelly, and Ljungqvist (2014).
Firms issuing earnings guidance should have more transparent information environment.
• InstHolding: Average fraction of shares held by institutional investors in four quarters before listing. Higher institutional holding is likely to reduce information asymmetry becausewith significant risk exposure from large positions, institutional investors have the incentive
to collect private information as argued by Amihud and Li (2006).
• NumInst: Number of institutional investors at the time of listing.
• AccrualQuality: Standard deviation of residuals from a regression of current accruals on
lagged, current, and future cash flows from operations as in Dechow and Dichev (2002). We
run the regressions using quarterly data from three years before listing. Dechow and Dichev(2002) argue that a close mapping between accounting accruals and cash is more desirable interms of accounting quality, leading to a small standard deviation of residuals in the proposed
regression. Therefore, AccrualQuality is inversely related to information transparency.
For each conditioning variable, the listing stocks are sorted into quintile portfolios by ascendingorder except for Guidance, which allows us to separate the sample into only two groups that either
issue (High) or do not issue (Low) earnings guidance. Table 11 presents the average treatment
effect on APIN for each quintile portfolio as well as the difference between the top and bottom
quintiles with robust t-statistics.

[Table 11 about here]

We examine the options liquidity effect first. Both Columns (1) and (2) show that the listing
effects on APIN are negative in all quintile portfolios except for the lowest options volume and O/Squintile portfolios, suggesting that reduction of information asymmetry is a general phenomenon
in the cross section of stocks that experience option listings. Moreover, it is clear that the listing34

effect becomes stronger (more negative) when options liquidity increases. The difference between
the top and bottom quintile groups reaches −0.218 (−0.16) with a t-statistic of −5.27 (−3.72) for
sorting based on OpVol (O/S). The evidence is consistent with the notion that an active options
market brings more informational benefit to investors, supporting our Hypothesis 4.
Next, we turn to Hypothesis 5, which posits that firms with weaker information environment
before option listing benefit more. Our analysis generates the following results. First, regardlessof the information proxy we use, the option listing effect on APIN is always negative in all characteristic groups. Second, focusing on the difference between the top and bottom quintile groups,
the listing effect is stronger (more negative) for stocks with large bid-ask spread, small market
capitalization, low analyst coverage, large absolute earnings surprise, little earnings guidance, lowinstitutional ownership, low number of institutional investors, and low accrual quality. Since such
firms are typically less transparent, the evidence is consistent with Hypothesis 5 that opaque firms
benefit more from option listings. Finally, the average difference between the top and bottom characteristic groups is statistically significant for all information environment measures except analyst
coverage and the t-statistics range between 1.86 and 3.06.
In Table 12, we perform multivariate regression analysis of the option listing effects on APIN
using all of the conditioning firm characteristics. Column (1) reports the regression results on
options liquidity proxies, OpVol and O/S, only. Although both OpVol and O/S are negatively
correlated with the listing effect on APIN in Table 11, only OpVol retains the significant effectin the multivariate regression. Column (2) reports the results using all information environment
proxies. Although the signs of the estimated coefficients are largely consistent with the resultsin Table 11, the only significant coefficient belongs to |SU E|. It is possible that the information
environment proxies are correlated. When all variables are included in the regression in Column(3), the results are similar. OpVol and |SU E| have significant and negative coefficients, indicatingthat higher options volume and larger earnings surprises before listing are correlated with stronger(more negative) option listing effects on information asymmetry. This result is robust in Column(4) when a time trend is added to the model.
[Table 12 about here]35

4.4. short-sale constraint and option listing effects
Out of the six parameters in the DY model, the analysis so far excludes the probability of information being good, β, because this parameter does not affect the calculation of either APIN
or PSOS. However, β can also be affected by option listing if getting around short-sale constraints is an important motivation of informed traders. In this subsection, we examine the roleof short sale constraint in option listing effects on APIN. Specifically, following Brennan, Huh,and Subrahmanyam (2015), we decompose APIN into a probability of informed trading based ongood news (APIN Good = β ∗ APIN) and a probability of informed trading based on bad news
(APIN Bad = (1 − β) ∗ APIN) and investigate the asymmetric effect from option listing.
In Table 13, we replicate the option listing effect analysis in Table 4 on β, APIN Good, andAPIN Bad. Panel A reports the average estimates before and after listing. The listing stocks havean average β of 0.588 before listing and 0.566 after listing. The control stocks have an average β of
0.572 before listing and 0.569 after listing. The fact that β is well above 0.5 in all the periods indicates that the observed information shocks can be asymmetric. This could be due to the short-saleconstraints in place because the estimated probability of bad news is lower than the true probability of bad news if informed traders cannot trade ahead of negative news under binding short-saleconstraints. A large β also makes APIN Good greater than APIN Bad in the sample. In Panel B,
we find β reduces by 5% for the listing stocks (t-statistic = −5.48) but does not have a significant
reduction in the sample of control stocks (t-statistic = −0.73). As a result, the treatment effect on
β is negative and significant (−2.9%, t-statistic = −3.04). The negative option listing effect on β isconsistent with the notion that options help informed traders get around the short-sale constraints
on the underlying stocks and the stock has a greater chance of experiencing selling pressure after
option listing. Turning to the asymmetric effects on the probability of informed trading, we findthat both APIN Good and APIN Bad have negative and significant treatment effects. However, theeffect on APIN Good is almost three times that on APIN Bad (−14.9% versus −5.4%) and thet-statistic is also much larger on APIN Good (−5.93 versus −1.68). The results support our Hypothesis 6 that option listing has asymmetric effects on the probability of informed trading and the
reduction in APIN Good is larger than the reduction in APIN Bad. In Panels C and D, we repli-36cate the conditional analysis on APIN Good and APIN Bad in the cross section. The results canbe summarized as follows. First, the option listing effects on APIN Good and APIN Bad have thesame sign as the effect on APIN for all the conditioning variables. This means that both options
liquidity and listing stock’s information environment affect APIN Good as well as APIN Bad.
Second, comparing the conditional option listing effect based on options liquidity in the first twocolumns, it seems that the impact of options liquidity is comparable between APIN Good and
APIN Bad in terms of both the high minus low result and statistical significance. Third, the option listing stock’s information environment has a more significant impact on APIN Good than
APIN Bad because significant difference in APIN Good between the high and low groups is foundfor Spread, |SU E|, and Guidance while such significant difference in APIN Bad exists only for|SU E|. The results in the last two panels are again consistent with the asymmetric effect of option
listing on the probability of informed trading.

[Table 13 about here]

4.5. Option listing and price efficiency
If options trading reveals private information, the information value of earnings announcements
will become lower after option listing. Empirically, Skinner (1990), Ho (1993), and Mendenhall
and Fehrs (1999) find mixed evidence regarding the role of options in conveying earnings information for option listing stocks. We examine this question in a matched sample based on the option
listing probability using a fixed effect regression method because some stocks do not have analyst
forecasts in the I/B/E/S database in some quarters. Specifically, we regress the cumulative abnormal return from days [−1,1] on the earnings surprise (SU E), the option listing dummy (Listing), atime dummy (A f ter) that equals one for observations after listing and zero otherwise, and interactions of these variables. The option listing effect is identified by the three-way interaction variable
of SU E, Listing, and A f ter. We include firm and year fixed effects in the regressions and apply
year clustering in calculating the t-statistics. Table 14 reports the results. Column (1) shows that in
the univariate model, the stock price response to earnings surprise is positive and significant as the37

estimated coefficient (0.002) has a t-statistic of 4.31. We add the listing dummy and the interactionof Listing and SU E to the model in Column (2). The coefficient on SU E remains positive andsignificant and the coefficient of the interaction term SU E ∗ Listing is negative with a t-statisticof −1.89, indicating that on average, the option listing stocks have lower price response to earnings surprise conditioning on the level of surprise. Column (3) includes the time dummy A f ter,
its interactions with SU E and Listing, and the three-way interaction. The variable of interest, thethree-way interaction, has a coefficient estimate of −0.008 with a t-statistic of −3.2. The significant negative estimate indicates that the price sensitivity to earnings surprise becomes weaker for
option listing stocks after listing, supporting Hypothesis 7. 16[Table 14 about here]

4.6. Other option listing effects
Concerning that the APIN model may not well capture the information risk, we examine the option
listing effect on five additional stock market metrics in this subsection, including the percentage
bid-ask spread (Spread), absolute order imbalance (AV OI), return standard deviation (ST D), realized volatility (RVol)) defined as the squared root of the daily sum of five-minute return squares,
and V PIN of Easley, Lopez de Prado, and O’Hara (2012). V PIN is proposed as an alternative
measure of information asymmetry based on the original PIN model:
V PIN =∑nτ=1 |VτS −VτB |
,
nV

(5)

where VτS , VτB , and V are the seller-initiated, buyer-initiated, and total volumes in a bucket, and n is
the number of buckets in a measurement window (say, a day). The buckets are divided in volume
time so that each bucket contains the same number of shares traded. Compared to the original PIN,
V PIN is more flexible and utilizes the volume information. This paper follows Easley, Lopez dePrado, and O’Hara (2012) to use 50 buckets in calculating daily V PIN. Spread, AV OI, ST D, and
16 In Section E of the internet appendix, we test the effect on price efficiency using delayed price response to orderimbalance following Chordia and Subrahmanyam (2004). The results also support the hypothesis that price efficiency
improves after option listing.

