We introduce a literature review considering articles on artificial neural networks in business published in last two decades.412 suitable articles are identified and classified according to defined methodology.We focus on date, area of interest, type of neural network, benchmark method, journal and citations.The most applied are multilayer feedforward networks with backpropagation learning performed by gradient descent algorithm.Majority (25.73%) of the examined articles were published in Expert Systems with Applications. In recent two decades, artificial neural networks have been extensively used in many business applications. Despite the growing number of research papers, only few studies have been presented focusing on the overview of published findings in this important and popular area. Moreover, the majority of these reviews were introduced more than 15 years ago. The aim of this work is to expand the range of earlier surveys and provide a systematic overview of neural network applications in business between 1994 and 2015. We have covered a total of 412 articles and classified them according to the year of publication, application area, type of neural network, learning algorithm, benchmark method, citations and journal. Our investigation revealed that most of the research has aimed at financial distress and bankruptcy problems, stock price forecasting, and decision support, with special attention to classification tasks. Besides conventional multilayer feedforward network with gradient descent backpropagation, various hybrid networks have been developed in order to improve the performance of standard models. Even though neural networks have been established as well-known method in business, there is enormous space for additional research in order to improve their functioning and increase our understanding of this influential area.

Artificial neural networks in business

This study examines the influence of working capital management (WCM) factors on the profitability of steel companies listed on the Stock Exchange of Vietnam. Data was collected from audited financial statements of companies for a period of 10 years, from 2010 to 2019. The number of samples eligible for research is 20 out of 26 companies, which is equivalent to 76.9%. With the help of dedicated software Stata version 14, the impact determination of WCM (through 8 independent variables: DIO, DPO, DSO, CCC, SIZ, CR, LEV, GRO) to the firm’s profitability (through the dependent variable) is performed through multivariate regression models. Research results from companies in the steel industry in Vietnam during this period indicate that WCM has a strong impact on the profitability of businesses. Among 8 factors affecting the profitability of steel enterprises, factors DPO, DIO, DSO, CR, SIZ, GRO have a positive impact, boosting profitability; 2 factors CCC and LEV have a negative impact on profitability; in which, the effect of CCC is negligible. This conclusion is almost in contrast to many previously published studies due to the specifics of the industry as well as the different stages of economic development associated with the economic management policies of the State.

https://www.koreascience.or.kr:443/article/JAKO202029062616666.pdf

Effect of Working Capital Management on the Profitability of Steel Companies on Vietnam Stock Exchanges

Management of working capital play very essential role in the firm’s profitability. Therefore, this study aims to inquire the impact of working capital management on firm’s profitability.  For measuring working capital management inventory conversion period, debtor’s conversion period, creditor’s conversion period, cash conversion cycle, and cash conversion efficiencyare taken as independent variables. Firm’s profitability is measured by using the four dependent variables which are net profit margin, return on assets, return on equity, and return on capital employed. Regression analysis is used to discover the impact of working capital management on firm’s profitability by using secondary data. This data is gathered from annual reports of 45 companies listed at KSE for five years from 2008-2012. Manufacturing industry is selected because major portion of total assets contain current assets so working capital management efficiency is necessary in this industry. From results of this study, working capital management has significant impact on firm’s net profit margin, return on assets, and returns on capital employed and insignificant impact is find out on return on equity of firms. Therefore, it is concluded that working capital management has significant impact on firm’s profitability in food sector. It is concluded that if firms in food sector efficiently manage working capital then, firm’s profitability is enhanced. This study also find descriptive statistics which tells how well performance of firms in food sector is and how efficiently these firms are managing working capital. Keywords: Working capital, food sector, financial performance, cash conversion cycle, ICP, CCE, DCP

/pdf/impact-of-working-capital-management-on-firm-s-profitability-34o2hy7u87.pdf

Impact of Working Capital Management on Firm’S Profitability

This study aimed to develop models of artificial neural networks to predict the financial performance of companies in and outside the governance levels of the B3 (before, BM&FBovespa). The models were developed using data from companies in the Brazilian stock market from 2005 to 2017. For these models, it was stipulated as a hypothesis that the models, which corresponded to the companies in the governance segments, would be most successful in predicting the performance of companies than those, which represent the ones that were not in the levels of governance. The results confirmed the hypothesis. In addition, they indicated that the performance of not belonging to corporate governance levels companies is more sensitive to oscillations in the external environment, making it difficult to forecast this indicator. This work was the first in Brazil to construct models through techniques of artificial intelligence – more specifically neural backpropagation network – to predict the performance of the company by relating it to aspects of corporate governance.

/pdf/nivel-de-governanca-corporativa-prediz-o-desempenho-5q64k13lw7.pdf

Nível de Governança Corporativa prediz o desempenho financeiro da empresa? Evidências do mercado brasileiro por meio de redes neurais artificiais

Lean implementation is a strategic decision. The capacity of organisation to be “Lean” can be identified before lean implementation by assessing leanness of an organisation. This study aims to attempt developing a holistic leanness assessment tool for assessing organisational leanness.,A neuro-fuzzy leanness assessment model for assessing the leanness of a manufacturing system is presented. The model is validated academically and industrially by conducting a case study.,Neuro-fuzzy hybridisation helped assess the leanness accurately. Fuzzy logic helped to perform the leanness assessment more realistically by accounting ambiguity and vagueness in organisational functioning and decision-making processes. Neural network increased the learning capacity of assessment model and increased the accuracy of leanness index.,The industrial case study in the paper shows the results in telecom equipment manufacturing industry. This may not represent entire manufacturing sector. The generic nature of the model developed in this research ensures its wide applicability.,The neuro-fuzzy hybrid model for assessing leanness helps to identify the potential of an organisation to become “Lean”. The organisational leanness index developed by the study helps to monitor the effectiveness and impact of lean implementation programmes.,The leanness assessment models available in literature lack depth and coverage of leanness parameters. The model developed in this research assesses leanness of an organisation by accounting for leanness aspects of inventory management, industrial scheduling, organisational flexibility, ergonomics, product, process, management, workforce, supplier relationship and customer relationship with the help of neuro-fuzzy hybrid modelling.

A neuro-fuzzy hybrid model for assessing leanness of manufacturing systems

http://www.isihome.ir/freearticle/ISIHome.ir-24073.pdf

A Decision Engineering Method to Identify the Competitive Effects of Working Capital: A Neural Network Model*

A Decision Engineering Method to Identify Effect on Product Market Competitive Performance

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A Decision Engineering Method to Identify the Competitive Effects of Working Capital: A Neural Network Model*

A Decision Engineering Method to Identify Effect on Product Market Competitive Performance