Cross-section without factors: a string model for expected returns
提出一个弦模型，用资产间的关联性溢价解释预期收益，无需共同因子；发现大股票在坏时期连接性更高，但作为关联性对冲工具，其暴露组合要求更低溢价，模型表现不逊于线性因子模型。
Many asset pricing models assume that expected returns are driven by common factors. We formulate a model where returns are driven by a string, and no-arbitrage restricts each expected return to capture the asset's granular exposure to all other asset returns: a correlation premium. The model predicts fresh properties for big stocks, which display higher connectivity in bad times, but also work as correlation hedges: they contribute to a negative fraction of the correlation premium, and portfolios that are more exposed to them command a lower premium. The string model performs at least as well as many existing linear factor models.