Can the SOM Analysis Predict Business Failure Using Capital Structure Theory? Evidence from the Subprime Crisis in Spain

Lucanera, Juan Pedro; Fabregat‐Aibar, Laura; Scherger, Valeria; Vigier, Hernán Pedro

doi:10.3390/axioms9020046

Cited by 5 publications

(4 citation statements)

References 43 publications

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“…Due to the nature of corporate financial difficulties/crises highly related to corporate's bad operating performance, the variables we chosen in financial difficulty forecasting can be taken as the surrogate for joining up with the predictors. According to the related works [67][68][69][70], the selected variables are represented in Table 5. Apart from prior works on building performance forecasting models that merely focus on numerical messages, this study aims at providing decision makers with an overarching decision making framework from different perspectives of corporate operations and equipping them with textual messages that can transmit future corporate performance without any time delay.…”

Section: The Resultsmentioning

confidence: 99%

An Advanced Decision Making Framework via Joint Utilization of Context-Dependent Data Envelopment Analysis and Sentimental Messages

Huang

Lin

Hsu

2021

Axioms

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Compared to widely examined topics in the related literature, such as financial crises/difficulties in accurate prediction, studies on corporate performance forecasting are quite scarce. To fill the research gap, this study introduces an advanced decision making framework that incorporates context-dependent data envelopment analysis (CD-DEA), fuzzy robust principal component analysis (FRPCA), latent Dirichlet allocation (LDA), and stochastic gradient twin support vector machine (SGTSVM) for corporate performance forecasting. Ratio analysis with the merits of easy-to-use and intuitiveness plays an essential role in performance analysis, but it typically has one input variable and one output variable, which is unable to appropriately depict the inherent status of a corporate’s operations. To combat this, we consider CD-DEA as it can handle multiple input and multiple output variables simultaneously and yields an attainable target to analyze decision making units (DMUs) when the data present great variations. To strengthen the discriminant ability of CD-DEA, we also conduct FRPCA, and because numerical messages based on historical principles normally cannot transmit future corporate messages, we execute LDA to decompose the accounting narratives into many topics and preserve those topics that are relevant to corporate operations. Sequentially, the process matches the preserved topics with a sentimental dictionary to exploit the hidden sentiments in each topic. The analyzed data are then fed into SGTSVM to construct the forecasting model. The result herein reveals that the introduced decision making framework is a promising alternative for performance forecasting.

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Section: The Resultsmentioning

confidence: 99%

An Advanced Decision Making Framework via Joint Utilization of Context-Dependent Data Envelopment Analysis and Sentimental Messages

Huang

Lin

Hsu

2021

Axioms

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“…Formula 1 Relevant Articles Accuracy tp+tn tp+ f p+tn+ f n [7,13,36,44,45,47,79,87,88,96,97,108,[111][112][113]116,117,[129][130][131][132][133] Error rate f p+ f n tp+ f p+tn+ f n [46,69,90,92,93,134] F-measure 2 * P * R P+R [56,74,79,135] Geometric mean √ tp * tn [50] 1 Note: tp-true positive, tn-true negative, fp-false positive, fn-false negative, P-precision, R-recall.…”

Section: Metricsmentioning

confidence: 99%

Predicting Construction Company Insolvent Failure: A Scientometric Analysis and Qualitative Review of Research Trends

Wang,

Li,

Skitmore

et al. 2024

Sustainability

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The construction industry is infamous for its high insolvent failure rate because construction projects require complex processes, heavy investment, and long durations. However, there is a lack of a comprehensive framework and a requirement for such a framework in predicting the financial distress of construction firms. This paper reviews relevant literature to summarize the existing knowledge, identify current problems, and point out future research directions needed in this area using a scientometric analysis approach. Based on a total of 93 journal articles relating to predicting construction company failure extracted from multiple databases, this study conducts a holistic review in terms of chronological trends, journal sources, active researchers, frequent keywords, and most cited documents. Qualitative analysis is also provided to explore the data collection and processing procedures, model selection and development process, and detailed performance evaluation metrics. Four research gaps and future directions for predicting construction company failure are presented: selecting a broader data sample, incorporating more heterogeneous variables, balancing model predictability and interpretability, and quantifying the causality and intercorrelation of variables. This study provides a big picture of existing research on predicting construction company insolvent failure and presents outcomes that can help researchers to comprehend relevant literature, directing research policy-makers and editorial boards to adopt the promising themes for further research and development.

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“…Also, most studies that are reporting the vitality of the method relate to core SOMs [35]. Some applications have been in the broad financial field, however, the research scope, and especially the goals, differs considerably from studying the banking sector, e.g., [38][39][40], up to financial studies of the corporate sector [41]. Insurance industry's sectoral properties and European integration were studied [42].…”

Section: Scr = Scr Nonli F E + Scr LI F Ementioning

confidence: 99%

New Suptech Tool of the Predictive Generation for Insurance Companies—The Case of the European Market

Jagrič,

Zdolšek,

Horvat

et al. 2023

Information

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Financial innovation, green investments, or climate change are changing insurers’ business ecosystems, impacting their business behaviour and financial vulnerability. Supervisors and other stakeholders are interested in identifying the path toward deterioration in the insurance company’s financial health as early as possible. Suptech tools enable them to discover more and to intervene in a timely manner. We propose an artificial intelligence approach using Kohonen’s self-organizing maps. The dataset used for development and testing included yearly financial statements with 4058 observations for European composite insurance companies from 2012 to 2021. In a novel manner, the model investigates the behaviour of insurers, looking for similarities. The model forms a map. For the obtained groupings of companies from different geographical origins, a common characteristic was discovered regarding their future financial deterioration. A threshold defined using the solvency capital requirement (SCR) ratio being below 130% for the next year is applied to the map. On the test sample, the model correctly identified on average 86% of problematic companies and 79% of unproblematic companies. Changing the SCR ratio level enables differentiation into multiple map sections. The model does not rely on traditional methods, or the use of the SCR ratio as a dependent variable but looks for similarities in the actual insurer’s financial behaviour. The proposed approach offers grounds for a Suptech tool of predictive generation to support early detection of the possible future financial distress of an insurance company.

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Can the SOM Analysis Predict Business Failure Using Capital Structure Theory? Evidence from the Subprime Crisis in Spain

Cited by 5 publications

References 43 publications

An Advanced Decision Making Framework via Joint Utilization of Context-Dependent Data Envelopment Analysis and Sentimental Messages

An Advanced Decision Making Framework via Joint Utilization of Context-Dependent Data Envelopment Analysis and Sentimental Messages

Predicting Construction Company Insolvent Failure: A Scientometric Analysis and Qualitative Review of Research Trends

New Suptech Tool of the Predictive Generation for Insurance Companies—The Case of the European Market

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