The DOI matches its Crossref record; 153 of the 153 references it cites were found in a registry or an index.
WHAT DO WE KNOW ABOUT THE PROFITABILITY OF TECHNICAL ANALYSIS?
Abstract
Abstract The purpose of this paper is to review the evidence on the profitability of technical analysis. The empirical literature is categorized into two groups, ‘early’ and ‘modern’ studies, according to the characteristics of testing procedures. Early studies indicate that technical trading strategies are profitable in foreign exchange markets and futures markets, but not in stock markets. Modern studies indicate that technical trading strategies consistently generate economic profits in a variety of speculative markets at least until the early 1990s. Among a total of 95 modern studies, 56 studies find positive results regarding technical trading strategies, 20 studies obtain negative results, and 19 studies indicate mixed results. Despite the positive evidence on the profitability of technical trading strategies, most empirical studies are subject to various problems in their testing procedures, e.g. data snooping, ex post selection of trading rules or search technologies, and difficulties in estimation of risk and transaction costs. Future research must address these deficiencies in testing in order to provide conclusive evidence on the profitability of technical trading strategies.
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Citations
Cites
Common risk factors in the returns on stocks and bonds
Fama, E. F., & French, K. R. (1993) · Journal of Financial Economics · doi:10.1016/0304-405x(93)90023-5 · Cited by 25,754
EFFICIENT CAPITAL MARKETS: A REVIEW OF THEORY AND EMPIRICAL WORK*
Malkiel, B. G., & Fama, E. F. (1970) · The Journal of Finance · doi:10.1111/j.1540-6261.1970.tb00518.x · Cited by 15,699
Efficient capital markets: A review of theory and empirical work
Fama, E. F. (1970) · The Journal of Finance · doi:10.2307/2325486 · Cited by 13,635
Genetic Programming: On the Programming of Computers by Means of Natural Selection
Koza, J. R. (1992) · Medical Entomology and Zoology · Cited by 13,082
Detecting strange attractors in turbulence
Takens, F. (1981) · Lecture Notes in Mathematics · doi:10.1007/bfb0091924 · Cited by 10,459
Witten, I. H. (2008) · Cited by 9,143
Noise Trader Risk in Financial Markets
De Long, J. B., Shleifer, A., Summers, L. H., & Waldmann, R. J. (1990) · Journal of Political Economy · doi:10.1086/261703 · Cited by 6,234
The relationship between return and market value of common stocks
Banz, R. W. (1981) · Journal of Financial Economics · doi:10.1016/0304-405x(81)90018-0 · Cited by 5,922
The Econometrics of Financial Markets
Campbell, J. Y., Lo, A. W., & MacKinlay, A. C. (2012) · Princeton University Press eBooks · doi:10.2307/j.ctt7skm5 · Cited by 5,875
By Force of Habit: A Consumption‐Based Explanation of Aggregate Stock Market Behavior
Campbell, J. Y., & Cochrane, J. H. (1999) · Journal of Political Economy · doi:10.1086/250059 · Cited by 5,210
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Forecasting the equity risk premium: the role of technical indicators
Neely, C. J., Rapach, D. E., Tu, J., & Zhou, G. (2014) · Management Science · doi:10.1287/mnsc.2013.1838 · Cited by 1,003
Rapach, D., & Zhou, G. (2013) · Handbook of Economic Forecasting · doi:10.1016/b978-0-444-53683-9.00006-2 · Cited by 613
Stock Market Analysis: A Review and Taxonomy of Prediction Techniques
Shah, D., Isah, H., & Zulkernine, F. (2019) · International Journal of Financial Studies · doi:10.3390/ijfs7020026 · Cited by 465
The evolution of stock market efficiency over time: A survey of the empirical literature
Lim, K.-P., & Brooks, R. (2011) · Journal of Economic Surveys · doi:10.1111/j.1467-6419.2009.00611.x · Cited by 410
The use of technical analysis by fund managers: International evidence
Menkhoff, L. (2010) · Journal of Banking & Finance · doi:10.1016/j.jbankfin.2010.04.014 · Cited by 302
Metadata
- Authors
- Park, C., & Irwin, S. H.
- Venue
- Journal of Economic Surveys
- Publisher
- Wiley
- Published
- 2007-07-11
- Volume
- 21
- Issue
- 4
- Pages
- 786-826
- ISSN
- 0950-0804 (ISSN-L)
- 1467-6419
- OpenAlex
- W2134658143
- Cited by
- 607
- References
- 153
- FWCI
- 23.03
- Type
- journal article
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
