Editing Technical analysis (section)

==Empirical evidence==
Whether technical analysis actually works is a matter of controversy. Methods vary greatly, and different technical analysts can sometimes make contradictory predictions from the same data. Many investors claim that they experience positive returns, but academic appraisals often find that it has little [[predictive power]].<ref>{{cite news  | last =Browning | first =E.S. | title =Reading market tea leaves | work =The Wall Street Journal Europe | pages =17–18 | publisher =Dow Jones | date =31 July 2007 }}</ref> Of 95 modern studies, 56 concluded that technical analysis had positive results, although [[data-snooping bias]] and other problems make the analysis difficult.<ref name=SurveysReview>{{cite journal | last1 = Irwin | first1 = Scott H. | last2 = Park | first2 = Cheol-Ho | year = 2007 | title = What Do We Know About the Profitability of Technical Analysis? | journal = Journal of Economic Surveys | volume = 21 | issue = 4| pages = 786–826 | doi = 10.1111/j.1467-6419.2007.00519.x | s2cid = 154488391 }}</ref> Nonlinear prediction using [[Artificial neural network|neural networks]] occasionally produces [[Statistical significance|statistically significant]] prediction results.<ref>Skabar, Cloete, [http://crpit.com/confpapers/CRPITV4Skabar.pdf Networks, Financial Trading and the Efficient Markets Hypothesis] {{Webarchive|url=https://web.archive.org/web/20110718234410/http://crpit.com/confpapers/CRPITV4Skabar.pdf |date=18 July 2011 }}</ref> A [[Federal Reserve]] working paper<ref name=Osler/> regarding [[support and resistance]] levels in short-term foreign exchange rates "offers strong evidence that the levels help to predict intraday trend interruptions", although the "predictive power" of those levels was "found to vary across the exchange rates and firms examined".

Technical trading strategies were found to be effective in the Chinese marketplace by a 2007 study that states, "Finally, we find significant positive returns on buy trades generated by the contrarian version of the [[moving-average crossover]] rule, the channel breakout rule, and the Bollinger band trading rule, after accounting for transaction costs of 0.50%."<ref>Nauzer J. Balsara, Gary Chen and Lin Zheng [https://web.archive.org/web/20081204112239/http://findarticles.com/p/articles/mi_qa5466/is_200704/ai_n21292807/pg_1?tag=artBody;col1 "The Chinese Stock Market: An Examination of the Random Walk Model and Technical Trading Rules"] ''The Quarterly Journal of Business and Economics, Spring 2007''</ref>

An influential 1992 study by Brock et al. appeared to find support for technical trading rules.<ref>Brock, William, et al. “Simple Technical Trading Rules and the Stochastic Properties of Stock Returns.” The Journal of Finance, vol. 47, no. 5, 1992, pp. 1731–64. JSTOR, https://doi.org/10.2307/2328994. Accessed 8 Dec. 2024.</ref> Sullivan and Timmerman tested the 1992 study for data snooping and other problems in 1999;<ref name=Sullivan1999>{{cite journal | author = Sullivan, R. |author2=Timmermann, A. |author3=White, H. | year = 1999 | title = Data-Snooping, Technical Trading Rule Performance, and the Bootstrap | journal = The Journal of Finance | volume = 54 | issue = 5 | pages = 1647–1691 | doi = 10.1111/0022-1082.00163
|citeseerx=10.1.1.50.7908 }}</ref> they determined the sample covered by Brock et al. was robust to data snooping.

Subsequently, a comprehensive study of the question by Amsterdam economist Gerwin Griffioen concludes that: "for the U.S., Japanese and most Western European stock market indices the recursive out-of-sample forecasting procedure does not show to be profitable, after implementing little transaction costs. Moreover, for sufficiently high transaction costs it is found, by estimating [[Capital asset pricing model|CAPM]]s, that technical trading shows no statistically significant risk-corrected out-of-sample forecasting power for almost all of the stock market indices."<ref name="autogenerated1">Griffioen, ''[https://ssrn.com/abstract=566882 Technical Analysis in Financial Markets]''</ref> Transaction costs are particularly applicable to "momentum strategies"; a comprehensive 1996 review of the data and studies concluded that even small transaction costs would lead to an inability to capture any excess from such strategies.<ref name=Chan1996>{{cite journal | author = Chan, L.K.C. |author2=Jegadeesh, N. |author3=Lakonishok, J. | year = 1996 | title = Momentum Strategies | journal = The Journal of Finance |
volume = 51 | issue = 5 | pages = 1681–1713| doi = 10.2307/2329534 | jstor=2329534}}</ref>

