scholarly journals Research on Intelligent Prediction Method of Financial Crisis of Listed Enterprises Based on Random Forest Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Mingxia Jiang ◽  
Xuexia Wang
Keyword(s):  
Decision Making ◽  
Random Forest ◽  
Financial Crisis ◽  
Financial Data ◽  
Actual State ◽  
Random Forest Algorithm ◽  
Publicly Traded ◽  
Publicly Traded Companies ◽  
Decision Making Unit ◽  
Prediction Approach

Traditional financial crisis prediction approaches have a tough time extracting the properties of financial data, resulting in financial crisis prediction with insufficient accuracy. As a result, based on the random forest algorithm, an intelligent financial crisis prediction approach for listed enterprises is proposed. The random forest method is used to mine the characteristics of financial data based on financial index data from publicly traded companies. This research develops a financial crisis prediction index system based on the findings of data feature mining. The CCR model is used to assess the efficiency of listed firms’ decision-making units with more input and output, and the efficiency index of each decision-making unit is calculated. The efficiency evaluation index of publicly traded companies is used to divide the severity of the financial crisis. The experimental results reveal that, when compared to standard prediction methods, this method’s forecast accuracy is commensurate with the actual state of businesses, and it can reduce the time it takes to predict financial crises.

scholarly journals The impact of the financial crisis on earnings management: Empirical evidence from Italian and Spanish listed companies

2019 ◽  
Vol 16 (2) ◽  
pp. 121-130 ◽  
Author(s):  
Francesco De Luca ◽  
Francesco Paolone
Keyword(s):  
Financial Crisis ◽  
Financial Markets ◽  
Performance Indicators ◽  
Listed Companies ◽  
Publicly Traded ◽  
Publicly Traded Companies ◽  
Time Period ◽  
Security Offerings ◽  
The Impact ◽  
Giving Bad News

Our study adopts a reliable and widely acknowledged model to detect accounts manipulation in order to assess the impact of the financial crisis on Italian and Spanish listed companies’ propensity to manage their earnings. The analysis is conducted on 565 publicly traded companies on the Italian and Spanish financial markets during the time period 2005-2013. We find a lower propensity to manipulate earnings in both countries during the pre-crisis period (2005-2008) as suggested by a decrease in the number of high-risk manipulators until 2008 included. With the spread of the financial crisis, companies become more manipulators. We believe that the reason for this is to avoid giving bad news to markets, investors, and lenders after that the crisis may have impacted too negatively on firms’ performance indicators and financial equilibrium. Our empirical results provide various implications for further studies related to managements’ incentives concurrently with security offerings.

scholarly journals A Oferta de Trade Credit Pelas Empresas Brasileiras de Capital Aberto

2011 ◽  
Vol 9 (4) ◽  
pp. 585 ◽  
Author(s):  
Rafael Felipe Schiozer ◽  
João Alberto Peres Brando
Keyword(s):  
Financial Crisis ◽  
Price Discrimination ◽  
Trade Credit ◽  
International Literature ◽  
Publicly Traded ◽  
Publicly Traded Companies ◽  
Credit Supply ◽  
International Financial Crisis ◽  
Main Determinants

This paper investigates the determinants of trade credit supply by Brazilian publicly traded companies between the years of 2005 and 2008. International literature (both theoretical and empirical) documents that the main determinants of trade credit supply are the size of the firm and the size of its debt. Both indicate that the availability of resources to the firm is an important factor for the supply of trade credit. In addition, the literature confirms strategic uses of trade credit such as those for price discrimination purposes. The results obtained using a sample of 157 Brazilian companies do not support that size and indebtedness are relevant determinants for trade credit supply, but they confirm the supply of trade credit as a strategic tool for the firms. Additionally we observed a significant decrease in trade credit supply in 2008, the year in which a severe international financial crisis took place.

scholarly journals Prediction of hotel bookings cancellation using hyperparameter optimization on Random Forest algorithm

2020 ◽  
Vol 9 (1) ◽  
pp. 15-21
Author(s):  
Yufis Azhar ◽  
Galang Aji Mahesa ◽  
Moch. Chamdani Mustaqim
Keyword(s):  
Decision Making ◽  
Random Forest ◽  
Business Performance ◽  
Model Performance ◽  
Machine Learning Algorithms ◽  
Model Parameters ◽  
Random Forest Algorithm ◽  
Managerial Decision ◽  
Hyperparameter Optimization ◽  
Managerial Decision Making

Cancellation of hotel bookings by customers greatly influences hotel managerial decision making. To minimize losses by this problem, the hotel management made a fairly rigid policy that could damage the reputation and business performance. Therefore, this study focuses on solving these problems using machine learning algorithms. To get the best model performance, hyperparameter optimization is applied to the random forest algorithm. It aims to obtain the best combination of model parameters in predicting hotel booking cancellations. The proposed model is proven to have the best performance with the highest accuracy results of 87 %. This study's results can be used as a model component in hotel managerial decision-making systems related to future bookings' cancellation.

scholarly journals Predicting financial distress in publicly-traded companies

2017 ◽  
Vol 28 (75) ◽  
pp. 390-406 ◽  
Author(s):  
Felipe Fontaine Rezende ◽  
Roberto Marcos da Silva Montezano ◽  
Fernando Nascimento de Oliveira ◽  
Valdir de Jesus Lameira
Keyword(s):  
Decision Making ◽  
Financial Distress ◽  
Publicly Traded ◽  
Final Model ◽  
Publicly Traded Companies ◽  
Explanatory Variables ◽  
Financial Variables ◽  
Asset Turnover ◽  
Short Time ◽  
The Relationship

