发表机构
Carey Business School, Johns Hopkins University; Mendoza College of Business, University of Notre Dame（约翰斯·霍普金斯大学凯瑞商学院; 圣母门多萨商学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出将交易数量信息纳入因子定价框架的BTQ模型，可预测样本外月度股票收益，解释横截面风险-收益关联并缓解因子动物园问题。
AI 中文摘要
我们提出了一种新的预期股票收益模型，将市场交易活动中的数量信息纳入因子定价框架。我们假设股票的预期收益不仅由其因子风险敞口（beta）决定，还由交易流引发的因子数量波动（q）决定，因此将该模型命名为beta乘以数量（BTQ）。其理论依据是，当成熟投资者吸收了对某因子具有高载荷的股票的噪声交易流时，他们会要求更高的因子溢价。BTQ模型为股票收益提供了令人信服的基于风险的解释，若不考虑数量信息，这一解释将被掩盖。无条件下近乎平坦的横截面风险-收益关联，强烈依赖于数量变量。结构化的BTQ模型能可靠地预测样本外的月度股票收益，并通过选择少量因子解决了因子动物园问题。
英文摘要
We propose a new model of expected stock returns that incorporates quantity information from market trading activities into the factor pricing framework. We posit that the expected return of a stock is determined by not only its factor risk exposures (beta) but also the factor's quantity fluctuations (q) induced by trading flows, and hence term the model beta times quantity (BTQ). The rationale is that sophisticated investors should demand a higher factor premium when they have absorbed noise trading flows of stocks with high loadings to that factor. The BTQ model provides a compelling risk-based explanation for stock returns, which is otherwise obscured without considering the quantity information. The cross-sectional risk-return association, which is nearly flat unconditionally, strongly depends on the quantity variable. The structured BTQ model reliably predicts monthly stock returns out of sample, and addresses the factor zoo problem by selecting a small number of factors.
