Soft computing hybrids for FOREX rate prediction: A comprehensive review

• Published in: Computers & operations research 2018-11, Vol.99, p.262-284
• Main Authors: Pradeepkumar, Dadabada, Ravi, Vadlamani
• Format: Article
• Language: English
• Subjects: Artificial intelligence; Artificial neural networks; Cloud computing; Evolutionary computation; Foreign exchange rates; FOREX rate prediction; Fuzzy logic; Hybrid control systems; Hybrid model; Intelligent systems; Machine learning; Neural networks; Operations research; Review; Soft computing
• ISSN: 0305-0548; 1873-765X; 0305-0548
• DOI: 10.1016/j.cor.2018.05.020

•Soft Computing (SC) hybrids for forecasting FOREX rate were proposed to obtain predictions that are more accurate.•A comprehensive review of 82 articles published during the 1998–2017 presented•All hybrids outperformed their constituents in terms of accuracy.•ANN-based hybrids turned out to be more pervasive and more powerful.•Both Evolutionary Computation and Fuzzy logic based hybrids do also contain some neural networks as a predominant constituent.•Any future research might necessarily contain either traditional or sophisticated neural networks or Support Vector Machine.•Critique of all families of soft computing hybrids is presented along with future directions Foreign exchange rate prediction is an important problem in finance and it attracts many researchers owing to its complex nature and practical applications. Even though this problem is well studied using various statistical and machine learning techniques in stand-alone mode, various soft computing hybrids were also proposed to solve this problem with the aim of obtaining more accurate predictions during 1998–2017. This paper presents a comprehensive review of 82 such soft computing hybrids found in the literature. Almost all authors in this area demonstrated that their proposed hybrids outperformed the stand-alone statistical and intelligent techniques in terms of accuracy. It is conspicuous from the review that artificial neural network based hybrids turned out to be more prevalent, more pervasive and more powerful. This observation is corroborated by the fact that both evolutionary computation based hybrids as well as fuzzy logic based hybrids also contained some architecture of neural networks as a predominant constituent. The review concludes with a set of insightful remarks and future directions that are very much useful to budding researchers and practitioners alike.