Predictive modeling and statistical inference for Commodity Trading Advisors
Year: 2025
Type: article
Abstract: This article focuses on predicting trends in Commodity Trading Advisors (CTAs), also known as trend-following hedge funds. The article applies a Hidden Markov Model (HMM) for classifying trends. By incorporating additional features, a regularized logistic regression model is used to enhance prediction capability. The model demonstrates success in identifying positive trends in CTA funds, with particular emphasis on precision and risk-adjusted return metrics. In the context of regularized regression models, techniques for statistical inference such as bootstrap resampling and Markov Chain Monte Carlo are applied to estimate the distribution of parameters. The findings suggest the model’s effectiveness in predicting favorable CTA performance and mitigating equity market drawdowns.
Source: Cogent Economics & Finance
Authors: Christian Oliver Ewald, Oskar Fransson
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $1,165
OpenAlex ID: W7117372898