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Preface and Acknowledgements
This thesis was submitted to the Rotterdam School of Management, Erasmus University (RSM), as partial fulfilment of the requirements for the degree of Master of Science in Finance and Investments.
The research project was conducted under the supervision of Dr. Mathijs Cosemans and Teodor Dyakov, M.Sc., between April and August 2013. I want to thank both of my supervisors for their invaluable feedback and support.
Since this thesis marks the end of an exciting academic journey, I also want to take the opportunity to express my deepest gratitude to my parents for their emotional and financial support during my studies. I will always be indebted to you for letting me follow my dreams, to live in different countries and to study at the schools of my choosing. I never took this freedom for granted and acknowledge that I would not be where I am today without your continuous support and trust. Danke!
Finally, I would like to thank Stefan Rohm, Julian Spruck, and other fellow students for their feedback, econometric advice, and enlightening discussions over the course of this project.
I hereby declare that this thesis is an original work that I have written entirely by myself. All sources and resources that contributed to this project have been properly cited and referenced. All direct and indirect citations from other publications have been indicated as such.
The copyright of this thesis rests with the author, Marco Klapper. However, the author also acknowledges the intellectual property of contributions made by the thesis supervisors. The author is responsible for the contents of this thesis. The Rotterdam School of Management is only responsible for the educational supervision and cannot be held liable for the content.
2. Literature Review..
2.1 The value of mergers and acquisitions .
2.1.1 Occurrence and characteristics.
2.1.2 Event studies on the wealth effects of acquisitions.
18.104.22.168 Abnormal returns methodology.
22.214.171.124 Takeover premiums.
126.96.36.199 Bidding firms' announcement returns.
2.1.3 Disentangling synergistic gains and other informational effects.
188.8.131.52 Information asymmetry and the method of payment
184.108.40.206 Revelation, truncation, and information timing.
2.2 Cross-section of options and stocks: Evidence from M&A transactions .
2.2.1 Price discovery process in the options and stock market
220.127.116.11 The role of informed traders.
18.104.22.168 The predictive power of option implied volatilities.
2.2.2 Event studies: predicting announcement returns with option trading proxies.
22.214.171.124 Trading activity around corporate announcements (earnings)
126.96.36.199 Predictability of M&A announcement returns.
2.3 Conceptual framework and hypothesis development
3. Data and methodology.
3.1 Data .
3.2 Methodology and Summary Statistics .
3.2.1 Cumulative abnormal returns.
3.2.2 Primary explanatory variables.
3.2.3 Hypothesis testing.
188.8.131.52 Pre-announcement liquidity.
184.108.40.206 Cross-sectional regression and control variables.
220.127.116.11 Effect of deal characteristics on CAR predictability.
4. Empirical results.
4.1 Option liquidity .
4.2 Predictive power of options for CARs .
4.2.1 Sorted portfolio approach.
4.2.2 Regression results.
4.2.3 Deal and target characteristics.
4.2.4 Significance of the results.
Financial markets are driven by information and by individuals that generate, process, and disclose this information to the market. Naturally, there have to be individuals who possess more information about a firm or a future event than other market participants. Mergers and acquisitions are particularly interesting events in this regard because they can have significant implications for the firms and stakeholders involved, as well as for the competitive dynamics in the respective market. Because of the large potential price impact of such transactions, traders with private information about a prospective takeover are expected to trade on this information to make a profit. But who are these “informed traders” and what kind of information do they possess?
A vast body of literature deals with the value effects of mergers and acquisitions, as well as with firm characteristics that determine the size of the takeover premium. The asset pricing literature, on the other hand, is concerned with the link between stock and options markets, i.e. which of these is the primary market in the price discovery process of stocks. Research combining these two strands of the literature is, however, fairly scarce. This thesis elaborates on the cross-sectional relationship between options and stocks around takeover announcements. While some research has already been conducted on the predictive power of certain informed option trading proxies for takeover announcement returns, most papers are concerned with the predictability of bidder returns. Target firms are often neglected because few targets are publicly listed and even fewer have (liquid) options traded on their stock, which decreases both the sample and the statistical significance of the results. Target firms also play a smaller role in the literature because it is mainly the announcement returns of bidding firms that allow researchers to draw inferences about the wealth effects of acquisitions. However, target firms might be a lot more attractive for informed traders since target shareholders usually receive a large premium, while the returns of bidding firms are slightly negative on average.
