This paper is concerned with testing the time series implications of the capital asset pricing model (CAPM) due to Sharpe (1964) and Lintner (1965), when the number of securities, N, is large relative to the time dimension, T, of the return series. In the case of cross-sectionally correlated...

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...

This paper is concerned with testing the time series implications of the capital asset pricing model (CAPM) due to Sharpe (1964) and Lintner (1965), when the number of securities, N, is large relative to the time dimension, T, of the return series. In the case of cross-sectionally correlated...

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...

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...