FinTSBridge: A New Evaluation Suite for Real-world Financial Prediction with Advanced Time Series Models
The document describes advanced financial time series forecasting methods, dataset structures, evaluation metrics, and trading strategy backtesting across financial markets.
What it examines
This paper introduces FinTSBridge, a framework that connects advanced time series forecasting models with real-world financial tasks. It presents specialized financial datasets, new evaluation metrics (msIC, msIR), and tailored prediction tasks to better capture asset pricing, risk management, and algorithmic trading challenges.
What it concludes
The study shows that integrating cutting-edge forecasting models with financial data improves practical trading strategy design and risk management. Future work may incorporate foundational models and agent-based systems, with applications in automated trading, portfolio optimization, and enhanced financial decision-making.
Evidence objects
FinTSBridge unveils a comprehensive evaluation suite linking advanced time series forecasting models with real-world financial applications through specialized datasets encompassing global stock indices, options, and Bitcoin futures, showing practical utility.
key_findings bullet 1 · key_findings · validation V0
The study reveals that error metrics $$MSE$$ and $$MAE$$ can mislead for non-stationary financial data, prompting new correlation metrics $$msIC$$ and $$msIR$$ which better capture temporal dependencies and model reliability.
key_findings bullet 2 · key_findings · validation V0
Comparing over a dozen forecasting approaches, the study uses datasets and strategy simulation, while limited hyperparameter tuning and a narrow focus reveal areas for improvement in financial time series evaluation.
key_findings bullet 3 · key_findings · validation V0
This paper innovatively bridges advanced time series forecasting models with financial portfolio and market challenges, employing curated datasets and introducing novel metrics such as $msIC$ and $msIR$. Its tailored, finance-specific evaluation tasks bring freshness to established methods, making it a compelling, balanced exploration of quantitative innovation in financial forecasting applications.
key_findings bullet 4 · key_findings · validation V0
Raw abstract and provenance
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… ▽ More 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. △ Less
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