Zero-Shot Learning for S&P 500 Forecasting via Constituent-Level Dynamics: Latent Structure Modeling Without Index Supervision

Abstract

Market indices, such as the S&P 500, serve as compressed representations of complex constituent-level dynamics. This study proposes a zero-shot forecasting framework capable of predicting index-level trajectories without direct supervision from index data. By leveraging a Variational AutoEncoder (VAE), the model learns a latent mapping from constituent-level price movements and macroeconomic factors to index behavior, effectively bypassing the need for aggregated index labels during training. Using hourly OHLC data of S&P 500 constituents, combined with the U.S. 10-Year Treasury Yield and the CBOE Volatility Index, the model is trained solely on disaggregated inputs. Experimental results demonstrate that the VAE achieves superior accuracy in index-level forecasting compared to models trained directly on index targets, highlighting its effectiveness in capturing the implicit generative structure of index formation. These findings suggest that constituent-driven latent representations can provide a scalable and generalizable approach to modeling aggregate market indicators, offering a robust alternative to traditional direct supervision paradigms.