To the best of my knowledge, no attempt has been made in the existing literature to reconcile theories on the predictability of stock returns by means of options with empirical evidence relating firm and takeover attributes to announcement returns of target firms. Why would this be relevant? If certain deal characteristics determine the size of the takeover premium, they should also affect the predictive power of options for stock returns. In other words, they may contribute to our understanding of informed trading and the role options markets play in the price discovery process of stocks. Are informed traders corporate or industry insiders? Do they have knowledge of the details about the offer terms? For example, do they know whether the takeover will be financed primarily with cash or with stocks; is the ownership structure of the target firm relevant; and how certain are informed traders that the takeover will take place? These questions constitute the conceptual framework of this thesis. More generally, my research question is as follows:
What information do “informed” option traders have with regard to the target firm and the deal before the official takeover announcement?
To answer this question, I merge M&A data from SDC Platinum with options and stock data from OptionMetrics and CRSP, respectively, resulting in a sample of 2,390 takeovers. Additionally, I obtain information from Compustat, I/B/E/S, and Corporate Library (all via WRDS). In a first step, I determine if option liquidity rises to abnormal levels in the five days prior to a takeover announcement. Using a logit regression model, I then try to find preliminary evidence that option liquidity proxies such as open interest and bid ask spread can predict takeovers. The results indicate that liquidity rises before an offer and that it has some predictive abilities, showing that informed trading is taking place in options markets.
Second, I replicate a recent paper by Chan et al. (2012) who use an event study, sorted quintile portfolios, and multivariate regressions to examine whether three informed option trading proxies ‒ implied volatility spread, implied volatility skew, and the relative option-to-stock trading volume O/S ‒ have predictive power for takeover announcement returns. For my sample of target firms, I find no evidence that the implied volatility spread is positively related to the takeover premium. Using the three-day average spread, the relationship is positive but remains insignificant. I confirm that implied volatility skew is a negative predictor of the premium. In direct contrast to the existing literature, I find the O/S ratio to be negatively ‒ not positively ‒ related to target announcement returns.
In the final part of this thesis, I interact the three informed option trading proxies with dummy variables representing certain deal characteristics. For example, I argue that cash offers should attract more informed option trading than stock offers because a cash premium is less uncertain and usually needs to be higher due to the tax advantages of stock payments. I find no firm statistical evidence that the predictive power of the three proxies is dependent on how the takeover is financed. Using proxies for managerial entrenchment, I further argue that informed traders with accurate information about the takeover would trade in options even when liquidity is low, because the expected premium is higher if target management is entrenched. On the other hand, less informed traders trade in stocks because entrenchment decreases the probability that the takeover will occur. The interaction term on the O/S ratio supports this narrative. Lastly, I show that informed trading is more pronounced if the target and the bidding firm are in the same industry, i.e. that industry know-how drives informed trading.
This thesis adds to the existing body of literature in several ways: (i) it combines findings from the corporate finance and asset pricing literature; (ii) it focuses on target firms where other studies investigate acquiring firms; (iii) it uses a larger sample than comparable studies; (iv) it shows that option liquidity has limited predictive power for takeover announcements; and (v) it provides some evidence that informed option trading is driven by rational agents with detailed information about future takeovers, and perhaps by industry insiders.
The remainder of this paper is organized as follows: Section 2 will summarize the relevant literature, draw a conceptual framework, and formulate a number of hypotheses; Section 3 presents the data and methodology; Section 4 discusses the empirical results; and Section 5 concludes.
This review summarizes the key contributions of the mergers and acquisition literature and adds to this literature by integrating studies on corporate finance and M&A related asset pricing. While the first part of this section focuses on theories and empirical findings related to the nature of takeover deals and the problems associated with measuring the shareholder value of these transactions, the second part delves into a more recent strand of research that relates option trading proxies to M&A announcement returns. In part 3, the findings from both fields are combined to develop a conceptual framework and to formulate a number of research hypotheses.
