An intelligent financial ratio selection mechanism for earning forecast

Conventionally, linear and univariate time series models are broadly used in earning forecast. However, their forecasting accuracies are seriously limited without considering sufficient important factors. On the other hand, using more variables does not guarantee to obtain better forecasting accuracy and may cause inefficiency. The Multi-Objective Genetic Algorithms (MOGA) have been shown to be able to select the variable set with population diversity and to perform efficient search in large space. In addition, the multiple regression model can efficiently evaluate the predicting accuracy by using the least sum of squared errors. We therefore combine the advantages of both multiple regression and MOGA to form a new efficient forecasting mechanism which maximizes the forecasting accuracy with minimal number of financial ratios. Furthermore, this mechanism includes the Sliding Window Multiple Regression mechanism which retrains our predictor periodically in order to get more accurate earning forecast.