Diffolio: A Diffusion Model for Multivariate Probabilistic Financial Time-Series Forecasting and Portfolio Construction
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Diffolio：多变量金融时序概率预测与组合构建的扩散模型
- 研究问题
- 如何在多资产收益概率预测中捕捉横截面依赖，并将预测分布用于同时考虑资产特征和市场信息的组合构建？
- 方法与思路
- 作者提出扩散模型，使用资产层与市场层的分层注意力去噪网络，并加入由目标相关矩阵稳定估计指导的正则项。
- 材料陈述的贡献
- 摘要报告在十二个行业组合的日度超额收益实验中，预测准确性及组合表现优于所比较基线，包括夏普比率与确定性等价。
- 阅读关注点
- 应核对训练与评估时间划分、相关矩阵的可用信息、预测分布评价方法，以及组合约束、交易成本和基线调参。
- 阅读边界
- 这是 arXiv 预印本，同行评审或接收未经核实；摘要未说明样本期、成本与数值差异，不能外推至其他市场或实际投资。
原文摘要
Probabilistic forecasting is crucial in multivariate financial time-series for constructing efficient portfolios that account for complex cross-sectional dependencies. In this paper, we propose Diffolio, a diffusion model designed for multivariate financial time-series forecasting and portfolio construction. Diffolio employs a denoising network with a hierarchical attention architecture, comprising both asset-level and market-level layers. Furthermore, to better reflect cross-sectional correlations, we introduce a correlation-guided regularizer informed by a stable estimate of the target correlation matrix. This structure effectively extracts salient features not only from historical returns but also from asset-specific and systematic covariates, significantly enhancing the performance of forecasts and portfolios. Experimental results on the daily excess returns of 12 industry portfolios show that Diffolio outperforms various probabilistic forecasting baselines in multivariate forecasting accuracy and portfolio performance. Moreover, in portfolio experiments, portfolios constructed from Diffolio's forecasts show consistently robust performance, thereby outperforming those from benchmarks by achieving higher Sharpe ratios for the mean-variance tangency portfolio and higher certainty equivalents for the growth-optimal portfolio. These results demonstrate the superiority of our proposed Diffolio in terms of not only statistical accuracy but also economic significance.