Mergers and acquisitions are among the most popular research topics in corporate finance. There is a vast literature on the value effects of these transactions as well as on the question why mergers occur in the first place. For example, Bradley et al. (1988, p. 4) suggest that the primary driving force behind mergers is “an attempt by the bidding firm to exploit a profit opportunity created by a change in economic conditions.” These changes can include shifts in demand or supply, technological advances, as well as deregulation and government policy changes. In a comprehensive literature survey, Andrade et al. (2001) present other plausible explanations for the occurrence of mergers: synergy and efficiency considerations, attempts to create market power, replacement of incompetent target managers, and self-serving motives of the acquiring firm's management (personal diversification, entrenchment, hubris). The empirical literature finds evidence for the hypothesis that merger activity in the late twentieth century was to a large extent the result of economic shocks that triggered entire “waves” of takeovers. During each of these waves, the level of merger activity in some industries was higher than in others, an observation that Mitchell and Mulherin (1996) ascribe to industry-specific shocks such as deregulation and foreign competition that ultimately forced firms into restructurings. The authors hypothesize that takeovers are a relatively inexpensive way to react to a shock, as compared to internal restructurings. Andrade et al. (2001) identify U.S. industries that experienced major deregulation and, subsequently, merger activity, e.g. airlines in the late 1970s, broadcasting in the 1980s, and media/telecommunications in the 1990s. Additionally, each merger wave shows distinct characteristics such as different deal volume, degree of hostility, usage of stock as method of payment, etc.
Determining the wealth effects of takeovers is a non-trivial task. Most studies fall back on Brown and Warner’s (1985) basic event study methodology that assesses the impact of corporate events on share prices. The method uses daily stock return observations over a longer “estimation period” prior to the event announcement and then estimates the expected or “normal” returns for the stock during a shorter time frame around the observed event (“event period”). Normally, this is done with an ordinary least squares (OLS) regression model. The sum of the differences between actual and normal returns is commonly referred to as the cumulative abnormal return (CAR). It is a cornerstone in almost all empirical studies on corporate announcements, including mergers and acquisitions.
Brown and Warner (1985) find that the plain market model is well-specified in the context of event study methodologies, i.e. with regard to the non-normality of returns and return autocorrelation. But there are several other problems that come with this approach. For example, Andrade et al. (2001) point out that the benchmark model for estimating normal returns becomes more important for longer event periods, as expected returns can vary tremendously over long-term horizons. This issue is relevant for M&A studies since not all value effects from an announced acquisition are immediately priced in during the event period. There is always a probability that the deal will not be consummated or that another bidder will ultimately succeed in acquiring the target firm (cf. Bhagat et al. 2005). Technically, the event period should therefore include all days between the announcement and the completion date to ensure that all expected value effects are incorporated. However, this becomes unfeasible in practice as it can take several months before a merger ultimately succeeds (or fails). Extending the event window accordingly adds considerable noise to the model as other news about the merging firms enters the market. Furthermore, returns would have to be discounted over a longer event window, requiring an additional estimate of the appropriate discount rate. According to Bhagat et al. (2005), short-window event studies therefore suffer from a so-called “truncation” bias. Methods of how to isolate the pure takeover value under these circumstances will be discussed in more detail below.
As for the benchmark model, the simple market model (or CAPM) is still widely used and considered accurate enough for short event periods. However, the three- and four-factor models suggested by Fama and French (1993) and Carhart (1997), respectively, generally prove to be a better fit for the empirical data and are recommended for longer event windows as they increase the accuracy of expected return estimates.
The aforementioned problems with the common event study methodology might explain the mixed results coming from empirical M&A studies. Most studies agree that target firms can expect a positive average announcement return between 20 and 50 percent (e.g. Jarrell and Poulsen 1989; Chan et al. 2012; and Croci et al. 2012). Bidding firms pay these large premiums to incentivize target shareholders to tender their shares, conditional to a minimum number of shareholders agreeing to the deal. However, more recent literature finds that the premiums paid for targets vary significantly depending on bidder, target, and deal characteristics. For instance, a survey by Eckbo (2009) finds that the offer premium is higher in single-bid takeover contests than in multi-bid contests (consistent with preemptive bidding strategies). On the other hand, premiums are lower for toehold bidders (bidders acquiring a smaller stake in the target prior to the bid offer) and higher for public than for private bidders, as well as for targets with a higher level of managerial or institutional ownership (Bargeron et al. 2008). Additionally, because cash offers have direct tax implications for target shareholders, those offers should have higher premiums than stock-bids that allow capital gains to be deferred (Travlos 1987). Finally, Betton et al. (2008) find that the initial offer price rises by $0.80 if the target stock price increases by $1 during pre-bid run-ups, which usually occur as rumors about the takeover reach the market.