38

V PIN are calculated on a daily bases and then averaged in the two annual event windows before andafter option listings. ST D is calculated using all daily observations in the annual event windows.
All these variables should be positively correlated with the level of information asymmetry.
Table 15 examines the option listing effects on these variables. We find that the average bidask spread decreases by 45.3% in the listing group and by 28.1% in the control group after option
listing. The treatment effect of option listing is −17.2% with a t-statistic of −10.12. Analysis on
AV OI, as an approximation of the expected value of PIN, shows that trading becomes more balanced after option listing. The average annual imbalance decreases by 23.4% for the listing stocks
and 13.3% for the control stocks. The difference between the two groups averages at −10.1% andis significant at the 1% level. Similar to Mayhew and Mihov (2004), we do not find option listing
significantly reduces the return standard deviation. Although the listing stocks have an average
decrease of 2.4% in ST D, the control stocks have an average decrease of 1.9%, making the treatment effect of −0.005 statistically insignificant. However, when we turn to the realized volatility(RVol), we find a significant reduction after option listing. Table 15 shows that RVol reduces by
24% for the listing stocks (t-statistic = −6.32), but no significant change in RVol of the control
stocks. As a result, the treatment effect is −19.3%, statistically significant at the 1% level (tstatistic = −4.02). Finally, we find that the average V PIN decreases by 19.3% for the listing stocks
and 9.5% for the control stocks. The treatment effect is −9.8%, statistically significant at the 1%
level (t-statistic = −17.38). Collectively, these results suggest that using alternative measures of
information asymmetry, option listing still significantly reduces the level of asymmetry.

[Table 15 about here]

5. Conclusion
This article investigates the impact of option introductions on the information environment of theoverall market. Between February 11, 2001 and February 28, 2010, we match each of the 1517
option listing stocks to an eligible, but non-listing, stock in the same month, and study the treatment

39

effects of option listing on several stock market metrics based on the Duarte and Young (2009)
model. The results show that option listing significantly reduces the probability of informed trading
by 8.8% in the year after listing. Also reduced is the impact of liquidity shocks. Both informed anduninformed trading increase after option listing but the probability of information events does notchange compared to the control group. The decline in the risk of trading against an informed traderis due to disproportional increases in the informed and uninformed trading. The results are robust
to several alternative empirical methods including relaxing the full delta hedging assumption andusing alternative order flow calculations, and exist in subsamples of stocks and after controllingfor firm characteristics associated with the probability of informed trading. Dynamic analysis
shows that the most significant structural break occurs in the first quarter after option listings. A
quasi-natural experiment also confirms the causal effects of option listing by comparing stocks
on the margin of the SEC requirement of option listing. A placebo test on ETF options showsthat there is no significant change in the level of informed trading in these securities after option
listing although the increased uninformed trading also reduces information risk. Consistent with
our hypotheses, we also find that the listing effects are stronger for stocks with active otions trading
and weak information environment before listing. Short-sale constraints play an important role in
the option listing effects as the listing effect is stronger for the probability of informed trading
based on good news than bad news. Finally, we find stock price response to earnings surprise
weakens after options listing, indicating that the stock price efficiency improves after listing.
The findings suggest that information content in the options market documented by previous
studies such as Easley, O’Hara, and Srinivas (1998), Pan and Poteshman (2006), and Hu (2014)
does not come only from driving informed traders out of the stock market. Option introductions
reduce the overall information risk and improve market liquidity. There is a net gain of information
efficiency from options trading on top of substitutional effects to stock trading. The current analysis does not include informed trading on stock return volatilities. It is important to jointly analyze
informed trading on both the stock price and the return volatility to uncover a more comprehensive
picture of the options trading effects. Private information about volatility is profitable in the options market, but not so in the stock market given mixed empirical relations between the return andvolatilities. Grossman (1988) points out that even if options can be synthesized by a dynamic trad40

ing strategy, the real trading process transmits volatility information that will not be revealed by the
dynamic trading strategy. Back (1993) shows that information asymmetry and option introductions
together cause stochastic volatility in the underlying market. Informed trading on volatility in theoptions market is unlikely to bias the results in this study because it has neutral impact on the stock
prices and order imbalance if volatility traders use delta-neutral strategies such as straddles, or if
both volatility traders and option market makers dynamically hedge the delta exposure of their option positions. The hedging transactions on the stock market can increase the total trading volume
but will not make the stock order flow imbalanced. Therefore, these hedging trades can be viewed
as liquidity trades in the stock market. Empirically, it is difficult to study the option listing effecton volatility trading because volatility trading is unobservable without options trading. Moreover,current microstructure theories are unable to produce empirical measures to disentangle informed
trading on stock volatilities from that on stock prices. Without a reliable identification strategy of
informed volatility trading, we leave the challenge to future studies. It would also be interesting to
explore the role of index funds, ETFs, hedge funds, and other institutional investors in the increaseof trading demand post option listing.

41

References
Admati, A. R., Pfleiderer, P., 1988. A theory of intraday patterns: Volume and price variability.
Review of Financial Studies 1, 3–40.
Ajinkya, B., Bhojraj, S., Sengupta, P., 2005. The association between outside directors, institutional investors and the properties of management earnings forecasts. Journal of Accounting
Research 43, 343–376.
Aktas, N., de Bodt, E., Declerck, F., van Oppens, H., 2007. The PIN anomaly around M&A
announcements. Journal of Financial Markets 10, 169–191.
Amihud, Y., Li, K., 2006. The declining information content of dividend announcements and theeffects of institutional holdings. Journal of Financial and Quantitative Analysis 41, 637–660.
Anthony, J. H., 1988. The interrelation of stock and options market trading-volume data. Journalof Finance 43, 949–964.
Aslan, H., Easley, D., Hvidkjaer, S., O’Hara, M., 2008. Firm characteristics and informed trading:
Implications for asset pricing. Journal of Empirical Finance.
Back, K., 1993. Asymmetric information and options. Review of Financial Studies 6, 435–472.
Balakrishnan, K., Billings, M. B., Kelly, B., Ljungqvist, A., 2014. Shaping liquidity: On the causal
effects of voluntary disclosure. Journal of Finance 69, 2237–2278.
Bali, T., Hovakimian, A., 2009. Volatility spreads and expected stock returns. Management Science55, 1797–1812.
Bali, T., Peng, L., Shen, Y., Tang, Y., 2014. Liquidity shocks and stock market reactions. Review
of Financial Studies 27, 1434–1485.
Barclay, M. J., Hendershott, T., McCormick, T., 2003. Competition among trading venues: Information and trading on electronic communications networks. Journal of Finance 58, 2637–2665.

