Credit risk modeling using Bayesian network with a latent variable

Masmoudi, Khalil; Abid, Lobna; Masmoudi, Afif

doi:10.1016/j.eswa.2019.03.014

Cited by 64 publications

(20 citation statements)

References 30 publications

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“…The data of a product supplier are selected, processed after normalization, and input into the Bayesian network model [ 32 ], average support vector machine (ASVM) model [ 33 ], BP neural network model [ 34 ], PSO-BP neural network, RBF neural network model [ 35 ] and the designed and trained BP-GA model to test the evaluation results of the proposed model on enterprise performance and prove the effectiveness and feasibility of its application. The simulation test is based on factor analysis.…”

Section: Resultsmentioning

confidence: 99%

Influence of the enterprise’s intelligent performance evaluation model using neural network and genetic algorithm on the performance compensation of the merger and acquisition parties in the commitment period

Luo¹,

Da²

2021

PLoS ONE

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The purpose is to study the performance compensation of the bid purchased during the mergers and acquisitions (M&A) process. An intelligent model of enterprise performance appraisal is built to analyze the performances of the acquired enterprises. First, the evaluation indicators of enterprise performance are selected from both financial and non-financial aspects. An enterprise performance appraisal model is established based on the neural networks and optimized by the factor analysis method and Genetic Algorithm (GA). The principal factors affecting enterprise performance are analyzed. Then the M&A parties’ performances during the M&A commitment period under the earnings compensation mechanism are analyzed quantitatively. Corresponding hypotheses and evaluation indicators are established. Mean test results and regression analyses demonstrate that the hypotheses proposed are valid under particular circumstances. Introducing the earnings compensation mechanism during the M&A process can improve the enterprise performance effectively so that the earnings forecasted in the commitment period are significantly higher than the historical profitability. Hence, the earnings compensation mechanism plays a positive role in guiding enterprise performance. Comparison with models proposed in previous research reveals that the output error ratio of the designed corporate performance evaluation model is 1.16%, which can effectively evaluate corporate performance. The above results provide a reference for studying the impact of the earnings compensation mechanism on enterprise performance during the M&A process.

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Section: Resultsmentioning

confidence: 99%

Influence of the enterprise’s intelligent performance evaluation model using neural network and genetic algorithm on the performance compensation of the merger and acquisition parties in the commitment period

Luo¹,

Da²

2021

PLoS ONE

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show abstract

“…Bayesian Belief Networks (BBN) is a graphical network of causal connections between different nodes. In BBN models, the network structure is a directed acyclic graph (DAG) that graphically represents the logical relationship between nodes, and the conditional probability of quantifying the strength of this relationship is the network parameter (Castelletti and Soncini-Sessa 2007;Ghribi and Masmoudi 2013;Masmoudi et al 2019). The network structure and network parameter can be obtained via expert knowledge (Joseph et al 2010;Nadkarni and Shenoy 2001) or training from data (Kabir et al 2015).…”

Section: Bayesian Belief Networkmentioning

confidence: 99%

Probabilistic Evaluation of CPT-based Seismic Soil Liquefaction Potential: Towards the Integration of Interpretive Structural Modeling and Bayesian Belief Network

Ahmad

Tang

Ahmad

et al. 2021

Preprint

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This paper proposes a probabilistic graphical model that integrates interpretive structural modeling (ISM) and Bayesian belief network (BBN) approaches to predict CPT-based soil liquefaction potential. In this study, an ISM approach was employed to identify relationships between influence factors, whereas BBN approach was used to describe the quantitative strength of their relationships using conditional and marginal probabilities. The proposed model combines major causes, such as soil, seismic and site conditions, of seismic soil liquefaction at once. To demonstrate the application of the propose framework, the paper elaborates on each phase of the BBN framework, which is then validated with historical empirical data. In context of the rate of successful prediction of liquefaction and non-liquefaction events, the proposed probabilistic graphical model is proven to be more effective, compared to logistic regression, support vector machine, random forest and naïve Bayes methods. This research also interprets sensitivity analysis and the most probable explanation of seismic soil liquefaction appertaining to engineering perspective.

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“…Bayesian Networks (BN) are powerful modeling tools that replicate the essential features to ratiocinate uncertainty in a consistent, efficient and sound mathematic way [54]. In BN models, the network structure is a directed acyclic graph (DAG) that graphically represents the logical relationship between nodes, and the network parameter is the conditional probability that quantifies the strength of this relationship [55][56][57]. The network structure and network parameter can be acquired through professional opinion or knowledge inspiration [58,59] or training from data [60,61].…”

Section: Bayesian Network Principlementioning

confidence: 99%

Applying a Series and Parallel Model and a Bayesian Networks Model to Produce Disaster Chain Susceptibility Maps in the Changbai Mountain area, China

Han

Zhang

et al. 2019

Water

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The aim of this project was to produce an earthquake–landslide debris flow disaster chain susceptibility map for the Changbai Mountain region, China, by applying data-driven model series and parallel model and Bayesian Networks model. The accuracy of these two models was then compared. Parameters related to the occurrence of landslide and debris flow disasters, including earthquake intensity, rainfall, elevation, slope, slope aspect, lithology, distance to rivers, distance to faults, land use, and the normalized difference vegetation index (NDVI), were chosen and applied in these two models. Disaster chain susceptibility zones created using the two models were then contrasted and verified using the occurrence of past disasters obtained from remote sensing interpretations and field investigations. Both disaster chain susceptibility maps showed that the high susceptibility zones are situated within a 10 km radius around the Tianchi volcano, whereas the northern and southwestern sections of the study area comprise primarily very low or low susceptibility zones. The two models produced similar and compatible results as indicated by the outcomes of basic linear correlation and cross-correlation analyses. The verification results of the ROC curves were found to be 0.7727 and 0.8062 for the series and parallel model and BN model, respectively. These results indicate that the two models can be used as a preliminary base for further research activities aimed at providing hazard management tools, forecasting services, and early warning systems.

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Credit risk modeling using Bayesian network with a latent variable

Cited by 64 publications

References 30 publications

Influence of the enterprise’s intelligent performance evaluation model using neural network and genetic algorithm on the performance compensation of the merger and acquisition parties in the commitment period

Influence of the enterprise’s intelligent performance evaluation model using neural network and genetic algorithm on the performance compensation of the merger and acquisition parties in the commitment period

Probabilistic Evaluation of CPT-based Seismic Soil Liquefaction Potential: Towards the Integration of Interpretive Structural Modeling and Bayesian Belief Network

Applying a Series and Parallel Model and a Bayesian Networks Model to Produce Disaster Chain Susceptibility Maps in the Changbai Mountain area, China

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