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Machine Learning Algorithms for Financial Asset Price Forecasting

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  • Philip Ndikum

Abstract

This research paper explores the performance of Machine Learning (ML) algorithms and techniques that can be used for financial asset price forecasting. The prediction and forecasting of asset prices and returns remains one of the most challenging and exciting problems for quantitative finance and practitioners alike. The massive increase in data generated and captured in recent years presents an opportunity to leverage Machine Learning algorithms. This study directly compares and contrasts state-of-the-art implementations of modern Machine Learning algorithms on high performance computing (HPC) infrastructures versus the traditional and highly popular Capital Asset Pricing Model (CAPM) on U.S equities data. The implemented Machine Learning models - trained on time series data for an entire stock universe (in addition to exogenous macroeconomic variables) significantly outperform the CAPM on out-of-sample (OOS) test data.

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  • Philip Ndikum, 2020. "Machine Learning Algorithms for Financial Asset Price Forecasting," Papers 2004.01504, arXiv.org.
    • Handle: RePEc:arx:papers:2004.01504
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    References listed on IDEAS

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    1. Zongwu Cai & Pixiong Chen, 2024. "Online Investor Sentiment via Machine Learning," Mathematics, MDPI, vol. 12(20), pages 1-14, October.
    2. Xinyue Cui & Zhaoyu Xu & Yue Zhou, 2020. "Using Machine Learning to Forecast Future Earnings," Papers 2005.13995, arXiv.org.
    3. Jiaju Miao & Pawel Polak, 2023. "Online Ensemble of Models for Optimal Predictive Performance with Applications to Sector Rotation Strategy," Papers 2304.09947, arXiv.org.

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