具有跨资产溢出效应的随机折现因子
Stochastic Discount Factors with Cross-Asset Spillovers
AI总结：
本文提出了一种统一框架，通过最大化夏普比率联合估计预测信号和跨资产溢出效应，构建出能解释资产间预测影响的随机折现因子，并在多种投资领域和市场状态下表现优于传统基准。
摘要：
本文开发了一个统一的框架，将企业层面的预测信号、跨资产溢出效应和随机折现因子（SDF）联系起来。信号和溢出效应通过最大化夏普比率共同估计，得到一个可解释的SDF，既能排名特征相关性，又能揭示资产间预测影响的方向。在样本外测试中，SDF在各种投资领域和市场状态下均优于自我预测和预期收益基准。推断的信息网络突显了大型低流动性公司作为净发射者。该框架提供了清晰且具有经济基础的观点，揭示了横截面收益动态背后的信息架构。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
