Lars Lochstoer, University of California-Los Angeles
Stig Roar Lundeby, BI Norwegian Business School
Zhaneta Tancheva, BI Norwegian Business School
Abstract: We show that households forecast future restraint that does not materialize. In the New York Fed Survey of Consumer Expectations, households plausibly more exposed to selfcontrol problems underpredict consumption growth while overpredicting future improvements in personal finances; both errors widen during economic stress. The same households expect especially weak growth in non-essential relative to essential spending and are more likely to experience financial distress. Such opposite-signed errors are not what a uniform belief-formation rule would produce. We rationalize them with state-dependent partial naivete: present-biased households underestimate how much their future selves will overconsume
and undersave, especially in bad states. The mechanism implies priced self-control risk even though agents forecast aggregate outcomes correctly.
Discussant: Andrea Buffa, University of Colorado-Boulder
Abstract: This paper resolves a long-standing zero-beta rate puzzle—the empirical finding that estimated zero-beta rates remain persistently high across factor models. I show that this apparent robustness may arise from pervasive model misspecification rather than reflecting a genuinely high risk-free rate. When a factor model fails to perfectly price assets, the corresponding zero-beta rate is no longer uniquely identified, and conventional estimators, based on the minimum-variance zero-beta portfolio, tend to bias the estimate upward toward the mean return of the global minimum-variance portfolio. To quantify this mechanism, I introduce a new investment-based measure of model misspecification: the maximum Sharpe ratio attainable by zero-investment, zero-beta portfolios. This measure captures the economic magnitude of pricing errors and links model misspecification to empirically observable investment opportunities. Studying a comprehensive set of classical and modern factor models, I find substantial misspecification, explaining why all models yield similarly elevated zero-beta rates. Simulation analyses confirm that realistic degrees of misspecification can fully reproduce the empirical magnitude of the puzzle even when the true risk-free rate is low.
Discussant: Ricardo Delao, University of Southern California
Abstract: Financial event studies estimate causal effects by constructing counterfactual returns
using asset pricing factor models. By design, these factor models are designed to cap-
ture priced risk—factors that carry risk premia and explain the cross-section of expected
returns. But constructing a valid counterfactual return requires accounting for all sys-
tematic variation in returns, including factors that carry no risk premium. We show that
the gap between these two objects is inconsequential under conditions common in classic
applications: many randomly-timed events and stationary factor distributions, so that
unpriced factor realizations average out and the distribution of priced factor realizations
is representative. When these conditions fail—as with a single event date, event tim-
ing that coincides with unusual market conditions, or long horizons with shifting factor
distributions—traditional estimators can produce substantial bias. We derive precise iden-
tification conditions and analytic bias expressions, and propose synthetic control meth-
ods that match on realized pre-event return paths, implicitly capturing exposure to both
priced and unpriced factors. Revisiting four empirical applications, we show that some es-
tablished findings—the Geithner Treasury Secretary announcement effect on banks’ stock
prices (Acemoglu et al., 2016), pre-inclusion drift for index inclusion, and M&A acquirer
effects—may reflect unmeasured systematic risk rather than true treatment effects.
Discussant: Sofonias Alemu Korsaye, Johns Hopkins University
