Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/266865
Authors: 
Year of Publication: 
2021
Citation: 
[Journal:] Financial Internet Quarterly [ISSN:] 2719-3454 [Volume:] 17 [Issue:] 1 [Publisher:] Sciendo [Place:] Warsaw [Year:] 2021 [Pages:] 50-61
Publisher: 
Sciendo, Warsaw
Abstract: 
Financial data sets are growing too fast and need to be analyzed. Data science has many different techniques to store and summarize, mining, running simulations and finally analyzing them. Among data science methods, predictive methods play a critical role in analyzing financial data sets. In the current paper, applications of 22 methods classified in four categories namely data mining and machine learning, numerical analysis, operation research techniques and meta-heuristic techniques, in financial data sets are studied. To this end, first, literature reviews on these methods are given. For each method, a data analysis case (as an illustrative example) is presented and the problem is analyzed with the mentioned method. An actual case is given to apply those methods to solve the problem and to choose a better one. Finally, a conclusion section is proposed.
Subjects: 
data mining
machine learning
meta-heuristic technique
numerical computation
operation research
predictive methods
JEL: 
G17
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc-nd Logo
Document Type: 
Article

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