Machine Learning Approaches for Equity Market Predictions


Asset Pricing, Empirical Capital Market Research

Journal of Asset Management, forthcoming


Prof. Dr. Dominik Wolff Dr. Ulrich Neugebauer


We empirically analyze equity premium predictions with ‘traditional’ linear models and machine learning approaches. Based on a commonly used dataset of equity market predictors extended by additional fundamental, macroeconomic, sentiment and risk indicators, we find mixed results for machine learning algorithms for equity market predictions. In contrast to sophisticated linear models such as penalized least squares or principal component regressions (PCR), the analyzed machine learning algorithms fail to significantly outperform the historical average benchmark forecast. However, an investment strategy that uses machine learning predictions in a market timing strategy, outperforms a passive buy-and-hold investment. Compared to sophisticated linear prediction models, in our problem set, machine learning algorithms do not improve forecast accuracy.

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