scholarly journals Bankruptcy Prediction for Innovative Companies

2021 ◽  
Vol 15 (4) ◽  
pp. 36-55
Author(s):  
Ivan Lobeev
Keyword(s):  
Neural Network ◽  
Financial Distress ◽  
Network Architecture ◽  
Relevant Literature ◽  
Predictive Ability ◽  
Bankruptcy Prediction ◽  
Financial Data ◽  
The Novel ◽  
Novel Approach ◽  
Bankruptcy Probability

The main purpose of this article is to identify the best neural network model algorithm and relevant set of variables for predicting financial distress/bankruptcy in innovative companies. While previous articles in this area considered neural network analysis for large companies from primary sectors of the economy, we take the novel approach of examining theless-explored area of innovative companies. First, we complete a comprehensive review of the relevant literature in order to define the best configuration of factors which can influence bankruptcy, network architecture and learning methodology. We apply our chosen method to a sample of companies from around the world, from industries which are considered innovative, and identify the dependence of bankruptcy probability on a set of factors which are reflected in the financial data of a company. Our evaluation is based on the financial data of 300 companies – 50 of them are bankrupts, and 250 are ‘healthy’. Our results represent the set of relevant factors for bankruptcy prediction and the appropriate neural network. We have applied a total of 19 factors characterising efficiency, liquidity, profitability, sustainability, and level of innovation. Our proposed analysis is appropriate for all sizes of companies. We provided two models in order to cater for the most confidence in terms of obtained results. The total predictive ability of the model developed in our research is almost 98%, which is extremely efficient, and corresponds to the results of the most modern methods. Both approaches demonstrated almost the same level of influence of factor groups on final bankruptcy probability.

scholarly journals DO FIRM FAILURE PROCESSES DIFFER ACROSS COUNTRIES: EVIDENCE FROM FINLAND AND ESTONIA

2014 ◽  
Vol 15 (5) ◽  
pp. 810-832 ◽  
Author(s):  
Erkki K. Laitinen ◽  
Oliver Lukason
Keyword(s):  
Prediction Models ◽  
Bankruptcy Prediction ◽  
Financial Data ◽  
The Novel ◽  
Finnish Firms

This study considers the novel topic of comparing firm failure processes between different countries. For seventy bankrupt Finnish firms corresponding pairs are found among Estonian bankrupt firms based on industry, size and time of bankruptcy. Despite the similarity of firms from two countries, the analysis shows remarkable differences in both pre-failure financial data and reasons for failure. Based only on financial data, five failure processes are detected for Finnish and six for Estonian firms. Established failure processes associate with different failure reasons. The study contributes to literature by showing that for similar companies failure processes can differ across countries. In practice, the established information about different failure processes can be applied when building or using bankruptcy prediction models.

scholarly journals Pesquisa sobre empresas multinacionais brasileiras: análises descritivas e preditivas

Internext ◽  
2015 ◽  
Vol 10 (2) ◽  
pp. 64
Author(s):  
Mario Henrique Ogasavara ◽  
Gilmar Masiero ◽  
Marcio De Oliveira Mota ◽  
Lucas Souza
Keyword(s):  
Neural Network ◽  
Citation Analysis ◽  
Multinational Enterprises ◽  
Descriptive Analysis ◽  
Predictive Analysis ◽  
The Novel ◽  
Neural Network Approach ◽  
Novel Approach ◽  
The Neural Network ◽  
Eclectic Paradigm

<p><em>T</em>his study attempts to review recent research on the internationalization of Brazilian multinational enterprises (I-BMNEs) based on an analysis of the 174 published articles that have appeared in international and Brazilian academic journals, books, and conference proceedings. The descriptive analysis seeks to undertake a citation analysis as well as to provide a typology of the leading researchers and school affiliations, the predominating theoretical and methodological approaches. This paper also proposes a predictive analysis based on a novel approach of neural network in order to classify features of a manuscript and predict the fit of its publication. We find that the research on I-BMNEs is driven by a small number of leading institutions and researchers which utilize case studies as their research method and have the Uppsala and Eclectic Paradigm models as theoretical framework. The citation analysis shows that authors of foreigner origin are cited from journal publications or translated books. The novel technique and design of the neural network approach was modeled to fit for bibliometric studies and the outcomes of the predictive analysis were able to classify correctly 56.25% of the manuscripts. We conclude by providing a set of recommendations to advance the research on I-BMNEs.</p>

Security Model of Internet of Things Based on Binary Wavelet and Sparse Neural Network

