Extrapolation and Bubbles

Abstract

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. * The authors' affiliations are Yale School of Management, Harvard Business School, California Institute of Technology, and Harvard University, respectively. Comments are welcome.

Social Networks and Housing Markets

“…We show that individuals with networks that experienced more positive house price movements, and who thus believe that real estate is a more attractive investment, actually do invest more in real estate, and are willing to pay more for a given house. 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 andStein, 1999, 2007;Scheinkman and Xiong, 2003;Geanakoplos, 2009;Simsek, 2013a,b;Brunnermeier, Simsek and Xiong, 2014;Barberis et al, 2015). Furthermore, our county-level results document an important effect of investor disagreement on aggregate prices and trading volume in housing markets, and are thus highly consistent with aggregate predictions from these models.…”

“…For example, Kuchler and Zafar (2015) show that locally-experienced house prices influence individuals' expectations of national house prices. Guren (2015), Glaeser and Nathanson (2015), and Barberis et al (2015) explore the implications of such extrapolation for price dynamics. Recent experiences or events also affect expectations in other settings (e.g., Vissing-Jorgensen, 2003;Nagel, 2011, 2015;Choi et al, 2009;Greenwood and Shleifer, 2014;Armona, Fuster and Zafar, 2016).…”

Bubbles for Fama

“…Fourth, Goetzmann (2015) studies rapid price increases of national stock markets and their subsequent returns, but not characteristics of these markets beyond the price run-up. Finally, our paper connects to a vast theoretical literature on bubbles, including De Long et al (1990), Abreu and Brunnermeier (2003), Scheinkman and Xiong (2003), and Barberis, Greenwood, Jin, and Shleifer (2016). To be sure, there is a lot of research in finance on so-called rational bubbles (e.g., Blanchard andWatson 1982, Tirole 1985) but recent evidence has not been kind to these theories (Giglio, Maggiori, and Stroebel 2016).…”

The formation of forward freight agreement rates in dry bulk shipping: Spot rates, risk premia, and heterogeneous expectations

“…In the following, we present and discuss the results for the set ; namely, we allow speculators to “worry” about the “fundamental value signal” but weigh more heavily the “contrarian value” one (Barberis, Greenwood, Lawrence, & Shleifer, ). In addition, we assume that they receive the private value signal with 50% precision; thus, there is asymmetry of information in the market.…”