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.     

Restricted maximum likelihood estimation under Eisenhart model Ill

For a balanced two-way mixed model, the maximum likelihood (ML) and restricted ML (REML) estimators of the variance components were obtained and compared under the non-negativity requirements of the variance components by L ee and K apadia (1984). In this note, for a mixed (random blocks) incomplete block model, explicit forms for the REML estimators of variance components are obtained. They are always non-negative and have smaller mean squared error (MSE) than the analysis of variance (AOV) estimators. The asymptotic sampling variances of the maximum likelihood (ML) estimators and the REML estimators are compared and the balanced incomplete block design (BIBD) is considered as a special case. The ML estimators are shown to have smaller asymptotic variances than the REML estimators, but a numerical result in the randomized complete block design (RCBD) demonstrated that the performances of the REML and ML estimators are not much different in the MSE sense.     

Mean squared error efficient estimators of the variance components

Summary Pseudo Bayesian estimators for the variance components based on Jeffrey’s Rule are derived for the mixed balanced incomplete block design and are compared with the usual analysis of variance estimators in terms of mean squared error (MSE) efficiency. Numerical results show that Pseudo-Bayesian estimators are more efficient in numerical results.     

Delta-Hedged Gains and the Negative Market Volatility Risk Premium

We investigate whether the volatility risk premium is negativeby examining the statistical properties of delta-hedged optionportfolios (buy the option and hedge with stock). Within a stochasticvolatility framework, we demonstrate a correspondence betweenthe sign and magnitude of the volatility risk premium and themean delta-hedged portfolio returns. Using a sample of S&P500 index options, we provide empirical tests that have thefollowing general results. First, the delta-hedged strategyunderperforms zero. Second, the documented underperformanceis less for options away from the money. Third, the underperformanceis greater at times of higher volatility. Fourth, the volatilityrisk premium significantly affects delta-hedged gains, evenafter accounting for jump fears. Our evidence is supportiveof a negative market volatility risk premium.     

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.     

Minimal sufficient statistics for the group divisible partially balanced incomplete block design (GD-PBIBD) with interaction under an Eisenhart Model II

Summary In this paper, an Eisenmhart Model II with interaction for a GD-PBIB design withp replicates per cell is considered. Specifically the Model Y_ijl=µ+_i+_j+()_ij+e_ijl is assumed, wherei=1, 2, ...,b; j=1, 2, ...,t andl=0, 1, 2, ...p s _ij wheres _ij=1, if treatmentj appears in blocki, 0, otherwise.If _i, _j, ()_ij ande _ijl are normally and independently distributed, then a minimal sufficient (Vector-valued) statistic for the class of densities for this model is found, together with the distribution of each component in the minimal sufficient statistic. It is also shown that the minimal sufficient statistic for this class densities is not complete. Hence the solution of the problem of finding minimum variance unbiased estimators of the variance components is not straightforward.     

Complexity, concentration and contagion

This paper develops a network model of interbank lending in which unsecured claims, repo activity and shocks to the haircuts applied to collateral assume centre stage. We show how systemic liquidity crises of the kind associated with the interbank market collapse of 2007–2008 can arise within such a framework, with funding contagion spreading widely through the web of interlinkages. Our model illustrates how greater complexity and concentration in the financial network may amplify this fragility. The analysis suggests how a range of policy measures – including tougher liquidity regulation, macro-prudential policy, and surcharges for systemically important financial institutions – could make the financial system more resilient.     

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.