Name
Title | ||
|---|---|---|
Stock price prediction based on chaotic hybrid particle swarm optimisation-RBF neural network. |
Abstract | ||
|---|---|---|
The stock market is an important part of the capital market, which plays a significant role in optimising capital allocation, financing and increasing the value of assets and other areas. Hence, the correct model for estimating and predicting the stock price has a very important practical significance to provide investors with investment decision reference. In this paper, a novel chaotic hybrid PSO-based RBF neural network model (CHPSO-RBFNN) has been proposed for forecasting the stock price, which can effectively prevent the RBF neural network from the local minimum trap and provide great learning ability. The presented methodology was tested with stock 601998, and the results showed that CHPSO-RBFNN can improve the prediction of accuracy and a high efficient and accurate stock prediction model compared to the traditional RBFNN and PSO-RBFNN methods. |
Year | Venue | Field |
|---|---|---|
2017 | IJADS | Econometrics,Particle swarm optimization,Economics,Stock price,Capital market,Stock prediction,Artificial intelligence,Chaotic,Finance,Artificial neural network,Stock market,Capital allocation line |
DocType | Volume | Issue |
Journal | 10 | 2 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sainan Wang | 1 | 5 | 2.47 |
| Luda Wang | 2 | 0 | 1.35 |
| Shou-Ping Gao | 3 | 0 | 0.34 |
| Zhi Bai | 4 | 2 | 1.43 |