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Sequential smoothing for turning point detection with application to financial decisions

Sequential smoothing for turning point detection with application to financial decisions A fundamental problem in financial trading is the correct and timely identification of turning points in stock value series. This detection enables to perform profitable investment decisions, such as buying‐at‐low and selling‐at‐high. This paper evaluates the ability of sequential smoothing methods to detect turning points in financial time series. The novel idea is to select smoothing and alarm coefficients on the gain performance of the trading strategy. Application to real data shows that recursive smoothers outperform two‐sided filters at the out‐of‐sample level. Copyright © 2012 John Wiley & Sons, Ltd. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Applied Stochastic Models in Business and Industry Wiley

Sequential smoothing for turning point detection with application to financial decisions

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References (27)

Publisher
Wiley
Copyright
Copyright © 2014 John Wiley & Sons, Ltd.
ISSN
1524-1904
eISSN
1526-4025
DOI
10.1002/asmb.1945
Publisher site
See Article on Publisher Site

Abstract

A fundamental problem in financial trading is the correct and timely identification of turning points in stock value series. This detection enables to perform profitable investment decisions, such as buying‐at‐low and selling‐at‐high. This paper evaluates the ability of sequential smoothing methods to detect turning points in financial time series. The novel idea is to select smoothing and alarm coefficients on the gain performance of the trading strategy. Application to real data shows that recursive smoothers outperform two‐sided filters at the out‐of‐sample level. Copyright © 2012 John Wiley & Sons, Ltd.

Journal

Applied Stochastic Models in Business and IndustryWiley

Published: Jan 1, 2014

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