Extrapolation and bubbles

By Nicholas Barberis, Robin Greenwood, Lawrence J. Jin, A. Shleifer. Journal of Financial Economics, 2018

339 citations recorded by the source corpus.

We present an extrapolative model of bubbles. In the model, many investors form their demand for a risky asset by weighing two signals—an average of the asset’s past price changes and the asset’s degree of overvaluation. The two signals are in conflict, and investors “waver” over time in the relative weight they put on them. The model predicts that good news about fundamentals can trigger large price bubbles. We analyze the patterns of cash-flow news that generate the largest bubbles, the reasons why bubbles collapse, and the frequency with which they occur. The model also predicts that bubbles will be accompanied by high trading volume, and that volume increases with past asset returns. We present empirical evidence that bears on some of the model’s distinctive predictions.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/753d2d31cca1e2234639cf1746cddc2d680da820

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

In Finance

Citations in the corpus

Psychology-based Models of Asset Prices and Trading Volume (2018, background): “Recently, there has been a second wave of research on the topic, including papers such as Barberis et al. (2015, 2018), Adam et al. (2017), Glaeser and Nathanson (2017), Cassella and Gulen (2018), DeFusco et al. (2018), Jin and Sui (2018), and Liao and Peng (2018).”
Presidential Address: Social Transmission Bias in Economics and Finance (2020, neutral): “9 Barberis et al. (2018) offer a model of wavering of investor beliefs at the peak of bubbles.”
Extrapolative beliefs in the cross-section: What can we learn from the crowds? (2020, neutral): “3 Some social media platforms (e.g., StockTwits and Seeking Alpha) collect a textual form of user opinions about stock performance, but sometimes textual information cannot be easily converted to precise quantitative information.”
Diagnostic Expectations and Credit Cycles (2018, neutral): “, Muth (1961), Barberis, Shleifer, and Vishny (1998), Rabin and Vayanos (2010), Fuster, Laibson, and Mendel (2010), Hirshleifer, Li, and Yu (2015), Greenwood and Hanson (2015), Barberis et al. (2015)).”
Social Networks and Housing Markets (2018, positive): “These findings provide support for a large and important class of models in which expectation heterogeneity influences asset valuations and motivates individuals to trade (e.g., Miller, 1977; Harrison and Kreps, 1978; Varian, 1989; Harris and Raviv, 1993; Hong and Stein, 1999; Scheinkman and Xiong, ”
Bubbles for Fama (2019, background): “(1990 ), Abreu and Brunnermeier (2003), Scheinkman and Xiong (2003) , and Barberis et al. (2018) .”