Trimming the Sail: A Second-order Learning Paradigm for Stock Prediction

Chi Chen, Li Zhao, Wei Cao, Jiang Bian and Chunxiao Xing

Papers from arXiv.org

Abstract: Nowadays, machine learning methods have been widely used in stock prediction. Traditional approaches assume an identical data distribution, under which a learned model on the training data is fixed and applied directly in the test data. Although such assumption has made traditional machine learning techniques succeed in many real-world tasks, the highly dynamic nature of the stock market invalidates the strict assumption in stock prediction. To address this challenge, we propose the second-order identical distribution assumption, where the data distribution is assumed to be fluctuating over time with certain patterns. Based on such assumption, we develop a second-order learning paradigm with multi-scale patterns. Extensive experiments on real-world Chinese stock data demonstrate the effectiveness of our second-order learning paradigm in stock prediction.

Date: 2020-02
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ecm and nep-fmk
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