1 / 17

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

omana
Download Presentation

Quantitative Stock Selection

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  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

More Related