2019 ◽  
Vol 10 (1) ◽  
pp. 1-17
Author(s):  
Zhihui Wang ◽  
Jingjing Yang ◽  
Benzhen Guo ◽  
Xiao Zhang
Keyword(s):  
Neural Network ◽  
Internet Of Things ◽  
Network Architecture ◽  
Safety Management ◽  
The Internet ◽  
Security Model ◽  
The Novel ◽  
Simulated Environment ◽  
Proposed Model ◽  
The Internet Of Things

At present, the internet of things has no standard system architecture. According to the requirements of universal sensing, reliable transmission, intelligent processing and the realization of human, human and the material, real-time communication between objects and things, the internet needs the open, hierarchical, extensible network architecture as the framework. The sensation equipment safe examination platform supports the platform through the open style scene examination to measure the equipment and provides the movement simulated environment, including each kind of movement and network environment and safety management center, turning on application gateway supports. It examines the knowledge library. Under this inspiration, this article proposes the novel security model based on the sparse neural network and wavelet analysis. The experiment indicates that the proposed model performs better compared with the other state-of-the-art algorithms.

scholarly journals Is the External Audit Report Useful for Bankruptcy Prediction? Evidence Using Artificial Intelligence

2018 ◽  
Author(s):  
Nora Muñoz-Izquierdo ◽  
María-del-Mar Camacho-Miñano ◽  
María Jesús Segovia-Vargas ◽  
David Pascual-Ezama
Keyword(s):  
Artificial Intelligence ◽  
Financial Distress ◽  
Predictive Accuracy ◽  
Practical Implication ◽  
Predictive Ability ◽  
Bankruptcy Prediction ◽  
External Audit ◽  
Audit Data ◽  
Audit Reports ◽  
Audit Information

Despite the number of studies on bankruptcy prediction using financial ratios, very little is known about how external audit information can contribute to anticipating financial distress. A handful of papers show that a combination of ratios and audit data can provide significant predictive purposes, but a recent paper by Mu&ntilde;oz-Izquierdo et al. (2018) provided an 80% predictive accuracy solely by using the disclosures of audit reports. We complement this study. Applying an artificial intelligence method (the PART algorithm), we examine the predictive ability of more easily extracted information from the report and suggest a practical implication for each user. Simply by (1) finding the audit opinion, (2) identifying if a matter section exist, (3) and the number of comments disclosed, then any user may predict a bankruptcy situation with the same accuracy as if they had scrutinised the whole report. In addition, we also provide an extended literature review about previous studies on the interaction between bankruptcy prediction and the external audit information.

scholarly journals DEEP CONVOLUTIONAL NEURAL NETWORK FOR AUTOMATIC DETECTION OF DAMAGED PHOTOVOLTAIC CELLS

2018 ◽  
Vol XLII-2 ◽  
pp. 893-900 ◽  
Author(s):  
R. Pierdicca ◽  
E. S. Malinverni ◽  
F. Piccinini ◽  
M. Paolanti ◽  
A. Felicetti ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Photovoltaic Cells ◽  
Automatic Detection ◽  
Thermal Infrared ◽  
The Novel ◽  
Infrared Sensor ◽  
Novel Approach ◽  
Safety And Stability ◽  
Degradation Problem

The number of distributed Photovoltaic (PV) plants that produce electricity has been significantly increased, and issue of monitoring and maintaining a PV plant has become of great importance and involves many challenges as efficiency, reliability, safety, and stability. This paper presents the novel approach to estimate the PV cells degradations with DCNNs. While many studies have performed images classification, to the best of our knowledge, this is the first exploitation of data acquired with a drone equipped with a thermal infrared sensor. The experiments on “Photovoltaic images Dataset”, a collected dataset, are presented to show the degradation problem and comprehensively evaluate the method presented in this research. Results in terms of precision, recall and F1-score show the effectiveness and the suitability of the proposed approach.

scholarly journals SEMANTIC SEGMENTATION FOR SELF DRIVING CARS

2021 ◽  
Vol 6 (3) ◽  
Author(s):  
M Vaishnavi ◽  
K Varshitha ◽  
G Usha ◽  
C Mounika ◽  
C Narasimha
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Semantic Segmentation ◽  
Input Image ◽  
Computational Time ◽  
Neural Network Architecture ◽  
Novel Approach ◽  
Active Interest ◽  
Self Driving Cars ◽  
Efficient Memory

This paper proposes a novel approach for Semantic segmentation which is one of the biggest challenge increasing in an order and have been making humans hold keen active interest to result in fast and accurate semantic segmentation. Whereas At present, we are trying to solve this problem of semantic segmentation using the segnet which makes its more accurate interms of accuracy, computational time, and inference time. and here we are using segnet model to take this to the next level which includes max-pooling, Batch normalization techniques to map low-resolution features to input resolution for pixelwise classification and the architecture here consists of an encoder which takes the input image and is identical to 13 convolutional layers and a decoder that uses segnet followed by pixel-wise classification layer. and also when compared with other architectures segnet provides good performance with competitive inference time and most efficient memory. So, therefore here we are presenting deep fully convolutional neural network architecture for semantic pixel-wise segmentation termed SegNet.

