1. Investors' Horizons and the Amplification of Market Shocks Cristina Cella Stockholm School of Economics Andrew Ellul Indiana University Mariassunta Giannetti Stockholm School of Economics, CEPR and ECGI

2. Research Motivation Severe market shocks cause panic selling precisely when potential buyers are financially constrained Investors with shorter trading horizons are inclined or forced to sell in bad times  Because of preferences or specialization, they care about short-term returns  Because they face constraints, such as investor outflows or margin constraints, shortening their horizons De Long, Shleifer, Summers, Waldmann (1990)

3. Theoretical Mechanisms Selling pressure  Limits to arbitrage  Shleifer and Vishny, 1997  Expectations of outflows due to negative performance may lead investors to sell undervalued stocks  Financial market runs  Bernardo and Welch, 2003; Morris and Shin, 2004  Short horizon investors sell in anticipation of sales of other market participants  Investors with long horizons can wait out the storm and generate less selling pressure  Margin constraints  Brunnermeier and Pedersen, 2009 Lack of potential buyers  Market frictions  Duffie, 2010; Duffie and Strulovici, 2009  Market frictions prevent buying capital from moving quickly to buy temporary undervalued stocks  Sales of distressed assets  Shleifer and Vishny, 1992  When a distressed seller tries to sell an asset, she faces two types of potential buyers: (a) from the same industry and (b) outside the industry  If investors in the same industry are distressed as well, then buyers have to come from outside the industry

4. This Paper Can institutional investors’ trading horizons amplify the price effects of severe market shocks? Such shocks will naturally impact stocks’ fundamentals Stocks’ returns may vary cross-sectionally depending on the horizons of investors  Do stocks of firms held by short horizon investors experience larger temporary drops after negative shocks?

5. Hypotheses During severe market shocks, stocks mostly held by short-term investors should experience:  Larger drops – returns drop below their normal (expected) returns  Larger reversals If prices fall below their fundamental values then reversals should occur as buyers move in to buy these stocks. Reversals should be largest precisely for stocks that have suffered the most severe drops.

6. Related literature Fire sales Coval and Stafford (2007), Mitchell, Pedersen and Pulvino (2007), Pulvino (1998), Campbell, Giglio and Pathak (2008) and Ellul, Jotikasthira and Lundblad (2009)  We explore the role of investor horizon in accentuating fire sales Literature exploring the effects of investor horizon on corporate policies Bushee (1998 and 2000); Gaspar, Massa, and Matos (2005) etc… Papers exploring the effects of firm characteristics on returns during financial crises E.g. Fahlenbrach and Stulz (2011); Lemmon and Lins (2003); Mitton (2002)

7. The Main Event Large stock market declines after Lehman Brothers’ bankruptcy in September 2008

8. Data Sources Quarterly ownership information reported by Thomson Financial in 13Fs over the period 1990-2006 CRSP COMPUSTAT

9. Investor Horizons Horizon is identified as average holding period  (e.g., Carhart (1997), Barber and Odean (2000), Bushee (1998, 2000 and 2001), Gaspar, Massa and Matos (2005) and Yan and Zhang (2009)) We use the average investor’ turnover from 1990 to 2006

10. Investor Horizons Large variation in investor portfolio turnover  5 th percentile turns over less than 1% of their portfolio in a quarter  95 th percentile turn over more than 50% of their holdings in a quarter Short horizon investors are not necessarily active investors  Portfolio turnover has low correlation with the proportion of the portfolio that deviates from the relevant index (Cremers and Petajisto, 2009)

11. Investor Turnover The average Investor Turnover (IT) of a high IT firm is 0.37 and that of a low IT firm is 0.19 An average turnover of 0.37 (0.19) implies that institutional investors holding these stocks rotate almost 19% (9.5%) of a portfolio in each quarter, and 76% (37%) in each year This means that on average investors in high turnover firms hold their position for less than 16 months, while investors in low turnover firms hold their position for almost 33 months

12. Trading and Investors’Horizons In the aftermath of the Lehman shock, short-term investors sold 21% of their holdings compared to 7% sold by long-term investors

