Market timing using combined forecasts and machine learning. (25th May 2020)

• Record Type: Journal Article
• Authors: Mascio, David A.; Fabozzi, Frank J.; Zumwalt, J. Kenton
• Is Part Of: Journal of forecasting. Volume 40:Number 1(2021)
• Page Start: 1
• Page End: 16
• Publication Date: 2020-05-25
• Subjects: beta optimization -- combined forecast -- machine learning -- market timing strategy -- sentiment index; Forecasting -- Periodicals; Forecasting -- Mathematical models -- Periodicals; 003.2
• DOI: 10.1002/for.2690
• Languages: English
• ISSNs: 0277-6693

Abstract

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.