Name
Abstract | ||
|---|---|---|
Neural networks as data mining tools are becoming increasingly popular in business. In this paper, a hybrid mining model based on neural network and kernel smoothing technique is developed. The kernel smoothing technique is used to preprocess data and help decision-making. Neural network is employed to predict the long trends of stock price. In addition, some trading rules involving trading decision-making are considered. The China Shanghai Composite Index is as case study. The return achieved by the hybrid mining model is four times as large as that achieved by the buy and hold strategy, so the proposed model is promising and certainly warrants further research. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11428862_110 | International Conference on Computational Science (3) |
Keywords | Field | DocType |
long trend,preprocess data,trading decision-making,china shanghai composite index,neural network,trading rule,case study,data mining tool,hybrid mining model,kernel smoothing,data mining,indexation | Decision rule,Data mining,Kernel smoother,Computer science,Buy and hold,Model-based reasoning,Smoothing,Information extraction,Case-based reasoning,Artificial neural network | Conference |
Volume | ISSN | ISBN |
3516 | 0302-9743 | 3-540-26044-7 |
Citations | PageRank | References |
3 | 0.49 | 8 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Defu Zhang | 1 | 657 | 52.80 |
| Qingshan Jiang | 2 | 588 | 77.27 |
| Xin Li | 3 | 4 | 1.85 |