FEC-Real: A KAN based Model for Improving Investment Strategies

TMLR Paper4543 Authors

Abstract: The application of mathematical and computational techniques in financial investment has emerged as a prominent area of research, leading to the development of various tasks including factor mining, stock prediction, and analysis of financial statements. In this work we particularly focus on the task of predicting the future trend for stocks. In existing fintech research different transformer-based models have been explored for predicting future stock trend. This study is motivated by the need for a more efficient network architecture that can enhance the interpretation of real-time data. However, transformer based models are not always efficient for real world high speed trading data. To address this, we particularly explore the effectiveness of Kolmogorov Arnold Network (KAN) for financial time series model. We propose a KAN based encoder (FTS2K) which utilizes both KAN and transformer architecture to predict future stock price movements. Empirical results show that our proposed Encoder improves yields an average accuracy enhancement of $2.62\%$ across state-of-the-art (SOTA) time series models. Our approach consistently outperforms in four datasets (i.e. China A Daily, China A Min, China Futures Min, Dow 12 Daily), achieving superior results in both ACC and Top-100 ACC metrics.

Submission Length: Regular submission (no more than 12 pages of main content)

Assigned Action Editor: ~Hongfu_Liu2

Submission Number: 4543