Multi-step stock price prediction over a long-term horizon is crucial for
forecasting its volatility, allowing financial institutions to price and hedge
derivatives, and banks to quantify the risk in their trading books.
Additionally, most financial regulators also require a liquidity horizon of
several days for institutional investors to exit their risky assets, in order
to not materially affect market prices. However, the task of multi-step stock
price prediction is challenging, given the highly stochastic nature of stock
data. Current solutions to tackle this problem are mostly designed for
single-step, classification-based predictions, and are limited to low
representation expressiveness. The problem also gets progressively harder with
the introduction of the target price sequence, which also contains stochastic
noise and reduces generalizability at test-time. To tackle these issues, we
combine a deep hierarchical variational-autoencoder (VAE) and diffusion
probabilistic techniques to do seq2seq stock prediction through a stochastic
generative process. The hierarchical VAE allows us to learn the complex and
low-level latent variables for stock prediction, while the diffusion
probabilistic model trains the predictor to handle stock price stochasticity by
progressively adding random noise to the stock data. Our Diffusion-VAE (D-Va)
model is shown to outperform state-of-the-art solutions in terms of its
prediction accuracy and variance. More importantly, the multi-step outputs can
also allow us to form a stock portfolio over the prediction length. We
demonstrate the effectiveness of our model outputs in the portfolio investment
task through the Sharpe ratio metric and highlight the importance of dealing
with different types of prediction uncertainties.

使用深层次变分自动编码器（VAE）和扩散概率技术，结合随机生成过程来进行序列预测，解决多步股票价格预测的问题，通过形成投资组合评估模型输出的有效性和处理不同类型预测不确定性的重要性。