Development and analysis of intelligent computation based stock forecasting and trading systems

This dissertation proposes a computation based forecasting system that integrates the generalized regression neural network and two supporting technologies, namely information gain and principal component analysis, to manage stock portfolios. --Abstract, page iii

Using Neural Networks and Technical Analysis Indicators for Predicting Stock Trends

Recent studies reflect a growing interest in applying neural networks to answer stock behavior. Most of these studies rely heavily on fundamental analysis factors to determine future stock prices. In fact, there exists another approach, called technical analysis, which attempts to predict the stock trend by using data surrounding past prices and volumes. This paper investigates whether using these indicators as inputs to a neural network will provide more accurate predictions of future stock trends and whether they will yield higher trading profits than the traditional technical indicators. Feed-forward, probabilistic, and learning vector quantization neural networks are then examined to predict the short-term trend signals of several major stocks in different industries. The overall results indicate that the proportion of correct predictions and the profitability of trading exercises guided by these neural networks are consistently higher than those guided by the buy-and-hold strategy and the individual technical indicators

Neural Networks as a Decision Maker for Stock Trading: a Technical Analysis Approach

There has been a growing interest in applying neural networks and technical analysis indicators for predicting future stock behavior. However, previous studies have not practically evaluated the predictive power of technical indicators by employing neural networks as a decision maker to uncover the underlying nonlinear pattern of these indicators. The objective of this paper is to investigate if using these indicators as the input variables to a neural network will provide more accurate stock trend predictions, and whether they will yield higher trading profits than the traditional technical indicators. Three neural networks are examined in the study to predict the short-term trend signals of three stocks across different market industries. The overall results indicate that the proportion of correct predictions and the profitability of stock trading guided by these neural networks are higher than those guided by their benchmarks