13. Investors’ Horizons and Selling Pressure Selling pressure during episodes of market turmoil may not necessarily be related to investors’ trading horizons Some hedge funds had strict lock-up periods that limited outflows and had a lower propensity to sell during the crisis (Ben-David, Franzoni, and Moussawi (2010)) Index mutual funds without the protection of lock- up periods may face severe redemptions leading to severe selling pressure during periods of crisis

14. Investors’ Horizons and Selling Pressure

15. Investors Horizons and Selling Pressure In periods of market turmoil, all institutional investors’ net sales increase by the equivalent of almost 0.3 standard deviations for an investor with average net sales and average churn ratio The increase in net sales is equivalent to over 0.65 standard deviations for an investor with a churn ratio in the top quartile of the distribution but to less than 0.15 standard deviations for investors with a churn ratio in the lowest quartile

16. Cumulative Abnormal Returns Two (main) definitions of normal (expected) returns  Based on the market model Estimated using weekly returns from the beginning of 2003 until the end of the first quarter of 2008  Based on contemporaneous returns of 100 portfolios sorted on size and book to market Ikenberry, Lakonishok and Vermaelen, 1995 Robustness tests using (two) multifactor models including VIX and Pastor and Stambaugh’s liquidity factor

17. CARs: Market Model The stocks held to a larger extent by investors with shorter horizons experience more severe price drops and larger price reversals

18. CARs: Size & Value Portfolios

19. Economic and Statistical Significance

20. Innovations in Implied Volatility Innovations in time-varying market volatility, considered to reflect the probability of a market-wide meltdown, may either change the risk-return trade-off, or the expectations of future returns (Campbell (1996) and Chen (2002)) It can be argued that the price dynamics we uncover just reflect differences in the stocks’ exposure to the probability of a meltdown

21. Innovations in Implied Volatility Differences in exposure to aggregate volatility risk?  We estimate abnormal returns using Ang, Hodrick, Xing, and Zhang’s (2006) multifactor model  We use changes in aggregate volatility, measured by the VIX, as an additional factor in the computation of normal returns

22. Exposure to Aggregate Volatility

23. Exposure to Liquidity Risk Heterogeneity between the two groups of stocks driven by their differences in the exposure to liquidity risk?  We estimate a multifactor model including the market return and the aggregated liquidity factor as in Pastor and Stambaugh (2003)

24. Exposure to Liquidity Risk

25. Rest of Talk I will try to convince you that the previous striking figure does not depend on  firm heterogeneity  characteristics of the investors trading strategy (other than investor horizon) i.e., indexers vs active investors; momentum traders

26. Do Low and High IT Firms Differ? Low and High IT firms differ along a number of dimensions  High IT firms have lower insider ownership  High IT firms are more liquid and more volatile  High IT firms have lower leverage Yet, it’s important to notice that:  High IT firms have beta only slightly larger relative to low IT firms

27. Is it Really Investor Horizon? Portfolio sorts – looking at subsamples of more homogeneous firms  Our benchmarks already take into account differences in size and book-to market  We find larger drops and reversals for stocks held by short-term investors within portfolio quintiles sorted on Share turnover  Not merely a liquidity effect Return volatility Past returns  Not merely a momentum effect Cross-sectional & panel regressions

28. Portfolio Sorts

29. Price Drop - Cross-section Analysis

30. Price Drops – Panel Regressions

31. Price Reversals

32. Testing the Mechanisms (I) Investors with short horizons may sell more for some (unobserved) reason related to returns Investor portfolio turnover captures  trades whose motivation is to generate profits (or limit losses)  trades forced by other reasons such investor flows Historical correlation between assets under management and past performance related to forced trades  It can be used as an instrument Instrumental variable estimates confirm the results shown so far

33. Results from IV Methodology

34. Testing the Mechanism (II) Do short-term investors sell also the stocks mostly held by long-term investors in their portfolios?  If not some stock unobservable characteristics could explain their trades Net Asset Position as % of Total Asset Value (invested in each type of stock)

35. Other Major Market Shocks Do we point out something specific to the fall 2008?  Other events during the 2007-2008 crisis  Large mkt declines (15% drop in the S&P500 in one month)

36. Other Major Market Shocks

37. Other Major Market Shocks Multivariate Analysis

38. Conclusions Evidence that investors’ short trading horizons amplify negative shocks Future research: What determines investor horizon?