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Dynamics of Subjective Risk Premia
Author(s):
Stefan Nagel, University of Chicago and NBER
Zhengyang Xu, City University of Hong Kong
Discussant(s):
Monika Piazzesi, Stanford University and NBER
Abstract:

Nagel and Xu examine subjective risk premia implied by return expectations of individual investors and professionals for aggregate portfolios of stocks, bonds, currencies, and commodity futures. While in-sample predictive regressions with realized excess returns suggest that objective risk premia vary countercyclically with business cycle variables and aggregate asset valuation measures, the researchers find subjective risk premia extracted from survey data do not comove much with these variables. This lack of cyclicality of subjective risk premia is a pervasive property that holds in expectations of different groups of market participants and in different asset classes. A similar lack of cyclicality appears in out-ofsample forecasts of excess returns, which suggests that investors' learning of forecasting relationships in real time may explain much of the cyclicality gap. These findings cast doubt on models that explain time-varying objective risk premia inferred from in-sample regressions with countercyclical variation in perceived risk or risk aversion. Nagel and Xu further find a link between subjective perceptions of risk and subjective risk premia, which points toward a positive risk-return tradeoff in subjective beliefs.

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Do Teams Alleviate or Exacerbate Behavioral Biases? Evidence from Extrapolation Bias in Mutual Funds
Author(s):
Ricardo Barahona, Erasmus University
Stefano Cassella, Tilburg University
Kristy A.E. Jansen, Tilburg University
Discussant(s):
Anastassia Fedyk, University of California at Berkeley
Abstract:

Whether teams attenuate or exacerbate the behavioral biases which are pervasive at the individual level is an open question. To address this question, Barahona, Cassella, and Jansen use the mutual fund industry as a laboratory. Their focus is on how return extrapolation, a bias in investor behavior that has received considerable attention in recent work, is transmitted from individual fund managers to the team-managed funds they join. The researchers show that teams heavily attenuate the influence of extrapolation bias on funds' trading behavior. Additional analysis reveals that this attenuation is not due to differences in investment experience, compensation contracts, workload, and investment styles between solomanaged and team-managed funds. Rather, Barahona, Cassella, and Jansen's evidence suggests that the elicitation of team members' inner cognitive reflection can be responsible for teams' reduction in behavioral biases.Their results highlight the attenuation of the extrapolation bias as a potential benefit of team-based asset management.

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The Role of Beliefs in Asset Prices: Evidence from Exchange Rates
Author(s):
Joao Paulo Valente, Yale University
Kaushik Vasudevan, Yale University
Tianhao Wu, Yale University
Discussant(s):
Adrien Verdelhan, Massachusetts Institute of Technology and NBER
Abstract:

Motivated by evidence of systematic forecast errors by market participants and professional forecasters, Valente, Vasudevan, and Wu construct a model of exchange rate determination where investors each (1) receive noisy private signals about the future path of interest rate differentials between the US and other countries and (2) overestimate the persistence of interest rate differentials. Their model is able to explain the forward premium puzzle, a well-known failure of the uncovered interest rate parity condition implied by traditional models (UIP), in a manner consistent with the survey evidence, in addition to a number of additional puzzles that existing models have struggled to simultaneously explain. These include the initial underreaction and delayed overreaction of currencies in response to monetary news; positive short-horizon and negative long-horizon autocorrelations of currency excess returns; and the lower return predictability of interest rate differentials for UIP trades implemented with longer maturity bonds. Valente, Vasudevan, and Wu's model is also useful for understanding the strong relationship between survey-based measures of macroeconomic news and exchange rates despite the weak relationship between macroeconomic fundamentals and exchange rates, the persistence of subjective beliefs, and the seeming reversal of the failure of UIP in recent years. Valente, Vasudevan, and Wu's results highlight the important role that investors' beliefs may play in exchange rate behavior.

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The 2000s Housing Cycle With 2020 Hindsight: A Neo-Kindlebergerian View
Author(s):
Gabriel Chodorow-Reich, Harvard University and NBER
Adam Guren, Boston University and NBER
Timothy McQuade, University of California, Berkeley
Discussant(s):
Tim Landvoigt, University of Pennsylvania and NBER
Abstract:

