The Dollar Profits to Insider Trading

1. The Dollar Profits to Insider Trading Jasmin Gider Tilburg University Peter Cziraki University of Toronto March 22, 2019

2. Motivation I – Common prior Corporate insiders generate substantial abnormal returns Introduction Method Data Yearly Qrt/Mth Summary Whatevs

3. Motivation II – Returns ≠ profits Corporate insiders generate substantial abnormal returns BUT • Returns (alpha) can differ from extracted economic value (e.g. Berk and van Binsbergen 2015) This paper: • Returns ≠ profits when trade size and frequency are choice variables • Can dollar profits tell us more about insider trading? Introduction Method Data Yearly Qrt/Mth Summary Whatevs

4. Motivation III – Why care about dollar profits? Theory I: Informed (insider) trading • Quantities chosen strategically to balance costs and benefits (e.g. Kyle 1985, Huddart et al. 2001, Lenkey 2014) • How much the insider cares about trading profits • Insider’s concern for adverse selection costs or litigation risk • These determinants can create a wedge between returns and profits Theory II: Agency • Insiders’ returns as a measure of opportunism (e.g. Ali and Hirshleifer 2017) • But individual utility more strongly linked to $ profits than % returns • Dollar profits = f(abnormal return, trade quantities) • Joint distribution of returns and trade quantities unknown Introduction Method Data Yearly Qrt/Mth Summary Whatevs

5. Research questions 1. Given their superior information, how much value (dollar profits) do insiders extract? 2. What drives dollar profits and trade quantities? • Do predictors of returns also predict trade quantities and profits? • Trading intentions • Monitoring Introduction Method Data Yearly Qrt/Mth Summary Whatevs

6. Contribution • Literature focuses on percentage returns • First to analyze trade quantities and dollar profits • Insider trading as source of private benefits/compensation? (Manne 1996, Hue and Noe 2001, Roulstone 2003, Henderson 2011, Denis and Xu 2013, Cziraki et al. 2014) • First to use short-swing rule to capture trading intentions • Are profits large? • Can monitoring restrain insider trading? • Depends on how do trade quantities and profits respond Introduction Method Data Yearly Qrt/Mth Summary Whatevs

7. Preview of findings Using $ profits vs. % returns offers contrasting evidence on a number of important questions • Typical dollar profits are small • Informed trading proxies predict returns, but not profits • Proxies are negatively correlated with quantities • Frequency is first-order determinant of profits • Sole exception: new proxy of trading intentions based on trading around the short-swing rule threshold • Still, even profits of these insiders remain modest • Different insiders respond differently to increase in monitoring Introduction Method Data Yearly Qrt/Mth Summary Whatevs

8. Insider trading universe • Insider trading data from Thomson Reuters spanning 1986 to 2013 • Aggregate trades by insider-day Introduction Data Data Yearly Qrt/Mth Summary Whatevs

9. Calculating dollar profits – main measure Insider trade t2 t1 • Dollar profit(t1,t2) = return(t1,t2) × value traded • Subtract benchmark, e.g. FF3, to obtain abnormal profit • Abnormal dollar profit(t1,t2) = abnormal return(t1,t2) × value traded • Use window of (0,20): common in literature • Potential profit, insider does not necessarily pocket this • Sample selection: (1) if potential profit is negative, wait for price to adjust, (2) some insiders do not close trades at all Introduction Data Method Yearly Qrt/Mth Summary Whatevs

10. Calculating dollar profits – alternative measure Buy Sell Buy Buy t2 t1 t1’’ t1’ • Dollar profit(t1,t2) = return(t1,t2) × value traded • Return(t1,t2) = (p2 – p1) / p1 • Properties • Actual profit, insider does pocket this • How to calculate p1 if sale is preceded by multiple purchases? • Can insiders profit from price declines? Introduction Data Method Yearly Qrt/Mth Summary Whatevs

11. Summary statistics Introduction Data Turnover Yearly Qrt/Mth Summary Whatevs

12. Infrequent traders: high returns, but low profits Returns and yearly profits by frequency deciles Introduction Data Predictors Short-swing Qrt/Mth Summary Whatevs

13. Known predictors of percentage returns • Buy:purchases more likely to be information-driven, sales may be motivated by diversification or liquidity (e.g. Jeng, Metrick, and Zeckhauser 2003) • Opportunistic:trades deviating from routine trading patterns are more informative (Cohen, Malloy, and Pomorski 2012) • CFO:CFO trades more informative (Wang, Shin, and Francis 2015) • Executive: Trades by insiders closer to decision-making are more informative (e.g., Ravina and Sapienza 2010) Introduction Data Predictors Short-swing Qrt/Mth Summary Whatevs

