The Dog That Did not Bark: Insider Trading and Crashes

1. The Dog That Did not Bark:Insider Trading and Crashes Jose M. Marin Universitat Pompeu Fabra and CREA Jacques Olivier HEC School of Management, GREGHEC and CEPR Research Seminar Finrisk University of Zurich - 12/15/2006

2. Motivation (1) • A famous quote from Sir Arthur Conan Doyle (« Silver Blaze »): ‘Is there any other point to which you would wish to draw my attention?’ ‘To the curious incident of the dog in the night-time’ ‘The dog did nothing in the night-time’ ‘That was the curious incident’ remarked Sherlock Holmes Research Seminar Finrisk University of Zurich - 12/15/2006

3. Motivation (2) • This paper: • The mystery: why the price of individual stocks sometimes crashes without the arrival of fundamental news? • The dog: Insiders • The hypothesis: crashes may be caused by the absence of dog barking (insiders trading) Research Seminar Finrisk University of Zurich - 12/15/2006

4. Outline of the Talk • Theory • Outline of the model and key predictions • Existing literature • Models of crashes • Existing evidence on insider trading • What we can learn from the data • The data • Basic results • Our story vs. competing hypotheses • Robustness checks • Conclusions Research Seminar Finrisk University of Zurich - 12/15/2006

5. The Theory (1) • Theoretical Model: • Static CARA-normal REE model with floor constraints on holdings (e.g. short-sales constraints) • Uninformed investors: • Rational, risk averse • Observe the equilibrium price • Insiders: • Rational, risk averse • Income risk à la Bhattacharya-Spiegel • Observe a noisy signal about fundamentals • Subject to floor constraint on holdings • Key element: no noise traders, thus trading by insiders is known by uninformed investors Research Seminar Finrisk University of Zurich - 12/15/2006

6. Research Seminar Finrisk University of Zurich - 12/15/2006

7. The Theory (2) • When insiders are selling: • Uninformed investors observe the sales • Partial downward adjustment of price proportional to insider sales • When insiders do not trade but floor constraint non-binding • No adjustment of prices • When insiders do not trade but floor constraint binding • Uninformed investors suspect that the insiders received bad news (they don’t buy and they cannot sell) • Uninformed investors cannot infer how bad the news received by insiders really are • Lower expected payoff + higher uncertainty → stock price crashes Research Seminar Finrisk University of Zurich - 12/15/2006

8. The Theory (3) • Multiple equilibria with similar qualitative properties: Research Seminar Finrisk University of Zurich - 12/15/2006

9. The Theory (4) • Testable Implications: • Crash occurs when floor constraint is binding, thus: • Insider sales in the past should raise the probability of a crash today • Crash occurs when insiders are not trading, thus: • Insider sales today should lower the probability of a crash today • No ceiling constraint, thus: • No symmetric finding for (positive) jumps of stock price Research Seminar Finrisk University of Zurich - 12/15/2006

10. Existing Literature (1) • Two broad families of models of crashes • Models where crashes bring prices closer to fundamental values: • Bubbles (e.g. Allen, Morris and Postlewaite, 1993) • Herding (e.g. Devenou and Welch, 1996) • Trading constraints or transaction costs (e.g. Cao, Coval and Hirschleifer, 2002 ; Harrison and Hong, 2003) • Models where crashes are periods of higher uncertainty about true value • E.g. Barlevy-Veronesi (2003), Yuan (2005), us • What the data can tell us: do crashes coincide with informed investors (insiders) getting into the market or out of the market? Research Seminar Finrisk University of Zurich - 12/15/2006

11. Existing Literature (2) • Consensus of existing literature on insider trading (e.g. Lakonishok and Lee, 2001, Friederich et al., 2002, Fidrmuc et al. 2006) • Insider purchases contain information • Insider sales mostly driven by liquidity • What our model tells us: impact of insider sales on returns theoretically ambiguous • What the data can tell us: do insider sales contain information about crashes? Research Seminar Finrisk University of Zurich - 12/15/2006

12. The Data (1) • Insider Transactions: • TFIF Database • All insiders transactions for stocks traded on NYSE, NASDAQ, AMEX between 1985 and 2002 • Cleaning procedure taken directly from Lakonishok and Lee (2001) • Returns data: CRSP • Earning announcement dates: Compustat Research Seminar Finrisk University of Zurich - 12/15/2006

