Stock Price Forecasting Based On Neural Network Optimized By Time Value

Stock is a kind of high-risk and high-return investment, and has become an indispensable partof modern life，so investors always care for stock market, analyze storks, and research on pricetrend. There are many uncertain factors, and therefore price volatility shows strong uncertainty, sotraditional prediction technology is unsatisfied. Establishing a logical model for stock priceforecasting has theoretical significance and applicable value.This paper analyzes the theory of stock, contrasts common stock forecasting methods, anddiscusses the feasibility of using BP neural network on stock price forecasting. In theory, neuralnetwork studies the historical data in order to forecast exchange price in future. Specifically, BPnetwork constantly revises its weights and valve values, through learning historical datum, in orderto establishing a relatively reasonable model finally. The paper is apparently different from ideas oftraditional investment, studying on ultra-short-term speculative stock trading; results are from theanalysis of many predictions, not just one in particular.The paper proposes a new way that is dynamic-weight error function based on time-value, anddesigns a neural network model based on time value by using dynamic weight. In the paper, BPmodel has changed the way of fitting training data by introducing the dynamic-weight method, andis designed more flexibility to meet the practical forecasts. This paper does simulation experimentusing MATLAB on stocks of the pharmaceutical industry. Empirical results show that, comparedwith traditional methods and BP neural network, the model proposed by this paper has higheraccuracy and lower error, and further improves the network’s generalization ability and predictionaccuracy of the model, and achieves better optimization of stock prediction. To verify theeffectiveness of the model on economic and social benefits, the paper designs the simulation thatcan be achieved in reality transactions (T+0model), and verifies its realistic prospect.