Head and shoulders pattern recognition in stochastic processes

Conference paper


Tsinaslanidis, P. and Zapranis, A. 2008. Head and shoulders pattern recognition in stochastic processes.
AuthorsTsinaslanidis, P. and Zapranis, A.
TypeConference paper
Description

Technical analysis is the process of analyzing a security's historical prices in an effort to determine probable future prices. On the other hand, in the context of Markov Processes (e.g. Wiener, generalized Ito etc) that form the basis of financial markets theory the best estimation for future’s price is current price, and the path that a stock price followed in the past is irrelevant to the future. The second concept is consistent with the weak form of Efficient Market Hypothesis where the first is not. In this paper we examine if technical analysis can predict the unpredictable. Particularly we apply an automatic rule-based mechanism which can identify the Head and Shoulder pattern on a stochastic process, and we examine firstly if the aforementioned pattern occurs, and secondly when the pattern occurs, what is the price’s future behavior.

Year2008
Conference2nd International Conference in Accounting and Finance
Publication process dates
Deposited03 Nov 2014
Output statusPublished
Page range884-897
Permalink -

https://repository.canterbury.ac.uk/item/871wy/head-and-shoulders-pattern-recognition-in-stochastic-processes

  • 208
    total views
  • 0
    total downloads
  • 0
    views this month
  • 0
    downloads this month

Export as

Related outputs

A surrogate similarity measure for the mean-variance frontier optimization problem under bound and cardinality constraints
Guijarro, F. and Tsinaslanidis, P. 2019. A surrogate similarity measure for the mean-variance frontier optimization problem under bound and cardinality constraints. Journal of the Operational Research Society. https://doi.org/10.1080/01605682.2019.1657367
Subsequence dynamic time warping for charting: bullish and bearish class predictions for NYSE stocks
Tsinaslanidis, P. 2018. Subsequence dynamic time warping for charting: bullish and bearish class predictions for NYSE stocks. Expert Systems with Applications. 94, pp. 193-204. https://doi.org/10.1016/j.eswa.2017.10.055
Technical analysis for algorithmic pattern recognition
Tsinaslanidis, P. and Zapranis, A. 2016. Technical analysis for algorithmic pattern recognition. Springer.
Dynamic time warping as a similarity measure: applications in finance
Tsinaslanidis, P., Alexandridis, A., Zapranis, A. and Livanis, E. 2014. Dynamic time warping as a similarity measure: applications in finance.
An examination of the head and shoulders technical pattern; A support of the technical analysis’s subjective nature
Zapranis, A. and Tsinaslanidis, P. 2009. An examination of the head and shoulders technical pattern; A support of the technical analysis’s subjective nature.
A behavioral view of the head-and-shoulders technical analysis pattern
Zapranis, A. and Tsinaslanidis, P. 2010. A behavioral view of the head-and-shoulders technical analysis pattern.
Testing the generalised efficacy of technical analysis with bootstrapped aggregated regression trees
Zapranis, A. and Tsinaslanidis, P. 2012. Testing the generalised efficacy of technical analysis with bootstrapped aggregated regression trees.
Charting and weak-form market efficiency test: an empirical study on NASDAQ and NYSE components
Zapranis, A. and Tsinaslanidis, P. 2012. Charting and weak-form market efficiency test: an empirical study on NASDAQ and NYSE components. in: Essays in Honor of Prof. Dimitrios Papadopoulos Thessaloniki, Greece University of Macedonia.
A comprehensive review of hedge fund investment and trading strategies
Zapranis, A. and Tsinaslanidis, P. 2010. A comprehensive review of hedge fund investment and trading strategies. in: Essays in Honor of Late Professor J. Vartholomeos University of Piraeus. pp. 289-322
Identification of the head-and-shoulders technical analysis pattern with neural networks
Zapranis, A. and Tsinaslanidis, P. 2010. Identification of the head-and-shoulders technical analysis pattern with neural networks. in: Diamantaras, K., Duch, W. and Iliadis, L. (ed.) Artificial Neural Networks - ICANN 2010 Springer.
A novel, rule-based technical pattern identification mechanism: identifying and evaluating saucers and resistant levels in the US stock market
Zapranis, A. and Tsinaslanidis, P. 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), pp. 6301-6308. https://doi.org/10.1016/j.eswa.2011.11.079
Identifying and evaluating horizontal support and resistance levels: an empirical study on US stock markets
Zapranis, A. and Tsinaslanidis, P. 2012. Identifying and evaluating horizontal support and resistance levels: an empirical study on US stock markets. Applied Financial Economics. 22 (19), pp. 1571-1585. https://doi.org/10.1080/09603107.2012.663469
Business failure prediction using neural networks and wavelet neural networks
Alexandridis, A., Zapranis, A., Livanis, E. and Tsinaslanidis, P. 2013. Business failure prediction using neural networks and wavelet neural networks.
A prediction scheme using perceptually important points and dynamic time warping
Tsinaslanidis, P. and Kugiumtzis, D. 2014. A prediction scheme using perceptually important points and dynamic time warping. Expert Systems with Applications. 41 (15), pp. 6848-6860. https://doi.org/10.1016/j.eswa.2014.04.028