Appendix to 'Is Trading Indicator Performance Robust? Evidence from Semi-Parametric Scenario Building'

Andrea Thomann
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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.
附录“交易指标表现稳健吗?”来自半参数情景构建的证据
这是“交易指标表现稳健吗?”的在线附录。“我们从其他交易指标中提供了额外的实证结果。“交易指标表现稳健吗?”本文对广泛应用的交易指标产生稳健表现的能力提出了挑战。在本研究中,我们使用半参数情景构建方法来模拟基于观察到的价格特征的人为价格序列。除了在观察到的价格序列上测试交易指标,并保留一些观察到的数据进行形式样本外测试之外,我们的价格模拟还提供了一个回测环境,以测试人为创造的价格上的交易策略。这提供了一个额外的性能评估,允许测试交易指标的稳健性,在一组人工创建的价格序列具有与观察到的价格序列相似的特征。我们发现,许多交易指标为某些绩效指标提供了稳健的结果,然而,无法在所有报告的绩效指标中提供稳健的结果和改进。最重要的是,大多数交易策略影响收益分布的高阶矩;虽然它们改善了偏度,从而增加了正收益的数量,但在大多数情况下,它们也增加了峰度,在收益分布的尾部引入了不必要的额外观察值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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