Publication Type : Journal Article
Publisher : IOS Press
Source : Intelligent Decision Technologies, IOS Press, Volume 9, Number 2, p.99-140 (2015)
Url : http://www.scopus.com/inward/record.url?eid=2-s2.0-84928106988&partnerID=40&md5=2422a7a8050078d4e297b1845ef40e57
Keywords : Artificial intelligence, Data set size, Economic benefits, economics, Finance, Financial data, financial forecasting, Financial markets, Financial prediction, Forecast accuracy, Forecasting, Novel techniques, Signal processing, Soft computing, Stock market, Surveying, Surveys
Campus : Coimbatore
School : School of Engineering
Department : Electronics and Communication
Year : 2015
Abstract : Financial forecasting is an area of research which has been attracting a lot of attention recently from practitioners in the field of artificial intelligence. Apart from the economic benefits of accurate financial prediction, the inherent nonlinearities in financial data make the task of analyzing and forecasting an extremely challenging task. This paper presents a survey of more than 100 articles published over two centuries (from 1933 up to 2013) in an attempt to identify the developments and trends in the field of financial forecasting with focus on application of artificial intelligence for the purpose. The findings from the survey indicate that artificial intelligence and signal processing based techniques are more efficient when compared to traditional financial forecasting techniques and these techniques appear well suited for the task of financial forecasting. Some of the issues that need addressing are discussed in brief. A novel technique for selection of the input dataset size for ensuring best possible forecast accuracy is also presented. The results confirm the effectiveness of the proposed technique in improving the accuracy of forecasts. © 2015 - IOS Press and the authors. All rights reserved.
Cite this Research Publication : Dr. Binoy B. Nair and Mohandas, V. P., “Artificial intelligence applications in financial forecasting-a survey and some empirical results”, Intelligent Decision Technologies, vol. 9, pp. 99-140, 2015.