42

Barth, M. E., Hutton, A. P., 2004. Analyst earnings forecast revisions and the pricing of accruals.
Review of Accounting Studies 9, 59–96.
Baruch, S., Panayides, M., Venkataraman, K., 2015. Informed trading and price discovery before
corporate events. Journal of Financial Economics forthcoming.
Battalio, R., Hatch, B., Jennings, R., 2004. Toward a national market system for u.s. exchangelisted equity options. Journal of Finance 59, 933–962.
Benos, E., Jochec, M., 2007. Testing the PIN variable. Working paper. University of Illinois.
Bhattacharya, M., 1987. Price changes of related securities: The case of call options and stocks.
Journal of Financial and Quantitative Analysis 22, 1–15.
Biais, B., Hillion, P., 1994. Insider and liquidity trading in stock and options markets. Review of
Financial Studies 7, 743–780.
Black, F., 1975. Fact and fantasy in the use of options. Financial Analysts Journal 31, 36–72.
Black, F., Scholes, M., 1973. The pricing of options and corporate liabilities. Journal of Political
Economy 81, 637–654.
Bloomfield, R. J., O’Hara, M., Saar, G., 2005. The ”make or take” decision in an electronic market:
Evidence on the evolution of liquidity. Journal of Financial Economics 75, 165–199.
Bloomfield, R. J., O’Hara, M., Saar, G., 2015. Hidden liquidity: Some new light on dark trading.
Journal of Finance 70, 2227–2274.
Bollen, N. P., 1998. Valuing options in regime-switch models. Journal of Derivatives 6, 38–49.
Boyle, P. P., Vorst, T., 1992. Option replication in discrete time with transaction costs. Journal of
Finance 47, 271–293.
Branch, B., Finnerty, J. E., 1981. The impact of option listing on the price and volume of theunderlying stock. Financial Review 16, 1–15.

43

Brennan, M., Huh, S.-W., Subrahmanyam, A., 2015. High-frequency measures of information risk.
Working paper. UCLA.
Brown, S., Hillegeistb, S. A., Lo, K., 2004. Conference calls and information asymmetry. Journalof Accounting and Economics 37, 343–366.
Bushee, B. J., Noe, C. F., 2000. Corporate disclosure practices, institutional investors, and stock
return volatility. Journal of Accounting Research 38, 171–202.
Cao, H. H., 1999. The effect of derivative assets on endogenous information acquisition and pricebehavior in a rational expectations equilibrium. Review of Financial Studies 12, 131–163.
Chan, K., Chung, Y. P., Fong, W. M., 2002. The informational role of stock and option volume.
Review of Financial Studies 14, 1049–1075.
Chen, Q., Goldstein, I., Jiang, W., 2007. Price informativeness and investment sensitivity to stock
price. Review of Financial Studies 20, 619–650.
Chordia, T., Hu, J., Tong, Q., Subrahmanyam, A., 2015. Order flow volatility and expected stock
returns. Working paper. Emory University.
Chordia, T., Roll, R., Subrahmanyam, A., 2005. Evidence on the speed of convergence to market
efficiency. Journal of Financial Economics 76, 271–292.
Chordia, T., Subrahmanyam, A., 2004. Order imbalance and individual stock returns: Theory andevidence. Journal of Financial Economics 72, 485–518.
Collin-Dufresne, P., Fos, V., 2015. Do prices reveal the presence of informed trading?. Journal of
Finance 70, 1555–1582.
Conrad, J., 1989. The price effect of option introduction. Journal of Finance 44, 487–498.
Cremers, M., Weinbaum, D., 2010. Deviations from put-call parity and stock return predictability.
Journal of Financial and Quantitative Analysis 45, 335–367.

44Danielsen, B. R., Sorescu, S. M., 2001. Why do option introductions depress stock prices? astudy of diminishing short sale constraints. Journal of Financial and Quantitative Analysis 36,
451–484.
Danielsen, B. R., van Ness, B. F., Warr, R. S., 2007. Reassessing the impact of option introductions on market quality: A less restrictive test for event-date effects. Journal of Financial andQuantitative Analysis 42, 1041–1062.
de Jong, C., Koedijk, K. G., Schnitzlein, C. R., 2006. Stock market quality in the presence of a
traded option. Journal of Business 79, 2243–2274.
Dechow, P. M., Dichev, I. D., 2002. The quality of accruals and earnings: The role of accrual
estimation errors. The Accounting Review 77, 35–59.
Detemple, J., Jorion, P., 1990. Option listing and stock returns : An empirical analysis. Journal of
Banking and Finance 14, 781–801.
Diamond, D. W., Verrecchia, R. E., 1987. Constraints on short-selling and asset price adjustment
to private information. Journal of Financial Economics 18, 277–312.
Duarte, J., Hu, E., Young, L., 2015. What does the PIN model identify as private information?.
Working paper. Rice University.
Duarte, J., Young, L., 2009. Why is PIN priced?. Journal of Financial Economics 91, 119–138.
Easley, D., Engle, R., O’Hara, M., Wu, L., 2008. Time varying arrival rates of informed anduninformed trades. Journal of Financial Econometrics 6, 171–208.
Easley, D., Hvidkjaer, S., O’Hara, M., 2002. Is information risk a determinant of asset returns?.
Journal of Finance 57, 2185–2221.
Easley, D., Lopez de Prado, M., O’Hara, M., 2012. Flow toxicity and volatility in a high frequency
world. Review of Financial Studies 25, 1457–1493.
Easley, D., O’Hara, M., 1992. Time and the process of security price adjustment. Journal of Finance 47, 577–605.
45

Easley, D., O’Hara, M., Srinivas, P. S., 1998. Option volume and stock prices: Evidence on where
informed traders trade. Journal of Finance 53, 431–465.
Ellul, A., Pagano, M., 2006. Ipo underpricing and after-market liquidity. Review of Financial Studies 19, 381–421.
Ellul, A., Panayides, M., 2016. Do financial analysts restrain insiders informational advantage.
Working paper. Indiana University.
Fedenia, M., Grammatikos, T., 1992. Options trading and the bid-ask spread of the underlying
stocks. Journal of Business 65, 335–351.
Figlewski, S., Webb, G., 1993. Options, short sales, and market completeness. Journal of Finance48(2), 761–778.
Glosten, L. R., Milgrom, P. R., 1985. Bid, ask, and transaction prices in a specialist market withheterogenously informed traders. Journal of Financial Economics 14, 71–100.
Grossman, S. J., 1988. An analysis of the implications for stock and futures price volatility of
program trading and dynamic hedging strategies. Journal of Business 61, 275–298.
Grundy, B. D., Lim, B., Verwijmeren, P., 2012. Do option markets undo restrictions on short sales?
evidence from the 2008 short-sale ban. Journal of Financial Economics 106, 331–348.
Hakansson, N. H., 1982. Changes in the financial market: Welfare and price effects and the basic
theorems of value conservation. Journal of Finance 37, 977–1004.
Ho, L.-C. J., 1993. Option trading and the relation between price and earnings: A cross-sectional
analysis. Accounting Review 68, 368–384.
Holden, C. W., Subrahmanyam, A., 1992. Long-lived private information and imperfect competition. Journal of Finance 47, 247–270.
Holowczak, R., Hu, J., Wu, L., 2014. Agregating information in option transactions. Journal of
Derivatives 21, 9–23.

46

Holowczak, R., Simaan, Y., Wu, L., 2006. Price discovery in the u.s. stock and stock options
markets: A portfolio approach. Review of Derivatives Research 9, 37–65.
Hong, H., Lim, T., Stein, J. C., 2000. Bad news travels slowly: Size, analyst coverage, and the
profitability of momentum strategies. Journal of Finance 55, 265–295.
Hu, J., 2014. Does option trading convey stock price information?. Journal of Financial Economics
111, 625–645.
Huang, J., Wang, J., 1997. Market structure, security prices and informational efficiency. Macroeconomic Dynamics 1, 169–205.
Johnson, T. L., So, E. C., 2012. The option to stock volume ratio and future returns. Journal of
Financial Economics 106, 262–286.
Kaniel, R., Liu, H., 2006. So what orders do informed traders use?. Journal of Business 79, 1867–
1913.
Kumar, R., Sarin, A., Shastri, K., 1998. The impact of options trading on the market quality of theunderlying security: An empirical analysis. Journal of Finance 53, 717–732.
Lai, S., Ng, L., Zhang, B., 2014. Does PIN affect equity prices around the world?. Journal of
Financial Economics 114, 178–195.
Lambert, R., Leuz, C., Verrecchia, R. E., 2007. Accounting information, disclosure, and the costof capital. Journal of Accounting Research 45, 385–419.
Lee, C. M. C., Ready, M. J., 1991. Inferring trade direction from intraday data. Journal of Finance
46, 733–746.
Leland, H. E., 1985. Option pricing and relation with transaction costs. Journal of Finance 40,
1283–1301.
Longstaff, F. A., 1995. Option pricing and martingale restriction. Review of Financial Studies 8,1091–1124.

