基于深度学习技术的股价预测及量化交易策略探讨

近几年来,卷积神经网络(CNN)、长短期记忆网络(LSTM)、门控循环单元(GRU)和注意力机制模型等深度学习技术,在金融科技领域尤其是股价预测和量化交易策略的制定上,已经成为一个非常活跃的研究领域。论文通过分析上证50指数及其成分股的数据,验证了BiGRU-CNN-Attention模型在预测准确性上的优势;考虑到不同投资者的风险承受能力和收益预期,论文设计了保守型、稳健型和极端激进型三种不同风险偏好的投资策略,揭示了在风险和回报之间平衡的效果。结果表明,结合深度学习模型预测和适当的投资策略,不仅可以有效提升投资组合的性能,还可以为投资者提供了定制化的投资方案,进一步凸显深度学习技术在金融市场决策中的应用潜力。

数字经济对产业结构升级的影响研究

本文采用空间计量模型,研究了2011年至2022年中国31个省份和直辖市的数字经济与产业结构升级之间的关系,揭示了数字经济发展对产业结构高级化和合理化的空间溢出效应。研究发现,数字经济发展与产业结构的高级化和合理化存在显著的空间正相关性,且在研究期间保持稳定。尤其是上海、北京、天津等城市因其先发优势和政策支持,成为数字经济发展的前沿,显著促进了产业结构的优化和升级。研究还发现,社会消费和外商直接投资的增加为产业结构优化提供了资金和市场需求支持。基于研究发现,文章提出了有针对性的政策建议,以推动区域经济的均衡发展和产业的高质量升级。This paper employs a spatial econometric model to study the relationship between the digital economy and industrial structure upgrading across 31 provinces and municipalities in China from 2011 to 2022. The research reveals the spatial spillover effects of digital economy development on the advancement and rationalization of industrial structures. The findings indicate a significant positive spatial correlation between digital economy development and the advancement and rationalization of industrial structures, which remained stable throughout the study period. Notably, cities such as Shanghai, Beijing, and Tianjin, with their first-mover advantages and policy support, have become frontiers in digital economy development, significantly promoting the optimization and upgrading of industrial structures. The study also finds that increased social consumption and foreign direct investment have provided financial and market demand support for industrial structure optimization. Based on these findings, the paper proposes targeted policy recommendations to promote balanced regional economic development and high-quality industrial upgrading.