The use of predictive analytics in finance

被引:21
作者
Broby, Daniel [1 ]
机构
[1] Ulster Univ, Dept Accounting Finance & Econ, Cathedral Quarter, Belfast BT15 1ED, North Ireland
关键词
Predictive analytics; Finance; Fintech; Regtech; Risk; Statistics; Machine learning; Decision support systems; Information systems; ASSET PRICING MODEL; PORTFOLIO CONSTRUCTION; PERFORMANCE; METHODOLOGY; ECONOMICS; ACCURACY; OUTLIERS; RULES; FRAUD;
D O I
10.1016/j.jfds.2022.05.003
中图分类号
F8 [财政、金融];
学科分类号
0202 ;
摘要
Statistical and computational methods are being increasingly integrated into Decision Support Systems to aid management and help with strategic decisions. Researchers need to fully understand the use of such techniques in order to make predictions when using financial data. This paper therefore presents a method based literature review focused on the predictive analytics domain. The study comprehensively covers classification, regression, clustering, association and time series models. It expands existing explanatory statistical modelling into the realm of computational modelling. The methods explored enable the prediction of the future through the analysis of financial time series and cross-sectional data that is collected, stored and processed in Information Systems. The output of such models allow financial managers and risk oversight professionals to achieve better outcomes. This review brings the various predictive analytic methods in finance together under one domain. (c) 2022 The Authors. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
引用
收藏
页码:145 / 161
页数:17
相关论文
共 106 条
[1]   Enhancing Predictive Analytics for Anti-Phishing by Exploiting Website Genre Information [J].
Abbasi, Ahmed ;
Zahedi, Fatemeh Mariam ;
Zeng, Daniel ;
Chen, Yan ;
Chen, Hsinchun ;
Nunamaker, Jay F., Jr. .
JOURNAL OF MANAGEMENT INFORMATION SYSTEMS, 2015, 31 (04) :109-157
[2]   CREDIT SCORING, STATISTICAL TECHNIQUES AND EVALUATION CRITERIA: A REVIEW OF THE LITERATURE [J].
Abdou, Hussein A. ;
Pointon, John .
INTELLIGENT SYSTEMS IN ACCOUNTING FINANCE & MANAGEMENT, 2011, 18 (2-3) :59-88
[3]   Identifying and treating outliers in finance [J].
Adams, John ;
Hayunga, Darren ;
Mansi, Sattar ;
Reeb, David ;
Verardi, Vincenzo .
FINANCIAL MANAGEMENT, 2019, 48 (02) :345-384
[4]   Time-series clustering - A decade review [J].
Aghabozorgi, Saeed ;
Shirkhorshidi, Ali Seyed ;
Teh Ying Wah .
INFORMATION SYSTEMS, 2015, 53 :16-38
[5]  
Altman E.I., 1983, CORPORATE FINANCIAL
[6]   Data mining applications in accounting: A review of the literature and organizing framework [J].
Amani, Farzaneh A. ;
Fadlalla, Adam M. .
INTERNATIONAL JOURNAL OF ACCOUNTING INFORMATION SYSTEMS, 2017, 24 :32-58
[7]  
Ayodele T. O., 2010, New Advances in Machine Learning, P19, DOI DOI 10.5772/9385
[8]   Machine Learning and Portfolio Optimization [J].
Ban, Gah-Yi ;
El Karoui, Noureddine ;
Lim, Andrew E. B. .
MANAGEMENT SCIENCE, 2018, 64 (03) :1136-1154
[9]  
Bellovary J.L., 2007, J. Financ. Educ., P1
[10]   Data mining for credit card fraud: A comparative study [J].
Bhattacharyya, Siddhartha ;
Jha, Sanjeev ;
Tharakunnel, Kurian ;
Westland, J. Christopher .
DECISION SUPPORT SYSTEMS, 2011, 50 (03) :602-613