The authors present a machine learning approach to regime-based asset allocation. The
framework consists of two primary components:(1) regime modeling and prediction and (2) …

High inflation and aggressive monetary policy tightening in 2022 triggered one of the largest
return drawdowns for a US 60/40 portfolio in the last 100 years. In this article, the authors …

In this study, we present a two-step method for predicting price bubbles in precious metals,
which combines a widely recognized right-tailed unit root test to detect bubbles with various …

This work extends a previous work in regime detection, which allowed trading positions to
be profitably adjusted when a new regime was detected, to ex ante prediction of regimes …

The mean-variance optimization framework has been the traditional approach to decide
portfolio allocations based on return-risk trade-offs. However, it faces practical drawbacks …

In this project I used the supervised machine learning methods logistic regression, decision
tree classifier, k nearest neighbor classifier, and support vector classifier, to determine the …

In statistical arbitrage, paired trading, as a market-neutral strategy, is widely used because of
its simple method and easy implementation. This paper constructs a machine learning …

Abstract [eng] The work aims to construct machine learning models that recognize different
regimes in stock markets and compare their results with the autoregressive Markov regime …