14. Returns vs. profits: informed trading proxies Each cell contains results from a separate regression, with controls and FE Returns of infrequent traders 37% higher, but profits more than 100% lower. Introduction Data Predictors Frequent Qrt/Mth Summary Whatevs

15. Revealed preferences using the short-swing rule • Short-swing rule:Round-trip profits within less than 6 months have to be returned to issuer • Section 16(b) of the Securities Exchange Act of 1934 • Exploit potential discontinuity to infer trading motives • Bunching around points that feature discontinuities to elicit behavioral responses (Bach and Metzger 2018, Goncharov, Ioannidou and Schmalz 2018) • Manipulability: assignment variable is discrete choice – opposite of RD • Study insider behavior around the 6-month threshold • Null: If insiders did not care about keeping profits, distribution around 180 days between opposite trades shouldbe continuous • Close trade just after expiration → likely driven by profit-seeking Introduction Data Predictors Short-swing Qrt/Mth Summary Whatevs

16. Bunching around 180 days – Density McCrary t = 24.79 Cattaneo et al. t = 10.54 Introduction Data Predictors Short-swing Qrt/Mth Summary Whatevs

17. Bunching around 180 days – Tests Introduction Data Predictors Short-swing Qrt/Mth Summary Whatevs

18. Bunching around 180 days – Subsamples Introduction Data Predictors Short-swing Qrt/Mth Summary Whatevs

19. Discontinuity around 180 days – Profits Introduction Data Predictors Short-swing Qrt/Mth Summary Whatevs

20. Mechanical relation? • Stark differences between trades closed 181-200 days and other trades • Is this all mechanical? • Suppose insider happens to get lucky • This is why she closes the trade just after expiration • Disentangling intentions from a mechanical relation: • Does short-swing trading predict future behavior? • Inconsistent with mechanical interpretation Introduction Data Predictors Short-swing Qrt/Mth Summary Whatevs

21. “Short-swing closers” make higher future profits Only group where both abnormal returns and trade frequency are higher Short-swing trading predicts higher future returns and profits Introduction Data Predictors Short-swing Qrt/Mth Summary Whatevs

22. “Short-swing closers” make higher future profits New measure particularly good at predicting right tail of the distribution Short-swing trading predicts higher future returns and profits Introduction Data Frequent Short-swing Qrt/Mth Summary Whatevs

23. SEC enforcement budget as monitoring proxy How do abnormal returns and profits respond to variation in monitoring intensity? • Resource-based measure of enforcement (DelGuercio, Odders-White, and Ready 2017) • Determined through political process, not by amount of insider trading • Produces variation in attention by regulator/monitoring Introduction Data Predictors Short-swing Monitoring Summary Whatevs

24. SEC enforcement budget Returns, volume, and per-trade profits decrease with monitoring, but yearly profits do not Introduction Data Predictors Short-swing Monitoring Summary Whatevs

25. SEC budget: frequent vs. infrequent traders When SEC enforcement intensity is high: • Infrequent traders trade less, their trades are less profitable • Frequent traders trade (even) more,realizehigher profits Introduction Data Predictors Short-swing Monitoring Summary Whatevs

26. Summary $ profits and % returns offer contrasting evidence on insider trading! • Typical insider trading profits are small • Informed trading proxies predict returns, but not profits • Proxies are negatively correlated with quantities • Frequency is first-order determinant of profits • Our novel proxy of trading intentions predicts profits • Even insiders identified by this measure make modest profits • Different insiders respond differently to monitoring Introduction Data Predictors Short-swing Monitoring Summary Whatevs

27. Thank you for your attention!

28. Yearly abnormal profits over time Introduction Additional slides Data Turnover Yearly Qrt/Mth Summary Whatevs

29. Calculating dollar profits – long term P=16 P=10 P=8 P=12 Buy Sell Buy Buy t2 t1 t1’’ t1’ • What is the correct purchase price (~COGS) for the sale? • “Which” of the 12 shares did the insider sell? • 3 methods: Value-weighted (main), LIFO, FIFO (robustness) Introduction Additional slides Data Method Yearly Qrt/Mth Summary Whatevs

30. Volume-weighted price P=16 P=10 P=8 P=12 Buy Sell Buy Buy t2 t1 t1’’ t1’ • Purchase price used to evaluate the sale is VW average of the purchase prices • Purchase price = (4×8 + 6×10 + 2×12)/12 = 9.67 • Profit = 6×(16-9.67) = 37.98 Introduction Additional slides Data Method Yearly Qrt/Mth Summary Whatevs

