Stochastic Discount Factors with Cross-Asset Spillovers

TL;DR

This paper introduces a unified framework linking firm-level signals, cross-asset spillovers, and the stochastic discount factor, improving return predictions and revealing the informational structure of markets.

Contribution

It develops a novel method to jointly estimate signals and spillovers, producing an interpretable SDF that captures predictive influence and outperforms benchmarks.

Findings

SDF outperforms benchmarks across markets and states

Large, low-turnover firms act as net transmitters of information

Framework uncovers the informational architecture of return dynamics

Abstract

This paper develops a unified framework that links firm-level predictive signals, cross-asset spillovers, and the stochastic discount factor (SDF). Signals and spillovers are jointly estimated by maximizing the Sharpe ratio, yielding an interpretable SDF that both ranks characteristic relevance and uncovers the direction of predictive influence across assets. Out-of-sample, the SDF consistently outperforms self-predictive and expected-return benchmarks across investment universes and market states. The inferred information network highlights large, low-turnover firms as net transmitters. The framework offers a clear, economically grounded view of the informational architecture underlying cross-sectional return dynamics.