Multi-Step Approach to Forecasting Financial Market Regimes Using Machine Learning and Factor Analysis

In this article, I will go over the steps to create a structured approach to use Machine Learning and Factor Analysis to Forecast Financial Market regimes sequentially.

A versatile multi-step framework can exploit the power of machine learning and large datasets to forecast short-term financial market regimes. In this article, I will go over the steps to create a structured approach to use Machine Learning and Factor Analysis to Forecast Financial Market regimes sequentially.

Pre-Selecting the Most Informative Financial Predictors

The structured multi-step framework starts by pre-selecting the most informative financial predictors from a broad dataset. This initial step is crucial, as it helps identify the key variables that drive short-term financial market dynamics, while filtering out the noise and irrelevant information.

Techniques like sure independence screening can assess the individual predictive power of each variable, focusing the analysis on the most statistically significant ones. Alternatively, t-stat-based selection or Bayesian moving averaging can be used to identify the optimal subset of predictors.

For example, in forecasting the S&P 500…