Cluster-based Regression using Variational Inference and Applications in Financial Forecasting

TL;DR

This paper introduces a variational inference-based clustering method for regression, enabling better modeling of data with regime-specific patterns, demonstrated through financial market forecasting.

Contribution

It presents a novel approach combining clustering and regression via variational inference, offering interpretable results and broad applicability beyond finance.

Findings

Effective in identifying market regimes in financial data

Improves prediction accuracy over standard regression methods

Provides insights into variable importance across clusters

Abstract

This paper describes an approach to simultaneously identify clusters and estimate cluster-specific regression parameters from the given data. Such an approach can be useful in learning the relationship between input and output when the regression parameters for estimating output are different in different regions of the input space. Variational Inference (VI), a machine learning approach to obtain posterior probability densities using optimization techniques, is used to identify clusters of explanatory variables and regression parameters for each cluster. From these results, one can obtain both the expected value and the full distribution of predicted output. Other advantages of the proposed approach include the elegant theoretical solution and clear interpretability of results. The proposed approach is well-suited for financial forecasting where markets have different regimes (or clusters) with different patterns and correlations of market changes in each regime. In financial applications, knowledge about such clusters can provide useful insights about portfolio performance and identify the relative importance of variables in different market regimes. An illustrative example of predicting one-day S&P change is considered to illustrate the approach and compare the performance of the proposed approach with standard regression without clusters. Due to the broad applicability of the problem, its elegant theoretical solution, and the computational efficiency of the proposed algorithm, the approach may be useful in a number of areas extending beyond the financial domain.