Technical Analysis Profitability Without Data Snooping Bias: Evidence from Chinese Stock Market

Aug 27, 2026

Comprehensive evaluation of the profitability of a large set of technical analysis trading rules in the Chinese stock market addresses data snooping bias. A stepwise superior predictive ability (step-SPA) test identifies genuinely profitable rules among 28,909 technical signals. Using 19 years of daily data on Chinese aggregate A-share market returns, the study finds substantial outperformance measured by market timing ability and Sharpe ratio gains. Results persist across subperiods and remain valid with transaction costs.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages16

Reading Time40 min

AccessFree to download

Introduction

Data snooping bias concern

Trading-rule universe and indicator categories

Step-SPA methodology

International Review of Finance, 2017 DOI: 10 . 1111/irﬁ.12161 Technical Analysis Proﬁtability Without Data Snooping Bias: Evidence from Chinese Stock Market* FUWEI JIANG†, GUOSHI TONG‡ AND GUOKAI SONG† †School of Finance, Central University of Finance and Economics, Beijing, China and ‡Hanqing Advanced Institute of Economics and Finance, Renmin University of China, Beijing, China ABSTRACT We perform a comprehensive analysis on the proﬁtability of a large number of technical analysis based trading rules in Chinese stock market. To counter data snooping bias, we employ a stepwise superior predictive ability test to identify genuinely proﬁtable trading rules among more than 28,000 technical signals. Using 19 years of daily data on Chinese aggregate stock market return, we ﬁnd substantial evidence on the proﬁtability of technical trading rules measured by either the market timing ability or Sharpe ratio gain. Our results on the proﬁtability of technical rules hold during different subperiods and remain valid under the presence of transaction costs. JEL Codes: C12; C52; G14 Accepted: 15 September 2017 I. INTRODUCTION Technical analysis involves a set of trading rules that make buy and sell decisions on the underlying security based on its past price patterns, trading volumes and potentially other public information. Technical analysis is widely adopted among security analysts, covered in ﬁnancial news media and easily accessible to most investors through various trading platforms. From a theoretical perspective, the use of technical analysis to forecast future return is justiﬁed in a number of equilibrium models that feature heterogeneity in investors’ * We gratefully acknowledge the helpful comments from the Editor, an anonymous referee, and feedbacks from Huafeng Chen, Po-Hsuan Hsu, Guofu Zhou, and seminar participants at Renmin University and Central University of Finance and Economics. We also thank Prof. Po-Hsuan Hsu for sharing with us a series of MATLAB and Fortran codes on superior predictive ability tests. This article is supported by the National Natural Science Foundation of China (Nos. 71602198, 71572052), Beijing Natural Science Foundation (No. 9174045), and the Program for Innovation Research in Central University of Finance and Economics. International Review of Finance access and response to information or feedback trading (e.g., Cespa and Vives 2012; Han et al. 2016) . Empirically, however, evidence tends to be inconclusive. While previous studies have found proﬁtability of using technical analysis tools in aggregate stock markets (e.g., Brock et al. 1992; Gencay 1998b; Lo et al. 2000), in foreign exchange and bond markets (e.g., Gencay 1999; Goh et al. 2013; Neely et al. 2014) and for individual stocks and portfolios (e.g., Glabadanidis 2015, 2017), an inﬂuential work by Sullivan et al. (1999) documented the absence of out-of-sample proﬁtability and thus raised concerns on the genuine predictiveness of technical analysis signals. In this article, we add to the above studies by examining the proﬁtability of technical analysis trading rules using Chinese aggregate stock market data. Complementary to many existing works that focus on various moving average rules, we perform a comprehensive investigation on a large number of commonly used technical signals. In particular, we consider ﬁve categories of technical indicators: channel break rules; ﬁlter rules; moving average rules; oscillator rules and support resistance rules. These ﬁve categories combined with a range of plausible parameters setting provide us with 28,909 distinct technical trading rules, which encompass the 2049 moving average rules considered in Brock et al. (1992) and 7846 trading rules in Sullivan et al. (1999) . We then evaluate jointly all these trading rules against the performance metrics of market timing ability and Sharpe ratio gain against a buy and hold benchmark. We emphasize that since our empirical analysi

FAQs

What problem does the study address regarding technical analysis profitability?

It addresses data snooping bias arising from testing many trading rules using a single time series dataset, which can overstate genuine predictiveness.

How does the study measure whether technical trading rules are genuinely profitable?

It uses a stepwise superior predictive ability (step-SPA) test and evaluates rules using market timing ability and Sharpe ratio gain against a buy-and-hold benchmark.

What main empirical findings are reported for the Chinese stock market?

Among 28,909 technical rules, fewer than 1% are genuinely significant after controlling for data snooping bias; the study identifies 170 and 54 significant rules under the two performance metrics and reports declines under transaction costs.

Aug 27, 2026

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