Paper: arXiv 2303.16149

Authors: Davood Pirayesh Neghab, Mucahit Cevik, M. I. M. Wahab

Abstract

The complexity and ambiguity of financial and economic systems, along with frequent changes in the economic environment, have made it difficult to make precise predictions that are supported by theory-consistent explanations. Interpreting the prediction models used for forecasting important macroeconomic indicators is highly valuable for understanding relations among different factors, increasing trust towards the prediction models, and making predictions more actionable. In this study, we develop a fundamental-based model for the Canadian-U.S. dollar exchange rate within an interpretative framework. We propose a comprehensive approach using machine learning to predict the exchange rate and employ interpretability methods to accurately analyze the relationships among macroeconomic variables. Moreover, we implement an ablation study based on the output of the interpretations to improve the predictive accuracy of the models. Our empirical results show that crude oil, as Canada’s main commodity export, is the leading factor that determines the exchange rate dynamics with time-varying effects. The changes in the sign and magnitude of the contributions of crude oil to the exchange rate are consistent with significant events in the commodity and energy markets and the evolution of the crude oil trend in Canada. Gold and the TSX stock index are found to be the second and third most important variables that influence the exchange rate. Accordingly, this analysis provides trustworthy and practical insights for policymakers and economists and accurate knowledge about the predictive model’s decisions, which are supported by theoretical considerations.

Complexity vs Empirical Score

  • Math Complexity: 4.0/10
  • Empirical Rigor: 7.5/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper presents a solid empirical approach to exchange rate forecasting using interpretable machine learning (SHAP/feature importance) and ablation studies rather than heavy mathematical proofs. Its strengths lie in the practical application to the CAD/USD rate and the economically intuitive findings regarding commodity dynamics.

Research Flowchart

  flowchart TD
    A[Goal: Predict CAD/USD Exchange Rate & Interpret Drivers] --> B[Data Inputs: Macroeconomic Variables]
    B --> C[Crude Oil, Gold, TSX Index, Macro Indicators]
    C --> D[Interpretive Machine Learning Framework]
    D --> E[Ablation Study based on Feature Interpretations]
    E --> F[Outcomes: Improved Accuracy & Key Factor Identification]
    F --> G[Findings: Crude Oil is Top Driver with Time-Varying Effects; Gold & TSX Rank 2nd/3rd]