影响因子：6.4
DOI码：10.1086/s40854-024-00621-7
所属单位：哈尔滨工程大学船舶工程学院
发表刊物：Financial Innovation
刊物所在地：中国
关键字：Empirical wavelet transform; Support vector machine; Ant Lion algorithm; Stock price index forecasting
摘要：The new energy industry is strongly supported by the state, and accurate forecasting of stock price can lead to better understanding of its development. However, factors such as cost and ease of use of new energy, as well as economic situation and policy environment, have led to continuous changes in its stock price and increased stock price volatility. By calculating the Lyapunov index and observing the Poincaré surface of the section, we find that the sample of the China Securities Index Green Power 50 Index has chaotic characteristics, and the data indicate strong volatility and uncertainty. This study proposes a new method of stock price index prediction, namely, EWT-S-ALOSVR. Empirical wavelet decomposition extracts features from multiple factors affecting stock prices to form multiple sub-columns with features, significantly reducing the complexity of the stock price series. Support vector regression is well suited for dealing with nonlinear stock price series, and the support vector machine model parameters are selected using random wandering and picking elites via Ant Lion Optimization, making stock price prediction more accurate.
合写作者：Ruo‑Tong Zhang,Cen‑Cen Cao,彭丽玲,Yi‑Hsuan Yeh
第一作者：范国峰
论文类型：期刊论文
通讯作者：洪维强
论文编号：84
学科门类：管理学
文献类型：J
卷号：10
ISSN号：2199-4730
是否译文：否
发表时间：2024-01-01
收录刊物：SSCI
发布期刊链接：https://link.springer.com/article/10.1186/s40854-024-00621-7