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Predicting financial cycles with dynamic ensemble selection frameworks using leading, coincident and lagging indicators

Indranil Ghosh Orcid Logo, Tamal Datta Chaudhuri Orcid Logo, Layal Isskandarani Orcid Logo, Mohammad Abedin Orcid Logo

Research in International Business and Finance, Volume: 80, Start page: 103114

Swansea University Author: Mohammad Abedin Orcid Logo

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DOI (Published version): 10.1016/j.ribaf.2025.103114

Abstract

This paper develops a model for predicting financial cycles in India, and defines leading, coincident, and lagging indicators to achieve the research objective. The dependent variable is binary, and Synthetic Minority Oversampling Technique (SMOTE) is used for correcting imbalances in the dataset. T...

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Published in: Research in International Business and Finance
ISSN: 0275-5319 1878-3384
Published: Elsevier BV 2025
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URI: https://cronfa.swan.ac.uk/Record/cronfa70225
first_indexed 2025-08-26T11:12:31Z
last_indexed 2025-08-27T04:36:05Z
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spelling 2025-08-26T12:13:51.3875760 v2 70225 2025-08-26 Predicting financial cycles with dynamic ensemble selection frameworks using leading, coincident and lagging indicators 4ed8c020eae0c9bec4f5d9495d86d415 0000-0002-4688-0619 Mohammad Abedin Mohammad Abedin true false 2025-08-26 CBAE This paper develops a model for predicting financial cycles in India, and defines leading, coincident, and lagging indicators to achieve the research objective. The dependent variable is binary, and Synthetic Minority Oversampling Technique (SMOTE) is used for correcting imbalances in the dataset. The study utilizes six distinct Dynamic Ensemble Selection (DES) models, and five different pools of classifiers. Explainable Artificial Intelligence (XAI) is used to identify feature importance. The predictive framework is applied to different time periods with distinct characteristics, and all the DES frameworks yield efficient forecasts. The importance and role of the indicators, however, differ among phases. Our results show, that while during CYCLE phases, exchange rate fluctuations play a significant role in explaining financial cycles, in an UPWARD expansionary phase, expansion in bank credit, capital formation, and realty growth are significant factors. During a DOWNWARD phase and a bearish environment, VIX and oil prices emerge significant. Journal Article Research in International Business and Finance 80 103114 Elsevier BV 0275-5319 1878-3384 Financial Cycle; Leading Indicators; Coincident Indicators; Lagging Indicators; Dynamic Ensemble Selection; Explainable Artificial Intelligence 31 8 2025 2025-08-31 10.1016/j.ribaf.2025.103114 COLLEGE NANME Management School COLLEGE CODE CBAE Swansea University SU Library paid the OA fee (TA Institutional Deal) Swansea University 2025-08-26T12:13:51.3875760 2025-08-26T11:52:53.9133636 Faculty of Humanities and Social Sciences School of Management - Accounting and Finance Indranil Ghosh 0000-0001-7064-4774 1 Tamal Datta Chaudhuri 0000-0002-5086-6019 2 Layal Isskandarani 0000-0001-5025-0196 3 Mohammad Abedin 0000-0002-4688-0619 4 70225__34990__43b7f09c3f284eed867b95f503b7f84e.pdf 70225.VOR.pdf 2025-08-26T12:11:35.5160401 Output 5635127 application/pdf Version of Record true This is an open access article distributed under the terms of the Creative Commons CC-BY license. true eng http://creativecommons.org/licenses/by/4.0/
title Predicting financial cycles with dynamic ensemble selection frameworks using leading, coincident and lagging indicators
spellingShingle Predicting financial cycles with dynamic ensemble selection frameworks using leading, coincident and lagging indicators
Mohammad Abedin
title_short Predicting financial cycles with dynamic ensemble selection frameworks using leading, coincident and lagging indicators
title_full Predicting financial cycles with dynamic ensemble selection frameworks using leading, coincident and lagging indicators
title_fullStr Predicting financial cycles with dynamic ensemble selection frameworks using leading, coincident and lagging indicators
title_full_unstemmed Predicting financial cycles with dynamic ensemble selection frameworks using leading, coincident and lagging indicators
title_sort Predicting financial cycles with dynamic ensemble selection frameworks using leading, coincident and lagging indicators
author_id_str_mv 4ed8c020eae0c9bec4f5d9495d86d415
author_id_fullname_str_mv 4ed8c020eae0c9bec4f5d9495d86d415_***_Mohammad Abedin
author Mohammad Abedin
author2 Indranil Ghosh
Tamal Datta Chaudhuri
Layal Isskandarani
Mohammad Abedin
format Journal article
container_title Research in International Business and Finance
container_volume 80
container_start_page 103114
publishDate 2025
institution Swansea University
issn 0275-5319
1878-3384
doi_str_mv 10.1016/j.ribaf.2025.103114
publisher Elsevier BV
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
document_store_str 1
active_str 0
description This paper develops a model for predicting financial cycles in India, and defines leading, coincident, and lagging indicators to achieve the research objective. The dependent variable is binary, and Synthetic Minority Oversampling Technique (SMOTE) is used for correcting imbalances in the dataset. The study utilizes six distinct Dynamic Ensemble Selection (DES) models, and five different pools of classifiers. Explainable Artificial Intelligence (XAI) is used to identify feature importance. The predictive framework is applied to different time periods with distinct characteristics, and all the DES frameworks yield efficient forecasts. The importance and role of the indicators, however, differ among phases. Our results show, that while during CYCLE phases, exchange rate fluctuations play a significant role in explaining financial cycles, in an UPWARD expansionary phase, expansion in bank credit, capital formation, and realty growth are significant factors. During a DOWNWARD phase and a bearish environment, VIX and oil prices emerge significant.
published_date 2025-08-31T18:04:49Z
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score 11.08899

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