DiffSTOCK: Probabilistic relational Stock Market Predictions using
  Diffusion Models
Divyanshu Daiya Monika Yadav Harshit Singh Rao
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.