47

Madhavan, A., Porter, D., Weaver, D., 2005. Should securities markets be transparent?. Journal of
Financial Markets 8, 266–288.
Manaster, S., Rendleman, R. J., 1982. Option prices as predictors of equilibrium stock prices.
Journal of Finance 37, 1043–1058.
Mayhew, S., Mihov, V., 2004. How do exchanges select stocks for option listing?. Journal of
Finance 59(1), 447–471.
Mendenhall, R. R., Fehrs, D. H., 1999. Option listing and the stock-price response to earnings
announcements. Journal of Accounting and Economics 27, 57–87.
Merton, R. C., 1973. An intertemporal asset pricing model. Econometrica 41, 867–887.
Milgrom, P., Stokey, N., 1982. Information, trade and common knowledge. Journal of Economic
Theory 26, 17–27.
Mohanram, P., Rajgopal, S., 2009. Is pin priced risk?. Journal of Accounting and Economics 47,
226–243.
Muravyev, D., Pearson, N. D., Broussard, J. P., 2013. Is there price discovery in equity options?.
Journal of Financial Economics 107(2), 259–283.
Natter, M., Rohleder, M., Schulte, D., Wilkens, M., 2014. The impact of option use on mutual fundperformance. Working paper. University of Augsburg.
Pan, J., Poteshman, A. M., 2006. The information in option volume for future stock prices. Review
of Financial Studies 19, 871–908.
Roll, R., Schwartz, E., Subrahmanyam, A., 2009. Options trading activity and firm valuation.
Journal of Financial Economics 94, 345–360.
Roll, R., Schwartz, E., Subrahmanyam, A., 2010. O/S: The relative trading activity in options andstock. Journal of Financial Economics 96, 1–17.

48

Ross, S. A., 1976a. The arbitrage theory of capital asset pricing. Journal of Economic Theory 13,
341–360.
Ross, S. A., 1976b. Options and efficiency. Quarterly Journal of Economics 90, 75–89.
Skinner, D., 1990. Options markets and the information content of accounting earnings releases.
Journal of Accounting and Economics 13, 191–211.
Skinner, D. J., 1989. Options markets and stock return volatility. Journal of Financial Economics
23, 61–76.
Sorescu, S. M., 2000. The effect of options on stock prices: 1973 to 1995. Journal of Finance 55,
487–514.
Stein, J. C., 1987. Informational externalities and welfare-reducing speculation. Journal of Political
Economy 95, 1123–1145.
Stephan, J. A., Whaley, R. E., 1990. Intraday price change and trading volume relations in thestock and stock option markets. Journal of Finance 45, 191–220.
Vega, C., 2006. Stock price reaction to public and private information. Journal of Financial Economics 82, 103–133.
Xing, Y., Zhang, X., Zhao, R., 2010. What does individual option volatility smirks tell us about
future equity returns?. Journal of Financial and Quantitative Analysis 45, 641–662.

49

Table 1

Number of option listing events over time
The table reports the time series distribution of the number of stocks selected for option listing
between February 11, 2001 and February 28, 2010 as well as for the firm-month observations thatare qualified for option listing but not selected. The selection criteria include: (1) the underlying
must be listed on NYSE, AMEX, NASDAQ, or any other national stock exchange; (2) the stock
price is not below $3.00; (3) there must be at least seven million publicly-held shares; (4) there
must be at least 252 trading days prior to this date; (5) the listing stocks must have options trading
for at least 252 days after listing.
Year

Listing

Eligible Non-listing

2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
Total

105
105
136
246
136
229
179
211
143
27
1517

18117
16660
16516
17409
17372
16525
15127
11931
9792
828
14027750

Table 2

Determinants of option listing: logistic regression results
This table presents the regression results from a logit model for the option listing decisions. The
sample includes all firm-month observations that meet the option listing criteria between February
11, 2001 and February 28, 2010. The dependent variable is equal to one for the firm-month observation when an options exchange listed a stock without options traded on it previously, and zerootherwise. The independent variables are log market capitalization (Sizet−1 ) in the last month, log
average daily trading volume in the past 12 months (Volume t−1,t−12 ), log average daily volume inthe last month (Volumet−1 ), log average daily volume in month t − 12 (Volumet−12 ), log standard
deviation of daily returns in the past year (ST Dt−1,t−12 ), log standard deviation of daily returns
in the last month (ST Dt−1 ), log standard deviation of daily returns in month t − 12 (ST D t−12 ),average daily percentage bid-ask spread at market close in the past year (Spreadt−1,t−12 ), average daily closing spread in the last month (Spreadt−1 ), average daily closing spread in montht − 12 (Spreadt−12 ), average daily volume order imbalance in the past year (V OIt−1,t−12 ), average daily volume order imbalance in the last month (V OIt−1 ), average daily volume order imbalance in month t − 12 (V OIt−12 ), average daily absolute volume order imbalance in the past year
(AV OIt−1,t−12 ), average daily absolute volume order imbalance in the last month (AV OIt−1 ), average daily absolute volume order imbalance in month t −12 (AV OIt−12 ), institutional ownership lastmonth (Institutiont−1), institutional ownership one year ago (Institution t−12), 71 two-digit SIC
industry dummies, and year dummies. Associated t-statistics are in parentheses. ∗ ,∗∗ ,∗∗∗ denotestatistical significance at the 10%, 5%, and 1% level, respectively.
Model

(1)

(2)

(3)

Intercept−19.560(−0.09)0.115∗∗∗(3.51)−1.137∗∗∗(−18.79)
1.389∗∗∗(24.85)−19.769(−0.09)0.139∗∗∗(4.21)−0.769∗∗∗(−9.48)1.297∗∗∗(22.40)−0.294∗∗∗(−6.89)−11.143(−0.07)0.027(0.76)−1.217∗∗∗(−13.15)
1.494∗∗∗(23.27)−0.287∗∗∗(−5.69)Sizet−1
Volumet−1,t−12
Volumet−1
Volumet−1251

Table 2 (continued)

Model

(1)

(2)

(3)

ST Dt−1,t−120.676∗∗∗

0.416∗∗∗

ST Dt−1(7.34)
0.240∗∗∗(3.78)(3.58)
0.287∗∗∗(4.31)
0.242∗∗∗(3.46)−0.294∗∗∗(−4.39)−1.113∗∗∗(−9.86)
0.031∗∗(2.49)

0.335∗∗∗(2.71)
0.003(0.04)
0.289∗∗∗(4.00)
0.077(1.22)−0.423∗∗∗(−4.24)0.037∗∗(2.20)
2.366∗∗∗(3.66)
2.256∗∗∗(5.77)−0.271(−0.96)−7.264∗∗∗(−7.79)−5.873∗∗∗(−9.07)1.365∗∗∗(2.98)
1.556∗∗∗(7.77)−0.697∗∗∗(−3.41)
yes
yes
2.49%
87.6%

ST Dt−12
Spreadt−1,t−12
Spreadt−1−0.132∗∗(−2.26)−1.261∗∗∗(−11.46)

Spreadt−12
VOIt−1,t−12
VOIt−1
VOIt−12
AVOIt−1,t−12
AVOIt−1
AVOIt−12
Institutiont−1
Institutiont−12
Industry Fixed Effects
Year Fixed Effects
McFadden’s R2
Accuracy Ratio

yes
yes
2.08%
85.7%

yes
yes
2.12%
86%

52

Table 3

Matching option listing stocks to eligible but non-listing stocks
This table compares the characteristics of the listing stocks and those of the matched control stocks.
For each of 1517 listing stocks between February 11, 2011 and February 28, 2010, the eligible but
non-listing stock with the closest propensity score of option listing in the same month is chosen without replacement. Sample statistics are reported for the log market capitalization before
listing (Sizet−1 ), log average daily trading volume in the past 12 months (Volume t−1,t−12 ), log
average daily volume in the last month (Volumet−1 ), log average daily volume in month t − 12
(Volumet−12 ), log standard deviation of daily returns in the past year (ST Dt−1,t−12 ), log standard
deviation of daily returns in the last month (ST Dt−1 ), log standard deviation of daily returns inmonth t − 12 (ST Dt−12 ), average daily percentage bid-ask spread at market close in the past year
(Spreadt−1,t−12 ), average daily closing spread in the last month (Spreadt−1 ), average daily closing spread in month t − 12 (Spreadt−12 ), average daily volume order imbalance in the past year
(V OIt−1,t−12 ), average daily volume order imbalance in the last month (V OIt−1 ), average daily volume order imbalance in month t − 12 (V OIt−12 ), average daily absolute volume order imbalancein the past year (AV OIt−1,t−12 ), average daily absolute volume order imbalance in the last month(AV OIt−1), average daily absolute volume order imbalance in month t − 12 (AV OIt−12 ), current institutional ownership (Institution t−1), institutional ownership one year ago (Institution t−12), andfitted probability of listing (Prob f it ) from Model (3) in Table 2.