Despite a large body of literature, evidence for bidding firms is much less conclusive. Roll (1986) theorizes that takeovers have no value for the shareholders of bidding firms. His managerial hubris hypothesis suggests that managers pay large premiums for targets (i.e. also firms with a known market price) because they commit positive valuation errors and “convince [themselves] that the market does not fully reflect the value of the combined firm” (Roll 1986, p. 199). As a consequence, premiums only represent wealth transfers from bidding to target shareholders and acquiring firms systematically overpay. This hypothesis is based on the same behavioral assumptions as the agency theory, according to which managers act in their own selfish interests, which can lead them to engage in value destroying activities (e.g. empire building). Overpayment may become even more severe when several competing bidders are involved. In such cases, “the winning bid [...], on average, overstate[s] the market’s estimate of the expected takeover gain” and shareholders face negative average announcement returns of up to 14% (Varaiya and Ferris 1987, p. 64). The market also expects cross-industry takeovers (diversification) or acquisitions of growth firms to be primarily driven by managerial objectives. For these cases Morck et al. (1990) and Bhagat et al. (2005) find systematically lower bidder or combined bidder-target announcement returns.
Many empirical studies report “normal” (i.e. zero abnormal) announcement returns for bidding firms. This, however, might not have anything to do with the value gain expected from the takeover. Even if an acquisition promises a high positive net present value, the acquirer’s share price might not be affected if the target is too small relative to that acquirer. Not surprisingly, Asquith et al. (1983) show that bidder’s (absolute) announcement returns are positively related to the relative size of the target. Fuller et al. (2002) also point out that the stock price reaction to an announcement is only significant if that announcement comes as a surprise to the market: if a firm follows an active acquisition strategy, the market will only react to non-anticipated features of the deal. In these cases, not all information about the value of the takeover can be inferred from announcement returns. These facts make an objective empirical evaluation of tender offers more than difficult.
Empirical evidence for positive bidder announcement returns is scarce, but it exists. For instance, Asquith et al. (1983) show that average abnormal returns for bidding firms were small but significantly positive prior to the 1980s, ranging from 0.9% to 2.4% depending on the event window. Jarrell and Poulsen (1989) confirm this result but notice that returns were negative in the 1980s. More recent studies mostly observe negative abnormal returns for acquirers. For example, Andrade et al. (2001) report a range from ‒0.7% over a three-day event period to ‒3.8% over a longer window. Moeller et al. (2005) report positive equally weighted abnormal returns but show that shareholders lost 12 cents per dollar spent on acquisitions between 1998 and 2001, indicating a skewed distribution with a small number of “large loss” deals. Similarly, a working paper by Bayazitova et al. (2011) identifies so-called “mega-mergers,” i.e. the top 1% of mergers with regard to deal value. These mergers are reported to have negative abnormal announcement returns of ‒3.2%, while all other deals have positive returns of about 1.5%. This finding is consistent with Roll’s (1986) hubris theory: while the majority of deals is motivated by value-maximization, large M&A deals are likely to be driven by managerial interests and are therefore value-destroying.
These findings suggest that the deal size, the size of the target (relative to the acquirer), the number of competing bidders, the industry (degree of diversification), and managers tendency to hubris (possibly measured with corporate governance proxies) need to be taken into consideration in the analysis of takeover returns.
