Using artificial neural networks for forecasting per share earnings

Mohammad Sarchami
<span title="2012-03-21">2012</span> <i title="Academic Journals"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/7hjqacldwndljifv62rj5wul44" style="color: black;">African Journal of Business Management</a> </i> &nbsp;
Forecasting per share earnings in investments is very important because it is a significant factor in methods of stock evaluation; and in most of these cases, it is a fundamental factor in investing in the stock market. In order to forecast per share earnings using an "artificial neural network with an error backward propagation algorithm" and an "artificial neural network with a genetic algorithm", 61 firms in 7 financial years, from the beginning of 1381 until the end of 1387, with 9
more &raquo; ... (8 input variables and 1 output variable) were chosen; from which 3843 (61*7*9) data point were extracted. The hypotheses are based on the idea that: 1) an artificial neural network with an error backward propagation algorithm is able to forecast the earnings of per share; 2) a neural network with a genetic algorithm is able to forecast the earnings per share; and 3) the neural network with the error backward propagation algorithm has less error in forecasting the earnings per share than the neural network with the genetic algorithm. To test the hypotheses, MATLAB software, the statistical methods of mean square error and mean absolute error were used. The results confirm all hypotheses.
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