Name
Abstract | ||
|---|---|---|
Technical analysis of stocks mainly focuses on the study of irregularities, which is a non-trivial task. Because one time scale alone cannot be applied to all analytical processes, the identification of typical patterns on a stock requires considerable knowledge and experience of the stock market. It is also important for predicting stock market trends and turns. The last two decades has seen attempts to solve such non-linear financial forecasting problems using AI technologies such as neural networks, fuzzy logic, genetic algorithms and expert systems but these, although promising, lack explanatory power or are dependent on domain experts. This paper presents an algorithm, PXtract to automate the recognition process of possible irregularities underlying the time series of stock data. It makes dynamic use of different time windows, and exploits the potential of wavelet multi-resolution analysis and radial basis function neural networks for the matching and identification of these irregularities. The study provides rooms for case establishment and interpretation, which are both important in investment decision making. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1016/j.asoc.2006.01.007 | Appl. Soft Comput. |
Keywords | Field | DocType |
time series,neural network,neural networks,radial basis function neural,chart pattern,wavelet analysis,radial basis function network,stock market trend,wavelet multi-resolution analysis,technical analysis,chart pattern extraction,different time windows,stock forecasting,automatic extraction,stock data,financial forecast,cbr,forecasting,time scale,stock market,expert system,fuzzy logic,genetic algorithm | Data mining,Radial basis function network,Expert system,Fuzzy logic,Artificial intelligence,Chart,Artificial neural network,Stock market,Genetic algorithm,Machine learning,Mathematics,Technical analysis | Journal |
Volume | Issue | ISSN |
7 | 4 | Applied Soft Computing Journal |
Citations | PageRank | References |
12 | 0.70 | 10 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| James N. K. Liu | 1 | 529 | 44.35 |
| Raymond W. M. Kwong | 2 | 15 | 1.56 |