The Research on the Forecasting of Stock Price Based on the Improved Neural Network

In this paper, we reviews the traditional technical analysis and financial models, such as regression and time series model, which often lacks reference value due to that their poor forecasting accuracy and predictive lag. In the age of big data, ANN (artificial neural network) technology has developed rapidly. So, we introduces a variety of classical ANN models and analyzes the traditional use of ANN in financial forecasting. Then, from the perspective of experiential investment, we puts forward the improved neural network and we used it to do empirical analysis with some optimization methods like Dropout. In conclusion, we suggest that the traditional model should be preferred in the short term prediction in the range of acceptable error. On the other hand, the neural network model is recommended if the prediction results and the prediction are required. Introduction Nowadays, the general methods to predict financial price are time series model and some other regression models, such as ARIMA, GARCH and VAR. However, these models can only predict the one period value in the future of financial markets. And, the predictions of these models often shows lagging performance based on true values. In the second half of the twentieth century, so many methods of machine learning got development with a flourish like SVM(Support Vector Machine), random forest, ANN (Artificial Neural network) and so on. Thanks to the development of big data and machine learning, especially the ANN model, Internet and AI [1] got an unprecedented growth. The ANN model has many distinguishing features. The foremost advantage is, under the condition of big valid data, ANN model could approximate fit any nonlinearity model through the adjustment of network structure. Therefore, in this paper, we try to predict price value of financial markets based on ANN model. Furthermore, the analysis, compare of prediction results between ANN and other traditional financial models, is need to finish as well. Review With the explosion of machine learning modeling, the artificial neural network was born. Earliest, it could be traced back to M-P nerve cell model which was proposed by McCulloch and Pitts [2] in 1943. Until now, the most fundamental ANN model, which is researchers mentioned frequently, is BP neural network built by some brilliant scholars in 1986, such as D.E. Rumelhart, J.L. McCellandand [3] their group mates. In recent years, the explosion of computer’s hardware has contributed to a sharp increase in computing power. So, ANN models, high level demand for calculating, have been widely applied in various fields such as prediction, classification and recognition. In 2012, CiresAn D Et al. [4] built Multi-column deep neural network for traffic sign classification, and they got an amazing result that is more precise than 99.46% made by human self. In 2015, according to the features of twodimensional binary programs, Saxe J Et al. [5] detected malicious software on the web based on ANN methods. And the results suggested that the deep ANN not only accelerated model training process, but also improved detection accuracy. On the other hand, in financial markets, Sun Et al. [6] applied