Why would firms engage in acquisitions if the expected return is zero or negative? Put differently, do mergers and acquisitions actually create value, e.g. by means of synergies? In order to answer this question, one needs to consider the managerial motives behind an acquisition. Outside shareholders have an informational disadvantage relative to managers and inside investors. In the mergers and acquisitions literature, this information asymmetry problem is not limited to managerial hubris but extends to the basic question of how a takeover should be financed. At first, this notion seems to contradict one of the basic corporate finance principles that the source of financing of an investment project is irrelevant as long as that project has a positive net present value. However, Travlos (1987) provides empirical evidence that the method of payment can indeed explain a significant fraction of the variation in M&A announcement returns. While offers made primarily in cash are associated with normal returns, stock offers are found to cause significant losses for the shareholders of the bidding firm. The reason for this discrepancy lies in the nature of a stock-financed acquisition. It is not just an investment project (as is the case with cash offers) but also a financing activity: the issuance of new equity. Since Myers and Majluf (1984), we know that if managers act in the interest of incumbent shareholders, they will try to take advantage of the prevalent informational asymmetry and only issue new equity if the “share of existing assets and slack going to new shareholders [is smaller or equal than] the increment to firm value obtained by old shareholders” (Myers and Majluf 1984, p. 199). According to the authors’ equilibrium model, this is only the case if the firm’s equity is overvalued. Hence, the model predicts that a seasoned equity offering (SEO) will tend to reduce the stock price in response to the overvaluation signal. This theoretical result is consistent with empirical evidence by Mikkelson and Partch (1986), among others, who find large decreases in share prices upon the announcement of common stock or convertible debt issues, as compared to the issuance of other securities.
The same rationale can be applied to the announcement of stock-financed acquisitions. Golubov et al. (2011) try to estimate the value effect of a takeover net of the hypothetical stock price drop associated with an equity issue. As it turns out, the average short-term CAR for stock-financed acquisitions of ‒2.56% is entirely due to the overvaluation signal (see section 18.104.22.168 for other negative revelation effects). The “pure” return of the takeover is found to be +0.1%, which is statistically indistinguishable from cash offer announcement returns. Hence, the financing-irrelevance principle also seems to hold for mergers and acquisitions. It could be argued that, despite this result, shareholders are worse off after the announcement of a stock-financed takeover and do not care about a hypothetical positive return. This argument, however, disregards the positive long-term effect of a value-adding acquisition. The equity's overvaluation will eventually be discovered even without the announcement of the takeover. On the other hand, long-term shareholders should benefit if the pure value of the takeover is considered positive. Savor and Lu (2009) provide empirical evidence for this notion. They show that stock-acquirers use takeovers to convert their overvalued equity into (less overvalued) hard assets and that successful stock-acquirers outperform failed ones by 13.6%, 22.2%, and 31.2% over a one-, two-, and three-year horizon, respectively. This result indicates that long-term shareholders indeed benefit from stock-financed acquisitions, despite negative announcement returns. Hansen (1987) provides a theory that directly relates a bidder’s propensity to offer stock to the level of asymmetric information. Target shareholders will only tender their shares if the offer is higher than the actual value. The resulting adverse selection problem (high uncertainty about the target’s asset value) can be avoided if stock is offered as payment. One implication of the model is that the probability of a stock bid increases with the relative size of the target. Croci et al. (2012) add to these findings by showing that targets receive higher premiums if bidders possess favorable asymmetric information about the target, i.e. if they perceive the “firm-specific risk regarding future synergies” as low. Under these circumstances, bidders are more inclined to offer cash, which the market interprets as evidence for the bidder having superior (positive) information about the target.
As is evident from the discussion above, M&A announcements carry information that is unrelated to the actual deal, making it difficult to determine its “true” incremental value for shareholders. In fact, it is plausible to assume that a takeover announcement always releases some negative information about the stand-alone value of a bidding firm. For example, it could indicate that a firm is no longer able to create profitable internal growth opportunities, e.g. due to a limited market size or a lack of resources, innovation, or organizational flexibility. This additional uncertainty about the future prospects of the firm, combined with the probability of managerial hubris and the rational revaluation of the firm after an implied equity issue, gives rise to the so-called “revelation” bias (cf. Bhagat et al. 2005, p. 4). In order to determine whether an acquisition itself is perceived as beneficial by shareholders, it is necessary to filter out revelation effects first.
A second bias that announcement returns suffer from is “truncation,” which was discussed earlier. This bias is related to the success (or failure) probability of the announced acquisition. Since no deal is entirely certain to succeed (even less so without any post-announcement adjustments), the stock price reaction of both the bidder and the target will not reflect the full value effects of an ultimately successful transaction unless the event window is extended to the completion date, which introduces a great deal of noise (Bhagat et al. 2005).
