The extreme volatility of stock market values has been the subject of a large body of literature. Previous research focused on the short run because of a widespread belief that, in the long run, the market reverts to well understood fundamentals. Our work suggests this belief should be questioned as well. First, we show actual dividends cannot account for the secular trends of stock market valu...

Most of existing fuzzy forecasting models partition historical training time series into fuzzy time series and build fuzzy-trend logical relationship groups to generate forecasting rules. The determination process of intervals is complex and uncertainty. In this paper, we present a novel fuzzy forecasting model based on high-order fuzzy-fluctuation trends and the fuzzy-fluctuation logical relat...

Stock data analysis for price forecasting and trend prediction has been a challenging problem that attracts researchers from different fields. Some use statistical methods, while others use neural network based approaches. This paper reports on a preliminary study on stock market data analysis using a hyperspace data mining approach that is built upon a projective geometrical method. Discussion...

We examine the use and pro tability of technical trading rules in nancial markets by studying the Santa Fe Stock Market, an agent-based model of a stock market, in which traders make investment decisions by forecasting stock prices using technical and fundamental rules. We show that individual traders earn more by using technical rules, no matter what other traders do, so the use of technical t...

In this paper, we evaluate the performance of a number of forecasting models of U.S. business fixed investment spending growth over the recent 1995:1-2004:2 out-of-sample period at multiple forecast horizons. The forecasting models are based on the conventional Accelerator, Neoclassical, Average Q, and Cash-Flow models of investment spending, as well as empirical models developed more recently ...

To forecast a complex and non-linear system, such as a stock market, advanced artificial intelligence algorithms, like neural networks (NNs) and genetic algorithms (GAs) have been proposed as new approaches. However, for the average stock investor, two major disadvantages are argued against these advanced algorithms: (1) the rules generated by NNs and GAs are difficult to apply in investment de...

This thesis proposes an Artificial Neural Network (ANN) enhanced decision support system for financial risk management. The decision support system allows hedgers to maximise their expected return while practising the hedge against financial risks. The importance of the research stems from the fact that it can be used to reduce the risk associated with adverse price movements in the stock marke...