Market timing using combined forecasts and machine learning

David A. Mascio, Frank J. Fabozzi and J. Kenton Zumwalt

Journal of Forecasting, 2021, vol. 40, issue 1, 1-16

Abstract: Successful market timing strategies depend on superior forecasting ability. We use a sentiment index model, a kitchen sink logistic regression model, and a machine learning model (least absolute shrinkage and selection operator, LASSO) to forecast 1‐month‐ahead S&P 500 Index returns. In order to determine how successful each strategy is at forecasting the market direction, a “beta optimization” strategy is implemented. We find that the LASSO model outperforms the other models with consistently higher annual returns and lower monthly drawdowns.

Date: 2021
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https://doi.org/10.1002/for.2690

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Persistent link: https://EconPapers.repec.org/RePEc:wly:jforec:v:40:y:2021:i:1:p:1-16

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