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The Disposition effect and U nderreaction to news

The Disposition effect and U nderreaction to news. Abdullah Al- Ashi Jungha Woo Muna Albasman Talha Yasin. Current Objectives. Reproduce the following tables from “The Disposition Effect and Under-reaction to News”:

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The Disposition effect and U nderreaction to news

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  1. The Disposition effect and Underreaction to news Abdullah Al-Ashi Jungha Woo MunaAlbasman TalhaYasin

  2. Current Objectives • Reproduce the following tables from “The Disposition Effect and Under-reaction to News”: • Table IV: Post-Earning announcement drift, monthly alphas (2003-2009) – sorted using CAR only • Table V: Overhang Spread and Negative Overhang Spread Alphas (2003-2009) – sorted using CAR and Gt • Table VI: Monthly Alphas by Overhang Quintiles(2003-2009) – sorted using CAR and Gt • Table VII: Three Factors Time-Series Regressions: Alphas and Factor Loadings (2003-2009) – sorted using CAR and Gt • Analyze regression results, compare to paper to check if reasonable • Test and debug code (individually and collectively) • Compare Matlab regression vs. SAS regression to evaluate correctness

  3. Achieved (so far) • The following codes (used interchangeably to produce the four tables): • Cumulative abnormal returns CAR (SAS): Provides CAR for all companies within a certain time period, output txt file • Capital gain overhang Gt (SAS): Provides Gt for all companies within a certain time period , output txt file • Mindate (SAS): Provides the monthly starting trading date (to use in rolling portfolios) , output txt file • Company (SAS): Provides a valid list of all companies to include in rolling portfolios within a certain time period , output txt file • Regression (Matalb): Regress rolling portfolios excess returns to calculate alphas and reproduce tables using Matlab, output regression and alphas, (extra space benefited us) • Price (SAS): Provides a list of companies along with adjusted prices within a certain time period , output txt file • Regression (SAS): Regress rolling portfolios excess returns to calculate alphas using SAS, output regression and alphas, (working fine for small data set, experiencing errors when running whole data set, need to do in another method)

  4. Current Status • Integrated all codes into the Matlab regression code to produce the following: • Rolling Portfolios with different rolling periods (+1,+2,+3) within a certain period of time • Rolling Portfolios excess returns (way of presenting /calculation/sorting vary from table to table) • Divide Portfolios into quintiles (depending on table reproduced) • Regress per quintile to obtain alphas and reproduce graph • Problems from last week (when reproducing table IV): • Most alphas where negative (even for top quintiles) • Used the T-bill data incorrectly (used as percentage/ daily values) • Results where very far from what obtained in the paper • Still fixing the SAS regression code (making it more efficient, use less memory)

  5. Paper results (table IV)

  6. Our last week results (table IV) • All negative alphas • q3 should be: q2 < q3 < q4, same for q1 (No trend)

  7. Results after fixing T-bill only • Did not fix the negative excess returns (still generated all negative alphas)

  8. Alphas are still negative! Discovered another problem (1/3) of the portfolio showed: -1 -1 . . . . . Why?

  9. Recall, old regression quintile 5 (+1) rolling period (without removing -1’s)

  10. After fixing T-bill and removing (-1)’s • Still all negatives (but bigger than before) • Still, q3 should be: q2 < q3 < q4, no trend • Checked CAR code again (no problem) • Suggestions: try to remove initial (-1)’s

  11. New regression quintile 5 (+1) rolling period

  12. New regression quintile 5 (+1) rolling period (optimistic, removing all (-1)’s) Alpha is still negative for the top quintile q5= - 0.044744

  13. Questions?

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