Does the degree of internationalization influence the prediction of stock profitability? Evidence of the Brazilian stock market through artificial neural networks
DOI:
https://doi.org/10.18316/desenv.v8i1.4937Keywords:
Performance, corporate governance, artificial neural networksAbstract
Predicting the direction of change in stock prices is an important contribution to the development of effective strategies in financial market operations. Especially in Brazil, whose economy has been expanding since the mid-1990s after the macroeconomic stabilization achieved by the Real Plan and policies to improve the country's insertion in world markets. Thus, the present study aimed to develop models of artificial neural networks to predict the profitability of the shares of Brazilian companies with a greater and lesser degree of internationalization. The models developed to predict the profitability of the shares of the most and least internationalized companies on the BM&FBovespa in the period from 2007 to 2012 showed mean squared error of 0.2422 and 0.0988, respectively. The occurrence of the largest squared error in the neural network model for predicting the profitability of the shares of companies with a high degree of internationalization may be associated with the dependence of new business networks with exposure to different risks and also with differences between foreign countries, which increases the risks of international business. And, the smallest squared error in the neural network model for predicting the profitability of the shares of companies with a low degree of internationalization may be related to the gradual commitment of resources and the organizational structure, which would provide a lower exposure to risk, higher share profitability stable and a more effective forecasting model.References
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