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Topological applications of multilayer perceptrons and support vector machines in financial decision support systems

Mohammad Abedin Orcid Logo, Chi Guotai, Fahmida-E- Moula, A.S.M. Sohel Azad, Mohammed Shamim Uddin Khan

International Journal of Finance & Economics, Volume: 24, Issue: 1, Pages: 474 - 507

Swansea University Author: Mohammad Abedin Orcid Logo

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DOI (Published version): 10.1002/ijfe.1675

Abstract

The heart of this study is particularly on risk assessment of financial decision support systems (FDSSs), to advance the model performance and improve classification accuracy. To conquer the downsides of the classical models, statistical intelligence (SI) technologies, for example, multilayer percep...

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Published in: International Journal of Finance & Economics
ISSN: 1076-9307
Published: Wiley 2019
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URI: https://cronfa.swan.ac.uk/Record/cronfa64237
first_indexed 2023-09-19T14:44:05Z
last_indexed 2024-11-25T14:13:41Z
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spelling 2023-09-19T15:44:49.1394262 v2 64237 2023-08-31 Topological applications of multilayer perceptrons and support vector machines in financial decision support systems 4ed8c020eae0c9bec4f5d9495d86d415 0000-0002-4688-0619 Mohammad Abedin Mohammad Abedin true false 2023-08-31 CBAE The heart of this study is particularly on risk assessment of financial decision support systems (FDSSs), to advance the model performance and improve classification accuracy. To conquer the downsides of the classical models, statistical intelligence (SI) technologies, for example, multilayer perceptrons (MLPs) and support vector machines (SVMs), have been deliberated in FDSS applications. Recently, the prestigiousness of SI approaches has been confronted by the latest prediction learners. Therefore, to ensure the competitive performance of SI mechanisms, the current investigation scrutinizes the topological applications of MLPs and SVMs over eight different databases with equivalent combinations in credit scoring and bankruptcy predictions example sets. The experimental results reveal that MLP5-5 and MLP4-4, that is, the sigmoid activation function with five and four hidden layers, are the feasible topologies for the MLP algorithm, and on all databases in all performance criterions, SVM trained with the linear kernel function (SVM-1) achieves better prediction results. From the “Baseline” family, random forest learner brings significant improvements in financial decisions. Lastly, FDSSs are found to be correlated with the nature of databases and the performance criterions of the trained algorithms. The results of this study, however, have practical and managerial implications to make a range of financial and nonfinancial strategies. With these contributions, therefore, our study not only supplements earlier evidence but also enhances the predictive performance of SI algorithms for financial decision support applications. Journal Article International Journal of Finance &amp; Economics 24 1 474 507 Wiley 1076-9307 Bankruptcy predictions, credit scoring, financial decision support systems, multilayer perceptrons, statistical intelligence, support vector machines, topological applications 13 1 2019 2019-01-13 10.1002/ijfe.1675 http://dx.doi.org/10.1002/ijfe.1675 COLLEGE NANME Management School COLLEGE CODE CBAE Swansea University Key Projects of National Natural Science Foundation of China (Grant Numbers: 71731003, 71431002), General Projects of National Natural Science Foundation of China (Grant Numbers: 71471027, 71873103), National Social Science Foundation of China (Grant Number: 16BTJ017), Youth Project of National Natural Science Foundation of China (Grant Numbers: 71601041, 71503199). 2023-09-19T15:44:49.1394262 2023-08-31T17:38:44.7009908 Faculty of Humanities and Social Sciences School of Management - Accounting and Finance Mohammad Abedin 0000-0002-4688-0619 1 Chi Guotai 2 Fahmida-E- Moula 3 A.S.M. Sohel Azad 4 Mohammed Shamim Uddin Khan 5
title Topological applications of multilayer perceptrons and support vector machines in financial decision support systems
spellingShingle Topological applications of multilayer perceptrons and support vector machines in financial decision support systems
Mohammad Abedin
title_short Topological applications of multilayer perceptrons and support vector machines in financial decision support systems
title_full Topological applications of multilayer perceptrons and support vector machines in financial decision support systems
title_fullStr Topological applications of multilayer perceptrons and support vector machines in financial decision support systems
title_full_unstemmed Topological applications of multilayer perceptrons and support vector machines in financial decision support systems
title_sort Topological applications of multilayer perceptrons and support vector machines in financial decision support systems
author_id_str_mv 4ed8c020eae0c9bec4f5d9495d86d415
author_id_fullname_str_mv 4ed8c020eae0c9bec4f5d9495d86d415_***_Mohammad Abedin
author Mohammad Abedin
author2 Mohammad Abedin
Chi Guotai
Fahmida-E- Moula
A.S.M. Sohel Azad
Mohammed Shamim Uddin Khan
format Journal article
container_title International Journal of Finance &amp; Economics
container_volume 24
container_issue 1
container_start_page 474
publishDate 2019
institution Swansea University
issn 1076-9307
doi_str_mv 10.1002/ijfe.1675
publisher Wiley
college_str Faculty of Humanities and Social Sciences
hierarchytype
hierarchy_top_id facultyofhumanitiesandsocialsciences
hierarchy_top_title Faculty of Humanities and Social Sciences
hierarchy_parent_id facultyofhumanitiesandsocialsciences
hierarchy_parent_title Faculty of Humanities and Social Sciences
department_str School of Management - Accounting and Finance{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management - Accounting and Finance
url http://dx.doi.org/10.1002/ijfe.1675
document_store_str 0
active_str 0
description The heart of this study is particularly on risk assessment of financial decision support systems (FDSSs), to advance the model performance and improve classification accuracy. To conquer the downsides of the classical models, statistical intelligence (SI) technologies, for example, multilayer perceptrons (MLPs) and support vector machines (SVMs), have been deliberated in FDSS applications. Recently, the prestigiousness of SI approaches has been confronted by the latest prediction learners. Therefore, to ensure the competitive performance of SI mechanisms, the current investigation scrutinizes the topological applications of MLPs and SVMs over eight different databases with equivalent combinations in credit scoring and bankruptcy predictions example sets. The experimental results reveal that MLP5-5 and MLP4-4, that is, the sigmoid activation function with five and four hidden layers, are the feasible topologies for the MLP algorithm, and on all databases in all performance criterions, SVM trained with the linear kernel function (SVM-1) achieves better prediction results. From the “Baseline” family, random forest learner brings significant improvements in financial decisions. Lastly, FDSSs are found to be correlated with the nature of databases and the performance criterions of the trained algorithms. The results of this study, however, have practical and managerial implications to make a range of financial and nonfinancial strategies. With these contributions, therefore, our study not only supplements earlier evidence but also enhances the predictive performance of SI algorithms for financial decision support applications.
published_date 2019-01-13T14:28:04Z
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score 11.048149

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