Computational Intelligence and Financial Markets: A Survey and Future Directions

• Published in: Expert systems with applications Vol. 55; pp. 194 - 211
• Main Authors: Cavalcante, Rodolfo C., Brasileiro, Rodrigo C., Souza, Victor L.F., Nobrega, Jarley P., Oliveira, Adriano L.I.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15.08.2016
• Subjects: Algorithms; Computation; Computational intelligence; Data mining; Deep learning; Economic forecasting; Economics; Financial markets; Intelligence; Literature reviews; Markets; Online learning; Trading systems; Deep learning; Financial markets; Trading systems; Online learning; Computational intelligence
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2016.02.006

•We propose a survey of soft computing techniques applied to financial market.•We surveyed several primary studies proposed in the literature.•A framework for building an intelligent trading system was proposed.•Future directions of this research field are discussed. Financial markets play an important role on the economical and social organization of modern society. In these kinds of markets, information is an invaluable asset. However, with the modernization of the financial transactions and the information systems, the large amount of information available for a trader can make prohibitive the analysis of a financial asset. In the last decades, many researchers have attempted to develop computational intelligent methods and algorithms to support the decision-making in different financial market segments. In the literature, there is a huge number of scientific papers that investigate the use of computational intelligence techniques to solve financial market problems. However, only few studies have focused on review the literature of this topic. Most of the existing review articles have a limited scope, either by focusing on a specific financial market application or by focusing on a family of machine learning algorithms. This paper presents a review of the application of several computational intelligent methods in several financial applications. This paper gives an overview of the most important primary studies published from 2009 to 2015, which cover techniques for preprocessing and clustering of financial data, for forecasting future market movements, for mining financial text information, among others. The main contributions of this paper are: (i) a comprehensive review of the literature of this field, (ii) the definition of a systematic procedure for guiding the task of building an intelligent trading system and (iii) a discussion about the main challenges and open problems in this scientific field.