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The volatility mechanism and intelligent fusion forecast of new energy stock prices

Author

Listed:
  • Guo-Feng Fan

    (Ping Ding Shan University)

  • Ruo-Tong Zhang

    (Ping Ding Shan University)

  • Cen-Cen Cao

    (Ping Ding Shan University)

  • Li-Ling Peng

    (Ping Ding Shan University)

  • Yi-Hsuan Yeh

    (Asia Eastern University of Science and Technology)

  • Wei-Chiang Hong

    (Asia Eastern University of Science and Technology
    Yuan Ze University)

Abstract

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.

Suggested Citation

  • Guo-Feng Fan & Ruo-Tong Zhang & Cen-Cen Cao & Li-Ling Peng & Yi-Hsuan Yeh & Wei-Chiang Hong, 2024. "The volatility mechanism and intelligent fusion forecast of new energy stock prices," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-37, December.
    • Handle: RePEc:spr:fininn:v:10:y:2024:i:1:d:10.1186_s40854-024-00621-7
    DOI: 10.1186/s40854-024-00621-7
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    References listed on IDEAS

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    2. Jiang, Wei & Tang, Wanqing & Li, Jianfeng & Wei, Xiaokun, 2025. "Climate risk and renewable energy market volatility: Machine learning approach," Research in International Business and Finance, Elsevier, vol. 76(C).

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