Title: Time-series gradient boosting tree for stock price prediction

Authors: Kei Nakagawa; Kenichi Yoshida

Addresses: Innovation Lab, Nomura Asset Management Co. Ltd., Tokyo, Japan ' Graduate School of Business Sciences, University of Tsukuba, Tokyo, Japan

Abstract: We propose a time-series gradient boosting tree for a dataset with time-series and cross-sectional attributes. Our time-series gradient boosting tree has weak learners with time-series and cross-sectional attributes in its internal node, and split examples based on similarity between a pair of time-series or impurity between cross-sectional attributes. Dissimilarity between a pair of time-series is defined by the dynamic time warping method. In other words, the decision tree is constructed based on the shape that the time-series is similar or not similar to its past shape. We conducted an empirical analysis using major world indices and confirmed that our time-series gradient boosting tree is superior to prior research methods in terms of both profitability and accuracy.

Keywords: dynamic time warping method; time-series decision tree; time-series gradient boosting tree; stock price prediction.

DOI: 10.1504/IJDMMM.2022.10047893

International Journal of Data Mining, Modelling and Management, 2022 Vol.14 No.2, pp.110 - 125

Accepted: 05 Aug 2020
Published online: 11 Jun 2022 *

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