Anticipating bank distress in the Eurozone: An Extreme Gradient Boosting approach

Diranzo, Francisco José Climent; Momparler, Alexandre; Ibáñez, Pedro Carmona

doi:10.1016/j.jbusres.2018.11.015

Cited by 107 publications

(51 citation statements)

References 29 publications

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“…If two features are highly correlated, the tree when deciding upon a split will choose only one of them. Thus, the variance inflation issue involving least-square estimates is not present(Climent et al, 2019). An inspection of the correlation matrix of the 50 features reveals that the extent of overlap between the 50 features is not severe.…”

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confidence: 99%

A Textual Analysis of US Corporate Social Responsibility Reports

Clarkson

Ponn²,

Richardson

et al. 2020

Abacus

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We employ computer‐based textual analysis to examine disclosure patterns for a sample of US corporate social responsibility (CSR) reports from the period 2002–2016. Starting from 466 features commonly used in computational linguistics, our results show that the linguistics or disclosure patterns in CSR reports can be used to accurately predict the actual CSR performance type of CSR reporters. Specifically, we find that the two most commonly used disclosure characteristics, number of words and number of sentences, alone can be used to predict reporting firms’ CSR performance type with 81% accuracy. The accuracy of prediction increases to 96% when the top 50 linguistics features most relevant to firms’ CSR performance are included in the prediction model. In addition, we find that the linguistic features of CSR disclosure identified by our study are incrementally value relevant to investors even after controlling for the actual CSR performance score from the professional CSR rating agencies. This finding suggests that the linguistic features of CSR disclosure can be an important venue for capital market participants in evaluating firms’ CSR performance type, especially when professional CSR performance ratings are not available.

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confidence: 99%

A Textual Analysis of US Corporate Social Responsibility Reports

Clarkson

Ponn²,

Richardson

et al. 2020

Abacus

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“…For example, Botner et al 2015offered payments beyond school credits in each round of data collection, and Ittersum and Feinberg (2010) paid a USD 20.00 bonus for individuals who participated in all rounds of data collection. Our final sample was of 144 participants, an attrition rate consistent with previous longitudinal data collection (e.g., Douglass et al, 2016;Kim et al, 2015), and a sample size that allows the robust estimation of machine learning methods (e.g., Climent, Momparler, & Carmona, 2019).…”

Section: Methodsmentioning

confidence: 99%

“…Previous research endorses the efficacy of these variable selection procedures. For instance, similar techniques were employed for classification problems concerning the prediction of bank failures (Climent et al, 2019), categorization of online content (Salminen, Yoganathan, Corporan, Jansen, & Jung, 2019), prediction of consumer purchase intention (Bag, Tiwari, & Chan, 2019), and prediction of the helpfulness of online reviews (Singh, Irani, Rana, Dwivedi, Saumya, & Roy, 2017).…”

Section: Open Accessmentioning

confidence: 99%

An Integrative Model to Predict Product Replacement Using Deep Learning on Longitudinal Data

Brei

Nicolao

Pasdiora

et al. 2020

BAR, Braz. Adm. Rev.

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Past research on product upgrades has focused either on understanding who and when will upgrade or on figuring out why consumers will upgrade, but seldom on all. It has also neglected the interplay between these matters with decision context and timing. This manuscript depicts a comprehensive approach where, for the first time, product characteristics, individual differences, process, and contextual variables are analyzed on a predictive model of real product upgrades, identified through the systematic collection of primary data from a panel of smartphone consumers. We tested one traditional linear logistic regression model and two types of non-linear, state-of-the-art machine-learning models (extreme gradient boosting and deep learning) to explain upgrading behavior. Results provide an integrative, yet parsimonious, product-upgrade model showing the importance of resources; news about the smartphone brand; sentimental value; predicted, current, and remembered enjoyment; update capacity; and how much the smartphone meets the user's current needs as the most relevant variables to determine which consumers are more prone to upgrade their smartphones. Our findings advance upgrade decision theory by taking a holistic approach to the phenomenon and bridging different theoretical accounts of the replacement decision literature.

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“…However, past studies as those above mainly focus on a specific sector such as manufacture, bank, hotel, agribusiness, etc. [22][23][24]34]. Until now, to the best of our knowledge, only Doumpos et al [2] has explored corporate failure forecasting in the energy sector.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Second, non-financial variables are being more widely applied for corporate failure forecasting recently, though financial ratios are still the most popular variables [9,20], such as market information, macroeconomic, industry information, and so on [2,24,34]. With the development of artificial intelligence, some literature has started to adopt textual data to forecast corporate failure [5,27,28].…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting Corporate Failure in the Chinese Energy Sector: A Novel Integrated Model of Deep Learning and Support Vector Machine

Pan

Chen

et al. 2019

Energies

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Accurate forecasts of corporate failure in the Chinese energy sector are drivers for both operational excellence in the national energy systems and sustainable investment of the energy sector. This paper proposes a novel integrated model (NIM) for corporate failure forecasting in the Chinese energy sector by considering textual data and numerical data simultaneously. Given the feature of textual data and numerical data, convolutional neural network oriented deep learning (CNN-DL) and support vector machine (SVM) are employed as the base classifiers to forecast using textual data and numerical data, respectively. Subsequently, soft set (SS) theory is applied to integrate outputs of CNN-DL and SVM. Hence, NIM inherits advantages and avoids disadvantages of CNN-DL, SVM, and SS. It is able to improve the forecasting performance by taking full use of textual data and numerical data. For verification, NIM is applied to the real data of Chinese listed energy firms. Empirical results indicate that, compared with benchmarks, NIM demonstrates superior performance of corporate failure forecasting in the Chinese energy sector.

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Anticipating bank distress in the Eurozone: An Extreme Gradient Boosting approach

Cited by 107 publications

References 29 publications

A Textual Analysis of US Corporate Social Responsibility Reports

A Textual Analysis of US Corporate Social Responsibility Reports

An Integrative Model to Predict Product Replacement Using Deep Learning on Longitudinal Data

Forecasting Corporate Failure in the Chinese Energy Sector: A Novel Integrated Model of Deep Learning and Support Vector Machine

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