北京师范大学学报（自然科学版）2025，Vol.61Issue(6)：776-785,10.DOI:10.12202/j.0476-0301.2025146
融合视频数据的梯度提升算法在基金收益率预测中的应用研究
Gradient boosting algorithm integrating video data applied to prediction of mutual fund returns
摘要
Abstract
Roadshow video data from Chinese public mutual funds are used to construct a multimodal feature system incorporating textual semantics,linguistic structure,and vocal behaviors.A gradient boosting regression(GBR)model is used to predict next-day fund returns.Model parameters are optimized through cross-validation and grid search with the 2020 dataset of Chinese mutual funds.Comparative analyses with support vector regression(SVR),random forest(RF),and Lasso regression show that this GBR model achieves significantly higher predictive accuracy.Interpretability analysis further indicates that linguistic and acoustic features proportion of vague expressions,speaking rate,pitch variation make prominent contributions to prediction performance.These findings confirm that language style and communication patterns contain meaningful behavioral signals that affect investor judgments and market responses,offering forward-looking informational value.The present work extends the application of multimodal data in fund analysis,provides quantitative evidence to support fund managers in optimising video-based disclosures and investors in identifying non-financial signals.关键词
多模态特征/基金路演视频/收益率预测/梯度提升回归/可解释性分析Key words
multi-modal feature/fund roadshow video/return prediction/gradient boosting regression/explainable analysis分类
管理科学引用本文复制引用
谭晶桦,王硕,康明惠..融合视频数据的梯度提升算法在基金收益率预测中的应用研究[J].北京师范大学学报（自然科学版）,2025,61(6):776-785,10.基金项目
国家自然科学基金资助项目(72442024,72301188) （72442024,72301188）
