Five decades of research on capital budgeting – a systematic review and future research agenda
- Authors: Sureka, Riya , Kumar, Satish , Colombage, Sisira , Abedin, Mohammad
- Date: 2022
- Type: Text , Journal article
- Relation: Research in International Business and Finance Vol. 60, no. (2022), p.
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- Description: This study synthesizes and reviews the existing literature on capital budgeting (CB) practices and their application to theories, contexts, characteristics and methodology. It aims to identify the prevalent issues and gaps in the literature and provide potential avenues for future research. After comprehensive search and rigorous scrutiny, this review encompasses 185 articles. A systematic literature review (SLR), triangulated with the bibliometric method, is carried out, adopting a meticulous approach to achieve a comprehensive overview of the field. Based on cluster analysis, four distinct themes are identified. Additionally, a conceptual framework is developed that shows the antecedents, moderators and outcomes of research on capital budgeting. Grounded on the detailed content analysis, 11 actionable future research directions are proposed to advance this field of research. © 2021 Elsevier B.V.
Credit default prediction using a support vector machine and a probabilistic neural network
- Authors: Abedin, Mohammad , Guotai, Chi , Colombage, Sisira , Fahmida-E-Moula
- Date: 2018
- Type: Text , Journal article
- Relation: Journal of Credit Risk Vol. 14, no. 2 (2018), p. 1-27
- Full Text: false
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- Description: The design of consistent classifiers to forecast credit-granting choices is critical for many financial decision-making practices. Although a number of artificial and statistical techniques have been developed to predict customer insolvency, how to provide an inclusive appraisal of prediction models and recommend adequate classifiers is still an imperative and understudied area in credit default prediction (CDP) modeling. Previous evidence demonstrates that the ranking of classifiers varies for different criteria with measures under different circumstances. In this study, we address this methodological flaw by proposing the simultaneous application of support vector machine and probabilistic neural network (PNN)-based CDP algorithms, together with frequently used high-performance models. We fill the gap by introducing a set of multidimensional evaluation measures combined with some novel metrics that are helpful in discovering unseen features of the model’s performance. For effectiveness and feasibility purposes, six real-world credit data sets have been applied. Our empirical study shows that the PNN model is more robust than its rivals, and traditional performance evaluations are more or less consistent with their original counterparts. With these contributions, therefore, our investigations offer several advantages to practitioners of financial risk management.