Adventures in Financial Data Science

10.1142/12678 ◽  
2022 ◽  
Author(s):  
Graham L Giller
Keyword(s):  
2019 ◽  
Author(s):  
Chris Brooks ◽  
Andreas G. F. Hoepner ◽  
David G. McMillan ◽  
Andrew Vivian ◽  
Chardin Wese Simen

e-Finanse ◽  
2021 ◽  
Vol 17 (1) ◽  
pp. 50-61
Author(s):  
Reza Habibi

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.


2022 ◽  
Vol 70 (3) ◽  
pp. 6289-6304
Author(s):  
Anwer Mustafa Hilal ◽  
Hadeel Alsolai ◽  
Fahd N. Al-Wesabi ◽  
Mohammed Abdullah Al-Hagery ◽  
Manar Ahmed Hamza ◽  
...  

2018 ◽  
Vol 136 ◽  
pp. 160-164 ◽  
Author(s):  
Paolo Giudici
Keyword(s):  

2020 ◽  
Vol 27 (1-2) ◽  
pp. 1-7
Author(s):  
Andreas G. F. Hoepner ◽  
David McMillan ◽  
Andrew Vivian ◽  
Chardin Wese Simen

2019 ◽  
Vol 1 (1) ◽  
pp. 10-13
Author(s):  
Joseph Simonian ◽  
Frank J. Fabozzi
Keyword(s):  

2019 ◽  
Vol 25 (17) ◽  
pp. 1627-1636 ◽  
Author(s):  
Chris Brooks ◽  
Andreas G. F. Hoepner ◽  
David McMillan ◽  
Andrew Vivian ◽  
Chardin Wese Simen

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