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Volatility, Valuation Ratios, and Bubbles: An Empirical Measure of Market Sentiment
Author(s):
Can Gao, Imperial College London
Ian Martin, London School of Economics
Discussant(s):
Alan Moreira, University of Rochester and NBER
Abstract:

Gao and Martin define a sentiment indicator that exploits two contrasting views of return predictability, and study its properties. The indicator, which is based on option prices, valuation ratios and interest rates, was unusually high during the late 1990s, reflecting dividend growth expectations that in the researchers' view were unreasonably optimistic. The researchers interpret it as helping to reveal irrational beliefs about fundamentals. They show that the measure is a leading indicator of detrended volume, and of various other measures associated with financial fragility. The researchers also make two methodological contributions. First, they derive a new valuation-ratio decomposition that is related to the Campbell and Shiller (1988) loglinearization, but which resembles the traditional Gordon growth model more closely and has certain other advantages for the researchers' purposes. Second, they introduce a volatility index that provides a lower bound on the market's expected log return.

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Expected Returns and Cash-Flow Growth
Author(s):
Niels Joachim Gormsen, University of Chicago
Eben Lazarus, Massachusetts Institute of Technology
Discussant(s):
Tobias J. Moskowitz, Yale University and NBER
Abstract:

Gormsen and Lazarus find that most cross-sectional variation in expected stock returns can be summarized by cross-sectional variation in cash-flow duration. They show empirically and theoretically that most firm characteristics that predict high returns also predict a low cash-flow growth and thus a short cash-flow duration. A duration factor therefore explains the return to many equity anomalies, including factors based on valuation, profit, investment, low risk, and payout measures, both in the U.S. and globally. Using a novel dataset of single stock dividend futures, the researchers find evidence that this duration factor predicts returns exactly because it predicts the timing of cash flows and not because it predicts other firm characteristics. A simple theoretical framework can reproduce these findings and is consistent with the empirical properties of the aggregate market portfolio and the equity term structure.

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A Retrieved-Context Theory of Financial Decisions
Author(s):
Jessica Wachter, University of Pennsylvania
Michael J. Kahana, University of Pennsylvania
Discussant(s):
Cary Frydman, University of Southern California
Abstract:

Studies of human memory indicate that features of an event evoke memories of prior associated contextual states, which in turn become associated with the current event's features. This mechanism allows the remote past to influence the present, even as agents gradually update their beliefs about their environment. Wachter and Kahana apply the context framework from the memory literature to four problems in asset pricing and portfolio choice: over-persistence of beliefs, providence of financial crises, price momentum, and the impact of fear on asset allocation. These examples suggest a recasting of neoclassical rational expectations in terms of beliefs as governed by principles of human memory.

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Five Facts About Beliefs and Portfolios
Author(s):
Stefano Giglio, Yale University and NBER
Matteo Maggiori, Stanford University and NBER
Johannes Stroebel, New York University and NBER
Stephen Utkus, University of Pennsylvania
Discussant(s):
Lars P. Hansen, University of Chicago and NBER
Abstract:

Giglio, Maggiori, Stroebel, and Utkus administer a newly-designed survey to a large panel of retail investors who have substantial wealth invested in financial markets. The survey elicits beliefs that are crucial for macroeconomics and finance, and matches respondents with administrative data on their portfolio composition and their trading activity. The researchers establish five facts in this data: (1) Beliefs are reflected in portfolio allocations. The sensitivity of portfolios to beliefs is small on average, but varies significantly with investor wealth, attention, trading frequency, and confidence. (2) It is hard to predict when investors trade, but conditional on trading, belief changes affect both the direction and the magnitude of trades. (3) Beliefs are mostly characterized by large and persistent individual heterogeneity, demographic characteristics explain only a small part of why some individuals are optimistic and some are pessimistic. (4) Investors who expect higher cash flow growth also expect higher returns and lower long-term price-dividend ratios. (5) Expected returns and the subjective probability of rare disasters are negatively related, both within and across investors. These five facts challenge the rational expectation framework for macro-finance, and provide important guidance for the design of behavioral models.

