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

Shynkevich, Yauheniya, McGinnity, T.M., Coleman, Sonya A., Belatreche, Ammar and Li, Yuhua ORCID: https://orcid.org/0000-0003-2913-4478 2017. Forecasting price movements using technical indicators: Investigating the impact of varying input window length. Neurocomputing 264 , 71--88. 10.1016/j.neucom.2016.11.095

Full text not available from this repository.

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:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	Elsevier
ISSN:	0925-2312
Date of Acceptance:	5 November 2016
Last Modified:	07 Nov 2022 09:26
URI:	https://orca.cardiff.ac.uk/id/eprint/129125

Citation Data

Cited 112 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item