ABSTRACT Several models for forecasting bankruptcy have been developed over the years, one of the reasons for which is the important part it plays in decision-making. However, forecasting a company’s bankruptcy leaves a very short time for stakeholders to change the situation. It is in this context that this paper arises in order to develop a model for predicting financial distress, which is identified as a step prior to bankruptcy. The predictive model uses the logistic regression technique with panel data and a sample of Brazilian publicly-traded companies with shares listed on the São Paulo Stock, Commodities, and Futures Exchange between 2001 and 2014. As well as financial variables, the final model includes market expectations (macroeconomic) and sector variables. These variables are statistically tested and the hypothesis is confirmed that they improve the accuracy of the model. The research identified the existence of financial distress in 96% of the companies that went bankrupt. In addition, the relationship between the phenomena of bankruptcy and financial distress is verified, using financial and macroeconomic explanatory variables. The results demonstrate that most (83%) of the explanatory variables in the model for predicting bankruptcy are also present in the model for predicting the phenomenon of financial distress. The expected gross domestic product variables and the quick ratio, asset turnover, and net equity over total liabilities financial variables are statistically significant in predicting both phenomena. With this evidence, the study suggests the use of the concept of financial distress as a stage prior to bankruptcy and provides a model for predicting financial distress with 89% accuracy when applied to publicly-traded companies in Brazil in the period examined.

Studi Perilaku Anggota Unit Pengambil Keputusan Dalam Pembelian Organisasional: Sebuah Review Literatur

2017 ◽  
Vol 24 (1) ◽  
pp. 71-86
Author(s):  
Amin Wibowo
Keyword(s):  
Decision Making ◽  
Information Search ◽  
Search Time ◽  
Integrated Approach ◽  
Review Literature ◽  
Buying Behavior ◽  
Organizational Buying ◽  
Decision Making Unit ◽  
Behavior Based ◽  
Sharing Responsibility

Up to now, organizational buying is still interesting topic discussed. There are divergences among the findings in organizational buying researches. Different perspectives, fenomena observed, research domains and methods caused the divergences. This paper will discusse organizational buying behavior based on literature review, focused on behavior of decision making unit mainly on equipment buying. From this review literatures, it would be theoritical foundation that is valid and reliable to develop propositions in organizational buying behavior. Based on review literature refferences, variables are classified into: purchase situation, member of decision making unit perception, conflict among the members, information search, influences among members of decision making unit. Integrated approach is used to develop propositions relating to: purchasing complexity, sharing responsibility among the members, conflict in decision making unit, information search, time pressure as moderating variable between sharing responsibility and conflict in decision making unit, the influence among the members inside decision making unit and decision making outcome

Boards of directors in Russian publicly traded companies: the gender aspect

2018 ◽  
pp. 142-155 ◽  
Author(s):  
T. A. Garanina ◽  
A. A. Muravyev
Keyword(s):  
Boards Of Directors ◽  
Company Performance ◽  
Corporate Boards ◽  
Publicly Traded ◽  
Publicly Traded Companies ◽  
Representation Of Women ◽  
Female Directors ◽  
Female Director ◽  
Gender Aspect ◽  
Longitudinal Dataset

This article studies the gender composition of corporate boards of Russian companies, including its relation to company performance. The analysis is based on a unique longitudinal dataset of virtually all Russian companies whose shares were traded on the stock market in 1998-2014. It shows a relatively small representation of women, just 12% of all the seats, while about 40% of the companies did not have any female director. At the same time, both the share of companies that appoint female directors and the share of female directors on boards show a clear upward trend. The econometric analysis suggests a positive link between the presence of female directors on boards and company performance, especially when firms appoint several, rather than one, female directors.

Relative Efficiency of Decision Making Units Producing Both Desirable and Undesirable Outputs: A Case of Textile Processing Units in Pakistan.

2011 ◽  
Vol 50 (4II) ◽  
pp. 685-698
Author(s):  
Samina Khalil
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Relative Efficiency ◽  
Value Added ◽  
Data Envelopment ◽  
Undesirable Outputs ◽  
Textile Processing ◽  
Decision Making Unit ◽  
Desirable And Undesirable Outputs ◽  
Decision Making Units

This paper aims at measuring the relative efficiency of the most polluting industry in terms of water pollution in Pakistan. The textile processing is country‘s leading sub sector in textile manufacturing with regard to value added production, export, employment, and foreign exchange earnings. The data envelopment analysis technique is employed to estimate the relative efficiency of decision making units that uses several inputs to produce desirable and undesirable outputs. The efficiency scores of all manufacturing units exhibit the environmental consciousness of few producers is which may be due to state regulations to control pollution but overall the situation is far from satisfactory. Effective measures and instruments are still needed to check the rising pollution levels in water resources discharged by textile processing industry of the country. JEL classification: L67, Q53 Keywords: Data Envelopment Analysis (DEA), Decision Making Unit (DMU), Relative Efficiency, Undesirable Output

METHOD SUGGESTING CITY WALKING ROUTES FOR PEDESTRIANS USING AN EXAMPLE OF SAINT-PETERSBURG

2019 ◽  
pp. 71-76
Author(s):  
A.E. Semenov
Keyword(s):  
Random Forest ◽  
Develop Model ◽  
Random Forest Algorithm ◽  
Saint Petersburg ◽  
Pedestrian Navigation ◽  
Factors Influencing ◽  
The City

The method of pedestrian navigation in the cities illustrated by the example of Saint-Petersburg was investigated. The factors influencing people when they choose a route for their walk were determined. Based on acquired factors corresponding data was collected and used to develop model determining attractiveness of a street in the city using Random Forest algorithm. The results obtained shows that routes provided by the method are 14% more attractive and just 6% longer compared with the shortest ones.

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