World Embedding: The Daily Economic State and Bond Risk Premia

Abstract

I construct a world embedding: a daily, low-dimensional vector that compresses news narratives, financial market data, policy uncertainty, geopolitical risk, and macroeconomic releases into a unified representation of the aggregate economic state. A machine-learning-based multimodal encoder produces this embedding under a strict expanding-window protocol, ensuring the series is look-ahead-free at every point in time. Unsupervised clustering of the world embedding recovers known business-cycle regimes with higher fidelity than linear methods, and the representation carries incremental out-of-sample forecasting power for labor-market indicators. The primary contribution addresses the interest-rate spanning puzzle: embedding principal components capture unspanned macro risks that raise the in-sample R2 for bond excess returns by 10 to 34 percentage points beyond standard yield-curve factors. This predictive content originates from non-yield information and survives out-of-sample evaluation, orthogonalization, and a pseudo-out-of-sample extension through the Covid-19 pandemic.

Keywords: World embedding, economic state representation, multimodal deep learning, machine learning, bond risk premia, unspanned macro risks, macroeconomic forecasting, regime detection

JEL Classification: C45, C55, E37, E43, G12