Unveiling the Impact of Macroeconomic Policies: A Double Machine Learning Approach to Analyzing Interest Rate Effects on Financial Markets

TL;DR

This paper uses a novel Double Machine Learning approach combining gradient boosting and linear regression to analyze how US Federal Reserve interest rate changes impact fixed income and equity fund returns from 1986 to 2021.

Contribution

It introduces a new application of DML with ML techniques to causally analyze interest rate effects on different types of funds, highlighting the utility of gradient boosting.

Findings

Gradient boosting effectively predicts fund returns.

A 1% interest rate increase decreases active fund returns by approximately 12%.

Passive funds are less affected by interest rate changes.

Abstract

This study examines the effects of macroeconomic policies on financial markets using a novel approach that combines Machine Learning (ML) techniques and causal inference. It focuses on the effect of interest rate changes made by the US Federal Reserve System (FRS) on the returns of fixed income and equity funds between January 1986 and December 2021. The analysis makes a distinction between actively and passively managed funds, hypothesizing that the latter are less susceptible to changes in interest rates. The study contrasts gradient boosting and linear regression models using the Double Machine Learning (DML) framework, which supports a variety of statistical learning techniques. Results indicate that gradient boosting is a useful tool for predicting fund returns; for example, a 1% increase in interest rates causes an actively managed fund's return to decrease by -11.97%. This understanding of the relationship between interest rates and fund performance provides opportunities for additional research and insightful, data-driven advice for fund managers and investors