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.

翻译：本文构建了一个统一框架，将公司层面的预测信号、跨资产溢出效应与随机贴现因子（SDF）联系起来。通过最大化夏普比率对信号与溢出效应进行联合估计，得到一个可解释的SDF，该因子既能对特征相关性进行排序，又能揭示资产间预测影响的方向。在样本外测试中，该SDF在不同投资领域和市场状态下均持续优于自预测基准和预期收益基准。推断出的信息网络凸显了规模大、换手率低的公司作为净信息传递者的角色。该框架为理解横截面收益动态背后的信息结构提供了一个清晰且具有经济理论基础的视角。