In a 2000 paper published in the ''[[Journal of Finance]]'', professor [[Andrew W. Lo]] of MIT, working with Harry Mamaysky and Jiang Wang found that:

{{blockquote|Technical analysis, also known as "charting", has been a part of financial practice for many decades, but this discipline has not received the same level of academic scrutiny and acceptance as more traditional approaches such as fundamental analysis. One of the main obstacles is the highly subjective nature of technical analysis{{spaced ndash}}the presence of geometric shapes in historical price charts is often in the eyes of the beholder. In this paper, we propose a systematic and automatic approach to technical pattern recognition using nonparametric [[kernel regression]], and apply this method to a large number of U.S. stocks from 1962 to 1996 to evaluate the effectiveness of technical analysis. By comparing the unconditional empirical distribution of daily stock returns to the conditional distribution{{spaced ndash}}conditioned on specific technical indicators such as head-and-shoulders or double-bottoms{{spaced ndash}}we find that over the 31-year sample period, several technical indicators do provide incremental information and may have some practical value.<ref name=Foundations />}}

In that same paper Lo wrote that "several academic studies suggest that&nbsp;... technical analysis may well be an effective means for extracting useful information from market prices."<ref name=Foundations>{{Cite journal | doi = 10.1111/0022-1082.00265 | last1 = Lo | first1 = Andrew W. | last2 = Mamaysky | first2 = Harry | last3 = Wang | first3 = Jiang | year = 2000 | title = Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation | journal = Journal of Finance | volume = 55 | issue = 4| pages = 1705–1765 | citeseerx = 10.1.1.134.1546 }}</ref> Some techniques such as [[Drummond Geometry]] attempt to overcome the past data bias by projecting support and resistance levels from differing time frames into the near-term future and combining that with reversion to the mean techniques.<ref>David Keller, "Breakthroughs in Technical Analysis; New Thinking from the World's Top Minds," New York, Bloomberg Press, 2007, {{ISBN|978-1-57660-242-3}} pp.1–19</ref>

===Efficient-market hypothesis===
The [[efficient-market hypothesis]] (EMH) contradicts the basic tenets of technical analysis by stating that past prices cannot be used to profitably predict future prices. Thus it holds that technical analysis cannot be effective. Economist [[Eugene Fama]] published the seminal paper on the EMH in the ''Journal of Finance'' in 1970, and said "In short, the evidence in support of the efficient markets model is extensive, and (somewhat uniquely in economics) contradictory evidence is sparse."<ref>Eugene Fama, [http://www.e-m-h.org/Fama70.pdf  "Efficient Capital Markets: A Review of Theory and Empirical Work,"] ''The Journal of Finance'', volume 25, issue 2 (May 1970), pp. 383–417.</ref>

However, because future stock prices can be strongly influenced by investor expectations, technicians claim it only follows that past prices influence future prices.<ref name=Aronson>Aronson, David R. (2006). [http://www.wiley.com/WileyCDA/WileyTitle/productCd-0470008741,descCd-authorInfo.html ''Evidence-Based Technical Analysis''], Hoboken, New Jersey: John Wiley and Sons, pages 357, 355–356, 342. {{ISBN|978-0-470-00874-4}}.</ref> They also point to research in the field of [[behavioral finance]], specifically that people are not the rational participants EMH makes them out to be. Technicians have long said that irrational human behavior influences stock prices, and that this behavior leads to predictable outcomes.<ref name=Dichotomy>{{cite journal |author=[[Robert Prechter|Prechter, Robert R Jr]]; Parker, Wayne D |year=2007 |title=The Financial/Economic Dichotomy in Social Behavioral Dynamics: The Socionomic Perspective |journal=Journal of Behavioral Finance |volume=8 |issue=2 |pages=84–108 |doi=10.1080/15427560701381028|citeseerx=10.1.1.615.763 |s2cid=55114691 }}</ref> Author David Aronson says that the theory of behavioral finance blends with the practice of technical analysis:

<blockquote>By considering the impact of emotions, cognitive errors, irrational preferences, and the dynamics of group behavior, behavioral finance offers succinct explanations of excess market volatility as well as the excess returns earned by stale information strategies.... cognitive errors may also explain the existence of market inefficiencies that spawn the systematic price movements that allow objective TA [technical analysis] methods to work.<ref name=Aronson/></blockquote>