A third noise factor is discussed by Masulis et al. (2011). There is a possibility that bidders time the announcement of a deal in order to smooth the stock price reaction. For example, managers tend to announce an acquisition together with “bad” news, e.g. unmet earnings targets, which artificially lowers the announcement returns. Also, bidders might “inflate” their stock price by releasing positive news prior to the deal announcement.
There are several recent studies that attempt to isolate synergistic gains from takeovers by disentangling value creation, revelation signals, and the truncation effect. In their pioneer paper, Bhagat et al. (2005) develop two techniques that address the aforementioned biases. First, the probability scaling model tackles the truncation bias by inflating the combined value improvement (both bidder and target) with the sum of the (ex-post) probability that the first bidder succeeds and the probability that a later bid does. The second approach, the intervention method, allows inferring takeover value improvements from stock returns associated with intervening events such as competing bids. These bids decrease the probability that the first bidder will succeed in acquiring the target. Thus, if the stock reacts negatively to a competing bid, the expected value improvement is larger than the purchase price; if the stock reacts positively, the transaction is expected to be value destroying. Additionally, the method accounts for the chance of a price increase upon arrival of a competing bid (cf. Bhagat et al. 2005). The authors find that the combined value improvements, net of revelation and truncation bias, amount to about 13%, indicating that these acquisitions do create value.
In a similar manner, Masulis et al. (2011) examine takeover bids that failed for exogenous reasons. They find a 25.5% combined value gain for bidders and targets, and show that successful bidders significantly outperform failed bidders after consummation/failure. In her analysis of withdrawn and completed mergers, Risik (2010) argues that, when a merger is withdrawn, returns associated with the merger should reverse while those related to revelation should not. She reports that completed acquirers outperform withdrawn ones by 12.3% over the six months after the announcement.
To conclude this first part of the literature review on M&A related corporate finance, we can summarize as follows. Mergers occur in waves that seem to be triggered by exogenous shocks such as deregulation or technological innovations. While target shareholders receive a substantial premium upon announcement of a takeover, shareholders of bidding firms have either normal or negative abnormal returns. Especially stock-financed acquisitions were long considered to be value-destroying. However, there is evidence that most M&A transactions have positive wealth effects for bidding firms, regardless of the method of payment. Since announcement returns suffer from revelation and truncation biases, these positive value effects do not become immediately apparent. In order to isolate the incremental value from mergers and acquisitions, announcement returns need to be adjusted to account for the implied revaluation of the bidder’s stand-alone value as well as for the probability that the deal might fail.
The second part of this literature review covers theoretical and empirical evidence related to options and their predictive power for stock returns. Following an introduction on the basic principles of option trading, the first subsection examines the lead-lag relationship between options and equity markets. Since both markets are closely tied to one another, the following questions arise: How much of a firm’s stock price is actually determined in the forward-looking options market? If there are informed traders, in which market do they trade (first)? Accordingly, can option prices or other related variables predict stock prices? And if this is the case, how long does this predictive power persist? The second subsection builds upon the first, narrowing these questions down to corporate events. This part will also close the circle and return to the theme of M&A transactions, providing evidence on the question whether takeover announcements, abnormal returns, and (both short- and long-term) wealth effects can be predicted by informed option trading proxies.
According to basic financial theory, an option is a redundant security whose price is derived from an “underlying,” i.e. from a primary instrument such as a stock. Nevertheless, options can also be written on commodities, futures, and basically all assets that have an observable market price. In terms of stock options, redundancy means that every option can be replicated by a combination of the underlying stock and a risk-free bond. This holds, however, only in complete markets without transaction costs, taxes, arbitrage opportunities, and short selling constraints. The well-known and widely used option pricing model by Black and Scholes (1973) and Merton (1973) (henceforth BSM) builds upon these assumptions and derives the option price from the stock price, the strike price, the risk-free rate, the option’s maturity, and the stock volatility that is expected over the life of the option. While the buyer of a call option has the right (but not the obligation) to buy a certain amount of the underlying for the strike price from the seller (“writer”), a put option entitles the holder to sell the underlying at the strike price. The contract between the buyer and the seller specifies whether the option can be exercised at any time prior to the expiration date (“American” option) or only on the day of expiration (“European” option). The BSM formula can only be applied to European calls and puts on non-dividend paying stocks. Most traded options are, however, American and need to be valued by means of other models, e.g. binomial trees.
