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

ArXiv ID: 2404.07225 “View on arXiv”

Authors: Unknown

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

Keywords: Double Machine Learning, gradient boosting, causal inference, interest rate sensitivity, fixed income funds, Multi-asset (Fixed Income & Equity)

Complexity vs Empirical Score

• Math Complexity: 6.5/10
• Empirical Rigor: 8.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced causal inference techniques (Double Machine Learning, DAGs) and complex ML models (gradient boosting, VAR), indicating high mathematical sophistication, while its large-scale real-world dataset (7,000+ funds) and detailed methodological description demonstrate strong empirical rigor.

  flowchart TD
    A["Research Question: Impact of US FRS Interest Rates on Fixed Income & Equity Funds"] --> B["Data & Framework"]
    B --> C["Methodology: Double Machine Learning"]
    C --> D["Computation: Gradient Boosting vs. Linear Regression"]
    D --> E["Key Findings & Outcomes"]
    
    subgraph B ["Data & Framework"]
        B1["Period: Jan 1986 - Dec 2021"]
        B2["Target: Actively vs. Passively Managed Funds"]
        B3["Input: Interest Rate Changes (US FRS)"]
    end
    
    subgraph C ["Methodology: Double Machine Learning"]
        C1["Causal Inference Approach"]
        C2["Separates Prediction from Treatment Effect"]
    end
    
    subgraph D ["Computation: Gradient Boosting vs. Linear Regression"]
        D1["Gradient Boosting Outperforms Linear Regression"]
        D2["Predicts Fund Returns Accurately"]
    end
    
    subgraph E ["Key Findings & Outcomes"]
        E1["Result: 1% Interest Rate Increase ⟶ -11.97% Return Drop for Actively Managed Funds"]
        E2["Finding: Passively Managed Funds Less Sensitive to Rate Changes"]
        E3["Implication: Data-Driven Strategy for Investors & Fund Managers"]
    end