Tuesday, September 25th @ 11:00-12:30 PM
A Deep Learning Investigation of One-Month Momentum
Ben Gum, AXA Rosenberg
The one-month return reversal in equity prices was first documented by Jedadeesh (1990), who found that there was a highly significant negative serial correlation in the monthly return series of stocks. This is in contrast to the positive serial correlation of the annual stock returns. Explanations for this effect differ, but the general consensus has been that the trailing one-month return includes a component of overreaction by investors. Since 1990, the one-month return reversal effect has decayed substantially, which has led others to refine it. Asness, Frazzini, Gormsen, and Pedersen (2017) refine this idea by adjusting MAX5 (the average of the five highest daily returns over the trailing month) for trailing volatility. They define a measure SMAX (scaled MAX5), which is the MAX5 divided by the trailing month's daily return volatility. SMAX is designed to capture lottery demand in excess of volatility. They show that SMAX has an even stronger one-month return reversal than trailing month return In this talk, I first replicate the results of Jedadeesh and Asness as benchmark models. Iconfirm that SMAX outperforms simple return reversalover the test period 1993-2017. However, the effectiveness of SMAX declines substantially over the test period. Using an enhanced combination of return statistics, I improve upon SMAX. I further improve upon SMAX byapplying Neural Networks to trailing daily active returns. Note that all of these signals decay substantially in effectiveness over the common test period 1998-2017.