In reality, options cannot be perfectly replicated because of transaction costs and the indivisibility of common stocks. Additionally, traders with negative private information often have no choice but to trade options in lieu of stock due to short selling constraints in the stock market. Option prices are determined by the trading activity in both stock and options markets. The large trading volumes that can be observed in the market indicate that investors take on substantial positions in derivatives. It can therefore be concluded that options are in fact non-redundant securities that are value-adding to at least some market participants.
Since Black (1975) argued that the leverage in options could incentivize traders with private information to trade in options markets, an ever-increasing body of literature has tried to determine the role that options play in the price discovery process of stocks. In theory, new information disseminating into the marketplace should be incorporated in stock and options markets simultaneously. Yet because of market inefficiencies, this need not always be the case. Some early research in this area has revealed little evidence that options markets are particularly attractive to informed traders. For example, Vijh (1990) argues that while the implied leverage in options might attract some informed traders, this is equally true for noise traders (i.e. speculators). He measures the extent of information-related trading, using the adverse-selection component of the option's bid-ask spread (premium charged by dealers to cover losses from trades against better-informed traders) as a proxy, and finds evidence against active information trading. Stephan and Whaley (1990) compare stock price changes implied from option prices with actual stock price changes and conclude that stock markets reflect new information fifteen to twenty minutes earlier than options markets. Chan et al. (2002) focus on the net-trade volume of options and stocks and its effect on quote revisions. The net-trade volume refers to a temporary order imbalance, or more specifically, to the difference between buyer- and seller-initiated trading volumes. While option net-trade volume does not seem to have any predictive ability, stock net-trade carries information about both stock and option quote revisions.
Nevertheless, there is substantial evidence to the contrary, namely that options markets can lead stock markets. Using their sequential trade model, Easley et al. (1998) investigate the role of option trading volume in the price discovery process. Consistent with Vijh (1990), they find no evidence that call or put option volumes predict stock prices. However, when they aggregate “positive news” option volumes (i.e. trades entered into by traders informed of good news, e.g. buying a call or selling a put) and “negative news” volumes (i.e. trading on bad news, e.g. buying a put or selling a call), the authors show that these aggregate volumes do in fact carry information about future stock returns, and that options markets lead stock markets by about twenty to thirty minutes (Easley et al. 1998). Pan and Poteshman (2006) apply an approach similar to Chan et al. (2002), using call and put option volumes that are initiated by buyers to open new positions. These volumes are not publicly observable. They form stock portfolios based on put-call ratios and find that the quintile portfolio with the lowest put-call ratio outperforms the one with the highest put-call ratio by over 40bps per day or 1% per week. In comparison, publicly observable signals do not seem to have predictive power. The authors conclude that the economic source of their result is the “valuable private information in the option volume rather than an inefficiency across the stock and option market” (Pan and Poteshman 2006, p. 873). Chakravarty et al. (2004) show that informed traders trade in both stock and options markets, and estimate the option market’s contribution to price discovery at about 17%. More information can be derived from options if option liquidity is high (i.e. trading volume is high, spreads are narrow) relative to stock liquidity, and if implied leverage is high. Finally, Ni et al. (2008) note that options are the preferred instruments of traders with information about future volatility (but not about the directional movement). Arguing that vega (measure for an option’s exposure to volatility) is positive for both calls and puts, they construct a measure for volatility demand by treating the buy volume for calls and puts as positive demand and the sell volume as negative demand. The net volatility demand is found to predict future realized stock volatility, showing that there are informed volatility traders in the market. This effect is persistent over one week.
While most of the early research on the predictive power of options uses option prices and volumes, a relatively new strand of literature is concerned with more implicit option trading proxies such as option implied volatility. The implied volatility of an option (henceforth IV) is the market’s expectation of the future volatility of the underlying over the life of the option. IV is not directly observable but can be inferred from an option pricing model like BSM by using the observed market price and solving iteratively for volatility. Ceteris paribus, high IV options are more expensive than low IV options.
