Options Trading Activity and Firm Valuation

1. Options Trading Activity and Firm Valuation Richard Roll, Eduardo Schwartz, and Avanidhar Subrahmanyam UCLA

2. The Issue • Ross (1976) -- options can improve market efficiency by expanding contingencies covered by traded securities (they help to complete the market). Allocational efficiency. • Also, since informed traders may prefer to trade options rather than stock (more leverage), options may allow agents to trade more effectively on their information, thus improving informational efficiency.

3. The Issue, contd. • Cao and Wei (2007) find that informational asymmetries play a more dominant role in influencing options liquidity (relative to stocks). • Easley, O’Hara and Srinivas (1998), Chakravarty, Gulen, and Mayhew (2004) find that options order flows contain information about future direction of the underlying stock price.

4. The Issue, contd. • If prices reveal more information, then resources are allocated more efficiently, which translates to higher firm valuations. • In addition, greater informational efficiency could reduce investment risk because market prices reflect information more precisely. • These arguments suggest that firms with higher options trading volume should be more informationally efficient and thus valued more highly.

5. A point of clarification • The mere listing of an option does not necessarily imply a valuation benefit. • If the options market has insufficient volume, the valuation benefit from listing would be minor because informed traders see no advantage to trading in options (Admati and Pfleiderer, 1988). • Any valuation benefit of options listing should depend on the amount of trading activity. • To the best of our knowledge, the relation between options trading activity and firm valuation has not been examined previously.

6. The Analysis • We analyze the effect of options trading volume on firm value after controlling for other variables that may also affect firm value such as firm size, share turnover, return on assets, capital expenditures, leverage and dividend payments. • Following other studies we use a measure of Tobin’s q as the valuation metric.

7. Findings • We find strong evidence that firms with more options trading volume have higher value. • Firms with more options trading activity in a given period tend to have improved financial performance in the next period. • This is consistent with the premise that options trading, by enhancing information flows, may lead to better corporate resource allocation.

8. Findings, contd. • The results also show that the effect of options trading on firm valuation is greater in stocks with low analyst following. • This indicates that the impact of options trading on information production is larger in stocks where investment analysis produces comparatively less public information.

9. Data • Options trading data from Option Metrics – 1996 to 2005: 10 years of daily data (we aggregate to total annual options volume for each stock). • Matched with data from Compustat on Tobin’s q and a set of control variables. • Tobin’s q is computed as the sum of the market capitalization of the firm’s common equity, the liquidation value of its preferred stock, and the book value of its debt divided by the book value of the firm’s assets (total firm q). All results go through if we use M/B of equity instead of q.

10. Control variables • A proxy for the firm’s leverage, long-term debt to total assets, is intended to measure the likelihood of distress, LTD. We expect higher LTD, lower q. • Profitability, ROA, intended to capture the notion that more profitable firms may have more favorable investment opportunities. On the other hand, high ROA may also mean that the firm is in a mature phase, and has limited growth opportunities. The relation between ROA and q is an empirical issue. • Share turnover in the underlying stock: liquidity effects arising from stock trading activity as opposed to options activity.

11. Controls, contd. • A direct measure of investment opportunities is capital expenditures divided by sales (CapX) —high values should mean greater q. • A dummy variable for whether the firm pays a dividend proxies for capital constraints (firms that pay dividends may have more free cash flow, which may potentially be used to overinvest in marginal projects). • Firm size (market value of firm’s shares).

12. Number of firms with nonmissing data Natural bifurcation of sample

13. Summary Stats All Firms Positive Options Volume

14. Correlation matrices (all firms) Mature Firms?

15. Correlation matrices, Firms with Positive Options Volume

16. Correlations • The correlation between q and options volume is strongly positive (also with share turnover and CapX) • q is negatively related to leverage as well as the dividend dummy • q is negatively related to ROA: mature phase with fewer opp. for growth?

17. Preamble to main analysis • For sample with positive options volume: sorted into deciles volume each year. For each decile we compute the average value of q across all years. • Indicative that firms with higher options volume have higher q.

18. Figure 1: Average Tobin’s q vs. options volume Significant

19. Sort by size and then by volume • To allow for independent variation in size and volume. • Average q for the resulting 25 portfolios. • q increases with volume for every size group.

20. Options volume and q by size Strong effect

21. Regressions • Year-by-year cross-sectional regressions and then test the significance of the time series coefficients. t-statistics are corrected by procedure Newey/West: residuals of the c-s regressions are likely to be serially correlated due to the autocorrelation in q.

22. Regression results – all firms Dependent Variable: Tobin’s q Time series of annual cross-sections t-statistic corrected by Newey/West

23. Regression results – firms with positive options volume

24. Summary of results • Tobin’s q is positively and significantly related to options trading; the effect is economically significant, 16% to 23% increase in q for a one sigma increase in options volume, ceteris paribus • q is also negatively related to leverage and the dividend dummy, consistent with proposed hypotheses • Stock trading activity also bears a positive relation with q

25. Robustness Checks • Various checks were performed and in all cases the central results are unchanged • Skewness • Panel regression • Endogeneity issues • Industry effects • Additional explanatory variables

26. Results with log(options volume) for firms with positive options volume This checks whether the skewness in options volume affects the results; it doesn’t. From now on we use Ln(optvol).

