The Study On Intelligent Forecasting And Decision Of Stocks And Its Application

This dissertation mainly studies intelligent forecasting and decision making of stock market. Some new results are presented, and to be used to solve some problems in stock forecasting and decision making. These new methods are very useful to the field of financial applications as follows: Fuzzy statistics is used to get the membership functions of Fuzzy reasoning system and regulate fuzzy rules on line for better forecasting results. How to select suited stock to invest is very important. I put forward a new method with fuzzy logic to solve this problem, it can select the best stock for traders. A hybrid intelligent cognition system is provided. It is a combination of neural network and fuzzy logic, and is applied in the analysis and prediction of the trend of the stock market with satisfactory results. Using chaotic theory to calculate the embed dimension and relational dimension of a chaotic series or a normal economical series, we select the same number of variable to model and forecast this system. Examples show that, with this number of variable to model and forecast the modeled nonlinear-system, the forecasting error is minimal. Propose a new method for stock market forecasting. The chaotic theory is used to discover chaos in stock price time series. By calculating its Lyapunov exponent we found the max steps to predict the price of stock market. This method could also be used to other nonlinear system forecasting. According to Markowitz model, We introduce how to estimate the profit expectation and risk of portfolio by time series. And list two ways to solve portfolio selection model-Lagrange multiplication and critical line. A new simple method is given to solve the oversell problem of portfolio selection. After introducing the author抯 stock trading software , a new application architecture based on internet and data warehouse is presented. This software will benefit to traders and software developer.