The Directionality Function Defect Of Performance Evaluation Method In Regression Neural Network For Stock Price Prediction

The most important financial attribute of stock time series is the directionality of its price movement. This study found that the prediction error of neural network can only reflect the closeness between model predicted price and actual market price, but not the important financial attribute, the direction of stock price's ups and downs. Taking Chinese stock 600275 and American stock AMZN as examples, the former's prediction error is 0.0353, and its stock return rate is negative 59.49%, while the latter's prediction error is 0.0201, and its stock return rate is positive 26.49%, while the difference between the predictions of the two stocks is only 0.0152. It shows that under the same scale, absolute value constraint is the source of the problem that the prediction error has no stock up and down attributes. Therefore, in the absence of condition variable of trend direction state, it is unreliable to make the conclusion by judging the performance of regression neural network for stock price prediction based only on the value size of prediction error.