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Quantitative Stock Selection

Quantitative Stock Selection. Portable Alpha Gambo Audu Preston Brown Xiaoxi Li Vivek Sugavanam Wee Tang Yee. Stock Selection Approach. Identify short-term technical factors and fundamental value-oriented factors

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Quantitative Stock Selection

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  1. Quantitative Stock Selection Portable Alpha Gambo Audu Preston Brown Xiaoxi Li Vivek Sugavanam Wee Tang Yee

  2. Stock Selection Approach • Identify short-term technical factors and fundamental value-oriented factors • Combine factors in effort to produce excess returns relative to the market without extreme volatility • The potential securities were constrained: • Public US-based companies • Top 500 companies by market capitalization • For final screen, companies with stock prices lower than $5 were removed • In-sample 1990-1999, out-of-sample 2000-2005

  3. Final Screen Constituents • Final screen combined technical and value-oriented factors: • Current Yield/PE • Dividend Payout Ratio • Momentum • Reversal • Voom • Room for improvement is available

  4. Current Yield/PEIntroduction • Definition: Trailing current dividend yield over P/E ratio • We expect the factor to have a positive correlation with stock returns • If the indicator is high, the dividend is relatively high while the stock price is relatively low, which means the stock price may be undervalued • This ratio also shows how market participants evaluate the firm as the P/E ratio reflect market expectations • FactSet code FG_DIV_YLD(0) / FG_PE(0) • As fractiles increase we see • Declining returns • Higher standard deviation • Decreasing success at beating the benchmark • Consistent across up and down markets • Higher volatility spikes massive return in fractile 5 occasionally (e.g. 10/99- 1/00) over fractile 1

  5. Current Yield /PEReturn and Volatility • The declining returns through the in-sample period show that implementing a long/short trading strategy by buying quintile 1 and shorting quintile 5 is profitable. The out-of-sample test is less clear, but shows the same possibility • Both in-sample and out-of-sample, quintile 5 has a higher standard deviation than quintile 1

  6. Div. Payout RatioIntroduction • Definition: Dividend per share over EPS. • The payout ratio provides an idea of how well earnings support dividend payments. • More mature companies tend to have a higher payout ratio. • Low payout ratio means firms retain large portions of earnings to support long-term growth. • FactSet code FG_DIV_PAYOUT • As fractiles increase we see • Increasing returns • Higher standard deviation • Better success at beating the benchmark during up markets, but not during down markets. • Higher volatility leads to large returns in fractile 5 occasionally (e.g. 10/99 and 5/00).

  7. Div. Payout RatioReturn and Volatility • Increasing returns during the in-sample period show that implementing a long/short trading strategy by buying quintile 5 and shorting quintile 1 is profitable. The out-of-sample test confirms this possibility. • Both in-sample and out-of-sample, quintile 5 has a higher standard deviation than quintile 1, suggesting caution in using this factor.

  8. Momentum FactorIntroduction • Definition: 12 month price change/Previous 1 year price • Based on long-term over-reaction from investors • Formula: (CM_P(-1)-CM_P(-13))/CM_P(-13) • As fractiles increase, returns and standard deviation decrease • No significant differences between in-sample and out-of-sample returns

  9. Momentum FactorReturn and Volatility • From 12/89 to 1/05, declining returns through fractiles suggest the possibility of generating returns through a long-short strategy across high and low fractiles

  10. ReversalIntroduction • Definition: Price change over previous month • We expect previous month returns to reverse • Short-term momentum, not reversal takes place • Stocks that gained in the previous month continue to gain • Stocks that lost in the previous month continue to lose • FactSet code FG_PRICE_CHANGE(-22,NOW) • As fractiles increase we see • Decreasing returns • Mildly increasing standard deviation • Decreasing proportion of positive returns • Decreasing proportion of benchmark-beating returns • Consistent across up and down markets • Occasional volatility spikes (e.g. 1/99) when fifth fractile outperforms massively

  11. ReversalReturn and Volatility • From 12/89 to 1/05, declining returns through fractiles suggest the possibility of generating returns through a long-short strategy across high and low fractiles • High standard deviation on low fractiles are signs of high occasional spikes in last quintile returns

  12. Voom (Volume x Momentum)Introduction • Change in volume scaled by price magnitude and direction • 1 month price change * (10 day Avg Vol / 3 month Avg Vol) • Hypothesis was that large Voom could predict strong positive or negative trends • Reality was that Voom was much better at predicting sell-offs • When Voom was high, stock price tended to drop in the following month • Voom stayed consistent through both in and out of sample periods, and across up and down markets • Need to employ a long/short strategy to create a portfolio that is market neutral and is best positioned to have consistent returns regardless of market direction

  13. VoomReturn and Volatility • Returns are negative for the first quintile, and then grow positive. • 4th quintile performed well with low volatility • Equal weighted portfolio is more consistent through time • Suggests that 1st quintile can be used as a short strategy, and a blend of the 4th and 5th quintiles can be used for a long strategy

  14. The Weighted FactorIntroduction • Created from subjectively-weighted factors that were determined to best describe portfolio. Weighted factors include: • Momentum (Scored 4 for Quintile 1 & -2 for Quintile 5) • Reversal (5 for Quintile 1 & -5 for Quintile 5) • Voom (-4 for Quintile 1, 4 for Quintile 4 & 3 for Quintile 4) • Current Yield/PE (3 for Quintile 1 & -3 for Quintile 5) • Payout Ratio Score (5 for Quintile 5)

  15. The Weighted FactorReturns • Observed trend shows that annual returns decrease uniformly from Q1 to Q5,indicating that a long-short investing strategy would be effective • Cumulative Returns for Q1 > 5000% over time period (in and out of sample); cum. returns for Q5 < 100% over same period (mkt returns > 500% over the same period

  16. The Weighted FactorVolatility and Sharpe Ratio • Q5 has higher s than Q1, despite the fact that returns for Q5 are lower than for Q1 • This fact is validated by the comparing Sharpe Ratios – Q1 SR > 0.35, Q5 SR < 0

  17. Conclusion • Reversal and the weighted score formed the best factors with monthly F1-F5 returns of over 2% • Investors should guard against volatility spikes with options • Transactions costs may be high for some factors • Next steps • Incorporate forward-looking factors (e.g. FY2 P/E) • Optimize weights on weighted score • Examine interaction and macro variables as factors

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