{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T09:35:39Z","timestamp":1791970539778,"version":"4.3.4"},"reference-count":34,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2002,12,17]],"date-time":"2002-12-17T00:00:00Z","timestamp":1040083200000},"content-version":"vor","delay-in-days":868,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["The Journal of Finance"],"published-print":{"date-parts":[[2000,8]]},"abstract":"<jats:p>Technical analysis, also known as \u201ccharting,\u201d 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\u2014the 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 we 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\u2014conditioned on specific technical indicators such as head\u2010and\u2010shoulders or double bottoms\u2014we find that over the 31\u2010year sample period, several technical indicators do provide incremental information and may have some practical value.<\/jats:p>","DOI":"10.1111\/0022-1082.00265","type":"journal-article","created":{"date-parts":[[2003,3,12]],"date-time":"2003-03-12T07:34:12Z","timestamp":1047454452000},"page":"1705-1765","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":853,"title":["Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation"],"prefix":"10.1111","volume":"55","author":[{"given":"Andrew W.","family":"Lo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Harry","family":"Mamaysky","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2002,12,17]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0304-405X(98)00052-X"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.272.5270.1905"},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1540-6261.1994.tb04424.x"},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1540-6261.1992.tb04681.x"},{"key":"e_1_2_11_6_1","doi-asserted-by":"publisher","DOI":"10.1093\/rfs\/2.4.527"},{"key":"e_1_2_11_7_1","doi-asserted-by":"crossref","DOI":"10.1515\/9781400830213","author":"Campbell John","year":"1997","journal-title":"The Econometrics of Financial Markets"},{"key":"e_1_2_11_8_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1540-6261.1996.tb05222.x"},{"key":"e_1_2_11_9_1","author":"Chang Kevin","year":"1994","journal-title":"Evaluating chart\u2010based technical analysis: The head\u2010andshoulders pattern in foreign exchange markets, Working paper"},{"key":"e_1_2_11_10_1","volume":"4","author":"Csaki E.","year":"1984","journal-title":"Empirical distribution function, in P. 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