STOCK MARKET PERCEPTION OF INDUSTRIAL FIRM BANKRUPTCY

The objectives of this study are to determine (1) when the stock market first perceives the impending bankruptcy of a potentially bankrupt firm and (2) what firm-specific factors explain the interval between the perception time and the eventual date of bankruptcy (i.e., market lead time). A computational methodology based on the Hillmer-Yu technique is used to determine the month in which a structural change in the mean and variance of monthly stock return occurs for a potentially bankrupt firm. This parametric change month or the “market perception time” is computed for a sample of 47 industrial firms. The range of market lead times cautions against the common assumption of a uniform event period in event studies. The lead time interval (for both the mean and variance of monthly market return) of poteintially bankrupt firms is found to be positively related to the firm's earnings per share at the time of stock market perception of eventual bankruptcy. Neither the firm's asset size nor systematic risk appear to be significant indicators of lead time interval. Also, change in investment at market perception time is positively related to percentage change in the market lead times. This suggests that innovations in the investment variable are a source of new information to the security market in assesing the probability of future bankruptcy of a firm.     

Corporate governance and returns to emerging economy firm acquisitions in tax havens: An empirical test on Indian firms

Cross‐border acquisitions (CBAs) by emerging economy firms are known to yield positive stockholder returns. A nontrivial fraction of CBAs by emerging economy firms are in tax havens. We argue because of weak corporate governance in emerging economies and the secrecy afforded by tax havens, emerging economy firm CBAs in tax havens yield lower stockholder returns than their CBAs in nontax havens. We also argue the negative effect of tax haven destinations is greater for firms with greater business group ownership and for firms with greater foreign insider ownership. Furthermore, we argue the negative effect of tax haven destinations is mitigated for firms whose stock is actively traded in the market. Empirical tests in a sample of nearly 800 CBAs by Indian firms from 2002 to 2011 support our hypotheses. Our study contributes to a better understanding of stockholder returns to CBAs by emerging economy firms and the influence of corporate governance on these returns.     

The scarring effects of youth joblessness in Sri Lanka

Retrospective data on labor market spells for successive cohorts of youth in the school‐to‐work transition in Sri Lanka are used to examine whether early spells of joblessness lead to subsequent difficulty in finding or keeping a job. A balancing score approach is used to generate pairs of youth in the school‐to‐work transition who have similar expected levels of joblessness but who differ in realized levels of joblessness. Assuming that youth are not able to perfectly control whether they are employed or not employed, we argue that marginal differences in joblessness among otherwise observationally equivalent youth can be viewed similarly to a regression discontinuity in experienced joblessness. We find evidence of scarring in that spending the first year after leaving school without a job or training increases subsequent share of time spent jobless by 23–31 percentage points and lowers subsequent wages by 5.5%–7.5%.     

Effect of green innovation strategy on firm‐idiosyncratic risk: A competitive action perspective

Despite increasing concern for corporate environmental responsibility in numerous industries, the relationship between green innovation strategy (GIS) and idiosyncratic risk is a rarely scrutinised topic, particularly in the automotive domain. In this study, we empirically explore the association between GIS and idiosyncratic risk and analyse the moderating role played by the firm's competitive action. We rely on the secondary information sourced for 132 top automotive firms, in the period ranging from 2011 to 2017 by applying the system generalised methods of moments estimator to the dynamic panel data model. Our findings indicate that GIS significantly reduces the idiosyncratic risk of all firms, and this relationship strengthens with the increase in the competitive action of the firms. Our evidence supports “it pays to be green” firm heterogeneity argument. This study highlights the academic and managerial implications and focuses on the environmental issues published in environmental management literature.     

Allocating a Promotion Budget between Advertising and Sales Contest Prizes: An Integrated Marketing Communications Perspective

This paper develops and analyzes a normative model for allocating a fixed, short-term promotion budget between product advertising and prizes of a rank-order sales contest for a homogeneous sales force when sales are driven by both personal selling effort and advertising. The model provides insights into how the optimal budget allocations vary with the synergy between advertising and selling effort, sales force size, salesperson risk-tolerance, perceived cost of effort, selling effectiveness and sales response uncertainty. The analysis highlights the need for and value of close coordination between marketing and sales management in designing a promotion program involving both advertising and sales force incentives.     

