@misc{chen2020trimming,
author = {Chen, Chi and Zhao, Li and Cao, Wei and Bian, Jiang and Xing, Chunxiao},
title = {Trimming the Sail: A Second-order Learning Paradigm for Stock Prediction},
howpublished = {arXiv},
year = {2020},
month = {February},
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.},
url = {http://approjects.co.za/?big=en-us/research/publication/trimming-the-sail-a-second-order-learning-paradigm-for-stock-prediction/},
}