The instrumental machine learning methods for corporate bankruptcy prediction

2021 ◽  
Vol 27 (9) ◽  
pp. 2118-2138
Author(s):  
Aleksandr R. NEVREDINOV
Keyword(s):  
Machine Learning ◽  
Bankruptcy Prediction ◽  
Instrumental Method ◽  
Corporate Bankruptcy ◽  
Foreign Companies ◽  
Data Compilation ◽  
Analysis And Synthesis ◽  
Long Time ◽  
Methodological Principles ◽  
Company Analysis

Subject. It is very important for corporate governance and a choice of partners to evaluate the company’s position. Therefore, bankruptcy forecast methods have been actively studied in theoretical and practical proceedings for a long time. Recurring crises and high market dynamics make the subject especially relevant. Objectives. I develop the instrumental method based on machine learning to predict corporate bankruptcy. The study also reviews data sources, the potential of forecasting models, and chooses inputs for company analysis. Methods. I applied methods of analysis and synthesis, and the systematization, formalization, comparative analysis. I referred to theoretical and methodological principles set forth in national and foreign proceedings on the company analysis and bankruptcy prediction. I investigate issues of data compilation, and building the artificial neural network for teaching the model. Results. I proposed and tested the instrumental method to predict bankruptcy. I suggest using my own sets of indicators for forecasting, which I selected by analyzing key indicators of financial sustainability, efficacy, and key external factors influencing market actors. The article presents a data sample for teaching purposes, which includes both the Russian and foreign companies, thus expanding its size. I devised machine learning models generating high-precision forecasts. Conclusions and Relevance. The findings contribute to bankruptcy prediction methods and can be used for administrative decision-making to automate their own analysis or analyze other entities, which the company cooperates with.

Corporate Bankruptcy Prediction: An Approach Towards Better Corporate World

2020 ◽  
Author(s):  
Talha Mahboob Alam ◽  
Kamran Shaukat ◽  
Mubbashar Mushtaq ◽  
Yasir Ali ◽  
Matloob Khushi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Bankruptcy Prediction ◽  
Support Vector ◽  
Financial Indicators ◽  
Learning Models ◽  
Corporate Bankruptcy ◽  
Logistic Model Tree ◽  
Decision Forest ◽  
Machine Learning Models ◽  
Corporate Bankruptcy Prediction

Abstract The area of corporate bankruptcy prediction attains high economic importance, as it affects many stakeholders. The prediction of corporate bankruptcy has been extensively studied in economics, accounting and decision sciences over the past two decades. The corporate bankruptcy prediction has been a matter of talk among academic literature and professional researchers throughout the world. Different traditional approaches were suggested based on hypothesis testing and statistical modeling. Therefore, the primary purpose of the research is to come up with a model that can estimate the probability of corporate bankruptcy by evaluating its occurrence of failure using different machine learning models. As the dataset was not well prepared and contains missing values, various data mining and data pre-processing techniques were utilized for data preparation. Within this research, the task of resolving the issues induced by the imbalance between the two classes is approached by applying different data balancing techniques. We address the problem of imbalanced data with the random undersampling and Synthetic Minority Over Sampling Technique (SMOTE). We used five machine learning models (support vector machine, J48 decision tree, Logistic model tree, random forest and decision forest) to predict corporate bankruptcy earlier to the occurrence. We use data from 2009 to 2013 on Poland manufacturing corporates and selected the 64 financial indicators to be broken down. The main finding of the study is a significant improvement in predictive accuracy using machine learning techniques. We also include other economic indicators ratios, along with Altman’s Z-score variables related to profitability, liquidity, leverage and solvency (short/long term) to propose an efficient model. Machine learning models give better results while balancing the data through SMOTE as compared to random undersampling. The machine learning technique related to decision forest led to 99% accuracy, whereas support vector machine (SVM), J48 decision tree, Logistic Model Tree (LMT) and Random Forest (RF) led to 92%, 92.3%, 93.8% and 98.7% accuracy, respectively, with all predictive financial indicators. We find that the decision forest outperforms the other techniques and previous techniques discussed in the literature. The proposed method is also deployed on the web to assist regulators, investors, creditors and scholars to predict corporate bankruptcy.

scholarly journals Predicting Fraud Victimization Using Classical Machine Learning