Listing
Variable
Sizet−1
Volumet−1,t−12
Volumet−1
Volumet−12
ST Dt−1,t−12
ST Dt−1
ST Dt−12
Spreadt−1,t−12
Spreadt−1
Spreadt−12
V OIt−1,t−12
V OIt−1
V OIt−12
AV OIt−1,t−12
AV OIt−1
AV OIt−12
Institutiont−1
Institutiont−9
Prob f it

Mean
20.227
12.25912.65511.761−0.718−0.738−0.874
0.626
0.385
0.897
0.022
0.027
0.012
0.192
0.157
0.224
0.355
0.301
0.060

MatchedStd. dev.
1.038
0.858
0.944
1.106
0.503
0.591
0.609
0.779
0.450
1.587
0.064
0.085
0.116
0.067
0.060
0.104
0.225
0.231
0.073

53

Mean
20.10812.062
12.526
11.580−0.738−0.731−0.884
0.730
0.431
1.043
0.022
0.031
0.012
0.206
0.162
0.243
0.329
0.277
0.053

Std. dev.
1.453
1.477
1.448
1.708
0.546
0.667
0.626
1.019
0.421
1.910
0.062
0.082
0.125
0.088
0.067
0.133
0.251
0.243
0.057

Table 4

Option listing effects on market information environment
This table reports the option listing effect on the market information environment estimated using the Duarte
and Young (2009) model. For each of 1517 listing stocks between February 11, 2001 and February 28,
2010, a control stock is chosen based on the predicted probability of option listing from Model (3) inTable 2. For both listing and control stocks, Panel A reports the averages of the probability of information
events (α); the probability of a symmetry liquidity shock (θ); log daily order arrival rate of informed trades(µ); log additional uninformed order arrival rate on liquidity shock days (δ); log daily order arrival rate of
uninformed trades (ε); the adjusted probability of informed trading (APIN); and the probability of a liquidity
order from symmetric liquidity shocks (PSOS) estimated in one year before and after the listing date. PanelB reports the treatment effect of option listing on these variables. For each variable, the percentage change
is calculated as the natural logarithm of the post-listing value minus the natural logarithm of the pre-listingvalue. The cross-sectional mean and associated t-statistics calculated using year-clustered standard errors(in parentheses) are then reported for the listing stocks, the control stocks, and the paired difference between
these two groups. ∗ ,∗∗ ,∗∗∗ denote statistical significance at 10%, 5%, and 1% levels, respectively.

Panel A: Averages of DY model parameters and constructsαθ
log(µ)
log(δ)
log(ε)APIN
PSOS

Listing stocks
Before
After
0.304
0.322
0.212
0.208
7.085
7.302
7.463
7.753
6.213
6.805
0.155
0.133
0.317
0.262

Control stocks
Before
After
0.300
0.314
0.206
0.205
6.954
7.047
7.295
7.446
6.003
6.355
0.160
0.149
0.307
0.274

Panel B: Option listing effect
α
θ
µ
δ
ε
APIN
PSOS

Listing stocks
0.058∗∗(1.97)−0.026(−0.95)0.217∗∗∗(7.81)
0.290∗∗∗(10.67)0.592∗∗∗(33.74)−0.157∗∗∗(−16.78)−0.168∗∗∗(−15.55)

Control stocks
0.096∗∗∗(2.99)
0.011(0.36)
0.093∗∗∗(3.12)
0.150∗∗∗(5.18)
0.353∗∗∗(18.62)−0.069∗∗∗(−7.07)−0.096∗∗∗(−8.18)54

Listing − Control−0.038(−0.87)−0.037(−0.90)0.124∗∗∗(3.07)
0.140∗∗∗(3.51)
0.239∗∗∗(10.01)−0.088∗∗∗(−6.71)−0.072∗∗∗(−4.56)

Table 5

Deviation from full delta hedging
This table relaxes the full delta hedging assumption in the main analysis and reexamines option listing
effects on the market information environment estimated using the Duarte and Young (2009) model. For
each of 1517 listing stocks between February 11, 2001 and February 28, 2010, a control stock is chosen
based on the predicted probability of option listing from Model (3) in Table2. For listing stocks, the model
estimation after listing uses adjusted daily buyer- and seller-initiated stock volumes. Specifically, both buy
and sell stock volumes are increased by half of the potentially unhedged options volume, equal to 25%,
50%, 75%, and 100% of the total options volume of the same stock on the same day in Columns (1)-(4),respectively. Reported are the treatment effect of option listing on the probability of information events(α); the probability of a symmetry liquidity shock (θ); log daily order arrival rate of informed trades (µ); log
additional uninformed order arrival rate on liquidity shock days (δ); log daily order arrival rate of uninformed
trades (ε); the adjusted probability of informed trading (APIN); and the probability of a liquidity order fromsymmetric liquidity shocks (PSOS). For each variable, the percentage change is calculated as the natural
logarithm of the post-listing value minus the natural logarithm of the pre-listing value and the treatment
effect is the paired difference between the listing and control stocks. Associated t-statistics calculated using
year-clustered standard errors are reported in parentheses. ∗ ,∗∗ ,∗∗∗ denote statistical significance at 10%, 5%,
and 1% levels, respectively.

α
θ
µ
δ
ε
APIN
PSOS

25%
−0.039(−0.93)−0.037(−0.86)0.121∗∗∗(2.89)
0.147∗∗∗(3.63)
0.247∗∗∗(11.66)−0.082∗∗∗(−6.84)−0.074∗∗∗(−4.19)Unhedged options volume
50%
75%−0.042−0.038(−1.03)(−0.84)−0.036−0.035(−0.89)(−0.88)0.129∗∗∗
0.147∗∗∗(3.27)(3.15)∗∗∗
0.148
0.153∗∗∗(3.68)(3.39)
0.253∗∗∗
0.257∗∗∗(11.02)(12.03)−0.079∗∗∗−0.079∗∗∗(−6.23)(−6.10)−0.077∗∗∗−0.080∗∗∗(−5.58)(−5.12)55

100%−0.045(−0.95)−0.033(−0.75)0.141∗∗∗(2.96)
0.155∗∗∗(3.66)
0.252∗∗∗(11.05)−0.075∗∗∗(−5.48)−0.084∗∗∗(−5.64)

Table 6

Robustness checks of the option listing effects
For each of 1517 listing stocks between February 11, 2001 and February 28, 2010, a control
stock is chosen based on the predicted probability of option listing from Model (3) in Table 2.
Using the Duarte and Young (2009) model, this table examines the robustness of option listing
effects on the averages of the probability of information events (α); the probability of a symmetry
liquidity shock (θ); log daily order arrival rate of informed trades (µ); log additional uninformed
order arrival rate on liquidity shock days (δ); log daily order arrival rate of uninformed trades(ε); the adjusted probability of informed trading (APIN); and the probability of a liquidity orderfrom symmetric liquidity shocks (PSOS) estimated in one year before and after the listing date.
For each variable, the percentage change is calculated as the natural logarithm of the post-listingvalue minus the natural logarithm of the pre-listing value for both listing and control stocks. The
cross-sectional mean and associated t-statistics calculated using year-clustered standard errors (inparentheses) are then reported for the the paired difference between the two groups. In Column(1), the model parameters are estimated using the number of trades. Columns (2) and (3) reportresults in the subsamples of NASDAQ stocks, and NYSE and AMEX stocks, respectively. The
financial crisis period of 2008 and 2009 is excluded in Column (4). Columns (5) and (6) report theresults in subsamples that involve option listing on CBOE and non-CBOE exchanges, respectively.
∗ ,∗∗ ,∗∗∗ denote statistical significance at the 10%, 5%, and 1% level, respectively.

