Abstract
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Abstract
Today，stock market is not only providing financing for good listed companies,
but also providing a way out for some investors with a sense of investment. So that
social resources are better configured and macro-economic are controlled. However,
because of the uncertainty of the stock market and every investor the cognitive vision
same-sex and technical analysis of the complexity of the factors, makes the general
investors investment returns short of expectations on the stock market, some even lose
everything. So the stock market has been heavily focused on government, business,
and investors. The forecast of stock price trends is a hot spot in stock research. As is
known to all, due to the volatility of the stock market has many characteristics, such
as strong nonlinear, high noise, so is extremely difficult to stock price trend forecast,
the traditional stock forecasting methods often with little success. So how to set up
new stock price trend forecast model to enhance the accuracy of prediction, thereby
helping to effectively avoid risks, financial investors investment profit maximization,
has important theoretical significance and application value.
This article first expounds the traditional stock prediction methods. The basic
analysis mainly from the macro micro economy, the related information such as the
company's financial statements and cash flow Angle, through the relative valuations
and discount valuation, estimates the intrinsic value of the shares. Disadvantages: the
information is not accurate, such as the delay, accuracy of the information. The
market analysis is mainly on the basis of statistical charts, such as K line graph, its
shape can be divided into the form and trend line, according to the specific form to
determine the future trend of the stock market. Disadvantages: there is a wide variety
of methods of analysis, and there is a huge difference between the methods. Statistical
analysis method is mainly the least squares was used to construct various regression,
such as mixed regression model, autoregressive model of stock price trend forecast,
this kind of model prediction accuracy compared with the previous two kinds of
forecasting methods. Disadvantages: the regression model is usually too many
assumptions, and problem for strong nonlinear processing ability, and the stock price
trend forecast problem affected by many factors and strong nonlinear. Prediction
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