Study on Stock Trading and Portfolio Optimization using Genetic Network Programming

Research on stock price prediction and trading model using evolutionary computation has been done in recent years. As we know, prediction in the stock market is quite difficult for a number of reasons. First, the ultimate goal of our research is not to min-imize the prediction error, but to maximize the profits. Second, the weak relationships among variables tend to be nonlinear, and they may hold only in limited areas of the search space. Finally, since the stock market data are given in an event-driven way, they are highly influenced by the indeterminate dealing. Generally speaking, there are two kinds of methods for predicting stock prices and determining the timing of buying or selling stocks: one is fundamental analysis which analyzes stock prices us-ing the financial statement of each company, the economic trend and movements of the exchange rate; the other is technical analysis which analyzes numerically the past movement of stock prices. The proposed method belongs to technical analysis since it determines the stock trading actions based on the technical indices such as Relative Strength Index, MACD, Golden/Dead Cross and so on