Short Term Overreaction based on Price and Volume

1. Short Term Overreaction based on Price and Volume Jared Mackey Yuriy Polevoy Matt Wilson

2. Strategy Overview • Hypothesis: Assuming extreme price movements are driven by investor overreaction and information asymmetry, we should make a profit investing in stocks that are likely to exhibit a short term correction in price. • Strategy: Stocks with extreme past price movements and decreasing trading volume should exhibit extreme price movements in the opposite direction.

3. Details of Strategy 5 Step Approach: 1 – Select 300 largest NYSE/AMEX stocks 2 – Filter stocks based on the previous week’s lagged returns 3 – Filter stocks based on % change in trading volume 4 – Form strategy portfolio 5 – Liquidate portfolio at the end of the week (and repeat steps 2-5).

4. Details of Strategy Step 1 - Rank 300 largest NYSE/AMEX stocks. - At the beginning of each year, select the 300 largest stocks by market capitalization. - Only large stocks will be included in the contrarian portfolio. 300 Largest Capitalization Firms All NYSE/AMEX

5. Details of Strategy Step 2 – Filter stocks based on previous week lagged returns. • Define securities as winners or losers. • Only include if lagged weekly return moved up or down by a specified amount. • Isolate price changes due to investor overreaction. • Screen out price changes due to noise. Large + Returns Medium + Returns Small + Returns Small – Returns Medium – Returns Large – Returns

6. Details of Strategy Step 3 – Filter stocks based on % change in trading volume. • Further divide stocks based on volume. • Applied filters based on the percentage change in their trading volume. • Stocks with decreasing trading volume should exhibit greater reversals. Changes in Volume Best Returns Large Large Decrease Increase Large Large Decrease Increase Worst Returns

7. Details of Strategy Step 4 – Form strategy portfolio. • Buy stocks with the worst lagged returns and the largest decrease in trading volume. • Short stocks with the best past week returns and the largest decrease in trading volume. Step 5 – Liquidate portfolio at the end of the week. SHORT:BEST RETURNS & LARGEST DECREASE IN VOLUME BUY:WORST RETURNS & LARGEST DECREASE IN VOLUME

8. Motivation Short-Term Overreaction Lehmann (1990): Fads, Martingales, and Market Efficiency Uses one week past returns to create winner/loser portfolios Finds that winner (loser) returns are negative (positive) in the subsequent week but diminish over longer periods of time

9. Motivation Short-Term Overreaction • Empirical evidence suggests that investors overreact to new information. • “Price Reversals, Bid-Ask Spreads, and Market Efficiency” Allen B. Atkins and Edward A. Dyl • Short term (1 day to 1 month) overreaction prevails in the market • “Size, Seasonality, and Stock Market Overreaction,” Paul Zarowin

10. Motivation Why use the 300 largest NYSE/AMEX stocks? • Conrad, Gultekin, and Kaul (1997): Profitability and Riskiness of Contrarian Portfolio Strategies • Reversals on large capitalizations NYSE securities are less affected by bid-ask bounce problems than reversal evidence on small-capitalization securities. • Forester and Keim (1993): Direct Evidence of Non-Trading of NYSE and AMEX Stocks • -Incidents of non-trading, which tend to upwardly bias contrarian profits, are less likely in large-capitalization stocks.

11. Motivation Step 1 - Rank 300 largest NYSE/AMEX stocks. Keim and Madhavan (1997): Execution Costs and Investment Performance: An Empirical Analysis of Institutional Equity Trades -Large capitalization securities are less likely to be affected by high transaction costs. They have smaller relative bid-ask spreads and face smaller price pressure effects.

12. Motivation Step 2 – Filter stocks based on previous week lagged returns. DeBondt and Thaler (1985): Does the Stock Market Overreact? -Extreme movement in stock prices will be followed by extreme movement in the opposite direction. -The more extreme the initial movement, the greater will be the subsequent adjustment.

13. Motivation Step 3 – Filter stocks based on % change in trading volume. Wang (1994): A Model of Competitive Stock Trading Volume • Investors are heterogeneous in their information and private investment opportunities. • Uninformed investors cannot perfectly identify the informed investors' motive behind each trade. • As the true state of the economy is revealed, the uninformed investors realize their mistakes. • A high return accompanied by high volume implies high future returns.

14. Data • From CRSP (Centre For Research and Security Prices) download annual returns of all stocks listed on NYSE and AMEX, from 1993 to 2006. • From CRSP, download daily stock price, volume, and shares outstanding for the largest 300 firms from December 20, 1993 to December 31, 2006. • Download weekly risk free rates for January 1, 1994 to December 31, 2006.

