Name
Title | ||
|---|---|---|
Evolving Neural Network with Dynamic Time Warping and Piecewise Linear Representation System for Stock Trading Decision Making |
Abstract | ||
|---|---|---|
Stock turning points detection is a very interesting subject arising in numerous financial and economic planning problems. In this paper, evolving neural network model with dynamic time warping piecewise linear representation system for stock turning points detection is presented. The piecewise linear representation method is able to generate numerous stocks turning points from the historic data base, then evolving neural network model will be applied to train the pattern and retrieve similar stock price patterns from historic data for training the system. These turning points represent short-term trading signals for selling or buying stocks from the market. it is applied to forecast the future turning points from the set of test data. Experimental results demonstrate that the hybrid system integrating DPLR and evolving neural networks can make a significant and constant amount of profit when compared with other approaches using stock data. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/CSIE.2009.36 | CSIE (5) |
Keywords | Field | DocType |
economic forecasting,database management systems,stock trading decision making,historic data,stock turning points,linear representation system,stock trading decision,decision making,learning (artificial intelligence),hybrid system,stock turning point detection,economic planning,points detection,piecewise linear representation system,stock data,evolving neural network training model,historic database,share prices,planning,stock markets,stock selling-buying,stock price movement,numerous stock,dynamic time warping,test data,investment,historic data base,piecewise linear techniques,evolving neural network model,dynamics time warping system,financial planning,evolving neural network,similar stock price pattern,plr method,neural nets,future turning point forecasting,genetic algorithms,learning artificial intelligence,data mining,piecewise linear,artificial neural networks,profitability | Data mining,Dynamic time warping,Economic forecasting,Computer science,Test data,Stock (geology),Artificial neural network,Hybrid system,Genetic algorithm,Financial plan | Conference |
Volume | ISBN | Citations |
5 | 978-0-7695-3507-4 | 1 |
PageRank | References | Authors |
0.37 | 4 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pei-Chann Chang | 1 | 1752 | 109.32 |
| Chin-Yuan Fan | 2 | 473 | 28.27 |
| Chen-Hao Liu | 3 | 453 | 22.49 |
| Yen-Wen Wang | 4 | 201 | 15.59 |
| Jyun-Jie Lin | 5 | 150 | 7.31 |