Cremers and Weinbaum (2010) show that the difference between implied volatilities of call and put options on the same underlying equity, with the same strike price and with the same maturity can be used to identify price pressures and predict stock returns in the cross-section. Amin et al. (2004) call this difference the implied volatility spread (henceforth IV spread). IV spreads are deviations from the put-call-parity but do not usually represent arbitrage opportunities, which is due to the fact that the put-call-parity is an inequality for American options (cf. Hull 2012) and does not account for transaction costs. Along the lines of the sequential trade model of Easley et al. (1998), Cremers and Weinbaum (2010) argue that the presence of informed investors in the options market results in option price deviations from put-call parity in the direction of the private information. It can therefore be expected that a higher call-put IV spread is indicative of positive news not yet reflected in the stock price. In fact, the authors find a positive relation between IV spread and future stock returns. A portfolio that is long in stocks with relatively expensive calls and short in stocks with relatively expensive puts earns an abnormal return of about 50bps per week. This effect is stronger when option liquidity is high and stock liquidity is low, which is consistent with the findings of Chakravarty et al. (2004). Notably, the authors control not only for the four Fama and French (1993) and Carhart (1997) factors but also for systematic return skewness, as suggested by Harvey and Siddique (2000). They justify this additional factor with the concern that stocks with high call (put) prices might have positively (negatively) skewed return distributions to begin with. Bali and Hovakimian (2009) also provide evidence for a positive relation between IV spread and future returns. In their analysis, IV spread proxies for stock price jump risk, which commands a risk premium. In addition, they theorize that the difference between the (one-month) lagged realized volatility and implied volatility proxies for volatility risk. The authors find this second spread to be negatively related to future returns. Bollerslev et al. (2009) basically come to the same conclusion. They show that the difference between (model-free) implied and realized variances positively predicts stock market returns, especially over quarterly horizons.
Another recently introduced proxy for informed option trading is implied volatility skewness (henceforth IV skew). Xing et al. (2010) define IV skew as the difference between the implied volatilities of out-of-the-money (OTM) put options and at-the-money (ATM) call options on the same underlying and with the same expiration date. OTM puts usually trade at a risk premium relative to ATM options because investors have an aversion toward negative jumps in stock prices and overestimate the probability of downward movements. This common characteristic of stock options is also known as the volatility smirk or smile. If some investors have unfavorable private information, they will buy (cheap) OTM puts. As a result, these puts become relatively expensive, which steepens the IV smirk (Xing et al. 2010). Xing et al. find evidence that IV skew is negatively related to future stock returns, i.e. stocks exhibiting the steepest smirks underperform those with the least pronounced smirks by 21bps per week. The effect is persistent over about six months. The persistency found in the empirical literature suggests inefficiencies in the stock market that cannot instantaneously incorporate the information provided in the options market. Alternatively, it could be argued that it is not the stock market’s inefficiency that drives these results but rather a misperception of the importance of the options market in the price discovery process. If the IV smirk is not regarded as a gauge of informed trading but as a measure of common investor sentiment (overly pessimistic investors buy insurance in light of increasing trading activity and volatility), stock prices will only adjust very slowly.
Doran et al. (2007) point out that option pricing models that aim to improve the model-fit over the BSM (which assumes constant volatility) need to account for the volatility smirk and incorporate a jump risk premium. For example, Bakshi et al. (1997) show that the inclusion of stock price jumps improves the performance of their stochastic volatility model, especially for the pricing of short-term options. According to Todorov (2010), the premium investors require for taking variance risk (particularly jump risk) varies over time and increases immediately after the occurrence of a price jump. This shows that extreme price movements drive the risk aversion of market participants, i.e. risk aversion is time-varying. A more detailed review of other option pricing models that include volatility and jump risk is beyond the scope of this paper. Yan (2011) provides a comprehensive survey of the most relevant works in this field. Nevertheless, the finding that both IV spread and IV skew seem to proxy for jump risk make these factors particularly intriguing with regard to event studies that explore the predictability of corporate announcements, that are expected to cause large price jumps for involved firms. The last subsection of this review will therefore focus on these announcements, especially mergers and acquisitions.
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