Forecasting Stock Market Out-of-Sample with Regularised Regression Training Techniques

Jonathan Iworiso^1,

¹School of Computing and Digital Media, London Metropolitan University

Cite this paper:
Jonathan Iworiso. Forecasting Stock Market Out-of-Sample with Regularised Regression Training Techniques. International Journal of Econometrics and Financial Management. 2023; 11(1):1-12. doi: 10.12691/ijefm-11-1-1

Abstract

Forecasting stock market out-of-sample is a major concern to researchers in finance and emerging markets. This research focuses mainly on the application of regularised Regression Training (RT) techniques to forecast monthly equity premium out-of-sample recursively with an expanding window method. A broad category of sophisticated regularised RT models involving model complexity were employed. The regularised RT models which include Ridge, Forward-Backward (FOBA) Ridge, Least Absolute Shrinkage and Selection Operator (LASSO), Relaxed LASSO, Elastic Net and Least Angle Regression were trained and used to forecast the equity premium out-of-sample. In this study, the empirical investigation of the Regularised RT models demonstrate significant evidence of equity premium predictability both statistically and economically relative to the benchmark historical average, delivering significant utility gains. Overall, the Ridge gives the best statistical performance evaluation results while the LASSO appeared to be most economical meaningful. They seek to provide meaningful economic information on mean-variance portfolio investment for investors who are timing the market to earn future gains at minimal risk. Thus, the forecasting models appeared to guarantee an investor in a market setting who optimally reallocates a monthly portfolio between equities and risk-free treasury bills using equity premium forecasts at minimal risk.

Keywords:
regression training out-of-sample expanding window statistical predictability economic significance utility gains

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