Enhancing Portfolio Performance: A Random Forest Approach to Volatility Prediction and Optimization

Vedant Rathi, Meghana Kshirsagar, Conor Ryan

2024

Abstract

Machine learning has diverse applications in various domains, including disease diagnosis in healthcare, user behavior analysis, and algorithmic trading. However, machine learning’s use in portfolio volatility predictions and optimization has only been recently explored and requires further investigation to prove valuable in real-world settings. We thus propose an effective method that accomplishes both these tasks and is targeted at people who are new to the realm of finance. This paper explores (a) a novel approach of using supervised machine learning with the Random Forest algorithm to predict portfolio volatility value and categorization and (b) a flexible method taking into account users’ restrictions on stock allocations to build an optimized and customized portfolio. Our framework also allows a diversified number of assets to be included in the portfolio. We train our model using historical asset prices collected over 8 years for six mutual funds and one cryptocurrency. We validate our results by comparing the volatility predictions against recent asset prices obtained from Yahoo Finance. The research underlines the importance of harnessing the power of machine learning to improve portfolio performance.

Paper Citation

in Harvard Style

Rathi V., Kshirsagar M. and Ryan C. (2024). Enhancing Portfolio Performance: A Random Forest Approach to Volatility Prediction and Optimization. In Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-680-4, SciTePress, pages 1278-1285. DOI: 10.5220/0012464600003636

in Bibtex Style

@conference{icaart24,
author={Vedant Rathi and Meghana Kshirsagar and Conor Ryan},
title={Enhancing Portfolio Performance: A Random Forest Approach to Volatility Prediction and Optimization},
booktitle={Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2024},
pages={1278-1285},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012464600003636},
isbn={978-989-758-680-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Enhancing Portfolio Performance: A Random Forest Approach to Volatility Prediction and Optimization
SN - 978-989-758-680-4
AU - Rathi V.
AU - Kshirsagar M.
AU - Ryan C.
PY - 2024
SP - 1278
EP - 1285
DO - 10.5220/0012464600003636
PB - SciTePress