Learning about Risk and Return: A Simple Model of Bubbles and Crashes

This paper demonstrates that an asset pricing model with least-squares learning can lead to bubbles and crashes as endogenous responses to the fundamentals driving asset prices. When agents are risk-averse they need to make forecasts of the conditional variance of a stock’s return. Recursive updating of both the conditional variance and the expected return implies several mechanisms through which learning impacts stock prices. Extended periods of excess volatility, bubbles and crashes arise with a frequency that depends on the extent to which past data is discounted. A central role is played by changes over time in agents’ estimates of risk.

The Spillover Effects of Monitoring: A Field Experiment.

We provide field experimental evidence of the effects of monitoring in a context where productivity is multi-dimensional and only one dimension is monitored and incentivised. We hire students to do a job for us. The job consists of identifying euro coins. We study the effects of monitoring and penalising mistakes on work quality, and evaluate spillovers on non- incentivised dimensions of productivity (punctuality and theft). We .nd that monitoring improves work quality only if incentives are large, but reduces punctuality substantially irrespectively of the size of incentives. Monitoring does not affect theft, with ten per cent of participants stealing overall. Our setting also allows us to disentangle between possible theoretical mechanisms driving the adverse effects of monitoring. Our .ndings are supportive of a reciprocity mechanism, whereby workers retaliate for being distrusted.