Alberto Rossi, University of Maryland
Predicting Stock Market Returns with Machine Learning
We employ a semi-parametric method known as Boosted Regression Trees (BRT) to forecast stock returns and volatility at the monthly frequency. BRT is a statistical method that generates forecasts on the basis of large sets of conditioning information without imposing strong parametric assumptions such as linearity or monotonicity. It applies soft weighting functions to the predictor variables and performs a type of model averaging that increases the stability of the forecasts and therefore protects it against overfitting. Our results indicate that expanding the conditioning information set results in greater out-of-sample predictive accuracy compared to the standard models proposed in the literature and that the forecasts generate profitable portfolio allocations even when market frictions are considered. By working directly with the mean-variance investor’s conditional Euler equation we also characterize semi-parametrically the relation between the various covariates constituting the conditioning information set and the investor’s optimal portfolio weights. Our results suggest that the relation between predictor variables and the optimal portfolio allocation to risky assets is highly non-linear.
Alberto Rossi is an Associate Professor of Finance at the Smith School of Business, University of Maryland at College Park. His research interests include theoretical and empirical asset pricing, portfolio choice, machine learning and FinTech. His recent work concentrates on networks, institutional investors’ performance, and the risk-return trade-off in financial markets. He also studies stock return predictability and commodity markets. Before joining the Smith School, he worked as an economist at the Board of Governors of the Federal Reserve System in Washington DC. He received his PhD in Economics from the University of California, San Diego.
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