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Quantitative Stock Selection Strategies Based on Momentum

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?.

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Quantitative Stock Selection Strategies Based on Momentum

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

  2. Outline • Strategy Overview • Factor Analysis • Conclusion

  3. Strategy OverviewWhy 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

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

  5. Factor AnalysisFactors 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

  6. Factor AnalysisAverage 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

  7. Factor AnalysisBenchmark Outperformance • Two factors had performed well when analyzing % of benchmark outperformance

  8. Factor AnalysisCumulative Returns – In Sample • The cumulative returns for a long/short strategy show that Factor #4 outperforms the rest` Factor #4

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

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

  11. Factor AnalysisFactor #4 – Cumulative Returns • In-sample returns show a huge return in 1999 • Out-of-sample returns are somewhat inconclusive In Sample Out of Sample

  12. ConclusionFactor #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

  13. ConclusionMomentum 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

  14. Questions?

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