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Application of Predictive Methods to Financial Data Sets

Author

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  • Habibi Reza

    (Iran Banking Institute, Central Bank of Iran, Tehran, Iran)

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.

Suggested Citation

  • Habibi Reza, 2021. "Application of Predictive Methods to Financial Data Sets," Financial Internet Quarterly (formerly e-Finanse), Sciendo, vol. 17(1), pages 50-61, March.
    • Handle: RePEc:vrs:finiqu:v:17:y:2021:i:1:p:50-61:n:3
    DOI: 10.2478/fiqf-2021-0006
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    More about this item

    Keywords

    data mining; machine learning; meta-heuristic technique; numerical computation; operation research; predictive methods;
    All these keywords.

    JEL classification:

    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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