Research On The Stocks Prediction Based On LM-BP Neural Networks

The stock market after several years of development, in a market economy occupy an increasingly important position. The establishment and development of the stock market, not only affected the national economy, but also for the country’s economic construction always doing contribution. However, the equity market is not very stable and smooth, with a total trading volume and price volatility of the unexpected. Stock market investors is the stock trading platform,virtually between investors and fund-raisers to build a bridge to enhance profits. In the stock market, fund-raisers public offering, issuing shares, long-term sources of funding has provided a guarantee; At the same time, investors by purchasing IPO shares, equivalent to the total development of the company, listed companies will directly affect It returns to investors. Due to the psychological state of investors and investment preferences are different, they will choose different portfolios, will assume different investment risk. However, such investment is not has not lose, stock market volatility is large, strong speculative stock market inefficiency, poor stability which will jeopardize the further development of the stock itself. Efficiency is reflected in the stock market listed companies to rational allocation of funds and the utilization of the funds raised to the maximum, so the ability to strive for more profits. However, a large number of empirical proof, the stock market is not very effective. However, stock prices have a certain regularity at all,which is reflected in this trend can be described by a nonlinear function, it is predictable. A variety of factors that affect the stock price, the stock is also the role of the complex, in order to more accurately predict the artificial neural network is introduced to the field of financial forecasting. In principle, for a continuous function, the neural network can be trained to achieve good accuracy in a certain range. Artificial neural networks can solve the problem of the black box, it sidesteps the underlying causes of data changes, more scientifically trained through a specific machine learning samples, establish a model to describe the connection between the input and output variables.Therefore, the study Stock prediction based on neural networks, not only of theoretical significance, but also has important practical significance and reference value.In this paper, the existing stock prediction method, BP neural network and its problems,LM-BP neural network algorithm, LM-BP neural network to predict stock price and other issues has been systematically studied. During the study, the results achieved are:(1) the characteristics of the stock market and stock forecasting methods to analyze the advantages and disadvantages of this approach.(2) to predict the amount of data, using standard BP neural network computing problems of slow for the stock price, derived gives LM-BP neural network algorithm, and design and development of LM-BP neural network calculation program.(3) Application of LM-BP neural network to predict the NASDAQ Stock Exchange-listed shares Zhaopin open, high, low, closing price trend. The prediction results show higher precision,the opening price of the average relative error is 0.88%, the highest price average relative error is1.25%, the lowest average relative error is 1.26%, the closing price of the average relative error of1.4%.(4) the forecast on the basis of the calculation is given Moving Average(MA), a deviation rate(BIAS), Relative Strength Index(RSI), stochastics(KDJ), sentiment indicator(BOV), William indicators(W & R), and draw a moving average(MA), a deviation rate(BIAS), relative strength index(RSI), stochastics(KDJ), sentiment indicator(BOV), William indicators(W & R) curve for investor decision-making for reference.Finally, the stock market prediction in the future.