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 portfolios of business units, and the key to risk reduction is both diversification and synchronization compensation. This study introduces “desynchronicity”, a process that operationalizes synchronization compensation by assessing the degree of correlation between income streams of business units. Two samples of 737 and 332 firms (from COMPUSTAT) were used to empirically test the relationships between diversification and risk, and desynchronicity and risk. The results show that diversification alone will not always lead to a lower corporate risk. To reduce risk, firms also need to consider the desynchronicity of their business portfolios. Other practical implications include improved decisions on portfolio composition.

We revisit the problem of calculating the exact distribution of optimal investments in a mean variance world under multivariate normality. The context we consider is where problems in optimisation are addressed through the use of Monte-Carlo simulation. Our findings give clear insight as to when Monte-Carlo simulation will, and will not work. Whilst a number of authors have considered aspects of this exact problem before, we extend the problem by considering the problem of an investor who wishes to maximise quadratic utility defined in terms of alpha and tracking errors. The results derived allow some exact and numerical analysis. Furthermore, they allow us to also derive results for the more traditional non-benchmarked portfolio problem.

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 framework is extended to derive the corresponding results when the asset returns are multivariate Student’s t. In particular, we derive the probability density function and the moments of the cross-sectional momentum returns and examine in detail the special case of two underlying assets to demonstrate that many of the salient features reported in the empirical literature are consistent with the theoretical implications.

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 of aggregate dispersion only exists prior to 2005. The investor sentiment index, as a proxy of short-sale constraints used by many studies, can only explain the dispersion effect prior to 2005. The investor sentiment index and other proxies such as institutional ownership and put options cannot explain the significant weakening of the dispersion effect after the global financial crisis. We argue that the dispersion-return relation is partly driven by the correlation between dispersion and conditional equity premium. Our evidence suggests that the short-sale constrained stocks do not experience a higher dispersion effect, which is contrary to what the overpricing theory predicts.

While risk tolerance is often elicited using questionnaire-based instruments, in this paper, we evaluate the merits of an inversion-based technique, wherein risk aversion parameters are inferred from an individual’s portfolio holdings and a sequence of realized returns. We obtain expressions for the finite sample and asymptotic variance of the estimated risk aversion parameter under the inversion approach with a single risky asset, demonstrating that confidence intervals for parameter estimates are relatively wide. Extending the analysis, we show that inferring risk aversion from multiple risky assets does not typically serve to reduce the estimated parameter variance, but rather propagates estimation error.