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Profit Guided or Statistical Error Guided? A Study of Stock Index Forecasting Using Support Vector Regression

Journal of Systems Science & Complexity 2017年第6期30卷 1425-1442页

作者： HU Zhongyi BAO Yukun CHIONG Raymond XIONG Tao School of Information Management Wuhan University School of Management Huazhong University of Science and Technology School of Electrical Engineering and Computing The University of Newcastle College of Economics and Management Huazhong Agricultural University

Stock index forecasting has been one of the most widely investigated topics in the field of financial forecasting. Related studies typically advocate for tuning the parameters of forecasting models by minimizing learning errors measured using statistical metrics such as the mean squared error or mean absolute percentage error. The authors argue that statistical metrics used to guide parameter tuning of forecasting models may not be meaningful, given the fact that the ultimate goal of forecasting is to facilitate investment decisions with expected profits in the future. The authors therefore introduce the Sharpe ratio into the process of model building and take it as the profit metric to guide parameter tuning rather than using the commonly adopted statistical metrics. The authors consider three widely used trading strategies, which include a na¨?ve strategy, a filter strategy and a dual moving average strategy, as investment scenarios. To verify the effectiveness of the proposed profit guided approach, the authors carry out simulation experiments using three global mainstream stock market indices. The results show that profit guided forecasting models are competitive, and in many cases produce significantly better performances than statistical error guided models. This implies thatprofit guided stock index forecasting is a worthwhile alternative over traditional stock index forecasting practices.

关键词： Financial market investment trading strategy parameter optimization stock index forecasting support vector regression

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