Accounting and various aspects of finance
Zahra Yousefzadeh; Gholamreza Mansourfar; Farzad Ghayour
Abstract
Today, with rapid and sustained changes in business markets, a growing number of companies have turned diversification into new product segments or global markets by shifting their business to increase the importance of long-term financial viability and sustainability. Moreover, increasing the variety ...
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Today, with rapid and sustained changes in business markets, a growing number of companies have turned diversification into new product segments or global markets by shifting their business to increase the importance of long-term financial viability and sustainability. Moreover, increasing the variety of products, covering the uncertain demand of customers, managing inventories and timely action have been important issues in manufacturing companies. Accordingly, the main purpose of this study is to investigate the impact of diversification strategy on inventory performance, which is one of the topics of operations management by considering the classification of the diversity into related, unrelated and international. The statistical population studied includes all companies listed in the Tehran Stock Exchange during the years 2009-2018. Sampling has been done by screening method, and the number of companies in the final sample has reached 120 companies. The hypotheses have been tested by the estimated generalized least squares method. The results show that related and international diversification have positive and significant effects on inventory performance. The findings also indicate that unrelated diversification has an adverse effect on inventory performance, but this relationship is not statistically significant. Based on the acquired results, an increase in related and international variety of products, relying on higher safety stock, has led to an increase in sales. In addition, the insignificancy in the effect of unrelated diversification on inventory performance can be attributed to production costs and marketing programs of manufactured products.
Gholamreza Mansourfar; Farzad Ghayour; Shabnam Khaleghparast Athari
Abstract
The purpose of study is to investigate comparative ability of accountinginformation to predict indices volatility of companies listed in Tehran StockExchange using intelligent methods including Support Vector Machine,Artificial Neural Network and classic Logistic Regression model. Sample ofstudy includes ...
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The purpose of study is to investigate comparative ability of accountinginformation to predict indices volatility of companies listed in Tehran StockExchange using intelligent methods including Support Vector Machine,Artificial Neural Network and classic Logistic Regression model. Sample ofstudy includes 91 companies listed in Tehran Stock Exchange that have beenclassified in 9 industrious groups during time period of 2003-3013.Considering 11 corporate financial variables, study results show that despitepredicting ability of around 60% by Support Vector Machine and ArtificialNeural Network, there is significant difference between actual and predictedresults. Classic Logistic Regression model also can explain only 4%industries’ indices volatility using selected 11 corporate financial variables.Finally, although intelligent methods are superior to classic methods,accounting information solely are not well-explainer variables for predictingindustry index volatility and variety of variables such as financial, political,economical are effective in predicting industry index volatility.
Abstract
The purpose of the study is to investigate comparative ability of accounting information to predict indices volatility of companies listed in Tehran Stock Exchange using intelligent methods including Support Vector Machine, Artificial Neural Network and classic Logistic Regression model. Sample of study ...
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The purpose of the study is to investigate comparative ability of accounting information to predict indices volatility of companies listed in Tehran Stock Exchange using intelligent methods including Support Vector Machine, Artificial Neural Network and classic Logistic Regression model. Sample of study includes 91 companies listed in Tehran Stock Exchange which have been classified in 9 industry group during time period of 2003-3013. Considering 11 corporate financial variables, study results show that despite predicting ability of around 60% by Support Vector Machine and Artificial Neural Network, there is significant difference between actual and predicted results. Also, classic Logistic Regression model can explain only 4% industries’ indices volatility using selected 11 corporate financial variables. Finally, although intelligent methods are superior to classic methods, accounting information solely aren’t well-explainer variables for predicting industry index volatility and variety of variables such as financial, political, economical … are effective in predicting industry index volatility.
