A Multi-Indicator Multi-Output Mixed Frequency Sampling Approach for Stock Index Forecasting

Yuchen Pan (), Zhi Xiao (), Xianning Wang () and Daoli Yang
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Yuchen Pan: Corresponding Author. Business School at Southwest University of Political Science&Law. No. 399 Baosheng Road, Yubei, Chongqing, 401120, China.
Zhi Xiao: Corresponding Author, School of Economics and Business Administration at Chongqing University. No. 174 Shazhengjie, Shapingba, Chongqing, 400044, China.
Xianning Wang: School of Economics and Management at Chongqing Normal University. No.37, Middle Road of University Town, Shaping District, Chongqing, 401331, China; Big Data Marketing Research and Applications Center of Chongqing Normal University, No. 37, Middle Road of University Town, Shapingba District, Chongqing, 401331, China.
Daoli Yang: E-commerce Department of School of Business Planning at Chongqing Technology and Business University. No. 19, Xuefu Avenue, Nan’an, Chongqing, 400067, China

Journal for Economic Forecasting, 2019, issue 4, 100-123

Abstract: Compared to stock price index sampled at higher frequency, its indicators are usually sampled at lower frequencies. In practice, with higher frequency variables response to lower frequency variables, we can get multi-output for each period. This paper explores how to construct a multi-indicator multi-output (MIMO) mixed sampling frequency approach for stock price index forecasting. We also consider nonlinear relationship between dependent variables and independent variables in stock market. We establish a new model by applying multiple output support vector machine (MSVM) to modify mixed data sampling (MIDAS). We compare results with other models and make DM tests. The experiment shows that the proposed model performances better.

Keywords: Stock price index forecasting; MIMO; MIDAS; MSVM; nonlinearity (search for similar items in EconPapers)
JEL-codes: C53 E47 (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (1)

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