EMH advocates reply that while individual market participants do not always act rationally (or have complete information), their aggregate decisions balance each other, resulting in a rational outcome (optimists who buy stock and bid the price higher are countered by pessimists who sell their stock, which keeps the price in equilibrium).<ref name=Clark>Clarke, J., T. Jandik, and Gershon Mandelker (2001). "The efficient markets hypothesis," ''Expert Financial Planning: Advice from Industry Leaders'', ed. R. Arffa, 126–141. New York: Wiley & Sons.</ref> Likewise, complete information is reflected in the price because all market participants bring their own individual, but incomplete, knowledge together in the market.<ref name=Clark/>

====Random walk hypothesis====
The [[random walk hypothesis]] may be derived from the weak-form efficient markets hypothesis, which is based on the assumption that market participants take full account of any information contained in past price movements (but not necessarily other public information). In his book ''A Random Walk Down Wall Street'', Princeton economist [[Burton Malkiel]] said that technical forecasting tools such as pattern analysis must ultimately be self-defeating: "The problem is that once such a regularity is known to market participants, people will act in such a way that prevents it from happening in the future."<ref>Burton Malkiel, A Random Walk Down Wall Street, W. W. Norton & Company (April 2003) p. 168.</ref>  Malkiel has stated that while momentum may explain some stock price movements, there is not enough momentum to make excess profits. Malkiel has compared technical analysis to "[[astrology]]".<ref name=huebscher>Robert Huebscher. [http://www.advisorperspectives.com/newsletters09/pdfs/Burton_Malkiel_Talks_the_Random_Walk.pdf Burton Malkiel Talks the Random Walk]. 7 July 2009.</ref>

In the late 1980s, professors Andrew Lo and Craig McKinlay published a paper which cast doubt on the random walk hypothesis. In a 1999 response to Malkiel, Lo and McKinlay collected empirical papers that questioned the hypothesis' applicability<ref>Lo, Andrew; MacKinlay, Craig. ''A Non-Random Walk Down Wall Street'', Princeton University Press, 1999. {{ISBN|978-0-691-05774-3}}</ref> that suggested a non-random and possibly predictive component to stock price movement, though they were careful to point out that rejecting random walk does not necessarily invalidate EMH, which is an entirely separate concept from RWH. In a 2000 paper, [[Andrew Lo]] back-analyzed data from the U.S. from 1962 to 1996 and found that "several technical indicators do provide incremental information and may have some practical value".<ref name=Foundations/> Burton Malkiel dismissed the irregularities mentioned by Lo and McKinlay as being too small to profit from.<ref name=huebscher/>

Technicians argue that the EMH and random walk theories both ignore the realities of markets, in that participants are not completely rational and that current price moves are not independent of previous moves.<ref name=Kahn/><ref>Poser, Steven W. (2003). ''Applying Elliott Wave Theory Profitably'', John Wiley and Sons, p. 71. {{ISBN|0-471-42007-7}}.</ref> Some signal processing researchers negate the random walk hypothesis that stock market prices resemble [[Wiener process]]es, because the statistical moments of such processes and real stock data vary significantly with respect to window size and [[similarity measure]].<ref>Eidenberger, Horst (2011). "Fundamental Media Understanding" Atpress. {{ISBN|978-3-8423-7917-6}}.</ref> They argue that feature transformations used for the description of audio and [[biosignal]]s can also be used to predict stock market prices successfully which would contradict the random walk hypothesis.

The random walk index (RWI) is a technical indicator that attempts to determine if a stock's price movement is random in nature or a result of a statistically significant trend.  The random walk index attempts to determine when the market is in a strong uptrend or downtrend by measuring price ranges over N and how it differs from what would be expected by a random walk (randomly going up or down).  The greater the range suggests a stronger trend.<ref>{{cite web|url=http://www.asiapacfinance.com/trading-strategies/technicalindicators/RandomWalkIndex|title=AsiaPacFinance.com Trading Indicator Glossary|access-date=1 August 2011|archive-url=https://web.archive.org/web/20110901022339/http://www.asiapacfinance.com/trading-strategies/technicalindicators/RandomWalkIndex|archive-date=1 September 2011|url-status=dead}}</ref>

Applying Kahneman and Tversky's [[prospect theory]] to price movements, Paul V. Azzopardi provided a possible explanation why fear makes prices fall sharply while greed pushes up prices gradually.<ref>Azzopardi, Paul V. (2012), "Why Financial Markets Rise Slowly but Fall Sharply: Analysing market behaviour with behavioural finance", Harriman House, ASIN: B00B0Y6JIC</ref>  This commonly observed behaviour of securities prices is sharply at odds with random walk.  By gauging greed and fear in the market,<ref>{{Cite web|url=https://money.cnn.com/data/fear-and-greed/|title=Fear & Greed Index - Investor Sentiment}}</ref> investors can better formulate long and short portfolio stances.