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

作     者：HU Zhongyi BAO Yukun CHIONG Raymond XIONG Tao 

作者机构：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 

出 版 物：《Journal of Systems Science & Complexity》 (系统科学与复杂性学报（英文版）)

年 卷 期：2017年第30卷第6期

页      面：1425-1442页

核心收录：

学科分类：12[管理学] 02[经济学] 0202[经济学-应用经济学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 020204[经济学-金融学（含∶保险学）] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0701[理学-数学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the Natural Science Foundation of China under Grant Nos.71601147,71571080,and 71501079 the Central Universities under Grant No.104-413000017 the China Postdoctoral Science Foundation under Grant No.2015M582280 

主　　题：Financial market investment trading strategy parameter optimization stock index forecasting support vector regression 

摘      要：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.