α
θ
µ
δ
ε
APIN
PSOS
N

(1)
Number of trades(2)
NASDAQ(3)
NYSE+AMEX(4)
ex-GFC

(5)
CBOE

(6)
non-CBOE−0.019(−0.65)−0.050∗(−1.70)0.175∗∗∗(5.78)
0.182∗∗∗(6.00)
0.385∗∗∗(5.80)−0.084∗∗∗(−7.63)−0.108∗∗∗(−7.02)1517−0.080(−1.28)−0.082(−1.46)0.107∗(1.87)
0.112∗∗(2.00)
0.216∗∗∗(5.97)−0.100∗∗∗(−5.24)−0.097∗∗∗(−4.75)791

0.008(0.13)
0.012(0.20)
0.143∗∗(2.50)
0.169∗∗∗(2.99)
0.265∗∗∗(8.60)−0.075∗∗∗(−4.19)−0.044∗(−1.83)726−0.034(−0.70)−0.046(−0.97)0.121∗∗∗(2.68)
0.137∗∗∗(3.04)
0.244∗∗∗(8.79)−0.088∗∗∗(−6.06)−0.084∗∗∗(−4.63)1163−0.001(−0.02)−0.035(−0.54)0.099∗(1.80)
0.149∗∗(2.46)
0.322∗∗∗(8.55)−0.117∗∗∗(−5.95)−0.101∗∗∗(−3.93)648−0.066(−1.14)−0.039(−0.73)0.143∗∗∗(2.68)
0.133∗∗(2.51)
0.177∗∗∗(5.77)−0.066∗∗∗(−3.77)−0.050∗∗(−2.54)869

56

Table 7

Option listing effects controlling for firm characteristics
The table reports the ordinary least squares regression results in the sample of 1256 option listing
stocks and 1283 control stocks between February 11, 2001 and February 28, 2010:
dY =b0 Listing + b1dSize + b2 dGrowth + b3dAnalyst + b4 dTurnover + b5 dInsider+ b6 dInstitution + b7dROA + b8 dST D + b9 dTobinQ + η,
where all variables are the changes from one year before option listing to one year after, except
Listing, a dummy variable that is equal to one for the listing stocks and zero otherwise. Size is thenatural logarithm of the market capitalization. Growth is the annual growth rate in sales. Analyst
is the number of analysts following the company. Turnover is the annual stock market trading
volume scaled by shares outstanding. Insider is the percentage ownership of company insiders.
Institution is the percentage of shares held by institutional investors. ROA is the return on asset
calculated as net income after depreciation over total asset. ST D is the standard deviation of thedaily returns. TobinQ is the market value of equity plus book value of debt over total asset. The
dependent variables are annual changes in the estimates of the Duarte and Young (2009) model
including the probability of information events (α); the probability of a symmetry liquidity shock(θ); log daily order arrival rate of informed trades (µ); log additional uninformed order arrival
rate on liquidity shock days (δ); log daily order arrival rate of uninformed trades (ε); the adjusted
probability of informed trading (APIN); and the probability of a liquidity order from symmetricliquidity shocks (PSOS). The associated t-statistics calculated using year-clustered standard errors
are in parentheses. ∗ ,∗∗ ,∗∗∗ denote statistical significance at the 10%, 5%, and 1% level, respectively.
Model
Indep.\Dep.
ListingdSize
dGrowthdAnalyst
dTurnoverdInsiderdInstitution
dROA
dST D
dTobinQ
Adj R-sq(1)dα−0.004(−0.56)0.065(0.84)
0.002(1.03)
0.130 ∗∗
(2.10)
0.001(0.67)−0.004(−0.77)0.339(1.30)
0.090(0.75)−0.360 ∗∗∗(−4.36)0.010(0.63)
0.011

(2)
dθ
−0.004(−0.89)−0.055(−0.77)−0.001(−0.55)−0.168∗∗∗(−2.91)0.003 ∗∗∗
(2.84)
0.004(0.84)−0.073(−0.30)−0.040(−0.36)−0.123(−1.60)0.000(0.00)
0.006(3)
dlog(µ)0.153 ∗∗∗
(4.13)
0.322 ∗∗∗
(4.64)
0.000(0.32)
0.065(1.17)
0.000(0.44)
0.005(1.11)
0.224(0.95)−0.304 ∗∗∗(−2.84)0.773 ∗∗∗
(10.39)0.054 ∗∗∗
(3.92)
0.063(4)
dlog(δ)0.187∗∗∗(5.33)
0.403∗∗∗(6.11)
0.003 ∗∗
(2.43)
0.330 ∗∗∗
(6.23)−0.001(−1.03)−0.001(−0.21)0.287(1.29)−0.275∗∗∗(−2.70)0.774∗∗∗(10.95)0.076∗∗∗(5.87)
0.109

57

(5)
dlog(ε)0.343∗∗∗(15.25)0.537∗∗∗(12.76)0.003∗∗∗(2.88)
0.260∗∗∗(7.68)
0.001 ∗
(1.67)
0.002(0.81)
1.717 ∗∗∗
(12.06)−0.287∗∗∗(−4.43)0.378∗∗∗(8.37)
0.095∗∗∗(11.51)
0.379

(6)
dAPIN
−0.014∗∗∗(−8.99)−0.094∗∗∗(−3.94)0.000(−0.82)−0.019(−1.02)0.000(−0.43)−0.001(−0.71)−0.426∗∗∗(−5.30)0.064∗(1.73)−0.074∗∗∗(−2.90)−0.020∗∗∗(−4.18)
0.097

(7)
dPSOS
−0.030∗∗∗(−7.73)−0.133∗∗∗(−4.96)0.000(−0.03)−0.053 ∗∗(−2.46)0.001(1.64)
0.001(0.45)−0.774∗∗∗(−8.56)−0.037(−0.91)0.164∗∗∗(5.74)−0.006(−1.23)0.144

Table 8

Quarterly option listing effects
This table reports the quarterly option listing effects on the model parameters and constructs ofDuarte and Young (2009) around the option listing date. For each of 1517 listing stocks between
February 11, 2001 and February 28, 2010, a control stock is chosen based on the predicted probability of option listing from Model (3) in Table 2. Estimates with corner solutions are excluded
and N is the number of observations in each event quarter. α is the probability of information
events. θ is the probability of a symmetry liquidity shock. µ is the daily order arrival rate frominformed trades. δ is the increment in uninformed order arrival rate on liquidity shock days. ε isthe daily order arrival rate from uninformed trades. APIN is the adjusted probability of informed
trading. PSOS is the probability of a trade from liquidity shocks. For each variable, the treatment
effect is calculated as the difference in the quarterly percentage changes between the listing stocks
and the control stocks. The cross-sectional mean and t-statistics calculated using year-clustered
standard errors (in parentheses) of the treatment effects are then reported in event quarters relative to the option listing date. ∗ ,∗∗ ,∗∗∗ denote statistical significance at the 10%, 5%, and 1% level,
respectively.

