Portfolio Selection Using Random Forest Algorithm

Authors

  • Daname KOLANI Faculté des sciences juridiques économiques et sociales - Agdal Université Mohammed V de Rabat

Keywords:

Portfolio Selection, Decision Tree, Random Forest, Asset Allocation, Value Investing

Abstract

Portfolio selection has long been a main topic in finance.  What stocks should one invest in? How much should one allocate to each stock to maximize gain and minimize risk? These are the questions we aim to answer by demonstrating the possibility of obtaining abnormal returns above those offered by the benchmark by constructing a portfolio through a rule-based algorithm called Random Forest with Decision Tree as the base model. The use of Random Forest addresses the problem of over-fitting in the learning process and permits the prediction of a robust portfolio based on financial ratios. This approach has proven to outperform the S&P 500 Index and the Equal-Weight portfolio from 2013 to 2020.

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Published

2022-04-01

How to Cite

KOLANI, D. (2022). Portfolio Selection Using Random Forest Algorithm. International Journal of Computer Engineering and Data Science (IJCEDS), 2(1), 28–36. Retrieved from https://ijceds.com/ijceds/article/view/32