By allowing for imperfectly informed markets and the role of private information, we offer new insights about observed deviations of portfolio concentrations in domestic relative to foreign risky assets, or home bias, from what standard finance models predict. Our model ascribes the bias to...

Bitcoin in particular and so-called cryptocurrencies in general have shaken up the financial world and seem to be claiming an increasing size of the market share. These new virtual assets present investors with significant opportunities, but also with significant risks. This paper analyzes the...

By allowing for imperfectly informed markets and the role of private information, we offer new insights about observed deviations of portfolio concentrations in domestic relative to foreign risky assets, or "home bias", from what standard finance models predict. Our model ascribes the "bias" to...

We combine a customized survey and randomized controlled trial (RCT) to study the effect of higher-order beliefs on U.S. retail investors' portfolio allocations. We find that investors' higher-order beliefs about stock market returns are correlated with but distinct from their first-order...

This review paper articulates the relationship between prediction market data and event studies, with a special focus on applications in political economy. Event studies have been used to address a variety of political economy questions - from the economic effects of party control of government...

This paper is concerned with empirical and theoretical basis of the Efficient Market Hypothesis (EMH). The paper begins with an overview of the statistical properties of asset returns at different frequencies (daily, weekly and monthly), and considers the evidence on return predictability, risk...

We challenge the recent claim that mispricing in the experimental asset markets introduced by Smith, Suchanek, and Williams (1988) is merely an artefact of confusion over declining fundamental value, and can be eliminated through appropriate training. We instead propose that when training is...

A common approach to dealing with missing data is to estimate the model on the common subset of data, by necessity throwing away potentially useful data. We derive a new probit type estimator for models with missing covariate data where the dependent variable is binary. For the benchmark case of...