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

Shynkevich, Y; McGinnity, M; Coleman, S; Belatreche, A; Li, Y

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. 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.

Citation

Shynkevich, Y., McGinnity, M., Coleman, S., Belatreche, A., & Li, Y. (2017). Forecasting price movements using technical indicators : investigating the impact of varying input window length. Neurocomputing, 264, 71-88. https://doi.org/10.1016/j.neucom.2016.11.095

• Journal Article Type: Article
• Acceptance Date: Nov 5, 2016
• Online Publication Date: Jun 16, 2017
• Publication Date: Jun 16, 2017
• Deposit Date: Jan 16, 2017
• Publicly Available Date: Jun 16, 2018
• Journal: Neurocomputing
• Print ISSN: 0925-2312
• Publisher: Elsevier
• Volume: 264
• Pages: 71-88
• DOI: https://doi.org/10.1016/j.neucom.2016.11.095
• Publisher URL: http://dx.doi.org/10.1016/j.neucom.2016.11.095
• Related Public URLs: https://www.journals.elsevier.com/neurocomputing/
• Additional Information: Funders : InvestNI;Financial companies