A Hybrid LSTM-XGBoost Framework for Multi-Horizon Stock Return Prediction Across Diversified Equity Portfolios· 混合 LSTM-XGBoost 框架预测股票收益
Accurate prediction of equity returns remains a major challenge in computational finance due to the non-stationary, nonlinear, and low signal-to-noise ratio nature of financial time series. This paper proposes a hybrid two-stage architecture that combines a long short-term memory (LSTM) network with an XGBoost gradient-boosted regressor for multi-horizon stock return prediction across a diversified panel of 14 U.S. equities spanning six industry sectors. The LSTM component, comprising two stacked layers with 64 hidden units, processes 60-day sliding windows of five sequential market features to produce 64-dimensional temporal embeddings that encode learned sequential market dynamics. These embeddings are concatenated with 14 hand-crafted technical indicators to form a 78-dimensional hybrid
混合 LSTM 和 XGBoost 框架，提高股票收益预测精度。
- 核心方法
- 构建了一个两阶段的混合架构，第一阶段使用 LSTM 处理60天的市场特征，生成64维的时间嵌入，第二阶段将这些嵌入与14个技术指标结合，通过 XGBoost 进行多时域股票收益预测。
- 适合谁读
- 金融领域的研究者和工程师
- 要解决的问题
- 金融时间序列的非平稳、非线性和低信噪比特性使得股票收益预测极具挑战性。
- 关键实验
- 对14只跨六个行业的美国股票进行了多时域收益预测实验，验证了模型的有效性。
- 主要贡献
- 提出了一种新的混合模型，能够跨多样化的股票组合进行多时域收益预测，提高了预测准确率。
- 意义与局限
- 该模型在 financial time series 预测中的应用提供了新思路，但可能受限于数据集的大小和市场变动的不确定性。