Using Intermarket Data to Evaluate the Efficient Market Hypothesis with Machine Learning

TL;DR

This study uses machine learning to analyze intermarket data and provides empirical evidence that challenges the semi-strong form of the Efficient Market Hypothesis by showing predictive power in various asset classes.

Contribution

The paper demonstrates that intermarket data can significantly improve prediction accuracy, contradicting the semi-strong EMH, using multiple machine learning models across diverse financial assets.

Findings

Bonds, index futures, and commodities futures data outperform baselines.

Intermarket data improves prediction metrics significantly.

Results are statistically significant at 95% confidence level.

Abstract

In its semi-strong form, the Efficient Market Hypothesis (EMH) implies that technical analysis will not reveal any hidden statistical trends via intermarket data analysis. If technical analysis on intermarket data reveals trends which can be leveraged to significantly outperform the stock market, then the semi-strong EMH does not hold. In this work, we utilize a variety of machine learning techniques to empirically evaluate the EMH using stock market, foreign currency (Forex), international government bond, index future, and commodities future assets. We train five machine learning models on each dataset and analyze the average performance of these models for predicting the direction of future S&P 500 movement as approximated by the SPDR S&P 500 Trust ETF (SPY). From our analysis, the datasets containing bonds, index futures, and/or commodities futures data notably outperform baselines by substantial margins. Further, we find that the usage of intermarket data induce statistically significant positive impacts on the accuracy, macro F1 score, weighted F1 score, and area under receiver operating characteristic curve for a variety of models at the 95% confidence level. This provides strong empirical evidence contradicting the semi-strong EMH.