期刊
NEUROCOMPUTING
卷 264, 期 -, 页码 71-88出版社
ELSEVIER
DOI: 10.1016/j.neucom.2016.11.095
关键词
Stock price prediction; Financial forecasting; Technical trading; Decision making
资金
- InvestNI
- Citi
- First Derivatives
- Kofax
- Fidessa
- NYSE Technologies
- ILEX
- Queen's University of Belfast
- Ulster University
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. Crown Copyright (C) 2017 Published by Elsevier B.V. All rights reserved.
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