Yi-Le Chan, Jireh and Leow, Steven Mun Hong and Bea, Khean Thye and Cheng, Wai Khuen and Phoong, Seuk Wai and Hong, Zeng-Wei and Lin, Jim-Min and Chen, Yen-Lin (2022) A correlation-embedded attention module to mitigate multicollinearity: An algorithmic trading application. Mathematics, 10 (8). ISSN 2227-7390, DOI https://doi.org/10.3390/math10081231.
Full text not available from this repository.Abstract
Algorithmic trading is a common topic researched in the neural network due to the abundance of data available. It is a phenomenon where an approximately linear relationship exists between two or more independent variables. It is especially prevalent in financial data due to the interrelated nature of the data. The existing feature selection methods are not efficient enough in solving such a problem due to the potential loss of essential and relevant information. These methods are also not able to consider the interaction between features. Therefore, we proposed two improvements to apply to the Long Short-Term Memory neural network (LSTM) in this study. It is the Multicollinearity Reduction Module (MRM) based on correlation-embedded attention to mitigate multicollinearity without removing features. The motivation of the improvements is to allow the model to predict using the relevance and redundancy within the data. The first contribution of the paper is allowing a neural network to mitigate the effects of multicollinearity without removing any variables. The second contribution is improving trading returns when our proposed mechanisms are applied to an LSTM. This study compared the classification performance between LSTM models with and without the correlation-embedded attention module. The experimental result reveals that a neural network that can learn the relevance and redundancy of the financial data to improve the desired classification performance. Furthermore, the trading returns of our proposed module are 46.82% higher without sacrificing training time. Moreover, the MRM is designed to be a standalone module and is interoperable with existing models.
| Item Type: | Article |
|---|---|
| Funders: | Ministry of Education, Malaysia (FRGS/1/2019/STG06/UTAR/03/1), Ministry of Science and Technology, Taiwan (MOST-109-2628-E-027-004-MY3), Ministry of Science and Technology, Taiwan (MOST-110-2218-E-027-004), Ministry of Education, Taiwan (1100156712) |
| Uncontrolled Keywords: | Algorithmic trading; Multicollinearity; Feature selection; Neural network; Classification |
| Subjects: | H Social Sciences > HF Commerce > Business Q Science > QA Mathematics |
| Divisions: | Faculty of Business and Economics |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 11 Sep 2023 04:25 |
| Last Modified: | 11 Sep 2023 04:25 |
| URI: | http://eprints.um.edu.my/id/eprint/42944 |
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