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.  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

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)

Strategy Based on 1-Month Return 1-Month Return1-Month Alpha

Strategy Based on 6-Month Return 6-Month Return6-Month Alpha

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

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.

Book to Market Price Book to Price ReturnBook to Price Alpha

Price to Earnings P/E Return P/E Alpha

Revision Ratio Revision Ratio ReturnRevision Ratio Alpha

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%

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

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

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

Multivariate Model Multivariate Model ReturnMultivariate Model Alpha

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.

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

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

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%

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

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

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

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

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.

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.

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%

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%.

Long/Short Distributions Positively Skewed After Scoring

Concerns  Transaction Costs  Short Selling Constraints  Execution  Volatility/Exit Signals  Fact Set

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

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

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

Concerns  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. Volatility and Exit Signals

Concerns  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. Fact Set

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

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.