27. Panel Regression: pools cross-section and time-series data Balanced Panel, to accommodate serial correlation and cross correlation in the errors, using the Parks (1967) Procedure (see Appendix.) Firms included must be present in all years (502 firms).

28. Endogeneity • One could argue, albeit implausibly, that high q firms may attract more attention and this may translate to greater options volume (reverse causality). • One simple way to address this issue is to consider the relation between q and one-year lagged options trading volume. • Then we use an Instrumental Variable approach.

29. Regression results using lagged options volume

30. An instrument • We need an instrument for options trading volume that is inherently unrelated to q. Finding such an instrument is a difficult endeavor and inevitably involves an element of subjectivity. • We propose that options volume may be related to the average absolute moneyness, the relative difference between the stock’s market price and the option’s strike price (correlation 0.19). • An alternative instrument for options volume is the total open interest in options within a given year.

31. IV estimation (2SLS) First equation: q as a function of same variables, using optvol from Second equation: optvol as a function of instrument and size. Main result is not due to reverse causality.

32. Year-by-year coefficients on ln options volume (dependent variable – Tobin’s q) Are unusual years driving the results; they’re not. Are industry outliers (e.g., the tech bust) are responsible; they aren’t.

33. Other robustness checks • Results are robust to scaling options volume by shares outstanding, and to using log transformation of the positive controls. • To test that option trading activity does not proxy for stock riskiness which could potentially affect q: return volatility is not significant in the overall regression for Tobin’s q and the options volume variable remains significant.

34. Options trading and future firm performance: Further analysis • Identify the mechanism by which options trading enhances firm value. • If options trading activity leads to better corporate resource allocation, then there may be a relation between future firm profitability and options trading. • We regress ROA (our measure of financial performance) on lagged values of options volume and control variables.

35. Firm performance and options trading (LHS variable=ROA) Persistence Parks procedure to account for autocorrelation

36. Firm performance and options • There is a positive relation between future ROA and current options activity • This supports the information channel: that more options trading is associated with greater informational efficiency, which, in turn, leads to improved resource allocation.

37. Options Trading and Investment Sensitivity to Stock Price • The degree to which managers obtain information from market prices to make investment decisions can be captured by the sensitivity of corporate investment to market prices. • Several papers have theoretically and empirically analyzed this sensitivity. • But, managers might learn more from market prices when options volume is greater (produces private information).

38. Corporate Investment • Sum of capital expenditures and R&D expenses scaled by beginning of year book assets. • We look at the interaction variable of q with options volume. • We also include q to capture the baseline effect of market valuation on investment (both lagged) and other controls.

39. Corporate Investment (LHS) Lagged 1 yr

40. Corporate Investment • Positive sensitivity of investment to stock price (q). • Greater sensitivity to q when options trading is high. • Supports notion that options trading contributes to information production, which managers use in making corporate investment decisions.

41. Information Asymmetry • Effect of options trading on valuation may be more pronounced in stocks with greater levels of informed trading. • Difficult to find a measure for level of informed trading in options markets. • We use the PIN (probability of informed trading, computed with stock data) measure of Easley, Hvidkjaer and O’Hara (2002) as a proxy for information asymmetry.

42. Information Asymmetry • Using the structure of a sequential trade market microstructure model, they derive an explicit measure of the probability of information based trading (PIN) for an individual stock • For stocks with high PINs the effect of option volume on valuation should be greater.

43. Information Asymmetry Interaction Variable The effect of options volume on q is stronger in stocks where more information is produced by the trading process.

44. Information Asymmetry • Options volume variable remains significant and the interaction of options volume with PIN is positive and mostly significant. • Suggestive evidence that the effect of options volume on q is stronger in stocks where more information is produced by the trading process.

45. Security analysts and options trading • Options volume could proxy for another measure of information production, the extent of analysts following. If no. of analyst following a company is included in the regressions: not significant whereas option volume remains significant. • Results are also robust to the inclusion of the dispersion of long-term growth forecasts by analysts which is forward looking measure of uncertainty.

46. A role for analyst following • The effect of options in information production may be greater in stocks with low analyst following, where little public information is produced and trading on private information may be more important. • In these cases private information may play a stronger part in information production.

47. Testing the impact of analyst following • We sort the sample each year into three groups by analyst following, and label them 0,1,2. • We interact options volume with this indicator variable and include the interaction variable in the regression.

48. Regression results with interaction variable for analyst following

49. Interpretation of results with inclusion of analyst following • The impact of options volume on Tobin’s q is stronger in firms with less analyst following, but it remains significant even for firms with large analyst following. • Suggests private information production is more important in stocks where investment analysts produce less public information.

50. Bottom Line • The amount of options trading is associated with higher firm valuations. • This result is consistent with the dual notions that more options trading is associated with greater informational efficiency of prices and superior resource allocation. • The results survive when subjected to a variety of robustness checks, including different specifications of volume.