13. The Data (2) • Crash variables: • Constraint imposed by our model: • Has testable implications about when a crash occurs • Does not have testable implications about size of crash (because of multiple equilibria) • Thus, define crash variable as a 0/1 variable • Constraint imposed by regulation: • Prior to 2002, insider trade may be reported only month after trade • Thus, work at monthly frequencies • Constraint imposed by our model: • Makes sense at individual stock level • Makes less sense at the level of the market • Thus need to correct for market fluctuations Research Seminar Finrisk University of Zurich - 12/15/2006

14. The Data (3) • Crash variables (continued): • Thus, two alternative measures of crashes: • ERCRASHi,t = 1 if excess return of stock i in month t is more than 2 standard deviations away and below average excess return (computed over a 5-year rolling window) • MMCRASHi,t computed the same way as ERCRASH using 1-factor beta adjustment • Average threshold for a monthly return to be considered a crash: - 22% Research Seminar Finrisk University of Zurich - 12/15/2006

15. The Data (4) • Insider trading variables • INSSAL, INSPURCH and INSTV • Normalized by market capitalization of the stock at the close of the transaction day • Past returns • Included for two reasons: • Found to predict insider trading by existing literature • Found to predict negative skewness by existing literature • Total trading volume • Included for two reasons: • Found to predict negative skewness by existing literature • Want to make sure that insider trading is not a proxy for total trading volume Research Seminar Finrisk University of Zurich - 12/15/2006

16. Basic Results (1) • Preliminary regression: do crashes coincide with insiders getting into the market or out of the market ? Research Seminar Finrisk University of Zurich - 12/15/2006

17. Basic Results (2) • Leading regression: does the pattern of insider sales predict crashes the way suggested by the theory? Research Seminar Finrisk University of Zurich - 12/15/2006

18. Competing Stories (1) • Insiders trying to evade SEC scrutiny: • SEC investigates insider trades close to date of large stock market fluctuations • Insiders who do not want to see their trades investigated only exploit long-lived information • Thus selling by insiders today unlikely to coincide with crash in near future • Key factual element: SEC prosecutes at least as much insiders having purchased stocks before (positive) jumps as they do insiders having sold shares before crashes (e.g. Meulbroek, 1992) • Implication: • If the “evading SEC scrutiny” story is correct then pattern of insider purchases prior to (positive) jumps should be at least as strong as pattern of insider sales prior to crashes • If our story is correct, then pattern should disappear Research Seminar Finrisk University of Zurich - 12/15/2006

19. Competing Stories (2) • Is the pattern of insider purchases prior to jumps the same as pattern of insider sales prior to crashes? Research Seminar Finrisk University of Zurich - 12/15/2006

20. Competing Stories (3) • Result could be a pure artefact: • Many crashes occur on earning announcement dates • Insiders are not allowed to trade before earning announcement dates • Thus we observe in the data that times at which insiders have not traded also correspond to times at which crashes are more frequent • Implication: • If the “earning announcement date” story is correct then pattern of insider sales prior to crashes should disappear once we remove all observations corresponding to months where earning announcement occurred • If our story is correct, then the evidence should be at least as strong as in the entire sample Research Seminar Finrisk University of Zurich - 12/15/2006

21. Competing Stories (4) • What is the evidence in the subsample without earning announcement dates? Research Seminar Finrisk University of Zurich - 12/15/2006

22. Robustness Checks (1) • Estimation procedure: • This version: OLS with Newey-West standard errors • Next version: Conditional Logit with Fixed Effects Research Seminar Finrisk University of Zurich - 12/15/2006

23. Robustness Checks (2) • Definition of crash variable • Crashes defined using raw returns • One of two variables loses significance • Dividing the sample into two subsamples • Before and after 1996 • Stronger results after 1996 • Window for past insider trading • 6 months vs. 1 year vs. 2 years • Retain significance • 2 years works slightly better than 1 year which works slightly better than 6 months Research Seminar Finrisk University of Zurich - 12/15/2006

24. Conclusions • Insiders get out of the market shortly before it crashes • Implication: crashes are periods of higher uncertainty • Pattern of insider sales help predict crashes • Implication: insider sales also contain information • Pattern of insider sales before crashes and pattern of insider purchases before jumps are different Research Seminar Finrisk University of Zurich - 12/15/2006