Event quarter

(1)
N

(2)
α

(3)
θ

(4)
µ

(5)
δ

(6)
ε

(7)
APIN

(8)
PSOS

−3

1514

−2

1513

−1

1515

1

1514

2

1515

3

1513

4

1514

0.021
(0.54)−0.006(−0.15)0.038(0.96)−0.033(−0.83)0.068∗(1.77)−0.81∗(−1.92)0.009(0.23)−0.011(−0.32)0.049(1.36)−0.009(−0.26)−0.055(−1.48)−0.011(−0.32)0.051(1.46)−0.049(−1.40)0.053(1.31)
0.017(0.45)−0.036(−0.86)0.120∗∗∗(3.00)−0.049(−1.28)0.078∗∗(2.03)
0.003(0.07)

0.059(1.50)
0.032(0.86)−0.041(−1.01)0.197∗∗∗(4.89)
0.042(1.06)−0.076∗(−1.92)0.036(0.90)

0.043∗∗(2.24)
0.037∗(1.95)
0.030(1.61)
0.125∗∗∗(6.10)
0.055∗∗∗(3.33)
0.020(1.21)
0.012(0.73)

0.020(1.27)−0.025(−1.33)0.000(−0.02)−0.051∗∗∗(−3.36)−0.010(−0.62)−0.024(−1.43)0.005(0.28)−0.007(−0.44)0.035∗∗(2.07)−0.053∗∗∗(−3.06)0.014(0.78)−0.017(−0.99)−0.016(−0.95)−0.021(−1.15)58

Table 9

A quasi-natural experiment on option listingThis table reports the option listing effects in a quasi-natural experiment. The sample includes 56 listing
stocks with prices above the SEC mandated minimum price by less than one dollar between February 11,
2001 and February 28, 2010. Control stocks are the non-listing stocks with prices below the SEC mandated
minimum price by less than one dollar. Each listing stock is then matched to a control stock at the sametime that has the closest predicted probability of option listing. Reported are the average treatment effects
on Duarte and Young (2009) model’s parameters and constructs including the probability of information
events (α); the probability of a symmetry liquidity shock (θ); log daily order arrival rate of informed trades(µ); log additional uninformed order arrival rate on liquidity shock days (δ); log daily order arrival rate of
uninformed trades (ε); the adjusted probability of informed trading (APIN); and the probability of a liquidity
order from symmetric liquidity shocks (PSOS) estimated in one year before and after the listing date. For
each variable, the percentage change is calculated as the natural logarithm of the post-listing value minus
the natural logarithm of the pre-listing value. The cross-sectional mean and associated t-statistics calculatedusing year-clustered standard errors (in parentheses) are then reported for the listing stocks, the control
stocks, and the paired difference between these two groups. ∗ ,∗∗ ,∗∗∗ denote statistical significance at 10%,5%, and 1% levels, respectively.

α
θ
µ
δ
ε
APIN
PSOS
N

Listing stocks−0.064(−0.36)−0.110(−0.70)0.309∗∗∗(2.70)
0.309∗∗(2.18)
0.710∗∗∗(7.37)−0.218∗∗∗(−5.03)−0.263∗∗∗(−3.61)
56

Control stocks
0.058(0.37)
0.091(0.65)
0.022(0.15)−0.095(−0.69)0.283∗∗(2.05)−0.073∗(−1.74)−0.157∗∗∗(−3.01)
56

59

Listing − Control−0.121(−0.52)−0.200(−0.93)0.287∗∗(2.19)
0.404∗(1.82)
0.427∗∗(2.58)−0.145∗∗(−2.36)−0.107(−1.18)56

Table 10

Option listing effects on ETFs
This table examines the option listing effects on the information environment of 85 ETFs between February
11, 2001 and February 28, 2010. For each listing ETF, a control ETF is chosen based on the predictedprobability of option listing from the full-specification logistic model in Equation (1). Reported are theaverage treatment effects on the probability of information events (α); the probability of a symmetry liquidity
shock (θ); log daily order arrival rate of informed trades (µ); log additional uninformed order arrival rateon liquidity shock days (δ); log daily order arrival rate of uninformed trades (ε); the adjusted probabilityof informed trading (APIN); and the probability of receiving a liquidity order from symmetric liquidity
shocks (PSOS) based on Duarte and Young (2009) model in one year before and after option listing. For
each variable, the percentage change is calculated as the natural logarithm of the post-listing value minus
the natural logarithm of the pre-listing value. The cross-sectional mean and associated t-statistics calculatedusing year-clustered standard errors (in parentheses) are then reported for the listing stocks, the control
stocks, and the paired difference between the two groups. ∗ ,∗∗ ,∗∗∗ denote statistical significance at the 10%,5%, and 1% level, respectively.

α
θ
µ
δ
ε
APIN
PSOS
N

Listing ETFs

Control ETFs

Listing − Control

0.181∗∗(2.12)
0.208(1.37)
0.175∗∗(2.03)
0.178(1.41)
0.704∗∗∗(8.96)−0.139∗∗∗(−5.04)−0.103∗(−1.73)85

0.164(1.57)
0.030(0.22)
0.138(1.23)
0.006(0.06)
0.224∗∗∗(3.25)
0.091(0.60)−0.166∗(−1.71)85

0.017(0.12)
0.179(0.85)
0.040(0.27)
0.172(0.92)
0.480∗∗∗(4.76)−0.230∗(−1.72)0.064(0.56)
85

60

61
(2)
O/S

(3)
Spread

Low
0.038
0.010
−0.031
2−0.060−0.063−0.083
3−0.106−0.102−0.0924−0.143−0.137−0.095High−0.180−0.149−0.156High−Low −0.218∗∗∗ −0.160∗∗∗ −0.125∗∗∗(−5.27)(−3.72)(−2.97)

(1)
OpVol

(5)
Analyst(6)|SUE|−0.136 −0.099 −0.033−0.106 −0.065 −0.046−0.095 −0.115 −0.075−0.068 −0.066 −0.130−0.037 −0.072 −0.146
0.099∗∗ 0.027 −0.113∗∗∗(2.42) (0.58)(−2.58)(4)Size−0.0280.079∗(1.88)−0.106−0.096−0.076−0.097−0.121−0.0200.076∗∗(2.03)−0.131−0.065−0.067−0.116−0.0070.123∗∗∗(3.06)−0.059−0.075−0.070−0.097−0.138−0.080∗(−1.86)

(7)
(8)
(9)
(10)
Guidance InstHolding NumInst AccrualQuality

This table reports the option listing effects conditioning on the underlying stock’s characteristics, including the total options trading volume(OpVol), the average options to stock volume ratio (O/S), average daily closing bid-ask spread in the year before listing (Spread), market
capitalization (Size), number of analysts covering the stock (Analyst), average absolute earnings surprise (|SUE|) measured as the absolutedifference between the actual earnings and the median analyst forecast scaled by the stock price at the end of the previous month, an earnings
guidance dummy (Guidance) which is one for listing firms that provide any earnings guidance in the year before listing, and zero otherwise,
average fraction of shares held by institutional investors in the four quarters before listing (InstHolding), the number of institutional investors at
the time of listing (NumInst), and accounting accrual quality measured as the standard deviation of residuals from a regression of current accruals
on lagged, current, and future cash flows from operations (AccrualQuality). The two options volume variables are calculated in the first year
after listing and the other firm characteristics are calculated at the time of listing. For each characteristic, the listing stocks are sorted into quintile
portfolios based on ascending order. The average option listing effects on the adjusted probability of informed trading (APIN) within each quintile
portfolio are presented as well as the differences between the top and bottom quintiles with robust t-statistics.∗ ,∗∗ ,∗∗∗ denote statistical significanceat the 10%, 5%, and 1% level, respectively.

Option listing effects in firm characteristic portfolios

Table 11

Table 12

Multivariate regressions of option listing effects
The table reports the OLS results of the option listing effect on APIN, the adjusted probability of informed
trading based on the model of Duarte and Young (2009). The percentage change of APIN is calculated
as the log of the post-listing value minus the log of the pre-listing value. The dependent variable of theregressions, the option listing effect, is the difference between the percentage change of the listing stockand that of the control stock matched on the propensity score of listing. OpVol is the average daily trading
volume of all options on the same underlying stock in the first year after listing. O/S is the average daily
ratio of options volume and the underlying stock trading volume. Spread is the average daily closing bidask spread in the year before listing. Size is market capitalization at the time of listing. Analyst is thenumber of analysts covering the stock. |SUE| is the average absolute earnings surprise measured as theabsolute difference between the actual earnings and the median analyst forecast scaled by the stock price atthe end of the previous month. Guidance is an earnings guidance dummy which is one for listing firms thatprovide any earnings guidance in the year before listing, and zero otherwise. InstHolding is the averagefraction of shares held by institutional investors in the four quarters before listing. NumInst is the numberof institutional investors at the time of listing. AccrualQuality is accounting accrual quality measured as
the standard deviation of residuals from a regression of current accruals on lagged, current, and future cash
flows from operations. And Trend is a year indicator. Associated t-statistics calculated using year-clustered
standard errors are in parentheses. ∗ ,∗∗ ,∗∗∗ denote statistical significance at the 10%, 5%, and 1% level,
respectively.

