5,373 research outputs found

    Hybrid model using logit and nonparametric methods for predicting micro-entity failure

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    Following the calls from literature on bankruptcy, a parsimonious hybrid bankruptcy model is developed in this paper by combining parametric and non-parametric approaches.To this end, the variables with the highest predictive power to detect bankruptcy are selected using logistic regression (LR). Subsequently, alternative non-parametric methods (Multilayer Perceptron, Rough Set, and Classification-Regression Trees) are applied, in turn, to firms classified as either “bankrupt” or “not bankrupt”. Our findings show that hybrid models, particularly those combining LR and Multilayer Perceptron, offer better accuracy performance and interpretability and converge faster than each method implemented in isolation. Moreover, the authors demonstrate that the introduction of non-financial and macroeconomic variables complement financial ratios for bankruptcy prediction

    A Comprehensive Survey on Enterprise Financial Risk Analysis: Problems, Methods, Spotlights and Applications

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    Enterprise financial risk analysis aims at predicting the enterprises' future financial risk.Due to the wide application, enterprise financial risk analysis has always been a core research issue in finance. Although there are already some valuable and impressive surveys on risk management, these surveys introduce approaches in a relatively isolated way and lack the recent advances in enterprise financial risk analysis. Due to the rapid expansion of the enterprise financial risk analysis, especially from the computer science and big data perspective, it is both necessary and challenging to comprehensively review the relevant studies. This survey attempts to connect and systematize the existing enterprise financial risk researches, as well as to summarize and interpret the mechanisms and the strategies of enterprise financial risk analysis in a comprehensive way, which may help readers have a better understanding of the current research status and ideas. This paper provides a systematic literature review of over 300 articles published on enterprise risk analysis modelling over a 50-year period, 1968 to 2022. We first introduce the formal definition of enterprise risk as well as the related concepts. Then, we categorized the representative works in terms of risk type and summarized the three aspects of risk analysis. Finally, we compared the analysis methods used to model the enterprise financial risk. Our goal is to clarify current cutting-edge research and its possible future directions to model enterprise risk, aiming to fully understand the mechanisms of enterprise risk communication and influence and its application on corporate governance, financial institution and government regulation

    Corporate Bankruptcy Prediction

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    Bankruptcy prediction is one of the most important research areas in corporate finance. Bankruptcies are an indispensable element of the functioning of the market economy, and at the same time generate significant losses for stakeholders. Hence, this book was established to collect the results of research on the latest trends in predicting the bankruptcy of enterprises. It suggests models developed for different countries using both traditional and more advanced methods. Problems connected with predicting bankruptcy during periods of prosperity and recession, the selection of appropriate explanatory variables, as well as the dynamization of models are presented. The reliability of financial data and the validity of the audit are also referenced. Thus, I hope that this book will inspire you to undertake new research in the field of forecasting the risk of bankruptcy

    Prediction of Banks Financial Distress

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    In this research we conduct a comprehensive review on the existing literature of prediction techniques that have been used to assist on prediction of the bank distress. We categorized the review results on the groups depending on the prediction techniques method, our categorization started by firstly using time factors of the founded literature, so we mark the literature founded in the period (1990-2010) as history of prediction techniques, and after this period until 2013 as recent prediction techniques and then presented the strengths and weaknesses of both. We came out by the fact that there was no specific type fit with all bank distress issue although we found that intelligent hybrid techniques considered the most candidates methods in term of accuracy and reputatio

    SME default prediction: A systematic methodology-focused review

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    This study reviews the methodologies used in the literature to predict failure in small and medium-sized enterprises (SMEs). We identified 145 SMEs’ default prediction studies from 1972 to early 2023. We summarized the methods used in each study. The focus points are estimation methods, sample re-balancing methods, variable selection techniques, validation methods, and variables included in the literature. More than 1,200 factors used in failure prediction models have been identified, along with 54 unique feature selection techniques and 80 unique estimation methods. Over one-third of the studies do not use any feature selection method, and more than one-quarter use only in-sample validation. Our main recommendation for researchers is to use feature selection and validate results using hold-out samples or cross-validation. As an avenue for further research, we suggest in-depth empirical comparisons of estimation methods, feature selection techniques, and sample re-balancing methods based on some large and commonly used datasets.publishedVersio

    Predicting Financial Distress Within Indian Enterprises: A Comparative Study on the Neuro-Fuzzy Models and the Traditional Models of Bankruptcy Prediction

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    The financial distresses is of major importance in the financial management system particularly in the case of this competitive environs. There are several traditional methods existing for predicting the financial distress within the country. Major factors influencing the financial distress is the stock market, credit risk and so on. Hence there is a need of models which could make dynamic predictions with the use of dynamic variables. There are several machine learning and artificial intelligence-based bankruptcy prediction models available. The neural network concepts and the computational intelligence-based methods are highly acceptable in the prediction arena. This research presents a comprehensive review of the existing prediction approaches and suggests future research directions and ideas. Some of the existing methods are support vector machines, artificial neural network, multi-layer perceptron, and the linear models such as principal component analysis. Neuro-fuzzy approaches, Deep belief neural networks, Convolution neural networks are also discussed

