Stock Trading using Neural Networks and the Ease of Movement Technical Indicator
Abstract
This research presents the profitability of a neural network model developed to predict the future value of the ease of movement (EMV) technical indicator on past S&P 500 index data. Trading systems using the neural network and EMV for stock trading are developed. the results show that the stock trading using the neural network and EMV, the stock trading using the neural network and EMV in conjunction with volume adjusted moving averages (VAMA) and simple moving averages (MA), outperform the results of stock trading generated from those without neural network assistance, the VAMA alone, the MA alone, and the buy-and-hold trading strategy.
Recommended Citation
T. Chavarnakul and D. Enke, "Stock Trading using Neural Networks and the Ease of Movement Technical Indicator," 2006 IIE Annual Conference and Exhibition, Institute of Electrical and Electronics Engineers, Dec 2006.
Department(s)
Engineering Management and Systems Engineering
Keywords and Phrases
Ease of movement; Financial engineering; Financial forecasting; Neural networks; Technical analysis
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
01 Dec 2006
