Job Market Paper
Imperfect knowledge about the structure of information flows can cause pricing mistakes in environments characterised by social learning. Using a natural experiment from the UK housing market that allows me to identify regular shocks to the information set of prospective sellers, I show that they overweight stale information when setting prices. I argue that this is inconsistent with Bayesian rationality and propose a model of naïve learning. I use the model, calibrated to the data, to measure the economic magnitude of the long-run effects arising from pricing mistakes. The results indicate that noisy signals bearing little information about future demand can have a long-lasting effect on aggregate prices when the dynamics of demand are highly persistent.
(with Francesco Nicolai)
By inverting the optimal portfolios of mutual fund managers in a fairly general setting, which allows us to partial out the effect of risk aversion and hedging demands, we provide an estimate of perceived expected excess returns and show that they are significantly affected by experienced returns. The effect of past returns is non-monotone: we provide reduced-form and structural evidence of managers displaying recency and primacy bias. Finally, we estimate an average coefficient of relative risk aversion close to unity.
Taxes that happen concurrently with the purchase are more salient than deferred taxes. Using a sharp geographical discontinuity between London Boroughs, we show that the incidence of property taxes deferred to the future is too small compared to the incidence of stamp duty taxes happening at the moment of buying the property. The difference in incidence implies very large discount rates that cannot be easily rationalized even after accounting for liquidity constraints. The lack of salience at the moment of purchase implies that the burden of the tax will be borne in the future to meet the budget constraint. This implies that there is an optimal tax mix, even though one of the two taxes is more distortionary than the other.
I analyze the problem of a firm choosing its optimal investment plan by maximising its value in the presence of a simple type of private information regarding future marginal productivity of capital. While, as it is standard in the investment literature, marginal Q is a sufficient statistic for the firm’s investment decision, the presence of foresight complicates the estimation of marginal Q. I show that the shocks recovered by ignoring the problem are not exogenous and can be predicted using past information. Moreover, I propose a news shock which explains a large portion of the variability in investment. Finally, I show that a new measure of marginal Q, constructed by taking into account the presence of foresight, significantly explains corporate investment and reduces the sensitivity of investment to cash-flows.