Identifying Dominant Industrial Sectors in Market States of the S&P 500 Financial Data

TL;DR

This study uses explainable AI to identify key industrial sectors influencing market states in the S&P 500, revealing that energy and IT sectors dominate correlation structures and enabling simplified models to predict market states effectively.

Contribution

The paper introduces a novel application of XAI techniques to identify dominant sectors in market states and develops an aggregation and Bayesian analysis method for feature significance.

Findings

Energy and IT sectors are key in market state determination.

A reduced model with top sector correlations predicts 90% of cluster assignments.

Market dynamics can be effectively summarized by a few sector correlations.

Abstract

Understanding and forecasting changing market conditions in complex economic systems like the financial market is of great importance to various stakeholders such as financial institutions and regulatory agencies. Based on the finding that the dynamics of sector correlation matrices of the S&P 500 stock market can be described by a sequence of distinct states via a clustering algorithm, we try to identify the industrial sectors dominating the correlation structure of each state. For this purpose, we use a method from Explainable Artificial Intelligence (XAI) on daily S&P 500 stock market data from 1992 to 2012 to assign relevance scores to every feature of each data point. To compare the significance of the features for the entire data set we develop an aggregation procedure and apply a Bayesian change point analysis to identify the most significant sector correlations. We show that the correlation matrix of each state is dominated only by a few sector correlations. Especially the energy and IT sector are identified as key factors in determining the state of the economy. Additionally we show that a reduced surrogate model, using only the eight sector correlations with the highest XAI-relevance, can replicate 90% of the cluster assignments. In general our findings imply an additional dimension reduction of the dynamics of the financial market.