Defining Ownership: An empirical assessment of the ownership measures

Ownership is considered to be one of the crucial governance mechanisms; however, there have been no systematic attempts at validating the construct and measures used to operationalize ownership. We review the current understanding of ownership and the measures used by each perspective, namely blockholder/dispersed shareholder perspective, owner identity perspective, and aggregated ownership perspective. We thereafter critique each of these perspectives, offer hypotheses regarding their validity, and empirically assess each ownership measure vis‐à‐vis firm performance outcomes. We utilize a sample of 3,990 US firms to test our hypotheses and find no consistent results for the blockholder measure, or for the owner identity measure. However, the aggregated ownership measure consistently accounts for significant increases in explanation of variance in firm performance. Copyright © 2018 ASAC. Published by John Wiley & Sons, Ltd.     

Whose firm is it anyway? Analyzing ownership effects on IPO performance

This study revisits current practice that ownership holding at IPO has a homogeneous impact on IPO performance. Using signalling theory, we develop and test a conceptual model explaining the relationships between the aggregated ownership structure and IPO price premium. We argue that aggregated ownership has a direct effect on issue price premium, and offer specific hypotheses on the effect of the shares sold during the offering by each type of owner on IPO performance. We use archival data from a sample of US firms that issued IPOs between 1996 and 2000 and find a significant direct effect of ownership configuration, namely, heterogeneity in effect of each ownership type on IPO performance as well as interaction effects between different ownership types. Copyright © 2013 ASAC. Published by John Wiley & Sons, Ltd.     

Analyzing Real Estate Data Problems Using the Gibbs Sampler

Real estate data are often characterized by data irregularities: missing data, censoring or truncation, measurement error, etc. Practitioners often discard missing- or censored-data cases and ignore measurement error. We argue here that an attractive remedy for these irregularity problems is simulation-based model fitting using the Gibbs sampler. The style of the paper is primarily pedagogic, employing a simple illustration to convey the essential ideas, unobscured by implementation complications. Focusing on the missing-data problem, we show dramatic improvement in inference by retaining rather than deleting cases of partially observed data. We also detail Gibbs-sampler usage for other data problems.     

Regulation of the iberoatlantic sardine fishery

The basic aim of this paper is to use a case study to illustrate the way in which controls can achieve an optimum exploitation of marine resources. The specific fishery under study is the Iberoatlanti sardine fishery. A bioeconomic deterministic model for this fish resource is developed and applied. The most relevant results and estimates are showed. Finally, we present some economic policy recommendations for the fishery, as well as considerations for the application of the regulatory measures.I thank M. C. Gallastegui for all his help. I also thank the anonymous referees for helpful comments and suggestions.     

Disentangling CEO compensation: A simultaneous examination of time,industry, and firm‐level effects

Despite decades of research, how CEO compensation is determined remains an enigma. Drawing on agency, managerial hegemony, and institutional theoretical perspectives, we use hierarchical linear modelling—a multilevel analytic technique—to examine how firm‐, industry‐, and time‐level effects drive CEO compensation in US corporations. Results show that while cash salary is mostly driven by firm‐specific factors, equity‐based compensation responds to time‐level effects with firm‐ and industry‐level effects playing a marginal role. We argue that such evidence is consistent with the institutionalization of the CEO compensation determination process through the widespread adoption of benchmark peer‐group comparisons. Such practices underlie economy‐wide changes in CEO compensation that are increasingly disconnected from other fundamental firm‐ or industry‐specific factors. Copyright © 2015 ASAC. Published by John Wiley & Sons, Ltd.     

Sri Lanka’s Rural Non-Farm Economy: Removing Constraints to Pro-Poor Growth

We use a survey of rural enterprises from Sri Lanka to explore characteristics of the informal non-farm sector and identify obstacles to its expansion and productivity. Value added in this sector amounts to 80% of agricultural GDP participating households’ incomes are significantly higher than those of households who do not participate. Barriers to entry are low and the impact of non-farm development on inequality modest, implying a large potential contribution to growth and poverty reduction. Infrastructure constraints negatively affect new startups, investment in, and productivity of existing enterprises, with small enterprises being particularly affected.     

Saying Sayonara to the Farm: Hierarchical Bayesian Modeling of Farm Exits in Japan

Off‐farm employment opportunities are thought to have an effect on farm exit rates, though evidence on the sign of this effect has been mixed. Examining this issue in the context of Japanese agriculture, we find that farm exits are related to off‐farm income as a share of household income, and more specifically to the nature of off‐farm work. Two econometric models are developed: a hierarchical Bayesian linear model and a hierarchical Bayesian Poisson model. Both models perform well in predicting exit rates across the towns and prefectures of Japan.     

Computing optimal rebalance frequency for log-optimal portfolios

Log-optimal investment portfolio is deemed to be impractical and cost-prohibitive due to inherent need for continuous rebalancing and significant overhead of trading cost. We study the question of how often a log-optimal portfolio should be rebalanced for any given finite investment horizon. We develop an analytical framework to compute the expected log of portfolio growth when a given discrete-time periodic rebalance frequency is used. For a certain class of portfolio assets, we compute the optimal rebalance frequency. We show that it is possible to improve investor log utility using this quasi-passive or hybrid rebalancing strategy. Simulation studies show that an investor shall gain significantly by rebalancing periodically in discrete time, overcoming the limitations of continuous rebalancing.     

Computing optimal rebalance frequency for log-optimal portfolios in linear time

The pure form of log-optimal investment strategies are often considered to be impractical due to the inherent need for continuous rebalancing. It is however possible to improve investor log utility by adopting a discrete-time periodic rebalancing strategy. Under the assumptions of geometric Brownian motion for assets and approximate log-normality for a sum of log-normal random variables, we find that the optimum rebalance frequency is a piecewise continuous function of investment horizon. One can construct this rebalance strategy function, called the optimal rebalance frequency function, up to a specified investment horizon given a limited trajectory of the expected log of portfolio growth when the initial portfolio is never rebalanced. We develop the analytical framework to compute the optimal rebalance strategy in linear time, a significant improvement from the previously proposed search-based quadratic time algorithm.