Boosting Stock Price Prediction with Anticipated Macro Policy Changes

ArXiv ID: 2311.06278 “View on arXiv”

Authors: Unknown

Abstract

Prediction of stock prices plays a significant role in aiding the decision-making of investors. Considering its importance, a growing literature has emerged trying to forecast stock prices with improved accuracy. In this study, we introduce an innovative approach for forecasting stock prices with greater accuracy. We incorporate external economic environment-related information along with stock prices. In our novel approach, we improve the performance of stock price prediction by taking into account variations due to future expected macroeconomic policy changes as investors adjust their current behavior ahead of time based on expected future macroeconomic policy changes. Furthermore, we incorporate macroeconomic variables along with historical stock prices to make predictions. Results from this strongly support the inclusion of future economic policy changes along with current macroeconomic information. We confirm the supremacy of our method over the conventional approach using several tree-based machine-learning algorithms. Results are strongly conclusive across various machine learning models. Our preferred model outperforms the conventional approach with an RMSE value of 1.61 compared to an RMSE value of 1.75 from the conventional approach.

Keywords: macroeconomic policy changes, tree-based machine-learning, feature engineering, RMSE, economic environment analysis, Equities (Stocks)

Complexity vs Empirical Score

• Math Complexity: 2.0/10
• Empirical Rigor: 6.5/10
• Quadrant: Street Traders
• Why: The paper’s mathematics is relatively elementary, focusing on standard regression and machine learning metrics like RMSE without advanced derivations. However, it demonstrates strong empirical rigor through its use of multiple real-world datasets, feature engineering, cross-model validation, and clear performance metrics.

  flowchart TD
    A["Research Goal: Enhance Stock Price Prediction"] --> B["Methodology: Feature Engineering & ML"]
    B --> C["Data Inputs: Historical Stock Prices, Current &<br>Anticipated Future Macro Policy Changes"]
    C --> D["Computational Process: Tree-Based ML Models"]
    D --> E["Model Training &<br>Performance Evaluation"]
    E --> F{"Comparison:<br>Proposed vs Conventional"}
    F -->|Outcomes| G["Proposed Model Outperforms"]
    G --> H["Key Findings: Lower RMSE (1.61 vs 1.75)<br>Future Policy Data Significantly Improves Accuracy"]