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

Aug 4, 2026

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This study examines how macroeconomic policies influence financial markets through a combined Machine Learning and causal inference framework. It evaluates the impact of interest rate changes by the US Federal Reserve System on the returns of fixed income and equity funds from January 1986 to December 2021. The analysis distinguishes actively and passively managed funds, testing the idea that passive funds are less sensitive to rate shifts. Results compare gradient boosting with linear regression under Double Machine Learning, showing stronger predictive performance and quantifying substantial negative effects for active funds.

LanguageEnglish

CategoryResearch & Report

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Pages6

Reading Time15 min

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Unveiling the Impact of Macroeconomic Policies: A Double Machine Learning Approach to Analyzing Interest Rate Effects on Financial Markets Anoop Kumar 1, Suresh Dodda2, Navin Kamuni 3, Rajeev Kumar Arora4 [1](1Anoop.kumar.2612@gmail.com)[Anoop.kumar.2612@gmail.com](1Anoop.kumar.2612@gmail.com), [2](2 sureshr.dodda@gmail.com)[ sureshr.dodda@gmail.com](2 sureshr.dodda@gmail.com), [3](3navin.kamuni@gmail.com)[navin.kamuni@gmail.com](3navin.kamuni@gmail.com), [4](4rajeev04.study@gmail.com)[rajeev04.study@gmail.com](4rajeev04.study@gmail.com) 1 IIT Roorkee, India 2 IT Department, Eudoxia Research center, USA 3AI-ML, BITS Pilani WILP, USA 4Ph.D Himalayan University, India 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. Index Terms— Double Machine Learning, Financial, Federal Reserve System, Machine Learning I. INTRODUCTION Interpreting the impact of interest rates, where a hypothetical 1% increase in rates implies an 11% decline in returns from actively managed funds, is central to the study. This substantial impact is consistent with theory, but given the variety offactors affecting market performance, further investigation is required. The intricate dynamics of the financial market are exemplified by the quick market response to interest rate changes, as opposed to the delayed responses in other economic sectors. The flexibility of the Double Machine Learning (DML) framework, indicating the possibility of its wider use in financial research by various studies such as [1]–[3] . However, the studies acknowledges certain limits, such as the difficulties presented by complex data and the requirement for advanced modelling methods in order to precisely represent the underlying economic events. This study examines the effectiveness of the DML framework for evaluating the relationship between fund returns and the growth in interest rates of the US Federal Reserve System (FRS), especially for actively and passively managed funds between January 1986 and December 2021. Approximately 7,000 funds are involved in this study, which uses gradient boosting and linear regression models to demonstrate the intricacy of the financial sector. Because of its known impact on market dynamics, DML is proposed as a novel method for evaluating average treatment impacts, with a particular emphasis on the interest rate of the FRS. One of the main questions in financial analysis is whether DML is feasible and can accurately assess causal effects on fund returns. Preliminary results show that DML aligns well with the intricacies of financial data, but the method needs to be handled carefully because financial markets are complex. Furthermore, gradient boosting exhibits strong predictive ability, suggesting its possible use in financial DML. Therefore, the empirical research conducted for the study shows a strong negative correlation, supported by a highly precise gradient boosting model, between interest

FAQs

What macroeconomic factor does the study analyze and why is it important?

The study analyzes US Federal Reserve interest rate changes because interpreting their effect on fund returns is central to understanding how monetary policy influences financial markets.

How does the paper use Double Machine Learning (DML) in its evaluation?

It applies the DML framework to estimate average treatment effects linking interest rate growth to fund returns, using machine learning models to support causal inference.

What difference in results is reported between actively and passively managed funds?

The findings show a strong negative relationship for actively managed funds, while results for passively managed funds are inconsistent, indicating the need for further investigation.

Aug 4, 2026

Upload by Liam