Forecasting price movements using technical indicators: investigating the impact of varying input window length

Shynkevich, Y., McGinnity, T.M. ORCID: 0000-0002-9897-4748, Coleman, S.A., Belatreche, A. and Li, Y., 2017. Forecasting price movements using technical indicators: investigating the impact of varying input window length. Neurocomputing, 264, pp. 71-88. ISSN 0925-2312

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Abstract

The creation of a predictive system that correctly forecasts future changes of a stock price is crucial for investment management and algorithmic trading. The use of technical analysis for financial forecasting has been successfully employed by many researchers. Input window length is a time frame parameter required to be set when calculating many technical indicators. This study explores how the performance of the predictive system depends on a combination of a forecast horizon and an input window length for forecasting variable horizons. Technical indicators are used as input features for machine learning algorithms to forecast future directions of stock price movements. The dataset consists of ten years daily price time series for fifty stocks. The highest prediction performance is observed when the input window length is approximately equal to the forecast horizon. This novel pattern is studied using multiple performance metrics: prediction accuracy, winning rate, return per trade and Sharpe ratio.

Item Type: Journal article
Publication Title: Neurocomputing
Creators: Shynkevich, Y., McGinnity, T.M., Coleman, S.A., Belatreche, A. and Li, Y.
Publisher: Elsevier
Date: 15 November 2017
Volume: 264
ISSN: 0925-2312
Identifiers:
NumberType
10.1016/j.neucom.2016.11.095DOI
S0925231217311074Publisher Item Identifier
Divisions: Schools > School of Science and Technology
Depositing User: Linda Sullivan
Date Added: 22 Feb 2018 11:34
Last Modified: 12 Apr 2018 08:16
URI: http://irep.ntu.ac.uk/id/eprint/32787

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