In this work, we propose an approach to generalize denoising diffusion
probabilistic models for stock market predictions and portfolio management.
Present works have demonstrated the efficacy of modeling interstock relations
for market time-series forecasting and utilized Graph-based learning models for
value prediction and portfolio management. Though convincing, these
deterministic approaches still fall short of handling uncertainties i.e., due
to the low signal-to-noise ratio of the financial data, it is quite challenging
to learn effective deterministic models. Since the probabilistic methods have
shown to effectively emulate higher uncertainties for time-series predictions.
To this end, we showcase effective utilisation of Denoising Diffusion
Probabilistic Models (DDPM), to develop an architecture for providing better
market predictions conditioned on the historical financial indicators and
inter-stock relations. Additionally, we also provide a novel deterministic
architecture MaTCHS which uses Masked Relational Transformer(MRT) to exploit
inter-stock relations along with historical stock features. We demonstrate that
our model achieves SOTA performance for movement predication and Portfolio
management.

我们提出了一种通用去噪扩散概率模型的方法，用于股票市场预测和投资组合管理。我们展示了去噪扩散概率模型在处理不确定性方面的有效性，并利用模型在时间序列预测中模拟更高的不确定性。此外，我们还提供了一种新颖的确定性架构 MaTCHS，利用蒙版关系转换网络来利用股票之间的关系。我们展示了该模型在预测市场动向和投资组合管理方面的最新性能。