Problem
Research questions and friction points this paper is trying to address.
expected stock returns
factor pricing
quantity information
Quantity, Risk, and Return
🤖 AI Summary
该研究提出了一种新的股票预期收益模型（BTQ），通过将市场交易活动的数量信息纳入因子定价框架，解决了风险与收益关系的预测问题。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
quantity information
factor pricing framework
beta times quantity (BTQ)
noise trading flows
risk-return association
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