Forecasting Chinese Stock Market Prices using Baidu Search Index with a Learning-Based Data Collection Method

Jichang Dong, Wei Dai, Ying Liu, Lean Yu () and Jie Wang
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Jichang Dong: School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, P. R. China
Wei Dai: School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, P. R. China
Ying Liu: School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, P. R. China†The Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences, Beijing 100190, P. R. China
Jie Wang: #xA7;Department of Civil and Environmental Engineering, Stanford University, Stanford, CA 94305, USA

International Journal of Information Technology & Decision Making (IJITDM), 2019, vol. 18, issue 05, 1605-1629

Abstract: In this study, to address search index selection and volatility problems, we propose a learning-based search index collection method that collects the search data fraction for modeling by learning the best criteria from robust statistics. Based on the fraction of collected search index from internet search engine (Baidu.com) data sources, a novel model is formulated for Chinese stock market price forecasting. We empirically test our method on the two main Chinese stock market price indexes and discover that the prediction accuracy is equivalent or superior to the benchmarks from previous studies that used alternative search index collection methods or lagged data prediction models. All prediction results outstand the importance of an effective data collection method for the robustness of forecast models and demonstrate the utility of a learning-based collection method for addressing search index collection problem, leading to a significant improvement in Chinese stock market price prediction accuracy.

Keywords: Learning-based collection method; search engine data; financial time series forecasting; stock market prices (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1142/S0219622019500287

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