Abstract
| Language | English |
|---|---|
| Pages | 71-88 |
| Journal | Neurocomputing |
| Volume | 264 |
| Early online date | 16 Jun 2017 |
| DOIs | |
| Publication status | E-pub ahead of print - 16 Jun 2017 |
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Keywords
- stock price prediction
- financial forecasting
- technical trading
- decision making
Cite this
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Forecasting Price Movements using Technical Indicators: Investigating the Impact of Varying Input Window Length. / Shynkevich, Yauheniya; McGinnity, T.Martin; Coleman, Sonya; Belatreche, Ammar; Li, Yuhua.
In: Neurocomputing, Vol. 264, 16.06.2017, p. 71-88.Research output: Contribution to journal › Article
TY - JOUR
T1 - Forecasting Price Movements using Technical Indicators: Investigating the Impact of Varying Input Window Length
AU - Shynkevich, Yauheniya
AU - McGinnity, T.Martin
AU - Coleman, Sonya
AU - Belatreche, Ammar
AU - Li, Yuhua
PY - 2017/6/16
Y1 - 2017/6/16
N2 - 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.
AB - 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.
KW - stock price prediction
KW - financial forecasting
KW - technical trading
KW - decision making
U2 - 10.1016/j.neucom.2016.11.095
DO - 10.1016/j.neucom.2016.11.095
M3 - Article
VL - 264
SP - 71
EP - 88
JO - Neurocomputing
T2 - Neurocomputing
JF - Neurocomputing
SN - 0925-2312
ER -