With "2020 hindsight,'' the 2000s housing cycle is not a boom-bust but rather a boom-bust-rebound at both the national level and across cities. Chodorow-Reich, Guren, and McQuade argue this pattern reflects a larger role for fundamentally-rooted explanations than previously thought. The researchers construct a city-level long-run fundamental using a spatial equilibrium regression framework in which house prices are determined by local income, amenities, and supply. The fundamental predicts not only 1997-2019 price and rent growth but also the amplitude of the boom-bust-rebound and foreclosures. This evidence motivates their neo-Kindlebergerian model, in which an improvement in fundamentals triggers a boom-bust-rebound. Agents learn about the fundamentals by observing "dividends'' but become over-optimistic due to diagnostic expectations. A bust ensues when over-optimistic beliefs start to correct, exacerbated by a price-foreclosure spiral that drives prices below their long-run level. The rebound follows as prices converge to a path commensurate with higher fundamental growth. The estimated model explains the boom-bust-rebound with a single fundamental shock and accounts quantitatively for cross-city patterns in the dynamics of prices and foreclosures.

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This paper was distributed as Working Paper 29140, where an updated version may be available.

The Inference-Forecast Gap in Belief Updating
Author(s):
Tony Qiaofeng Fan, Stanford University
Yucheng Liang, Carnegie Mellon University
Cameron Peng, London School of Economics
Discussant(s):
Daniel J. Benjamin, University of California, Los Angeles and NBER
Abstract:

Individual forecasts of economic variables show widespread overreaction to news, but laboratory experiments on belief updating typically find underinference from signals. Fan, Liang, and Peng provide new experimental evidence to connect these two seemingly inconsistent phenomena. Building on a classic experimental paradigm, Fan, Liang, and Peng study how people make inferences and revise forecasts in the same information environment. Subjects underreact to signals when inferring about underlying states, but overreact to signals when revising forecasts about future outcomes. This gap in belief updating is largely driven by the use of different simplifying heuristics for the two tasks. Additional treatments link their results to the difficulty of recognizing the conceptual connection between making inferences and revising forecasts.

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Model-free and Model-based Learning as Joint Drivers of Investor Behavior
Author(s):
Nicholas C. Barberis, Yale University and NBER
Lawrence J. Jin, California Institute of Technology
Discussant(s):
Alexander M. Chinco, Baruch College
Abstract:

In the past decade, researchers in psychology and neuroscience studying human decision-making have increasingly adopted a framework that combines two systems, namely "model-free" and "model-based" learning. Barberis and Jin import this framework into a simple financial setting, study its properties, and link it to a wide range of applications. Barberis and Jin show that it provides a foundation for extrapolative demand and experience effects; resolves a puzzling disconnect between investor allocations and beliefs in both the frequency domain and the cross-section; helps explain the dispersion in stock market allocations across investors as well as the inertia in these allocations over time; and sheds light on the persistence of household investment mistakes. More broadly, the framework offers a way of thinking about individual behavior that is grounded in recent evidence on the computations that the brain undertakes when estimating the value of a course of action.

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Participants

Markus Baldauf, University of British Columbia
Ricardo Barahona, Erasmus University
Matteo Benetton, University of California at Berkeley
Stefano Cassella, Tilburg University
Fousseni Chabi-Yo, University of Massachusetts Amherst
Thummim Cho, London School of Economics
Ricardo De la O, University of Southern California
Tony Qiaofeng Fan, Stanford University
Anastassia Fedyk, University of California at Berkeley
Leandro Gomes, Yale University
Daniel D. Graves, Yale University
Xindi He, University of Chicago
Xing Huang, Washington University in St. Louis
Kristy A.E. Jansen, Tilburg University
Kose John, New York University
Mark Kamstra, York University
Peter Kelly, University of Notre Dame
Lisa A. Kramer, University of Toronto
Eben Lazarus, Massachusetts Institute of Technology
Gen Li, University of British Columbia
Yucheng Liang, Carnegie Mellon University
Jiacheng Liu, Purdue University
Steven Malliaris, University of Georgia
Ben Matthies, University of Notre Dame
Alexander Michaelides, Imperial College London
Indrajit Mitra, Federal Reserve Bank of Atlanta
Claudia Moise, Duke University
Saurin Patel, Western University, Ivey Business Schoo
Elena Pikulina, University of British Columbia
Claudia Robles Garcia, Stanford University
Paul Schmidt-Engelbertz, Yale University
Maya O. Shaton, Hebrew University
Oliver G. Spalt, University of Mannheim
Andrea Tamoni, Rutgers University
Sheri Tice, Tulane University
Nathan Tribble, Yale University
Joao Paulo Valente, Yale University
Michela Verardo, London School of Economics
Chen Wang, University of Notre Dame
Tianhao Wu, Yale University
Scott E. Yonker, Cornell University
Guihai Zhao, Bank of Canada

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