Sales Quota Plans: Mechanisms For Adaptive Learning

A vexing problem in managing sales forces occurs when the sales manager does not know the characteristics of a sales territory. In this paper we show how sales quota-bonus plans can be optimally used to learn about unknown territory characteristics over a multiperiod horizon. We demonstrate this process in two scenarios—one in which the sales manager and the rep are symmetrically informed about the territory characteristics, and the other in which the rep is better informed than the manager. Overall our analysis underscores the importance of Bayesian techniques in sales force management.     

Environments, unobserved heterogeneity, and the effect of market orientation on outcomes for high-tech firms

The interaction between market orientation and facets of the environment is theoretically compelling and is hence the primary interaction studied in market orientation literature. Yet empirical literature offers mixed findings regarding these interaction effects. We suggest that these mixed findings may result from the failure of extant research to control for unobserved heterogeneity that may mask the true relationships among market orientation, facets of the environment, and firm outcomes. Such unobserved heterogeneity might arise due to presence of higher order (e.g., three-way, four-way) moderators (e.g., firm size and innovativeness). To illustrate our assertions on unobserved heterogeneity and the role of firm size and innovativeness, we present two studies that use firm performance or new product performance as the outcome variable; the studies (1) include market orientation, two facets of the environment (technological turbulence and market dynamism), and the interactions between market orientation and facets of the environment as explanatory variables, (2) employ finite mixture regression models to estimate the relationships of interest while explicitly accounting for unobserved heterogeneity in the form of latent regimes (segments), and (3) use firm size and innovativeness as concomitant profiling variables in the finite mixture model specification. The results indicate that disaggregate models (i.e., multi-regime solutions) offer the best fit in both studies. The effects across the latent regimes differ, demonstrating the possibility of an aggregation bias in empirical literature and suggesting the need for using disaggregated analyses to study important marketing phenomena. In theoretical terms, these results also suggest the possibility of developing theories that incorporate unobserved heterogeneity and perhaps higher order (e.g., three-way) interaction effects.     

Dynamic competitive retail pricing behavior with uncertainty and learning

When competing retailers lack full information about rivals' decision processes, how will dynamic pricing behavior vary from patterns observed in more traditional static or full-information models? We investigate this question in a dynamic alternating-moves duopoly model. Retailers update (linear) conjectures about rivals' future prices in a Bayesian fashion. We show that as observed and expected prices converge, a pricing equilibrium is always achieved, whether or not the conjectured and actual values of the slope of the rival's best response function are consistent. Assuming specific parameter values, we compare equilibrium prices and associated profits in our Bayesian learning model with those obtained under the assumptions of static Nash behavior, collusive behavior and dynamically optimal behavior with full information. We apply the notions of strategic substitutability and strategic complementarity to the analysis and find that when products are strategic complements, conjectures of higher rival price responsiveness lead to higher steady-state prices and profits. The reverse is true for strategic substitutes. We also find that learning about a rival's behavior proceeds more quickly, the less intensely related in demand are products. We find, in general, that equilibrium pricing patterns and profits can vary considerably from those in full-information environments, but that even with grossly wrong beliefs about rival behavior, competing retailers are still attracted to an equilibrium. The analysis suggests not only the value of investigating lessthan-full information situations but also the potential incremental value of signalling greater or less aggressiveness than truly characterizes one's behavior as a strategic option.     

Facility location under demand uncertainty: Response to a large-scale bio-terror attack

In the event of a catastrophic bio-terror attack, major urban centers need to efficiently distribute large amounts of medicine to the population. In this paper, we consider a facility location problem to determine the points in a large city where medicine should be handed out to the population. We consider locating capacitated facilities in order to maximize coverage, taking into account a distance-dependent coverage function and demand uncertainty. We formulate a special case of the maximal covering location problem (MCLP) with a loss function, to account for the distance-sensitive demand, and chance-constraints to address the demand uncertainty. This model decides the locations to open, and the supplies and demand assigned to each location. We solve this problem with a locate-allocate heuristic. We illustrate the use of the model by solving a case study of locating facilities to address a large-scale emergency of a hypothetical anthrax attack in Los Angeles County.