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

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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

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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

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4 Current Yield/PE Introduction 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

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5 Current Yield /PE Return 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

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6 Div. Payout Ratio Introduction 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).

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7 Div. Payout Ratio Return 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.

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8 Momentum Factor Introduction 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

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9 Momentum Factor Return 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

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10 Reversal Introduction 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

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11 Reversal Return 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

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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

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

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14 The Weighted Factor Introduction 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)

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15 The Weighted Factor Returns 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 500% over the same period

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16 The Weighted Factor Volatility and Sharpe Ratio Q5 has higher 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

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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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McGraw-Hill/Irwin © 2007 The McGraw-Hill Companies, Inc., All Rights Reserved. Efficient Markets & The Behavioral Critique CHAPTE R 8.

McGraw-Hill/Irwin © 2007 The McGraw-Hill Companies, Inc., All Rights Reserved. Efficient Markets & The Behavioral Critique CHAPTE R 8.

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