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Nowadays, machine learning methods have been widely used in stock prediction.Traditional approaches assume an identical data distribution, under which alearned model on the training data is fixed and applied directly in the testdata. Although such assumption has made traditional machine learning techniquessucceed in many real-world tasks, the highly dynamic nature of the stock marketinvalidates the strict assumption in stock prediction. To address thischallenge, we propose the second-order identical distribution assumption, wherethe data distribution is assumed to be fluctuating over time with certainpatterns. Based on such assumption, we develop a second-order learning paradigmwith multi-scale patterns. Extensive experiments on real-world Chinese stockdata demonstrate the effectiveness of our second-order learning paradigm instock prediction.

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