{"title":"Appendix to 'Is Trading Indicator Performance Robust? Evidence from Semi-Parametric Scenario Building'","authors":"Andrea Thomann","doi":"10.2139/ssrn.3246671","DOIUrl":null,"url":null,"abstract":"This is the online Appendix to \"Is Trading Indicator Performance Robust? Evidence from Semi-Parametric Scenario Building\"<br><br>We provide additional empirical results from other trading indicators.<br><br>Abstract of \"Is Trading Indicator Performance Robust? Evidence from Semi-Parametric Scenario Building\"<br><br>This paper challenges widely applied trading indicators in their ability to generate robust performance. In this study we use a semi-parametric scenario building approach to simulate artificial price series based on the characteristics of the observed price. In addition to testing the trading indicators on the observed price series and holding back some observed data for pro forma out-of-sample testing, our price simulations provide a back-testing environment to test trading strategies on artificially created prices. This provides an additional performance assessment by allowing to test the trading indicators for robustness on a large set of artificially created price series with similar characteristics as the observed price series. We find that many trading indicators deliver robust results for certain performance metrics, however, are unable to deliver robust results and improvements across all reported performance metrics. On top, most trading strategies influence the higher order moments of the return distribution; while they improve the skewness—thereby increasing the number of positive returns—in most cases they also increase the kurtosis, introducing undesired additional observations in the tail of the return distributions.","PeriodicalId":264857,"journal":{"name":"ERN: Semiparametric & Nonparametric Methods (Topic)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ERN: Semiparametric & Nonparametric Methods (Topic)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2139/ssrn.3246671","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This is the online Appendix to "Is Trading Indicator Performance Robust? Evidence from Semi-Parametric Scenario Building"
We provide additional empirical results from other trading indicators.
Abstract of "Is Trading Indicator Performance Robust? Evidence from Semi-Parametric Scenario Building"
This paper challenges widely applied trading indicators in their ability to generate robust performance. In this study we use a semi-parametric scenario building approach to simulate artificial price series based on the characteristics of the observed price. In addition to testing the trading indicators on the observed price series and holding back some observed data for pro forma out-of-sample testing, our price simulations provide a back-testing environment to test trading strategies on artificially created prices. This provides an additional performance assessment by allowing to test the trading indicators for robustness on a large set of artificially created price series with similar characteristics as the observed price series. We find that many trading indicators deliver robust results for certain performance metrics, however, are unable to deliver robust results and improvements across all reported performance metrics. On top, most trading strategies influence the higher order moments of the return distribution; while they improve the skewness—thereby increasing the number of positive returns—in most cases they also increase the kurtosis, introducing undesired additional observations in the tail of the return distributions.