Intercept
OpVol
O/S
SpreadSize
Analyst|SUE|
Guidance
InstHolding
NumInstAccrualQuality(1)
0.486∗∗∗(4.63)−0.061∗∗∗(−5.37)0.581∗∗(2.22)(2)−0.297(−1.38)−0.033(−1.14)0.011(1.05)
0.008(0.25)−0.028∗(−1.66)0.025(0.68)−0.149(−0.42)0.001(0.36)−0.646(−1.05)

Trend

62

(3)
0.258
(1.04)−0.060∗∗∗(−4.35)0.637∗∗(2.12)−0.024(−0.82)0.008(0.77)
0.039(1.14)−0.029∗(−1.70)0.035(0.97)−0.088(−0.25)−0.001(−0.41)−0.531(−0.85)(4)
0.278(1.11)−0.06∗∗∗(−4.35)0.642∗∗(2.13)−0.029(−0.93)0.008(0.79)
0.036(1.04)−0.028∗(−1.65)0.034(0.92)−0.084(−0.24)−0.001(−0.34)−0.534(−0.86)−0.004(−0.47)

Table 13

Decomposing the option listing effects
This table reports the option listing effect on probabilities of directional informed trading estimated using theDuarte and Young (2009) model. For each of 1517 listing stocks between February 11, 2001 and February
28, 2010, a control stock is chosen based on the predicted probability of option listing from Model (3) inTable 2. For both listing and control stocks, Panel A reports the averages of the probability of an information
shock being positive (β); the adjusted probability of informed trading for good news (APIN Good); and theadjusted probability of informed trading for bad news (APIN Bad) estimated in one year before and after thelisting date. Panel B reports the treatment effect of option listing on these variables. For each variable, thepercentage change is calculated as the natural logarithm of the post-listing value minus the natural logarithmof the pre-listing value. The cross-sectional mean and associated t-statistics calculated using year-clustered
standard errors (in parentheses) are then reported for the listing stocks, the control stocks, and the paired
difference between these two groups. Panels C and D replicate the analysis in Table11 using APIN Goodand APIN Bad. ∗ ,∗∗ ,∗∗∗ denote statistical significance at 10%, 5%, and 1% levels, respectively.

Panel A: Averages of DY model parameters and constructs

β
APIN Good
APIN Bad

Listing stocks
Before
After
0.588
0.566
0.092
0.076
0.063
0.058

Control stocks
Before
After
0.572
0.569
0.091
0.084
0.068
0.065

Panel B: Option listing effectβ
APIN Good
APIN Bad

Listing stocks−0.050∗∗∗(−5.48)−0.210∗∗∗(−15.72)−0.101∗∗∗(−4.59)Control stocks−0.007(−0.73)−0.061∗∗∗(−3.80)−0.047∗∗(−2.21)

63

Listing − Control−0.029∗∗∗(−3.04)−0.149∗∗∗(−5.93)−0.054∗(−1.68)

64

Low
0.082
2
0.019
3
−0.041
4−0.216High−0.126High−Low −0.208∗∗(−2.14)

Panel D: APIN Bad

Low−0.0862−0.0403−0.0494−0.259High−0.309High−Low −0.223∗∗(−2.34)

Panel C: APIN Good

(1)
OpVol

Table 13 (continued)(3)
Spread0.084
0.008−0.152−0.160−0.066−0.150(−1.62)−0.020−0.092−0.045−0.065−0.066−0.046(−0.50)−0.1000.004−0.160 −0.247−0.127 −0.147−0.166 −0.101−0.207 −0.261−0.107 −0.265∗∗(−1.27) (−2.25)

(2)
O/S

−0.007−0.113−0.053−0.048−0.067−0.060(−0.67)−0.041−0.187−0.153−0.201−0.173−0.132(−1.23)

(4)
Size

(6)
|SUE|

−0.003 −0.018−0.094 −0.011−0.108 −0.012−0.020 −0.087−0.036 −0.195−0.033 −0.177∗∗(−0.32) (−2.00)−0.054 −0.074−0.230 −0.061−0.193 −0.084−0.110 −0.198−0.122 −0.219−0.069 −0.145∗(−0.71) (−1.65)(5)Analyst−0.011
0.052(0.77)−0.063−0.0100.169∗∗(2.51)−0.179−0.030−0.104−0.167−0.0670.026
0.130(1.58)−0.285−0.096−0.088−0.119−0.1640.121(1.43)−0.102−0.0510.030−0.082−0.0560.046(0.51)−0.080−0.097−0.097−0.281−0.1900.091(1.03)−0.053−0.013−0.101−0.015−0.140−0.087(−0.99)−0.061−0.217−0.010−0.215−0.191−0.131(−1.14)

(7)
(8)
(9)
(10)
Guidance InstHolding NumInst AccrualQuality

Table 14

Option listing and price response to earnings surprise
The table investigates the option listing effect on stock price response to earnings surprise. For the listing
stocks and matched control stocks in the study, four quarterly earnings announcements before and after thelisting date are collected. Reported are the OLS regression results with year and firm fixed effects. The
dependent variable is the cumulative abnormal return surrounding an earnings announcement. Earnings
surprise SUE is calculated as the actual earnings minus the median analyst forecast then scaled by the stock
price at the end of the previous month. Listing is a dummy variable that equals one for option listing stocks
and zero for control stocks. A f ter is a dummy variable that equals one for observations after the listing
day and zero for those before the listing day. Associated t-statistics calculated using year-clustered standard
errors are in parentheses. ∗ ,∗∗ ,∗∗∗ denote statistical significance at the 10%, 5%, and 1% level, respectively.

Intercept
SUE(1)0.001∗∗(2.41)
0.002∗∗∗(4.31)(2)
0.002∗∗(2.29)
0.004∗∗∗(3.86)−0.001(−0.94)−0.002∗(−1.89)

Yes
Yes
0.121
19244

Yes
Yes
0.121
19244

Listing
SUE ∗ ListingA f ter
SUE ∗ A f ter
Listing ∗ A f ter
SUE ∗ Listing ∗ A f ter
Firm FE
Year FE
Adj. Rsq
N

65(3)0.003∗∗∗(2.63)
0.002∗∗∗(2.89)
0.000(−0.18)−0.002)(−1.40)0.000(0.26)
0.007∗(1.79)−0.001(−0.68)−0.008∗∗∗(−3.20)
Yes
Yes
0.122
19244

Table 15

Option listing effects on other stock market metrics
This table analyzes the treatment effects of option listing on Spread, the one-year average ofdaily percentage bid-ask spread; AV OI, the one-year average of absolute volume order imbalance;
ST D, the one-year standard deviation of daily returns; RVol, the one-year average of daily realized
volatility; and V PIN, the order flow toxicity measure based on Easley, Prado, and O’Hara (2012).
For each of 1517 listing stocks between February 11, 2001 and February 28, 2010, a control stock
is chosen based on the predicted probability of option listing from the full-specification logistic
regression in Column (3) of Table 2. For each dependent variable, X , the percentage change iscalculated as the natural logarithm of the post-listing value minus the natural logarithm of the prelisting value: log(Xa f ter ) − log(Xbe f ore ). The cross-sectional mean and the associated t-statistics
calculated using year-clustered standard errors (in parentheses) are then reported for the listing
stocks, the control stocks, and the paired differences between these two groups. ∗ ,∗∗ ,∗∗∗ denotestatistical significance at the 10%, 5%, and 1% level, respectively.
Variable
Spread
AVOI
STD
RVol
VPIN

Listing−0.453∗∗∗(−26.65)−0.234∗∗∗(−33.43)−0.024∗∗(−2.40)−0.240∗∗∗(−6.32)−0.193∗∗∗(−43.18)

Control−0.281∗∗∗(−16.53)−0.133∗∗∗(−19.00)−0.019(−1.58)−0.047(−1.24)−0.095∗∗∗(−22.14)

66

Listing - Control−0.172∗∗∗(−10.12)−0.101∗∗∗(−11.22)−0.005(−0.42)−0.193∗∗∗(−4.02)−0.098∗∗∗(−17.38)

Average daily O/S ratio
0.1
0.09
0.08

O/S ratio

0.07
0.06
0.05
0.04
0.03
0.02
0

50

100
150
Days relative to options listing

200

252

Figure 1

Options to stock volumes ratio in the first year after option listingThis figure plots the daily cross-sectional average of the O/S ratio in the first year after option
listing for 1517 listing stocks between February 11, 2001 and February 28, 2010. The O/S iscalculated as the total options volume divided by the total stock volume on the same day.

67