Entropy ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. 300
Author(s):  
Mark Lokanan ◽  
Susan Liu
Keyword(s):  
Machine Learning ◽  
Financial Literacy ◽  
Learning Algorithm ◽  
Demographic Characteristics ◽  
Financial Knowledge ◽  
The Past ◽  
Machine Learning Model ◽  
Long Time ◽  
Regulatory Organization ◽  
Investment Fraud

Protecting financial consumers from investment fraud has been a recurring problem in Canada. The purpose of this paper is to predict the demographic characteristics of investors who are likely to be victims of investment fraud. Data for this paper came from the Investment Industry Regulatory Organization of Canada’s (IIROC) database between January of 2009 and December of 2019. In total, 4575 investors were coded as victims of investment fraud. The study employed a machine-learning algorithm to predict the probability of fraud victimization. The machine learning model deployed in this paper predicted the typical demographic profile of fraud victims as investors who classify as female, have poor financial knowledge, know the advisor from the past, and are retired. Investors who are characterized as having limited financial literacy but a long-time relationship with their advisor have reduced probabilities of being victimized. However, male investors with low or moderate-level investment knowledge were more likely to be preyed upon by their investment advisors. While not statistically significant, older adults, in general, are at greater risk of being victimized. The findings from this paper can be used by Canadian self-regulatory organizations and securities commissions to inform their investors’ protection mandates.

scholarly journals Machine Learning Use for Prognostic Purposes in Multiple Sclerosis

Life ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 122
Author(s):  
Ruggiero Seccia ◽  
Silvia Romano ◽  
Marco Salvetti ◽  
Andrea Crisanti ◽  
Laura Palagi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Multiple Sclerosis ◽  
High Impact ◽  
Prognostic Models ◽  
Early Prediction ◽  
Disease Course ◽  
Slow Progression ◽  
Relapsing Remitting ◽  
Long Time ◽  
Effective Drugs

The course of multiple sclerosis begins with a relapsing-remitting phase, which evolves into a secondarily progressive form over an extremely variable period, depending on many factors, each with a subtle influence. To date, no prognostic factors or risk score have been validated to predict disease course in single individuals. This is increasingly frustrating, since several treatments can prevent relapses and slow progression, even for a long time, although the possible adverse effects are relevant, in particular for the more effective drugs. An early prediction of disease course would allow differentiation of the treatment based on the expected aggressiveness of the disease, reserving high-impact therapies for patients at greater risk. To increase prognostic capacity, approaches based on machine learning (ML) algorithms are being attempted, given the failure of other approaches. Here we review recent studies that have used clinical data, alone or with other types of data, to derive prognostic models. Several algorithms that have been used and compared are described. Although no study has proposed a clinically usable model, knowledge is building up and in the future strong tools are likely to emerge.

scholarly journals Snow Depth Fusion Based on Machine Learning Methods for the Northern Hemisphere

2021 ◽  
Vol 13 (7) ◽  
pp. 1250
Author(s):  
Yanxing Hu ◽  
Tao Che ◽  
Liyun Dai ◽  
Lin Xiao
Keyword(s):  
Machine Learning ◽  
Northern Hemisphere ◽  
Snow Depth ◽  
Learning Algorithm ◽  
Random Forest Regression ◽  
Machine Learning Methods ◽  
Long Time ◽  
In Situ Observations ◽  
Input Variables

