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

Shynkevich, Y, McGinnity, M, Coleman, S, 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.

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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. Window size 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 a window size 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 window size 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: Article
Schools: Schools > School of Computing, Science and Engineering > Salford Innovation Research Centre (SIRC)
Journal or Publication Title: Neurocomputing
Publisher: Elsevier
ISSN: 0925-2312
Related URLs:
Funders: InvestNI, Financial companies
Depositing User: Yuhua Li
Date Deposited: 16 Jan 2017 15:44
Last Modified: 09 Mar 2018 04:12
URI: /id/eprint/41188

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