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

Blair Hull, Petra Bakosova and Alexander Kment

Chapter 14 in Handbook of Applied Investment Research, 2020, pp 281-321 from World Scientific Publishing Co. Pte. Ltd.

Abstract: 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.

Keywords: Applied Investments; Financial Forecasting; Portfolio Theory; Investment Strategies; Fundamental and Economic Anomalies; Behaviour of Investors (search for similar items in EconPapers)
JEL-codes: G1 G11 G17 (search for similar items in EconPapers)
Date: 2020
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