FinTSBridge: A New Evaluation Suite for Real-world Financial Prediction with Advanced Time Series Models
This work addresses the gap between time series forecasting research and real-world financial applications, providing a new evaluation suite for researchers and practitioners in finance, though it is incremental as it focuses on benchmarking existing methods.
The authors tackled the challenge of applying advanced time series forecasting models to financial asset pricing by constructing three financial datasets, evaluating over ten models, and developing new metrics (msIC and msIR) to assess performance, showing that some models achieve competitive results in financial-specific tasks.
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.