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

arXiv: https://arxiv.org/abs/2404.07225 · HTML: https://mlsanity.com/paper/2404.07225/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 24 references that are in the map.
- First posted on arXiv 31 Mar 2024.

## Builds on (most cited first, 7 of 7)

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- [Roulette-Wheel Selection-Based PSO Algorithm for Solving the Vehicle Routing Problem with Time Windows](https://mlsanity.com/paper/2306.02308/) (2023)
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- [Enhancing End-to-End Multi-Task Dialogue Systems: A Study on Intrinsic Motivation Reinforcement Learning Algorithms for Improved Training and Adaptability](https://mlsanity.com/paper/2401.18040/) (2024)
- [Financial Markets Prediction with Deep Learning](https://mlsanity.com/paper/2104.05413/) (2021)
- [Advancing Audio Fingerprinting Accuracy Addressing Background Noise and Distortion Challenges](https://mlsanity.com/paper/2402.13957/) (2024)

## Built on by (most cited first, 2 of 2)

- [Riesz Regression As Direct Density Ratio Estimation](https://mlsanity.com/paper/2511.04568/) (2025)
- [Optimizing Photometric Light Curve Analysis: Evaluating Scipy's Minimize Function for Eclipse Mapping of Cataclysmic Variables](https://mlsanity.com/paper/2406.00071/) (2024)

## Abstract (arXiv)

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