Bankruptcy prediction for private firms in developing economies: a scoping review and guidance for future research

Matenda, Frank Ranganai; Sibanda, Mabutho; Chikodza, Eriyoti; Gumbo, Victor

doi:10.1007/s11301-021-00216-x

Cited by 7 publications

(6 citation statements)

References 86 publications

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“…We adopted the above-mentioned databases because these databases include a plethora of studies and are considered as some of the world's most extensive databases of peer-reviewed studies. Only studies extracted in these databases are reviewed to guarantee the research work's consistency, repeatability, dependability, and quality (Matenda et al, 2021;Stenbacka, 2001;Trochim and Donnelly, 2006). Pooling studies from several databases suppresses database or source bias (Matenda et al, 2021;Schlosser, 2007) and eliminate geographic bias since studies from all angles of the universe are reviewed (Matenda et al, 2021;Alaka et al, 2018).…”

Section: Methodsmentioning

confidence: 99%

“…Only studies extracted in these databases are reviewed to guarantee the research work's consistency, repeatability, dependability, and quality (Matenda et al, 2021;Stenbacka, 2001;Trochim and Donnelly, 2006). Pooling studies from several databases suppresses database or source bias (Matenda et al, 2021;Schlosser, 2007) and eliminate geographic bias since studies from all angles of the universe are reviewed (Matenda et al, 2021;Alaka et al, 2018). We extracted studies from all databases in fifteen days, i.e., from 02 August 2021 to 16 August 2021.…”

Section: Methodsmentioning

confidence: 99%

“…The final versions of the search terms were first used on Google Scholar and then applied to other databases. We checked the reference lists (employing a snowballing technique) of the relevant reviewed studies (to guarantee sufficient coverage and find possible missing studies to be included in the preliminary sample), supplemented by a forward citation check on Google Scholar to trail where these studies are quoted after that (Matenda et al, 2021;Booth, Papaioannou and Sutton, 2012;Webster and Watson 2002). Further, we hand-searched some journals to select studies that have been omitted in the reference list and database searches (Matenda et al, 2021;Arksey and O'Malley, 2005).…”

Section: Methodsmentioning

confidence: 99%

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South African business rescue regime

Matenda

Sibanda

Nomlala

et al. 2023

IJRBS

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In South Africa, the devastating effects of the COVID-19 plague forced several corporates to resort to business rescue proceedings to guarantee the continuance of their operations. However, the low success rates associated with the current South African business rescue regime are a thing of concern among policymakers. In this article, we conduct a systematic literature review highlighting the shortcomings of the current South African business rescue mechanism and recommendations that can be adopted to improve its success rates. The study then proposes a broad agenda for future research. We discover that even though the South African business rescue mechanism is an economically significant, promising framework that gives companies time and space to find solutions for their problems, it is associated with numerous shortcomings that lessen its success rates. Also, the study results indicate that the regime can be improved in more than a few dimensions to augment its success rates. Further, the review divulges that implementing the current South African business rescue framework has some uncharted areas that need to be investigated. As a recommendation, policymakers should improve the business rescue regime to increase its success rates.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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South African business rescue regime

Matenda

Sibanda

Nomlala

et al. 2023

IJRBS

Self Cite

View full text Add to dashboard Cite

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“…The design of this research involved two stages. The first stage started on 13 January 2021 with an inductive scoping review to gather the dominant thematic concepts (Colicchia & Strozzi, 2012;Matenda et al, 2021;Webster & Watson, 2002). We included the peerreviewed scholarly literature from the Scopus database and "gray" sources from the practitioner literature (i.e., reports, industry surveys, F I G U R E 3 How machine and deep learning work (source: Authors) magazines, and news) to identify gaps between research and practice.…”

Section: Methodsmentioning

confidence: 99%

Three and a half decades of artificial intelligence in banking, financial services, and insurance: A systematic evolutionary review

Herrmann

Masawi

2022

Strategic Change

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The banking, financial services, and insurance (BFSI) sector is one of the earliest and most prominent adopters of artificial intelligence (AI). However, academic research substantially lags behind the adoption of AI in practice. At the beginning of this century, AI research has been centered on the sector's credit risk. In the 2010s decade, expert systems were increasingly replaced by data‐driven, “algorithmic” AI. Big data enjoyed much hype in that decade, which diminished later mostly due to unsuccessful implementations. Much published research on big data actually relates to machine and deep learning but not to big data per se. These terms are often found to be conflated in research and practice. The insurance sector is substantially underrepresented in published AI research, and current research is dominated by banking and investments. Governance frameworks for “responsible AI” (RAI) are yet to be incorporated into practice by fintech companies as well as incumbent organizations. RAI is a particular issue for decentralized finance (DeFi). The most successful implementations of AI in BFSI practice, as well as dominant academic research areas, are in investments, securities, market making, customer relationships, lending, risk management, and compliance.

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“…Market value variable proxied by PBV has a significant negative effect on the prediction of financial distress. Matenda (2021) introduced accounting-based bankruptcy forecasting models applying accounting data including financial ratios deduced from financial statements (e.g. profitability, liquidity, efficiency and leverage ratios) to forecast firm bankruptcy.…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting the risk at infractions: an ensemble comparison of machine learning approach

2021

IMDS

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PurposeThe infraction of securities regulations (ISRs) of listed firms in their day-to-day operations and management has become one of common problems. This paper proposed several machine learning approaches to forecast the risk at infractions of listed corporates to solve financial problems that are not effective and precise in supervision.Design/methodology/approachThe overall proposed research framework designed for forecasting the infractions (ISRs) include data collection and cleaning, feature engineering, data split, prediction approach application and model performance evaluation. We select Logistic Regression, Naïve Bayes, Random Forest, Support Vector Machines, Artificial Neural Network and Long Short-Term Memory Networks (LSTMs) as ISRs prediction models.FindingsThe research results show that prediction performance of proposed models with the prior infractions provides a significant improvement of the ISRs than those without prior, especially for large sample set. The results also indicate when judging whether a company has infractions, we should pay attention to novel artificial intelligence methods, previous infractions of the company, and large data sets.Originality/valueThe findings could be utilized to address the problems of identifying listed corporates' ISRs at hand to a certain degree. Overall, results elucidate the value of the prior infraction of securities regulations (ISRs). This shows the importance of including more data sources when constructing distress models and not only focus on building increasingly more complex models on the same data. This is also beneficial to the regulatory authorities.

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Bankruptcy prediction for private firms in developing economies: a scoping review and guidance for future research

Cited by 7 publications

References 86 publications

South African business rescue regime

South African business rescue regime

Three and a half decades of artificial intelligence in banking, financial services, and insurance: A systematic evolutionary review

Forecasting the risk at infractions: an ensemble comparison of machine learning approach

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