Characteristics, Risk, and Returns
Paper Session
Monday, Jan. 5, 2026 8:00 AM - 10:00 AM (EST)
- Chair: Jun Li, University of Texas at Dallas
Profitability Retrospective: What Have We Learned?
Abstract
Profitability subsumes all of "quality" investing, explaining both the performance of the strategies that industry markets and the factors that academics employ. It also has striking power pricing "defensive equity" strategies that overweight low-beta or low-volatility stocks. Profitability tilts explain all the abnormal performance of popular "alternative value" strategies, including those adjusted for "intangibles," and half of value's post-2007 underperformance. Profitability is crucial for pricing a wide array of seemingly unrelated anomalies, yielding a more parsimonious understanding of the cross section of expected returns.Macro Strikes Back: Term Structure of Risk Premia
Abstract
We provide a novel priced Wold representation theorem that sharply identifies shocks common to financial markets and the macroeconomy, their propagation, and the term structure of macro risk premia. Our identification leverages the fact that, in equilibrium models, asset prices are jump variables that reveal priced risks in large cross-sections of returns. We find that macro factors’ risk premia are strongly time-varying and countercyclical, with sharply increasing unconditional term structures. This pattern is driven by the slow propagation of a priced common shock, distinct from TFP, that captures most of the persistence in macroeconomic variables. Macro risk premia are negligible in the short run, yet grow to match equity risk premia at the business cycle horizon. This finding is not a mechanical byproduct of persistence: while GDP, consumption, industrial production, and employment exhibit upward-sloping term structures, other similarly persistent factors—e.g., VIX or intermediary-based variables—display downward-sloping or flat ones.Quantity, Risk, and Return
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 (β) but also the factor’s quantity fluctuations (q) induced by noise trading flows, and hence term the model beta times quantity (BTQ). The rationale is that a factor’s premium should be higher when sophisticated investors have absorbed flows of stocks with high exposure 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.Discussant(s)
Cesare Robotti
,
University of Warwick
Amit Goyal
,
University of Lausanne
Dongho Song
,
Johns Hopkins University
Wei Wu
,
Texas A&M University
JEL Classifications
- G1 - General Financial Markets