SelectedWorks @ Melbourne Business School (The University of Melbourne)
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The Role of Ethical Standards in the Relationship Between Religious Social Norms and M&A Announcement Returns Title
No full textPrior studies suggest that firms headquartered in areas with strong religious social norms have higher ethical standards. In this study, we examine whether the ethical standards associated with local religious norms influence the M&A announcement returns. We document that the M&A announcement returns of acquirer firms increase with the strength of religious social norms in the area surrounding firms’ headquarters. We also document that the relationship is attenuated when acquirer firms have strong corporate social responsibility credentials, is amplified when public trust that firms act in the best interest of stakeholders suffers a negative shock and when the M&A deal has greater economic significance for the acquirer, and manifests predominantly in the lower tail of the distribution of M&A returns. Our findings are consistent with investor assessments of firms’ ethical standards driving the relationship between local religious social norms and M&A announcement returns. We find no evidence for the competing explanation—that investor assessments of firms’ risk preferences drive the documented relationship
Inversion copulas from nonlinear state space models with an application to inflation forecasting
No full textWe propose the construction of copulas through the inversion of nonlinear state space models. These copulas allow for new time series models that have the same serial dependence structure as a state space model, but with an arbitrary marginal distribution, and flexible density forecasts. We examine the time series properties of the copulas, outline serial dependence measures, and estimate the models using likelihood-based methods. Copulas constructed from three example state space models are considered: a stochastic volatility model with an unobserved component, a Markov switching autoregression, and a Gaussian linear unobserved component model. We show that all three inversion copulas with flexible margins improve the fit and density forecasts of quarterly U.S. broad inflation and electricity inflation
Time Series Copulas for Heteroskedastic Data
No full textWe propose parametric copulas that capture serial dependence in stationary heteroskedastic time series. We suggest copulas for first-order Markov series, and then extend them to higher orders and multivariate series. We derive the copula of a volatility proxy, based on which we propose new measures of volatility dependence, including co-movement and spillover in multivariate series. In general, these depend upon the marginal distributions of the series. Using exchange rate returns, we show that the resulting copula models can capturetheir marginal distributions more accurately than univariate and multivariate generalized autoregressive conditional heteroskedasticity models, and produce more accurate value-at-risk forecasts
The influence of CEO equity incentives on licensing
No full textIn a study of life science firms, we find that, in accordance with predictions drawn from agency theory and behavioral agency theory, CEO stock ownership is negatively associated with licensing while CEO stock options are positively associated with licensing. Furthermore, by combining theoretical insights from the capabilities literature with both agency theory and behavioral agency theory, we predict that a key measure of capabilities in the licensing context—a firm\u27s alliance experience—significantly influences the ways in which CEO equity incentives impact licensing. More specifically, we find that, in accordance with our theoretical predictions, alliance experience positively (negatively) moderates the relationship between CEO stock ownership (CEO stock options) and licensing. Our study contributes to the wider literature on the determinants of licensing by examining whether licensing is sensitive to CEO equity incentives. We also extend the capabilities literature on licensing by examining the contrasting influences of a firm\u27s alliance experience on the relationship between CEO equity incentives and licensing. Our findings also inform behavioral agency-based research on the effects of equity incentives by highlighting the usefulness of a capabilities perspective in augmenting our understanding of the behavioral role of CEO equity incentives
Behavioral Agency and Social Norms.docx
No full textDrawing on arguments from institutional theory, this study examines how social norms—specifically, local religious social norms—affect the motivational impact of equity-based incentives. We test our model using longitudinal data on local religious norms, CEO equity incentives and firm value. Consistent with our theoretical predictions, we find that local religious social norms attenuate the impact of CEO option incentives upon firm value. Furthermore, we find that the attenuating impact of local religious social norms increases with managerial discretion. These findings provide valuable insight for human resource professionals and boards aiming to design compensation contracts that are aligned with firm goals. Our findings also contribute to research on the motivational effect of equity incentives by demonstrating importance of considering the social context in which executives are embedded
