# Machine Learning Algorithms for Financial Asset Price Forecasting

arXiv: https://arxiv.org/abs/2004.01504 · HTML: https://mlsanity.com/paper/2004.01504/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Mar 2020.

## Built on by (most cited first, 3 of 3)

- [Advancing Investment Frontiers: Industry-grade Deep Reinforcement Learning for Portfolio Optimization](https://mlsanity.com/paper/2403.07916/) (2024)
- [Online Ensemble Learning for Sector Rotation: A Gradient-Free Framework](https://mlsanity.com/paper/2304.09947/) (2023)
- [ARTEMIS: A Neuro Symbolic Framework for Economically Constrained Market Dynamics](https://mlsanity.com/paper/2603.18107/) (2026)

## Abstract (arXiv)

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.