Working Paper: NBER ID: w21944
Authors: Nicholas Barberis; Robin Greenwood; Lawrence Jin; Andrei Shleifer
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.
Keywords: bubbles; extrapolation; trading volume
JEL Codes: G02; G12
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| cash flow news (O16) | increased prices (P22) |
| cash flow shocks (F32) | increases in asset prices (G19) |
| extrapolators (C51) | increased demand (J23) |
| cash flow news (O16) | behavior of extrapolators (C51) |
| increased demand from extrapolators (J23) | price bubbles (E32) |
| cash flow news subsides (H23) | prices begin to decline (E30) |
| trading volume during bubble periods (E32) | behavior of extrapolators (C51) |
| magnitude and timing of cash flow shocks (E44) | size of bubbles (E32) |
| extrapolators' reaction to overvaluation and past price increases (F31) | trading volume increase (G15) |