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Shynkevich, Y, McGinnity, TM 
ORCID: https://orcid.org/0000-0002-9897-4748, Coleman, SA, 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: | Number Type 10.1016/j.neucom.2016.11.095 DOI S0925231217311074 Publisher Item Identifier |
| Divisions: | Schools > School of Science and Technology |
| Record created by: | Linda Sullivan |
| Date Added: | 22 Feb 2018 11:34 |
| Last Modified: | 16 Jun 2019 03:00 |
| URI: | https://irep.ntu.ac.uk/id/eprint/32787 |
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