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Applied Financial Economics Volume 19, 2009 - Issue 15
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Original Articles

Is technical analysis profitable on US stocks with certain size, liquidity or industry characteristics?

Ben R. Marshall Department of Finance, Banking and Property, College of Business, Massey University, Private Bag 11222, Palmerston North, New ZealandCorrespondence[email protected]
,
Sun Qian Xiamen University, Xiamen, China
&
Martin Young Department of Finance, Banking and Property, College of Business, Massey University, Private Bag 11222, Palmerston North, New Zealand
Pages 1213-1221 | Published online: 30 Jun 2009

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José Luis Miralles-Quirós & María Mar Miralles-Quirós. (2022) Intraday Bitcoin price shocks: when bad news is good news. Journal of Applied Economics 25:1, pages 1294-1313.
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Gil Cohen & Elinor Cabiri. (2015) Can technical oscillators outperform the buy and hold strategy?. Applied Economics 47:30, pages 3189-3197.
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Achilleas Zapranis & Prodromos E. Tsinaslanidis. (2012) Identifying and evaluating horizontal support and resistance levels: an empirical study on US stock markets. Applied Financial Economics 22:19, pages 1571-1585.
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Articles from other publishers (14)

Prodromos Tsinaslanidis & Francisco Guijarro. (2020) What makes trading strategies based on chart pattern recognition profitable?. Expert Systems 38:5.
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Shaharudin Jakpar, Michael Tinggi, Akmal Hisham Tak & Wen Yi Chong. (2018) Fundamental Analysis VS Technical analysis: The Comparison of Two Analysis in Malaysia Stock Market. UNIMAS Review of Accounting and Finance 1:1.
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Louie Ren, Peter Ren & Yong Glasure. (2018) Is the simple trading range break-out rule profitable from the NASDAQ index?. Benchmarking: An International Journal 25:2, pages 763-771.
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Prodromos E. Tsinaslanidis. (2018) Subsequence dynamic time warping for charting: Bullish and bearish class predictions for NYSE stocks. Expert Systems with Applications 94, pages 193-204.
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Dirk F. Gerritsen. (2016) Are chartists artists? The determinants and profitability of recommendations based on technical analysis. International Review of Financial Analysis 47, pages 179-196.
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Prodromos E. Tsinaslanidis & Achilleas D. ZapranisProdromos E. Tsinaslanidis & Achilleas D. Zapranis. 2016. Technical Analysis for Algorithmic Pattern Recognition. Technical Analysis for Algorithmic Pattern Recognition 161 192 .
Prodromos E. Tsinaslanidis & Achilleas D. ZapranisProdromos E. Tsinaslanidis & Achilleas D. Zapranis. 2016. Technical Analysis for Algorithmic Pattern Recognition. Technical Analysis for Algorithmic Pattern Recognition 57 83 .
Prodromos E. Tsinaslanidis & Achilleas D. ZapranisProdromos E. Tsinaslanidis & Achilleas D. Zapranis. 2016. Technical Analysis for Algorithmic Pattern Recognition. Technical Analysis for Algorithmic Pattern Recognition 45 55 .
Prodromos E. Tsinaslanidis & Achilleas D. ZapranisProdromos E. Tsinaslanidis & Achilleas D. Zapranis. 2016. Technical Analysis for Algorithmic Pattern Recognition. Technical Analysis for Algorithmic Pattern Recognition 1 28 .
Prodromos E. Tsinaslanidis & Dimitris Kugiumtzis. (2014) A prediction scheme using perceptually important points and dynamic time warping. Expert Systems with Applications 41:15, pages 6848-6860.
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Ryuichi Yamamoto. (2012) Intraday technical analysis of individual stocks on the Tokyo Stock Exchange. Journal of Banking & Finance 36:11, pages 3033-3047.
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Achilleas Zapranis & Prodromos E. Tsinaslanidis. (2012) A novel, rule-based technical pattern identification mechanism: Identifying and evaluating saucers and resistant levels in the US stock market. Expert Systems with Applications 39:7, pages 6301-6308.
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Audrius Dzikevičius & Svetlana Šaranda. (2011) SMOOTHING TECHNIQUES FOR MARKET FLUCTUATION SIGNALS. Business: Theory and Practice 12:1, pages 63-74.
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Dries Heyman, Koen Inghelbrecht & Stefaan Paul Pauwels. (2012) Technical Trading Rules in Emerging Stock Markets. SSRN Electronic Journal.
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