@article {Awjoi.2022.1.220, author = {Edward N. W. Aw and Joshua Jiang and John Q. Jiang}, title = {Factor Investing with Classification-Based Supervised Machine Learning}, elocation-id = {joi.2022.1.220}, year = {2022}, doi = {10.3905/joi.2022.1.220}, publisher = {Institutional Investor Journals Umbrella}, abstract = {There are two types of supervised machine learning (SML): regression and classification. In this study, the authors propose classification-based machine learning algorithms for factor investing with artificial neural networks in which the cross section of stock returns is grouped into five categories: strong buy, buy, neutral, sell, and strong sell. Their empirical out-of-sample results demonstrate some advantages of classification-based machine learning relative to regression-based learning in which the actual stock returns denote the response variable. The classification-based models also deliver slight outperformance relative to the ordinary least squares model, although the outperformance is not statistically significant. Furthermore, the out-of-sample results show that {\textquotedblleft}deep{\textquotedblright} learning with multilayers of neuron layers struggles to surpass a less sophisticated {\textquotedblleft}shallow{\textquotedblright} learning for both classification-based and regression-based SML algorithms. Their findings suggest that market noise, common in the financial markets, during the training process overwhelms the nonlinear association uncovered in the machine learning process; and the classification of the cross section of stock returns may have reduced some of the noise.}, issn = {1068-0896}, URL = {https://joi.pm-research.com/content/early/2022/01/04/joi.2022.1.220}, eprint = {https://joi.pm-research.com/content/early/2022/01/04/joi.2022.1.220.full.pdf}, journal = {The Journal of Investing} }