Seasonal Effects, Trends and Pre-Announcement Drifts: Turning Anomalies into Investment Strategies

We revisit a series of popular anomalies: seasonal, announcements, and momentum. We confirm statistical significance and persistence of these effects and propose useful investment strategies to incorporate this information. We investigate the creation of a seasonal anomaly and trend model composed of the Sell-in-May (SIM or the Halloween effect), Turn of the Month (TOM), Federal Open Market Committee pre-announcement drift (FOMC), and State Dependent Momentum (SDM). Using the total return S&P 500 dataset starting in 1975, we estimate the parameters of each model on a yearly basis based on an expanding window, and then proceed to form, in a walk forward manner, an optimized combination of the four models using a return to risk optimization procedure. We find that a real-time optimized strategy of the aforementioned four market anomalies produced 9.13% annualized returns with 5.19% volatility and a Sharpe ratio of 0.85. This strategy exceeds the Sharpe ratio of Buy-and-Hold in the same period by more than 100%. Furthermore, the strategy also shows low correlation to a number of popular investment approaches including previously published market-timing models.