Testing the Predictive Ability of Technical Analysis Using a New Stepwise Test Without Data Snooping Bias

Hsu, Po–Hsuan; Hsu, Yu‐Chin; Kuan, Chung‐Ming

doi:10.2139/ssrn.1087044

Cited by 56 publications

(124 citation statements)

References 39 publications

(61 reference statements)

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“…Hansen (2005) only considers a bootstrap single-step MTP. Hsu et al (2010) propose the same adjustment (4.1) in the context of a bootstrap stepwise MTP in the spirit of Romano and Wolf (2005).…”

Section: Asymptotic Adjustmentsmentioning

confidence: 99%

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Multiple Testing of One-Sided Hypotheses: Combining Bonferroni and the Bootstrap

Romano

Wolf

2017

SSRN Journal

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may AbstractIn many multiple testing problems, the individual null hypotheses (i) concern univariate parameters and (ii) are one-sided. In such problems, power gains can be obtained for bootstrap multiple testing procedures in scenarios where some of the parameters are 'deep in the null' by making certain adjustments to the null distribution under which to resample. In this paper, we compare a Bonferroni adjustment that is based on finite-sample considerations with certain 'asymptotic' adjustments previously suggested in the literature.

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Section: Asymptotic Adjustmentsmentioning

confidence: 99%

“…The adjustment (4.1) is of asymptotic nature, since one does not have to pay any 'penalty' in the proposals of Hansen (2005) and Hsu et al (2010). In other words, the MTP procedure proceeds as if θ s (P A 0,n ) = θ s in case θ s (P A 0,n ) has been adjusted toθ n,s < 0.…”

Section: Asymptotic Adjustmentsmentioning

confidence: 99%

Multiple Testing of One-Sided Hypotheses: Combining Bonferroni and the Bootstrap

Romano

Wolf

2017

SSRN Journal

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“…When testing different investment strategies, there is a probability of having a given set of results purely due to chance rather 9 than these being truly based on the actual superior predictive ability of the competing strategies. (Sullivan et al, 1999, Hsu et al, 2010, and Neuhierl and Schlusche, 2011 Politis and Romano (1994). More details on this procedure are outlined in…”

Section: Superior Predictive Ability Testmentioning

confidence: 99%

Portfolio optimization and index tracking for the shipping stock and freight markets using evolutionary algorithms

Andriosopoulos

Doumpos

Papapostolou

et al. 2013

Transportation Research Part E: Logistics and Transportation Re

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“…Todea et al (2009) report that profitability of a moving-average based strategy in six Asian markets from 1997 to 2008 is episodic. Hsu et al (2010) report that technical patterns (moving averages, filter rules) loose much of their predictive power as market matures.…”

mentioning

confidence: 99%

Empirical evaluation of price-based technical patterns using probabilistic neural networks

Ahlawat

2017

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Abstract. Technical analysis is the art of identifying patterns in historical data with the belief that certain patterns foretell future price movements. An empirical evaluation of the effectiveness of technical analysis is confounded by the subjectivity involved in identifying patterns. This work presents a robust framework for pattern identification using probabilistic neural networks (PNN). The thirty components of the Dow Jones Industrial Average and a set of ten indices are considered. Fourteen patterns are analyzed. In order to test the possibility that technical patterns are more predictable in certain market environments, the period under study is partitioned into bull and bear markets and the statistical significance of profits earned by identified patterns observed in each environment is analyzed. A range of holding periods from 10 to 50 trading days is considered and a simple model of transaction costs is added. The study reveals that no pattern produces statistically and economically significant profits for a cross-section of stocks and indices analyzed, though a few patterns are more successful predictors. Bullish (bearish) patterns are more reliable predictors in bullish (bearish) market environments. These observations can be explained by the Adaptive Market Hypothesis with certain patterns becoming more accurate predictors in specific market environments.

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Testing the Predictive Ability of Technical Analysis Using a New Stepwise Test Without Data Snooping Bias

Abstract: for their guidance during his doctoral study at the Graduate School of Business of Columbia University. C.-M. Kuan thanks the NSC of Taiwan for research support (97-2410-H-002-217-MY3). All errors remain ours.

Cited by 56 publications

References 39 publications

Multiple Testing of One-Sided Hypotheses: Combining Bonferroni and the Bootstrap

Multiple Testing of One-Sided Hypotheses: Combining Bonferroni and the Bootstrap

Portfolio optimization and index tracking for the shipping stock and freight markets using evolutionary algorithms

Empirical evaluation of price-based technical patterns using probabilistic neural networks

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