Quantitative Stock Selection Strategies Based on Momentum Presented by: ICARUS MANAGEMENT GROUP Krista Deitemeyer Scott Dieckhaus Ian Enverga Jeremy Hamblin February 27, 2006

Outline  Strategy Overview  Factor Analysis  Conclusion

Strategy Overview Why Momentum?  Momentum strategy can help satisfy many client and portfolio objectives Determine which securities to overweight and underweight in an existing benchmark Use it for a long-short strategy  Many people in the industry dispute the validity of such strategies Test those pundits

Strategy Overview Universe Definition  US common stock  Market capitalization between $500 million and $1 billion (scaled for time) These firms may have greater price inefficiencies than those that have a larger market capitalization Hypothesis

Factor Analysis Factors Examined  Factor #1: (1m avg volume * 1m % price change) / 3m avg volume  Factor #2: Price / 3m avg price  Factor #3: Price / 1m avg price  Factor #4: 1m avg price / 1y avg price  Factor #5: 1m avg price / 3m avg price  Factor #6: 1m avg price / 6m avg price  Factor #7: 3m avg price / 6m avg price  Factor #8: 12m net sales / Year ago 12m net sales  Factor #9: (Price - 1m avg price) / 1m avg price

Factor Analysis Average Monthly Returns  A look a the average returns of the top and bottom fractiles of each factor shows that four of the factors are the most promising

Factor Analysis Benchmark Outperformance  Two factors had performed well when analyzing % of benchmark outperformance

Factor Analysis Cumulative Returns – In Sample Factor #4  The cumulative returns for a long/short strategy show that Factor #4 outperforms the rest`

Factor Analysis Factor #4 – Average Fractile Returns  Factor #4: 1m average price / 1y average price  Average In-Sample monthly returns for each fractile shows strong linear relationship

Factor Analysis Factor #4 - Yearly Returns Heat Map Out of Sample In Sample  Heat map indicates a long/short strategy would be profitable every year, except the first out of sample year

Factor Analysis Factor #4 – Cumulative Returns Out of SampleIn Sample  In-sample returns show a huge return in 1999  Out-of-sample returns are somewhat inconclusive

Conclusion Factor #4  Pros Profitable strategy both in-sample and out-of- sample  Cons Monthly turnover of around 80% means trading costs are very high Significant outperformance during 1999 skews results  Recommendation Improve on Strategy before implementation

Conclusion Momentum Strategies  Profitable opportunities do exist but trading cost issues need to be overcome  Further Exploration: Layer a predictive model for up or down markets, then implement the strategies that would perform the best based on the prediction Look at different universes (e.g. Large cap, all stocks, emerging markets) Optimize fractile size and rebalancing periods

Questions?