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This paper was distributed as Working Paper 25744, where an updated version may be available.

Post-FOMC Announcement Drift in U.S. Bond Markets
Author(s):
Jordan Brooks, AQR Capital Management
Michael Katz, AQR Capital Management
Hanno Lustig, Stanford University and NBER
Discussant(s):
Anna Cieslak, Duke University and NBER
Abstract:

The sensitivity of long-term rates to short-term rates represents a puzzle for standard macro-finance models. Post-FOMC announcement drift in Treasury markets after Federal Funds target changes contributes to the excess sensitivity of long rates. Mutual fund investors respond to the salience of Federal Funds target rate increases by selling short and intermediate duration bond funds, thus gradually increasing the effective supply to be absorbed by arbitrageurs. The gradual increase in supply generates post-announcement drift in longer Treasury yields, which spills over to other bond markets. Brooks, Katz, and Lustig's findings shed new light on the causes of time-series-momentum in bond markets. A model in which mutual fund investors slowly adjust their extrapolative expectations of future short rates after a target change can qualitatively match the dynamics of yields and fund flows.

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This paper was distributed as Working Paper 25127, where an updated version may be available.

Selling Fast and Buying Slow: Heuristics and Trading Performance of Institutional Investors
Author(s):
Klakow Akepanidtaworn, University of Chicago
Rick Di Mascio, Inalytics Ltd
Alex Imas, University of Chicago and NBER
Lawrence Schmidt, Massachusetts Institute of Technology
Discussant(s):
Bronson Argyle, Brigham Young University
Abstract:

Most research on heuristics and biases in financial decision-making has focused on non-experts, such as retail investors who hold modest portfolios. Akepanidtaworn, Di Mascio, Imas, and Schmidt use a unique data set to show that financial market experts - institutional investors with portfolios averaging $573 million -- exhibit costly, systematic biases. A striking finding emerges: while investors display clear skill in buying, their selling decisions underperform substantially -- even relative to strategies involving no skill such as randomly selling existing positions -- in terms of both benchmark-adjusted and risk-adjusted returns. Across many specifications, foregone profits from underperformance in selling relative to a random-sell strategy are of a similar order of magnitude (though somewhat smaller) as the gains accrued from buying. The researchers present evidence that an asymmetric allocation of cognitive resources such as attention towards buying relative to selling can explain this discrepancy. Looking at events when attention is more likely to be evenly split between prospective buys and sells -- earning announcement days -- the researchers find that stocks bought and sold both outperform counterfactual strategies. They show that a heuristic process associated with limited attention can explain selling but not buying decisions. Assets with salient features in the form of extreme past returns are 50% more likely to be sold than those with zero benchmark-adjusted returns. Past returns have little predictive power for buying decisions. Lastly, the researchers show that such heuristics are costly, funds that are more prone to use heuristic strategies exhibit the most underperformance in selling.

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This paper was distributed as Working Paper 29076, where an updated version may be available.

Participants

Stefano Cassella, Tilburg University
Can Gao, Imperial College London
Gustavo Grullon, Rice University
Xing Huang, Washington University in St. Louis
Byoung-Hyoun Hwang, Cornell University
Kose John, New York University
Bige Kahraman, University of Oxford
Mark Kamstra, York University
Peter Kelly, University of Notre Dame
Lisa A. Kramer, University of Toronto
Eben Lazarus, Massachusetts Institute of Technology
Indrajit Mitra, Federal Reserve Bank of Atlanta
Terrance T. Odean, University of California at Berkeley
Clemens Otto, Singapore Management University
Oguzhan Ozbas, University of Southern California
Elena Pikulina, University of British Columbia
Michela Verardo, London School of Economics

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