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GLOBAL ASSET ALLOCATION AND STOCK SELECTION. ASSIGNMENT # 1 SMALL CAP LONG-SHORT STRATEGY FIRST-YEAR BRAVES Daniel Grundman, Kader Hidra, Damian Olesnycky, Jason Trujillo, Alex Volzhin. Methodology. Goal: to identify long-short strategy for trading US small cap stocks using Fact Set.

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GLOBAL ASSET ALLOCATION AND STOCK SELECTION


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    1. GLOBAL ASSET ALLOCATION AND STOCK SELECTION • ASSIGNMENT # 1 • SMALL CAP LONG-SHORT STRATEGY • FIRST-YEAR BRAVES • Daniel Grundman, Kader Hidra, Damian Olesnycky,Jason Trujillo, Alex Volzhin

    2. Methodology • Goal: to identify long-short strategy for trading US small cap stocks using Fact Set. • Universe Definition: US stocks with market cap from $300M to $2B. • Strategy: Buy 1st quintile, Short 5th quintile. • Benchmark: S&P 500 • In-sample period: Jan, 1995 – Dec, 2004 • Out-of-sample period: Jan-Dec, 2005

    3. Factors • We tested many factors but settled on three: • One-month return • Six-month return • Current price to 52-week high • Additionally, we tried various combinations of these factors (two-factor and tree-factor models)

    4. Strategy Based on1-Month Return 1-Month Return 1-Month Alpha

    5. Strategy Based on6-Month Return 6-Month Return 6-Month Alpha

    6. Current Price to 52-Week High Price to 52-Week High Alpha Price to 52-Week High Return

    7. Other Explored Factors • In addition to the previous 3 factors, we tried several other metrics: • Book to Market Price • Price to Earnings • Dividend Yield • Return on Equity • Revision Ratio • However, we found all of them to be of little value.

    8. Book to Market Price Book to Price Return Book to Price Alpha

    9. Price to Earnings P/E Return P/E Alpha

    10. Revision Ratio Revision Ratio Return Revision Ratio Alpha

    11. Returns • Our one-factor models delivered good returns: • 1-Month Returns Model +6.98% • 6-Month Returns Model +4.26% • Price to 52-Week High +3.55% • However, two-factor models were even better: • 1-Month Return & Price to 52-Week High +6.95% • 6-Month Return & Price to 52-Week High +4.55%

    12. Bivariate Model: 1-Month Return & Price to 52-Week High

    13. Beta for Bivarate P to 52High & 1 Month Return Model

    14. Bivariate Model: 6-Month Return & Price to 52-Week High

    15. Multivariate Model Multivariate Model Return Multivariate Model Alpha

    16. Scoring • We used scoring for bi-variate model (1-month return and price to 52-week high) • For 1-month return: • 1st quintile +5, 5th quintile -5 • Price to 52-week high: • 1st quintile +3, 5th quintile -3 • More weight on 1-month return because single-factor model based on 1-month return is superior to that based on price to 52-week high.

    17. In-Sample Two-Factor Model:1-Month Return & Price to 52-Week High with Scoring In-Sample Model w/ Scoring Return In-Sample Model w/ Scoring Alpha

    18. Beta for Bivarate 52-P and 1- Month Return Scoring Model

    19. Out-of-Sample Testing • We used the period from January, 2005 to December, 2005 for the out-of-sample testing of our best model (two-factor: 1-month return & current price to 52-week high). • Annualized Returns - • Benchmark Return: 0.4% • Our model without scoring: 11.79% • Our model with scoring: 12.07%

    20. Out-of-Sample Two-Factor Model: 1-Month Return & Price to 52-Week High w/o Scoring Out-of-Sample Model Return Out-of-Sample Model Alpha

    21. Out-of-Sample Two-Factor Model Beta: 1-Month Return & Price to 52-Week High without Scoring

    22. Out-of-Sample Two-Factor Model: 1-Month Return & Price to 52-Week High with Scoring Out-of-Sample Model w/ Scoring Alpha Out-of-Sample Model w/ Scoring Return

    23. Out-of-Sample Two-Factor Scoring Model Beta: 1-Month Return & P to 52-W High with

    24. In-Sample Results (1/2) Heat Map In-Sample WITHOUT Scoring: • Quintile 1 has NOT the highest average return. • Only 3/10 years have the highest returns. • Here we are concerned by 2003 when we actually got the lowest returns in Quintile 1. • The spread would have crushed us! • Quintile 5 has the lowest average return. • 5/10 years have the lowest returns. • Here we are concerned by 2003 when we actually got the highest returns in Quintile 5.

    25. In-Sample Results (2/2) Heat Map In-Sample WITH Scoring: • The scoring screen alleviates our concerns: • Fractile 1 has the highest average return. • 8/10 years have the highest returns. • The scoring eliminates the 2003 crush! • Fractile 5 has the lowest average return. • 10/10 years have the lowest returns.

    26. Out-of-Sample Results (1/2) Heat Map Out of Sample WITHOUT Scoring: • Quintile 1 has the highest average return. • Only 3/12 months have the highest returns. • Here we are concerned by these 2 months where we actually got the lowest returns in quintile 1. • Quintile 5 has the lowest average return. • 8/12 months have the lowest returns. • Here we are concerned by these 2 months where we actually got the highest returns in quintile 5. • The Long/Short spread is satisfactory: 36%

    27. Out-of-Sample Results (2/2) Heat Map Out of Sample WITH Scoring: • The scoring screen alleviates our concerns: • Quintile 1 has the highest average return and outperform the unscored screen by far! • Quintile 1 has the highest average return. 10/12 months have the highest returns. • Quintile 5 has the lowest average return and underperformed the unscored screen by far! • Quintile 5 has the lowest average return. 9/12 months have the lowest returns. • The Long/Short spread is satisfactory: 147%.

    28. Long/Short DistributionsPositively Skewed After Scoring

    29. Concerns • Transaction Costs • Short Selling Constraints • Execution • Volatility/Exit Signals • Fact Set

    30. Concerns Transaction Costs • Monthly rebalancing • Many months have >50% change in fractile components. • Large number of securities • ~60 Stocks per fractile per month

    31. Concerns Short Selling Constraints • Dealing only with small cap securities. • May be limited opportunity to short sell some securities.

    32. Concerns Execution • How to execute as an actual trading strategy. • When to run model? • When do you make trades?

    33. Concerns Volatility and Exit Signals • Portfolios are not Beta neutral and overall betas are usually above 1. • No parameters set for liquidating portfolios. • In sample we had several very bad months. • Given the high volatility of small caps, there is the potential for very large losses.

    34. Concerns Fact Set • Limited knowledge of the tool. • Results seem almost too good. • In practice we would run tests to verify that what we believe is happening is actually happening.

    35. Limitations • Primary limitation is the fund size for which this is compatible. • Relatively few securities • Low market capitalizations • Solution: Change screen • Wider market cap range • Low trading volume requirement

    36. Summary • We find the results of our analysis to be very compelling. • The big challenge is efficient and proper execution. • Proper study of transaction costs is required. • We would also recommend a further review of the data before moving forward.