FinTSBridge: A New Evaluation Suite for Real-world Financial Prediction with Advanced Time Series Models

ArXiv ID: 2503.06928 “View on arXiv”

Authors: Unknown

Abstract

Despite the growing attention to time series forecasting in recent years, many studies have proposed various solutions to address the challenges encountered in time series prediction, aiming to improve forecasting performance. However, effectively applying these time series forecasting models to the field of financial asset pricing remains a challenging issue. There is still a need for a bridge to connect cutting-edge time series forecasting models with financial asset pricing. To bridge this gap, we have undertaken the following efforts: 1) We constructed three datasets from the financial domain; 2) We selected over ten time series forecasting models from recent studies and validated their performance in financial time series; 3) We developed new metrics, msIC and msIR, in addition to MSE and MAE, to showcase the time series correlation captured by the models; 4) We designed financial-specific tasks for these three datasets and assessed the practical performance and application potential of these forecasting models in important financial problems. We hope the developed new evaluation suite, FinTSBridge, can provide valuable insights into the effectiveness and robustness of advanced forecasting models in finanical domains.

Keywords: time series forecasting, financial asset pricing, evaluation metrics (msIC, msIR), financial datasets, benchmarking

Complexity vs Empirical Score

• Math Complexity: 4.0/10
• Empirical Rigor: 8.5/10
• Quadrant: Street Traders
• Why: The paper’s core contribution is an empirical evaluation suite with curated datasets, specific financial tasks, and new metrics (msIC, msIR), demonstrating high practical rigor. While it references advanced time series models (e.g., transformers, decomposition methods), the paper itself is more focused on application and benchmarking rather than deriving new mathematical theorems.

  flowchart TD
    A["Research Goal: Bridge TS models with Financial Asset Pricing"] --> B{"Methodology"}
    B --> C["Data Creation<br>3 Financial Datasets"]
    B --> D["Model Selection<br>10+ TS Forecasting Models"]
    B --> E["Metric Development<br>msIC, msIR, MSE, MAE"]
    C --> F["Financial Task Design"]
    D --> F
    E --> F
    F --> G["Computational Process:<br>Benchmarking & Evaluation"]
    G --> H["Key Findings:<br>FinTSBridge Evaluation Suite<br>Model Insights & Practical Potential"]