    Machine Learning based Wind Power Forecasting for Operational Decision Support

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    To utilize renewable energy efficiently to meet the needs of mankind's living demands becomes an extremely hot topic since global warming is the most serious global environmental problem that human beings are facing today. Burning of fossil fuels, such as coal and oil directly for generating electricity leads to environment pollution and exacerbates global warning. However, large-scale development of hydropower increases greenhouse gas emissions and greenhouse effects. This research is related to knowledge of wind power forecasting (WPF) and machine learning (ML). This research is built around one central research question: How to improve the accuracy of WPF by using AI methods? A pilot conceptual system combining meteorological information and operations management has been formulated. The main contribution is visualized in a proposed new framework, named Meteorological Information Service Decision Support System, consisting of a meteorological information module, wind power prediction module and operations management module. This conceptual framework has been verified by quantitative analysis in empirical cases. This system utilizes meteorological information for decision-making based on condition-based maintenance in operations and management for the purpose of optimizing energy management. It aims to analyze and predict the variation of wind power for the next day or the following week to develop scheduling planning services for WPEs based on predicting wind speed for every six hours, which is short-term wind speed prediction, through training, validating, and testing dataset. Accurate prediction of wind speed is crucial for weather forecasting service and WPF. This study presents a carefully designed wind speed prediction model which combines fully-connected neural network (FCNN), long short-term memory (LSTM) algorithm with eXtreme Gradient Boosting (XGBoost) technique, to predict wind speed. The performance of each model is tested by using reanalysis data from European Center for Medium-Range Weather Forecasts (ECMWF) for Meteorological observatory located in Vaasa in Finland. The results show that XGBoost algorithm has similar improved prediction performance as LSTM algorithm, in terms of RMSE, MAE and R2 compared to the commonly used traditional FCNN model. On the other hand, the XGBoost algorithm has a significant advantage on training time while comparing to the other algorithms in this case study. Additionally, this sensitivity analysis indicates great potential of the optimized deep learning (DL) method, which is a subset of machine learning (ML), in improving local weather forecast on the coding platform of Python. The results indicate that, by using Meteorological Information Service Decision Support System, it is possible to support effective decision-making and create timely actions within the WPEs. Findings from this research contribute to WPF in WPEs. The main contribution of this research is achieving decision optimization on a decision support system by using ML. It was concluded that the proposed system is very promising for potential applications in wind (power) energy management

    A revision of Altman’s Z- Score for SMEs: suggestions from the Italian Bankruptcy Law and pandemic perspectives

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    As the pandemic urged further investigations on the prediction of firms’ financial distress, this study develops and tests an alternative measure to the alert system elaborated by the NCCAAE which combines the benefits of the Z-score’s multivariate discriminant model with the background employed to develop the NCCAAE’ predictors. Using a sample of 43 viable and 43 non-viable Italian SMEs, we first compare the financial distress predictive accuracy of the NCCAAE’s alert system to that of the traditional Z-score over the period 2015-2019. On the basis of the results, we elaborate and compare the revised versions of both approaches which align the traditional Z-score to the current socio-economic conditions and provide an alternative measure to the NCCAAE’s alert system which embeds a Z-score calculated using the ratios elaborated by the NCCAAE for the alert system. The analysis of the two baseline approaches showed complementary results as the Z-score overperformed the alert system when predicting the status of non-viable firms whereas the opposite emerged as regards viable firms. The revised version of both approaches pointed out an enhanced predictive accuracy with respect to baseline models. In particular, the complementary role of the Z-score has been integrated into the new alert system as major contribute to its enhancement which pointed it out as the best measure employed. We, therefore, contribute to the literature studying the financial distress prediction developments by elaborating an alternative measure to the alert system developed by the NCCAAE which combines the benefits of the Z-score’s multivariate discriminant function with the background employed to develop the NCCAAE’ predictors. Our analysis enriches the post-pandemic debate on refined financial distressed prediction methods by pointing out the limits of the alert system as designed by the NCCAAE and suggests an alternative and better performing measure that may be used by third-party bodies to predict financial distress

    Interpretable Nonlinear Model for Enterprise Bankruptcy Prediction

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    The aim of this research was to model bankruptcy dependency of Lithuanian enterprises on their financial ratios and its dynamics over time by the integration of artificial neural networks and fuzzy logic technology using Adaptive Network – based Fuzzy Inference System (ANFIS). We used data from financial reports for three years’ of 230 Lithuanian going and failed enterprises. Input variables used for the ANFIS model training and testing composed of 13 financial ratios of the last year before bankruptcy and 13 variables characterizing changes of that ratios over time. It was checked 1716 subsets of input variables, each subset containing five input variables. This way the ANFIS model and the best subset of predictive variables with minimal training errors was found. Test of that model showed that percentage of right failure and success predictions reached 80 %. Fuzzy rules of the ANFIS were used to construct interpretable rules base, which can be useful for enterprise managers as knowledge for the linguistic diagnosis of failure or financial problems

    A multi-sensor based online tool condition monitoring system for milling process

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    Tool condition monitoring has been considered as one of the key enabling technologies for manufacturing optimization. Due to the high cost and limited system openness, the relevant developed systems have not been widely adopted by industries, especially Small and Medium-sized Enterprises. In this research, a cost-effective, wireless communication enabled, multi-sensor based tool condition monitoring system has been developed. Various sensor data, such as vibration, cutting force and power data, as well as actual machining parameters, have been collected to support efficient tool condition monitoring and life estimation. The effectiveness of the developed system has been validated via machining cases. The system can be extended to wide manufacturing applications
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