Predictive Modeling and Statistical Inference for CTA returns : A Hidden Markov Approach with Sparse Logistic Regression

Abstract: This thesis focuses on predicting trends in Commodity Trading Advisors (CTAs), also known as trend-following hedge funds. The paper applies a Hidden Markov Model (HMM) for classifying trends. Additionally, 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. For future research, it is recommended to explore alternative classification models and extend the methodology to different markets and datasets.