Investigating the Effects of Managerial Ability on Overconfidence and Attitude of Managers in Choosing Corporation’s Strategy

Document Type : Research Paper

Authors

Abstract

Introduction
Importance of macroeconomic variables is clear to everyone and announcements of them are seen and carefully scrutinized by different groups of users; however, initial estimates and economic forecasting of macro variable is raised as a serious challenge in economic planning. In this context, little or no evidence has been provided for exploring the relationship between accounting and economics (Macro Accounting) in developing countries like Iran. The idea of macro accounting was based on the idea that accounting variables such as aggregate accounting earnings convey information about Macroeconomics. This paper presents the use of fundamental accounting variables as the best leading indicators of macroeconomics variables.
 
Research Questions
The main questions of this paper are as follows:

Can the combination of Elman neural network and particle swarm optimization improve models prediction in comparison to others?
How can fundamental accounting variables improve the predictive power of the model?

 
Methods
In this study we rely on predictive power of various models including Elman neural networks and particle swarm optimizationn. An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems. There are many types of artificial neural networks such as Elman Networks. Elman Networks are a form of recurrent neural networks which have connections from their hidden layer back to a special copy layer. Particle swarm optimization (PSO) is a population based stochastic optimization technique developed by  Eberhart and  Kennedy  in 1995, inspired by social behavior of bird flocking or fish schooling. PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). We construct portfolio based on 88 largest firms in Tehran Stock Exchange. The sample period covers 20 semi annuals from 1385 to 1395. For this purpose, fundamental accounting variables (including net income, gross income, inventory, accounts receivable, administrative, general and sales expense, capital expenditures, debt and tax costs) have been chosen and their explanatory power in predicting macro.
 
Results
Taking into consideration more alternative measures for accounting can decrease model's errors. As mentioned in previous subsection, using fundamental accounting variables would enable producing more reliable and accurate results. Our findings suggest that fluctuations in accounting information including net income, gross income, inventory, account receivables, general and sales expense and capital expenditure are a leading indicator of macroeconomic variables. Results show that fundamental accounting variables have predictive power in predicting GDP growth and unemployment rate for the next one and two quarters respectively. Also the empirical results from combination of artificial intelligence models show that optimization of Elman artificial network with particle swarm optimization improves effectiveness of model in comparison to Elman artificial network.
 
Discussion and Conclusion
This study distinguishes itself from previous papers with the introduction of key variables that have not been studied previously in macro accounting subject such as fundamental accounting variables. Prior studies mostly address accounting earnings in general neglecting predictive power of fundamental accounting variables. The main consequences of this study are effective links between accounting and economic information that must be included in the economic and financial decisions. So we recommend studies in application of accounting numbers in modeling macro. Overall the consequences of this paper introduce a new idea that the informativeness of accounting variables is not only in the micro level, but also in macro economy level.
 
 
 

Keywords

References

 
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  • Receive Date: 11 April 2018
  • Accept Date: 11 April 2018

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