15. Data Analysis • Market capitalization rankings will be made on December 31 of each year. • Define filter breakpoints and ranges. • Each stock that meets the filter break points will be given an equal weight in the portfolio. • If there are no successful stocks, invest in the risk-free rate for that week.

16. Data Analysis • Market capitalization rankings will be made on December 31 of each year. • Each stock that meets the filter break points will be given an equal weight in the portfolio. • If there are no successful stocks, invest in the risk-free rate for that week.

17. Data Analysis • Market capitalization rankings will be made on December 31 of each year. • Each stock that meets the filter break points will be given an equal weight in the portfolio. • If there are no successful stocks, invest in the risk-free rate for that week.

18. Data Analysis • Determine the strength of our hypothesis. • Compare our strategy portfolio with the returns of the stocks in the other filter groups. • Compare against the average return of a holding the 300 largest capitalization stocks. • Compare against holding the broader market index (we will use the S&P 500).

19. Data Analysis • Find the mean (%) and standard deviation of the weekly returns. • Compute t-statistics for a mean = 0 % null hypothesis. • Computed t-statistics to measure the significance of the difference between two means.

20. Data Analysis • Investigate alternative time periods for stock selection and holding period. • Larger reversals for larger consecutive losses/gains? • Does a longer holding period improve profitability?

21. Data Analysis • Market capitalization rankings will be made on December 31 of each year. • Each stock that meets the filter break points will be given an equal weight in the portfolio. • No successful stocks, invest in the risk-free rate for that week.

22. Data Analysis • Market capitalization rankings will be made on December 31 of each year. • Each stock that meets the filter break points will be given an equal weight in the portfolio. • No successful stocks, invest in the risk-free rate for that week.

23. Data Analysis • Market capitalization rankings will be made on December 31 of each year. • Each stock that meets the filter break points will be given an equal weight in the portfolio. • No successful stocks, invest in the risk-free rate for that week.

25. Data Analysis Filter Plans • Filters will be defined based on the overall sample distribution from the annually ranked top 300 largest market capitalization NYSE stocks. • We will define breakpoints and ranges for weekly returns, low growth in volume, and high growth in volume based on the sample distributions. • We will target approximately the 1, 2.5, 5, 10, 25, 50, 75, 90, 95, 97.5, 99 percentile points. • - The following 2 slides are designed to illustrate what our filter groups will look like.

26. Data Analysis Price Filter Rules We will divide all 300 stocks into 1 of the 12 categories below based on their previous week return. Loser (0%): If 0% > Return in week t-1 ≥ -2% Loser (2%): If -2% > Return in week t-1 ≥ -4% Loser (4%): If -4% > Return in week t-1 ≥ -6% Loser (6%): If -6% > Return in week t-1 ≥ -8% Loser (8%): If -8% > Return in week t-1 ≥ -10% Loser (10%): If Return in week t-1 < -10% Winner (0%): If 0% ≤ Return in week t-1 < 2% Winner (2%): If 2% ≤ Return in week t-1 < 4% Winner (4%): If 4% ≤ Return in week t-1 < 6% Winner (6%): If 6% ≤ Return in week t-1 < 8% Winner (8%): If 8% ≤ Return in week t-1 < 10% Winner (10%): If Return in week t-1 ≥ 10%

27. Data Analysis Volume Filter Rules - To construct volume filters we will analyze individual security weekly percentage changes in volume, adjusted for the number of shares outstanding: %∆ volumei,t = [(V i,t / S i,t) – (V i,t-1 / S i,t-1)] [V i, t-1 / S i, t-1] Si,t = number of shares outstanding for security i Vi,t = the weekly volume for security i in week t Stocks will be subdivided based on volume approximately as follows: Low Growth (0%): If 0% > %∆ volumei, t-1 ≥ -15% Low Growth (15%): If -15% > %∆ volumei, t-1 ≥ -30% Low Growth (30%): If -30% > %∆ volumei, t-1 ≥ -45% Low Growth (45%): If -45% > %∆ volumei, t-1 ≥ -60% Low Growth (60%): If -60% > %∆ volumei, t-1 ≥ -75% Low Growth (75%): If %∆ volumei, t-1 < -75% High Growth (0%): If 0% ≤ %∆ volume i, t-1 < 15% High Growth (15%): If 15% ≤ %∆ volume i, t-1 < 30% High Growth (30%): If 30% ≤ %∆ volume i, t-1 < 45% High Growth (45%): If 45% ≤ %∆ volume i, t-1 < 60% High Growth (60%): If 60% ≤ %∆ volume i, t-1 < 75% High Growth (75%): If %∆ volume i, t-1 ≥ 75%

28. Data Analysis