ROBUST TRADING RULE SELECTION AND FORECASTING ACCURACY

作     者：SCHMIDBAUER Harald ROSCH Angi SEZER Tolga TUNALIOGLU Vehbi Sinan 

作者机构：Department of Business Administration Istanbul Bilgi University Santral Campus 34060 Eyiip IstanbulTurkey. FOM University of Applied Scienees Study Centre Munich Arnulfstr. 30 80335 Miinchen Germany. DIME University of Genoa Via Opera Pia 15 16145 Genoa Italy. 

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

年 卷 期：2014年第27卷第1期

页      面：169-180页

核心收录：

学科分类：0810[工学-信息与通信工程] 1205[管理学-图书情报与档案管理] 12[管理学] 02[经济学] 0202[经济学-应用经济学] 1202[管理学-工商管理] 020206[经济学-国际贸易学] 020202[经济学-区域经济学] 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）] 

主　　题：A-priori robustness data-snooping bias efficient market hypothesis evolutionary com-putation intra-day FX markets time-series bootstrap trading rule selection. 

摘      要：Trading rules performing well on a given data set seldom lead to promising out-of-sample results, a problem which is a consequence of the in-sample data snooping bias. Efforts to justify the selection of trading rules by assessing the out-of-sample performance will not really remedy this predica- ment either, because they are prone to be trapped in what is known as the out-of-sample data-snooping bias. Our approach to curb the data-snooping bias consists of constructing a framework for trading rule selection using a-priori robustness strategies, where robustness is gauged on the basis of time- series bootstrap and multi-objective criteria. This approach focuses thus on building robustness into the process of trading rule selection at an early stage, rather than on an ex-post assessment of trading rule fitness. Intra-day FX market data constitute the empirical basis of the proposed investigations. Trading rules are selected from a wide universe created by evolutionary computation tools. The authors show evidence of the benefit of this approach in terms of indirect forecasting accuracy when investing in FX markets.