Paper Info
Title
Forecasting stock price directional movements using technical indicators: Investigating window size effects on one-step-ahead forecasting
Abstract
Accurate forecasting of directional changes in stock prices is important for algorithmic trading and investment management. Technical analysis has been successfully used in financial forecasting and recently researchers have explored the optimization of parameters for technical indicators. This study investigates the relationship between the window size used for calculating technical indicators and the accuracy of one-step-ahead (variable steps) forecasting. The directions of the future price movements are predicted using technical analysis and machine learning algorithms. Results show a correlation between window size and forecasting step size for the Support Vector Machines approach but not for the other approaches.
Year
DOI
Venue
2014
10.1109/CIFEr.2014.6924093
Computational Intelligence for Financial Engineering & Economics
Keywords
Field
DocType
financial data processing,learning (artificial intelligence),share prices,stock markets,support vector machines,algorithmic trading,financial forecasting,forecasting step size,future price movements,investment management,machine learning algorithms,one-step-ahead forecasting,stock price directional changes,stock price directional movements forecasting,support vector machines,technical analysis,technical indicators,window size,window size effects
Econometrics,Technology forecasting,Time series,Economics,Demand forecasting,Support vector machine,Probabilistic forecasting,Investment management,Algorithmic trading,Technical analysis
Conference
ISSN
Citations 
PageRank 
2380-8454
1
0.42
References 
Authors
0
5
Name
Order
Citations
PageRank
Yauheniya Shynkevich1242.34
T. Martin Mcginnity251866.30
Sonya Coleman321636.84
Yuhua Li4111353.63
Ammar Belatreche525623.11