31. Last in, first out (LIFO) P=16 P=10 P=8 P=12 Buy Sell Buy Buy t2 t1 t1’’ t1’ • Purchase price used to evaluate the sale is based on the LIFO method • Purchase price = (2×12 + 4×10)/6 = 10.67 • Profit = 6×(16-10.67) = 31.98 Introduction Additional slides Data

32. First in, first out (FIFO) P=16 P=10 P=8 P=12 Buy Sell Buy Buy t2 t1 t1’’ t1’ • Purchase price used to evaluate the sale is based on the FIFO method • Purchase price = (4×8 + 2×10)/6 = 8.67 • Profit = 6×(16-8.67) = 43.98 Additional slides Introduction Data Whatevs

33. Summary Stats: Purchases vs. Sales While purchases have higher abnormal returns, sales volumes and sales profits to sales are substantially larger Additional slides Whatevs

34. Round-trip trades vs. other observations • Round-trip trades are smaller, but generate higher abnormal returns • Round-trip profitsare thus available for more profitable transactions, so they overestimate the profits to the average insider • 7.8% of overall sample comes from round-trip trades Introduction Additional slides Data Turnover Yearly Qrt/Mth Summary Whatevs

35. The short-swing rule – Details I • Section 16(b) of theSecuritiesExchangeAct of 1934 (15 U.S.Code Section 78p(b)) • https://www.law.cornell.edu/uscode/text/15/78p • “For the purpose of preventing the unfair use of information which may have been obtained by such beneficial owner, … • …any profit realized by him from any purchase and sale, or any sale and purchase, of any equity security of such issuer… • …within any period of less than six months… • …shall inure to and be recoverable by the issuer, irrespective of any intention on the part of such beneficial owner…” Introduction Additional slides Data Frequent Short-swing Governance Summary Whatevs

36. The short-swing rule – Details II • “Suit to recover such profit may be instituted at law or in equity in any court of competent jurisdiction by the issuer, or by the owner of any security of the issuer…” • Attorneys are aware of this: http://www.legalandcompliance.com/wp-content/uploads/2014/07/LC-White-Paper-070114.pdf • “Forms 3, 4 and 5 filed with the SEC arepublicly available and are routinely monitored by attorneys who make their living by threatening to file Section 16(b) suitson behalf of shareholders” • Short-swing liability calculator from Andrew Chin (UNC): http://16b.law.unc.edu/frontend/home.html Introduction Additional slides Data Frequent Short-swing Governance Summary Whatevs

37. Discontinuity around 180 days – Realized profits Introduction Additional slides Data Frequent Short-swing Qrt/Mth Summary Whatevs

38. Discontinuity around 180 days – Abnormal returns Introduction Additional slides Data Frequent Short-swing Qrt/Mth Summary Whatevs

39. Discontinuity around 180 days – Trade size Introduction Additional slides Data Frequent Short-swing Qrt/Mth Summary Whatevs

40. Subsample tests in more detail • Null: nr. trades before vs. nr. trades after equal across subsamples Introduction Additional slides Data Frequent Short-swing Qrt/Mth Summary Whatevs

41. “Short-swing closers” make higher profits • 6.5% of round-trip trades by short-swing closers • 75% higher abnormal returns and 150% more in yearly profits • Average (median) profit still modest! • Average total compensation is 3M Introduction Additional slides Data Predictors Short-swing Qrt/Mth Summary Whatevs

42. “Short-swing closers” make higher profits • 6.5% of round-trip trades by short-swing closers • 75% higher abnormal returns and 150% more in yearly profits • Average (median) profit still modest! • Average total compensation is 3M Introduction Additional slides Data Predictors Short-swing Qrt/Mth Summary Whatevs

43. Robustness: Profits from non-trading • Concern: earn profits not through trading, but by strategically not trading • Solution: track year-to-year changes in value based on reported holdings • Result: Profits from non-trading are negative both on average and at the median Introduction Additional slides Data Frequent Short-swing Governance Summary Whatevs

44. Robustness: Departing insiders I • Concern: Roundtrip transaction completed after insider leaves • Solution: • Measure lag between last reported trade and departure • Measure long-term profits over this gap • Result: Profits from non-trading are negative both on average and at the median Introduction Additional slides Data Frequent Short-swing Governance Summary Whatevs

45. Robustness: Departing insiders II • Results: • Gap between last transaction and leaving is 1-3 years • Abnormal profits smaller than those for observed round-trip transactions. • Also, they are negative when the last transaction is a purchase Overall, trades of insiders who leave cannot bias our results. Introduction Additional slides Data Frequent Short-swing Governance Summary Whatevs