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Time Series Technical Analysis via New Fast Estimation Methods: A Preliminary Study in Mathematical Finance

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Author Info
Michel Fliess (LIX, INRIA Saclay - Ile de France)
C\'edric Join (INRIA Saclay - Ile de France, CRAN)
Abstract

New fast estimation methods stemming from control theory lead to a fresh look at time series, which bears some resemblance to "technical analysis". The results are applied to a typical object of financial engineering, namely the forecast of foreign exchange rates, via a "model-free" setting, i.e., via repeated identifications of low order linear difference equations on sliding short time windows. Several convincing computer simulations, including the prediction of the position and of the volatility with respect to the forecasted trendline, are provided. $\mathcal{Z}$-transform and differential algebra are the main mathematical tools.

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File URL: http://arxiv.org/abs/0811.1561
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Paper provided by arXiv.org in its series Quantitative Finance Papers with number 0811.1561.

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Date of creation: Nov 2008
Date of revision: Nov 2008
Publication status: Published in IAR-ACD08 (23rd IAR Workshop on Advanced Control and Diagnosis) (2008)
Handle: RePEc:arx:papers:0811.1561

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  1. Durlauf, Steven N & Phillips, Peter C B, 1988. "Trends versus Random Walks in Time Series Analysis," Econometrica, Econometric Society, vol. 56(6), pages 1333-54, November. [Downloadable!] (restricted)
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  2. Michel Fliess & Cédric Join, 2009. "A mathematical proof of the existence of trends in financial time series," Post-Print inria-00352834_v1, HAL. [Downloadable!]
  3. Michel Fliess & C\'edric Join, 2009. "A mathematical proof of the existence of trends in financial time series," Quantitative Finance Papers 0901.1945, arXiv.org. [Downloadable!]
  4. Andrew W. Lo & Harry Mamaysky & Jiang Wang, 2000. "Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation," Journal of Finance, American Finance Association, vol. 55(4), pages 1705-1770, 08. [Downloadable!] (restricted)
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