发表机构
MSA University（MSA大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出一种结合LSTM与XGBoost的两阶段混合框架，用于多时间跨度股票收益率预测，在14只美股上验证，30天RMSE显著降低，并构建投资评分框架支持组合决策。
AI 中文摘要
由于金融时间序列具有非平稳、非线性和低信噪比的特点，股票收益率的准确预测仍然是计算金融领域的一项重大挑战。本文提出了一种混合两阶段架构，将长短期记忆（LSTM）网络与XGBoost梯度提升回归器相结合，用于对涵盖六个行业板块的14只美国股票的多元化面板进行多时间跨度股票收益率预测。LSTM组件由两个堆叠层组成，每层包含64个隐藏单元，处理五个序列市场特征的60天滑动窗口，生成64维时间嵌入，以编码学习到的序列市场动态。这些嵌入与14个手工制作的技术指标拼接，形成78维混合特征向量，随后传递给通过3折交叉验证网格搜索调优的XGBoost回归器。该框架在多股票池化语料库上训练，采用严格的时间顺序划分和每只股票的MinMaxScaling以防止前视偏差，并在30、90、252和365个交易日的四个预测时间跨度上进行评估。实验结果表明，混合模型在30天时间跨度上的测试RMSE为0.0949，约为独立LSTM基线的三分之一，同时在大多数股票上略微匹配或超越仅使用XGBoost的基线。方向准确率随时间跨度长度增加而上升，在365天时达到97.6%；然而，我们表明这很大程度上与样本中正长期收益的高基础比率相关，因此我们将方向准确率与朴素的全正向预测器进行基准比较，并将短期时间跨度上高于基础比率的差距视为更具信息量的信号。进一步提出了一种基于多时间跨度预测的复合投资评分框架，以支持投资组合排序和决策支持。
英文摘要
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 feature vector, which is subsequently passed to an XGBoost regressor tuned via 3-fold cross-validation grid search. The framework is trained on a multi-stock pooled corpus using strict chronological splits and per-stock MinMaxScaling to prevent look-ahead bias, and evaluated across four prediction horizons of 30, 90, 252, and 365 trading days. Experimental results demonstrate that the hybrid model achieves a test RMSE of 0.0949 on the 30-day horizon, roughly one-third that of the standalone LSTM baseline, while marginally matching or surpassing the XGBoost-Only baseline across the majority of stocks. Directional accuracy rises with horizon length, reaching 97.6% at 365 days; we show, however, that this largely tracks the high base rate of positive long-horizon returns in the sample, and we therefore benchmark directional accuracy against a naive always-positive predictor and treat the above-base-rate gap at short horizons as the more informative signal. A composite investment scoring framework derived from multi-horizon predictions is further proposed to support portfolio ranking and decision support.
