Is Trading Indicator Performance Robust? Evidence from Scenario Building

IF 0.1 Q4 BUSINESS, FINANCE Journal of Investment Strategies Pub Date : 2019-01-02 DOI:10.21314/jois.2020.119
Andrea Thomann
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Abstract

This paper challenges widely applied trading indicators with regard to their ability to generate a robust performance. In this study, we use a semiparametric scenario building approach to simulate artificial price series based on characteristics of the observed price. In addition to testing the trading indicators on the observed price series and holding back some observed data for proforma 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 us to test the trading indicators for robustness on a large set of artificially created price series with similar characteristics to the observed price series. We find that many trading indicators deliver robust results for certain performance metrics but are unable to deliver robust results and improvements across all reported performance metrics. In addition, most trading strategies influence the statistical moments of the return distribution. While they improve the skewness – and thereby increase the number of positive returns – in most cases, they also increase the kurtosis, introducing undesired additional observations in the tails of the return distributions.
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交易指标表现稳健吗?情景构建证据
本文对广泛应用的交易指标在产生稳健表现方面的能力提出了挑战。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反向测试环境,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,允许我们在一组与观察到的价格序列相似的人为创造的价格序列上测试交易指标的稳健性。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,但无法在所有报告的绩效指标中提供稳健的结果和改进。此外,大多数交易策略都会影响收益分布的统计矩。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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来源期刊
CiteScore
0.40
自引率
50.00%
发文量
7
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