Biases in Charitable Giving to International Humanitarian Aid: The Role of Psychic Distance
No full textAt a time when government support for international humanitarian aid is decreasing, organizations devoted to helping in times of disaster are looking ever more to the individual donor for financial contributions. In this paper, we explore the relationship between the donor and the distant other by introducing the concepts of psychic distance and psychic distance stimuli to the macromarketing literature and exploring the role of psychic distance in fundraising for international humanitarian aid. It is our contention that by better understanding the biases that psychic distance introduce into the system, an improved flow of donations for the betterment of the distant needy and a more effective marketing system can be achieved. We offer four propositions for future testing and exploration
CEO RISK-TAKING AND SOCIOEMOTIONAL WEALTH: THE BEHAVIORAL AGENCY MODEL, FAMILY CONTROL, AND CEO OPTION WEALTH
No full textWe combine behavioral agency and family business literature to analyze the role of dominant firm principals in constraining the managerial agent’s (CEO’s) response to equity-based pay. Behavioral agency research has made progress in understanding CEO risk behavior in response to equity-based incentives and family firm risk behavior driven by concentrated socioemotional and financial firm-specific risk bearing. However, both literatures have evolved independently, which has limited our understanding of how the risk bearing of agent and principal influence the predictions of the behavioral agency model (BAM). We combine these literatures in order to enhance BAM’s predictive validity with regard to firm risk-taking as a function of both agent and principal risk preferences. Our findings suggest that family principals are more likely than non-family principals to constrain CEO risk behavior that is perceived as immoderate (excessively risk-averse or excessively risk-seeking). We also offer evidence that CEO ties to the family influence the CEO’s response to equity based incentives. In doing so, we offer refinements to BAM’s formulation and advance our understanding of the unique nature of agency problems within family firms
Gaussian variational approximation with a factor covariance structure
No full textVariational approximations have the potential to scale Bayesian computations to large datasets and highly parameterized models. Gaussian approximations are popular, but can be computationally burdensome when an unrestricted covariance matrix is employed and the dimension of the model parameter is high. To circumvent this problem, we consider a factor covariance structure as a parsimonious representation. General stochastic gradient ascent methods are described for efficient implementation, with gradient estimates obtained using the so-called re-parametrization trick . The end result is a flexible and efficient approach to high-dimensional Gaussian variational approximation. We illustrate using robust P-spline regression and logistic regression models. For the latter, we consider eight real datasets, including datasets with many more covariates than observations, and another with mixed effects. In all cases, our variational method provides fast and accurate estimates
The Role of Affect in Shaping the Behavioral Consequences of CEO Option Incentives
No full textWe advance behavioral agency theory by exploring the influence of mood or “affect” on thebehavioral consequences of stock option incentives. Drawing on insights from psychology andbehavioral decision theory, we describe how affect influences agent risk behavior. We argue thatpositive affect amplifies both the extent to which executives reduce strategic risk taking inresponse to risk bearing and engage in strategic risk taking in response to incentives for furtherenrichment. Building again on the psychology literature, we describe how CEO accountabilityattenuates the influence of affect on CEO risk behavior in response to stock option incentives.We test our expectations in a longitudinal data set of CEO stock option incentives, affect, andstrategic risk taking by U.S. firms
Real-Time Macroeconomic Forecasting with a Heteroskedastic Inversion Copula
No full textThere is a growing interest in allowing for asymmetry in the density forecasts of macroeconomic variables. In multivariate time series, this can be achieved with a copula model, where both serial and cross-sectional dependence is captured by a copula function, and the margins are nonparametric. Yet most existing copulas cannot capture heteroskedasticity well, which is a feature of many economic and financial time series. To do so, we propose a new copula created by the inversion of a multivariate unobserved component stochastic volatility model, and show how to estimate it using Bayesian methods. We fit the copula model to real-time data on five quarterly U.S. economic and financial variables. The copula model captures heteroskedasticity, dependence in the level, time-variation in higher moments, bounds on variables and other features. Over the window 1975Q1 -- 2016Q2, the real-time density forecasts of all the macroeconomic variables exhibit time-varying asymmetry. In particular, forecasts of GDP growth have increased negative skew during recessions.The point and density forecasts from the copula model are competitive with those from benchmark models -- particularly for inflation, a short term interest rate and current quarter GDP growth