Machine Learning Algorithms for Financial Asset Price Forecasting

2020/03/31 by Philip Ndikum, Ndikum, Philip · 1 citation
Decision Sciences · Economics, Econometrics and Finance · #Econometrics (econ.EM) #FOS: Computer and information sciences #FOS: Economics and business #Financial Markets and Investment Strategies #Forecasting Techniques and Applications #I.2.1 #J.5 #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Statistical Finance (q-fin.ST) #Stock Market Forecasting Methods

paper · pdf · doi:10.48550/arxiv.2004.01504

openalex publication_date 2020/03/31 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• Recurrent Neural Network Regularization
• On the use of cross-validation for time series predictor evaluation
• Hyperparameter Search in Machine Learning
• Twitter mood predicts the stock market
• A Unified Approach to Interpreting Model Predictions
• "Why Should I Trust You?": Explaining the Predictions of Any Classifier
• NGBoost: Natural Gradient Boosting for Probabilistic Prediction

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