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

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Listed:
  • Anoop Kumar
  • Suresh Dodda
  • Navin Kamuni
  • Rajeev Kumar Arora

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

Suggested Citation

  • Anoop Kumar & Suresh Dodda & Navin Kamuni & Rajeev Kumar Arora, 2024. "Unveiling the Impact of Macroeconomic Policies: A Double Machine Learning Approach to Analyzing Interest Rate Effects on Financial Markets," Papers 2404.07225, arXiv.org.
    • Handle: RePEc:arx:papers:2404.07225
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    References listed on IDEAS

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    1. Michael C Knaus, 2022. "Double machine learning-based programme evaluation under unconfoundedness [Econometric methods for program evaluation]," The Econometrics Journal, Royal Economic Society, vol. 25(3), pages 602-627.
    2. Neng-Chieh Chang, 2020. "Double/debiased machine learning for difference-in-differences models," The Econometrics Journal, Royal Economic Society, vol. 23(2), pages 177-191.
    3. Samar Wazir & Gautam Siddharth Kashyap & Karan Malik & Alexander E. I. Brownlee, 2023. "Predicting the Infection Level of COVID-19 Virus Using Normal Distribution-Based Approximation Model and PSO," Springer Optimization and Its Applications, in: Zakia Hammouch & Mohamed Lahby & Dumitru Baleanu (ed.), Mathematical Modeling and Intelligent Control for Combating Pandemics, pages 75-91, Springer.
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