In this study, a machine learning algorithm was introduced to fuse gridded snow depth datasets. The input variables of the machine learning method included geolocation (latitude and longitude), topographic data (elevation), gridded snow depth datasets and in situ observations. A total of 29,565 in situ observations were used to train and optimize the machine learning algorithm. A total of five gridded snow depth datasets—Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) snow depth, Global Snow Monitoring for Climate Research (GlobSnow) snow depth, Long time series of daily snow depth over the Northern Hemisphere (NHSD) snow depth, ERA-Interim snow depth and Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) snow depth—were used as input variables. The first three snow depth datasets are retrieved from passive microwave brightness temperature or assimilation with in situ observations, while the last two are snow depth datasets obtained from meteorological reanalysis data with a land surface model and data assimilation system. Then, three machine learning methods, i.e., Artificial Neural Networks (ANN), Support Vector Regression (SVR), and Random Forest Regression (RFR), were used to produce a fused snow depth dataset from 2002 to 2004. The RFR model performed best and was thus used to produce a new snow depth product from the fusion of the five snow depth datasets and auxiliary data over the Northern Hemisphere from 2002 to 2011. The fused snow-depth product was verified at five well-known snow observation sites. The R2 of Sodankylä, Old Aspen, and Reynolds Mountains East were 0.88, 0.69, and 0.63, respectively. At the Swamp Angel Study Plot and Weissfluhjoch observation sites, which have an average snow depth exceeding 200 cm, the fused snow depth did not perform well. The spatial patterns of the average snow depth were analyzed seasonally, and the average snow depths of autumn, winter, and spring were 5.7, 25.8, and 21.5 cm, respectively. In the future, random forest regression will be used to produce a long time series of a fused snow depth dataset over the Northern Hemisphere or other specific regions.

scholarly journals Adaptive quantum state tomography with neural networks

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Yihui Quek ◽  
Stanislav Fort ◽  
Hui Khoon Ng
Keyword(s):  
Machine Learning ◽  
Quantum State ◽  
The State ◽  
Learning To Learn ◽  
Seamless Integration ◽  
Quantum State Tomography ◽  
Flexible Machine ◽  
Meta Learning ◽  
Long Time ◽  
State Tomography

AbstractCurrent algorithms for quantum state tomography (QST) are costly both on the experimental front, requiring measurement of many copies of the state, and on the classical computational front, needing a long time to analyze the gathered data. Here, we introduce neural adaptive quantum state tomography (NAQT), a fast, flexible machine-learning-based algorithm for QST that adapts measurements and provides orders of magnitude faster processing while retaining state-of-the-art reconstruction accuracy. As in other adaptive QST schemes, measurement adaptation makes use of the information gathered from previous measured copies of the state to perform a targeted sensing of the next copy, maximizing the information gathered from that next copy. Our NAQT approach allows for a rapid and seamless integration of measurement adaptation and statistical inference, using a neural-network replacement of the standard Bayes’ update, to obtain the best estimate of the state. Our algorithm, which falls into the machine learning subfield of “meta-learning” (in effect “learning to learn” about quantum states), does not require any ansatz about the form of the state to be estimated. Despite this generality, it can be retrained within hours on a single laptop for a two-qubit situation, which suggests a feasible time-cost when extended to larger systems and potential speed-ups if provided with additional structure, such as a state ansatz.

scholarly journals On the Form of Organizing the Institution of Condominium Ownership in Russia

Legal Concept ◽  
2019 ◽  
pp. 107-115
Author(s):  
Maxim Permyakov
Keyword(s):  
Legal Regulation ◽  
Law And Order ◽  
Real Form ◽  
Progressive Development ◽  
Root Cause ◽  
Foreign Countries ◽  
Domestic Legislation ◽  
Analysis And Synthesis ◽  
Long Time ◽  
Domestic Institution

Introduction: despite the fact that Russia is a country in which the majority of the population lives in apartment buildings, the institution of condominium ownership is one of the least developed, both in doctrinal and practical terms, in connection with which the theoretical and practical difficulties arise in the domestic legal order. The solution of such problems is impossible without the search for the root cause, which is the lack of choice of the form of organization of the legal institution, so that the legal regulation cannot be harmonious. Purpose: based on the study of the formation, evolution and unification of the institution of law in foreign countries, to address the problems of the domestic institution of condominium ownership. Methods: the methodological framework for this study is a set of methods of scientific knowledge, among which the main ones are the methods of specific historical, historical and comparative, social and legal, as well as the methods of analysis and synthesis. Results: the prerequisites for the emergence of condominium ownership in classical civil law were: the limitation of land as a natural resource, as well as capital for individual construction. The institution of condominium ownership is approved in the countries of continental law in two forms: “real” and “unreal”. In Russia, due to the lack of a long time of progressive development of property law, this institution was formed without taking into account its classical prerequisites, within the framework of privatization processes, which led to the emergence of the problems which are atypical for the European law and order. Conclusions: the domestic legislation tends to the organization of the institution of condominium ownership in the “real” form; however, the modern interpretation of this form entails many legal problems, which clearly indicates the need for its reform.

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