We show how to combine statistically efficient ways to design discrete choice experiments based on random utility theory with new ways of collecting additional information that can be used to expand the amount of available choice information for modeling the choices of individual decision...

Louviere et al. (2008, J. of Choice Modelling, 1, 126-163) present two main empirical examples in which a respondent rank orders the options in various choice sets by repeated best, then worst, choice. They expand the ranking data to various implied choices in subsets and fit the expanded data...

Recent macro-finance contributions explain a great deal of unconditional asset pricing by introducing persistent consumption risks and rare disasters. Only the volatility puzzles remain unresolved among the longer-established issues in this literature. Motivated by empirical finance...

A longstanding objective of managers is to reduce risk to their businesses. The conventional strategy for risk reduction is diversification; however, evidence for the effectiveness of diversification remains inconclusive. According to Organizational Portfolio Analysis, firms are viewed as...

In Kwon and Satchell (2018), a theoretical framework was introduced to investigate the distributional properties of the cross-sectional momentum returns under the assumption that the vector of asset returns over the ranking and holding periods were multivariate normal. In this paper, the...

Prior studies found that analyst forecast dispersion predicts future market returns. Some prior studies attribute this predictability to the short-sale constraints in the market according to the overpricing theory. Using the U.S. data from 1981 to 2014, we find that the return predictive power...

This paper provides a new approach to recover relative entropy measures of contemporaneous dependence from limited information by constructing the most entropic copula (MEC) and its canonical form, namely the most entropic canonical copula (MECC). The MECC can effectively be obtained by...