Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

8-2023

Abstract

Quantitative stock investment is a fundamental financial task that highly relies on accurate prediction of market status and profitable investment decision making. Despite recent advances in deep learning (DL) have shown stellar performance on capturing trading opportunities in the stochastic stock market, the performance of existing DL methods is unstable with sensitivity to network initialization and hyperparameter selection. One major limitation of existing works is that investment decisions are made based on one individual neural network predictor with high uncertainty, which is inconsistent with the workflow in real-world trading firms. To tackle this limitation, we propose AlphaMix, a novel three-stage mixture-of-experts (MoE) framework for quantitative investment to mimic the efficient bottom-up hierarchical trading strategy design workflow of successful trading companies. In Stage one, we introduce an efficient ensemble learning method, whose computational and memory costs are significantly lower comparing to traditional ensemble methods, to train multiple groups of trading experts with personalised market understanding and trading styles. In Stage two, we collect diversified investment suggestions through building a pool of trading experts utilizing hyperparameter level and initialization level diversity of neural networks for post hoc ensemble construction. In Stage three, we design three different mechanisms, namely as-needed router, with-replacement selection and integrated expert soup, to dynamically pick experts from the expert pool, which takes the responsibility of a portfolio manager. Through extensive experiments on US and Chinese stock markets, we demonstrate that AlphaMix significantly outperforms many state-of-the-art baselines in terms of 7 popular financial criteria.

Keywords

computational finance, deep learning, ensemble learning, mixture-of-experts, quantitative investment, stock prediction

Discipline

Artificial Intelligence and Robotics | Finance and Financial Management | Numerical Analysis and Scientific Computing

Areas of Excellence

Digital transformation

Publication

KDD '23: Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Long Beach, CA, August 6-10

First Page

2109

Last Page

2119

ISBN

9798400701030

Identifier

10.1145/3580305.3599424

Publisher

ACM

City or Country

New York

Creative Commons License

Creative Commons Attribution 3.0 License
This work is licensed under a Creative Commons Attribution 3.0 License.

Additional URL

https://doi.org/10.1145/3580305.3599424

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