Multi-Horizon Uncertainty-Aware Latent Scenario Learning for Real-Time Sequential Financial Analytics and Decision-Making with Asynchronous Data

Machine learning in the finance domain always involves multi-task sequential quantitative analytics and decision-making, in which research analysts extract predictive signals from diverse firm characteristics, form prospects of future financial disclosure and valuations of stock prices, then construct investment portfolios based on their return expectations. In reality, they have to deal with asynchronous financial data and thereby obsolete information, e.g., accounting-based fundamentals are released quarterly or annually with lags while market-based characteristics such as trading activities can be collected in a more timely manner. This imposes huge challenges in the sequential investment process, where input uncertainty can propagate through multi-task learning, leading to (i) distorted beliefs about firm fundamentals in upstream data analytics, (ii) misinformed return expectations in midstream asset pricing, and (iii) suboptimal downstream portfolio decisions. To tackle these challenges, we propose a novel unified multi-horizon uncertainty-aware latent scenario learning network that simultaneously (i) infers real-time multi-horizon probabilistic firm fundamentals jointly from the stale prints and the latest market data, (ii) estimates probability-weighted systematic risk exposures to common risk factors that produce realistic multi-horizon cross-sectional expected returns, and (iii) constructs various multi-horizon robust investment portfolios, which take the generative latent scenarios into account in optimization. Extensive experiments, ablation studies, and sensitivity analyses on the most comprehensive open-source stock market dataset show that our proposed model consistently outperforms seminal baselines in relevant literature across all evaluation metrics for sequential financial tasks. The results also emphasize the importance of real-time uncertainty-aware design of the latent scenario learning mechanism.