scholarly journals Is the External Audit Report Useful for Bankruptcy Prediction? Evidence Using Artificial Intelligence

2019 ◽  
Vol 7 (2) ◽  
pp. 20 ◽  
Author(s):  
Nora Muñoz-Izquierdo ◽  
María-del-Mar Camacho-Miñano ◽  
María-Jesús Segovia-Vargas ◽  
David Pascual-Ezama
Keyword(s):  
Artificial Intelligence ◽  
Financial Distress ◽  
Predictive Accuracy ◽  
Practical Implication ◽  
Predictive Ability ◽  
Bankruptcy Prediction ◽  
Support Vector ◽  
External Audit ◽  
Audit Reports ◽  
Audit Information

Despite the number of studies on bankruptcy prediction using financial ratios, very little is known about how external audit information can contribute to anticipating financial distress. A handful of papers have shown that a combination of ratios and audit data is significant for predictive purposes, but only one recent paper provided a predictive accuracy of 80% solely by using the disclosures contained in audit reports. This study was complemented by simplifying the analysis of audit reports for prediction purposes and the same predictive accuracy was achieved. By applying three artificial intelligence techniques (PART algorithm, random forest, and support vector machine), the predictive ability of more easily extracted information from the report was examined and a practical implication suggested for each user. Simply by (1) finding the audit opinion, (2) identifying if a matter section exists, and (3) the number of comments disclosed, any user may predict a bankruptcy situation with the same accuracy as if they had scrutinized the whole report. In addition, an extended literature review is included, on previous studies on the interaction between bankruptcy prediction and the external audit information.

scholarly journals Deep-insight visible neural network (DI-VNN) for improving interpretability of a non-image deep learning model by data-driven ontology

2021 ◽  
Author(s):  
Herdiantri Sufriyana ◽  
Yu Wei Wu ◽  
Emily Chia-Yu Su
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Predictive Ability ◽  
Image Data ◽  
Data Driven ◽  
Differential Analysis ◽  
Model Training ◽  
Internal Properties ◽  
Using Data ◽  
Construction Model

Abstract We aimed to provide a framework that organizes internal properties of a convolutional neural network (CNN) model using non-image data to be interpretable by human. The interface was represented as ontology map and network respectively by dimensional reduction and hierarchical clustering techniques. The applicability is to implement a prediction model either to classify categorical or to estimate numerical outcome, including but not limited to that using data from electronic health records. This pipeline harnesses invention of CNN algorithms for non-image data while improving the depth of interpretability by data-driven ontology. However, the DI-VNN is only for exploration beyond its predictive ability, which requires further explanatory studies, and needs a human user with specific competences in medicine, statistics, and machine learning to explore the DI-VNN with high confidence. The key stages consisted of data preprocessing, differential analysis, feature mapping, network architecture construction, model training and validation, and exploratory analysis.

SCORING MODELING BASED ON NEURAL NETWORKS FOR DETERMINING A BANK BORROWER'S RATING

2020 ◽  
Vol 2020 (10) ◽  
pp. 54-62
Author(s):  
Oleksii VASYLIEV ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Statistical Data ◽  
Activation Function ◽  
Decision Making Process ◽  
Neural Network Architecture ◽  
Acceptable Accuracy ◽  
The Neural Network ◽  
Sigmoid Activation Function

The problem of applying neural networks to calculate ratings used in banking in the decision-making process on granting or not granting loans to borrowers is considered. The task is to determine the rating function of the borrower based on a set of statistical data on the effectiveness of loans provided by the bank. When constructing a regression model to calculate the rating function, it is necessary to know its general form. If so, the task is to calculate the parameters that are included in the expression for the rating function. In contrast to this approach, in the case of using neural networks, there is no need to specify the general form for the rating function. Instead, certain neural network architecture is chosen and parameters are calculated for it on the basis of statistical data. Importantly, the same neural network architecture can be used to process different sets of statistical data. The disadvantages of using neural networks include the need to calculate a large number of parameters. There is also no universal algorithm that would determine the optimal neural network architecture. As an example of the use of neural networks to determine the borrower's rating, a model system is considered, in which the borrower's rating is determined by a known non-analytical rating function. A neural network with two inner layers, which contain, respectively, three and two neurons and have a sigmoid activation function, is used for modeling. It is shown that the use of the neural network allows restoring the borrower's rating function with quite acceptable accuracy.

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