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This paper revisits the controversial question of whether long-run abnormal returns are associated with major corporate events. Our analyses investigate initial public offerings (IPOs), seasoned equity offerings (SEOs), mergers and acquisitions (M&As), and dividend initiations. In an attempt to resolve ambiguous empirical evidence with respect to these events, we conduct a variety of tests for abnormal long-run performance, including buy-and-hold returns (BHARs), different calendar time approaches, and a recent standardized test. Empirical tests for these different methods consistently detect significant long-run abnormal returns for all four corporate events. We conclude that long-run abnormal returns exist with respect to these major corporate actions.

We study the directional predictability of monthly excess stock market returns in the U.S. and ten other markets using univariate and bivariate binary response models. We introduce a new bivariate (two-equation) probit model that allows us to examine the benefits of predicting the signs of returns jointly, focusing on the predictive power originating from the U.S. to foreign markets. Our in-sample and out-of-sample forecasting results indicate superior predictive performance of the new model over competing univariate binary response models, and conventional predictive regressions, by statistical measures and market timing performance. This highlights the importance of predictive information from the U.S. to the other markets providing also practical improvement in investors' market timing decisions.

Recently, a marked Poisson process (MPP) model for life catastrophe risk was proposed in [6]. We provide a justification and further support for the model by considering more general Poisson point processes in the context of extreme value theory (EVT), and basing the choice of model on statistical tests and model comparisons. A case study examining accidental deaths in the Finnish population is provided. We further extend the applicability of the catastrophe risk model by considering small and big accidents separately; the resulting combined MPP model can flexibly capture the whole range of accidental death counts. Using the proposed model, we present a simulation framework for pricing (life) catastrophe reinsurance, based on modeling the underlying policies at individual contract level. The accidents are first simulated at population level, and their effect on a specific insurance company is then determined by explicitly simulating the resulting insured deaths. The proposed microsimulation approach can potentially lead to more accurate results than the traditional methods, and to a better view of risk, as it can make use of all the information available to the re/insurer and can explicitly accommodate even complex re/insurance terms and product features. As an example we price several excess reinsurance contracts. The proposed simulation model is also suitable for solvency assessment., Comment: 32 pages, 9 figures

Bilevel optimization problems are characterized by a hierarchical leader-follower structure, in which the leader desires to optimize her own strategy taking the response of the follower into account. These problems are referred to as Stackelberg problems in the domain of game theory, and as bilevel problems in the domain of mathematical programming. In a number of practical scenarios, a bilevel problem is solved by a leader who needs to take multiple objectives into account and simultaneously deal with the decision uncertainty involved in modeling the follower’s behavior. Such problems are often encountered in strategic product design, homeland security applications, and taxation policy. However, the hierarchical nature makes the problem difficult to solve and they are commonly simplified by assuming a deterministic setup with smooth objective functions. In this paper, we focus our attention on the development of a flexible evolutionary algorithm for solving multicriterion bilevel problems with lower level (follower) decision uncertainty. The performance of the algorithm is evaluated in a comparative study on a number of test problems. In addition to the numerical experiments, we consider two real-world examples from the field of environmental economics and management to illustrate how the framework can be used to obtain optimal strategies.

The relationship between ubiquitous technologies and organisational matters remains under-theorised. In this article, ubiquitous revolution is scrutinised within the framework of the 'service space' concept. The article argues that the healthcare field evolves so rapidly because of the continual introduction of new technologies and disruptive innovations. The role of healthcare related technologies and the drive by service providers to incorporate them to internet of things systems is likely to herald a significant departure in service delivery in the healthcare sector. The Japanese case study suggests that ubiquitous technologies accelerate social and societal change by focusing greater emphasis on customers' voices in the healthcare 'service space'. Moreover, the complexity and dynamics of this evolving 'service space' provide pivotal challenges for organisational adaptivity challenging the traditional organisational business and knowledge management models used by technology providers in various ways. The article suggests that further research is required to place this topic more firmly on the healthcare agenda.

Electricity distribution is a prime example of local monopoly. In most countries, the costs of electricity distribution operators are regulated by the government. However, the cost regulation may create adverse incentives to compromise the quality of service. To avoid this, cost regulation is often amended with quality incentives. This study applies theory and methods of productivity analysis to model the frontier of service quality. A semi-nonparametric estimation method is developed, which does not assume any particular functional form for the quality frontier, but can accommodate stochastic noise and heteroscedasticity. The empirical part of our paper examines how underground cabling and location affect the interruption costs. As expected, higher proportion of underground cabling decreases the level of interruption costs. The effects of cabling and location on the variance of performance are also considered. Especially the location is found to be a significant source of heteroscedasticity in the interruption costs. Finally, the proposed quality frontier benchmark is compared to the current practice of Finnish regulation system. The proposed quality frontier is found to provide more meaningful and stable basis for setting quality targets than the average practice benchmarks currently in use.

Business travel has increased substantially during the past few decades. Business travel costs are one of the main controllable costs in international corporations, and thus companies are imposing stricter policies on corporate travel to create savings and efficiency. For travel management, the current literature suggests two alternative management strategies based on either a control-oriented or a commitment-oriented approach. In this paper we present an in-depth case study that investigates the impact that each type of strategy has on corporate travel policy compliance. Specifically, we investigate how the strategies are executed in a triadic travel supply chain setting, consisting of a corporate travel buyer, a business travel agency and a technology provider. Our findings show that both the control and commitment-based strategies are used in all stages of the travel process. The seminal finding is that the competitiveness and high quality of services provided internally by the buyer in collaboration with the triad members – rather than strict control and monitoring – is essential to travel policy compliance. This finding shows that corporate travel management shares similar perspectives to leisure travel in that service quality is key to securing business. Furthermore, a proactive approach to control via a well-established and reasonable travel policy is needed.

In the traveling salesman problem with pickup, delivery, and ride-time constraints (TSPPD-RT), a vehicle located at a depot is required to service a number of requests where the requests are known before the route is formed. Each request consists of (i) a pickup location (origin), (ii) a delivery location (destination), and (iii) a maximum allowable travel time from the origin to the destination (maximum ride-time). The problem is to design a tour for the vehicle that (i) starts and ends at the depot, (ii) services all requests, (iii) ensures that each request's ride-time does not exceed its maximum ride-time, and (iv) minimizes the total travel time required by the vehicle to service all requests (objective function). A capacity constraint that may be present is that the weight or volume of the undelivered requests on the vehicle must always be no greater than the vehicle's capacity. In this article, we concurrently analyze the TSPPD-RT with capacity constraints and without capacity constraints. We describe two mathematical formulations of the problem. These formulations are used to derive new lower bounds on the solution to the problem. Then, we provide two exact methods for finding the optimal route that minimizes the total travel cost. Our extensive computational analysis on both versions of the TSPPD-RT shows that the proposed algorithms are capable of solving to optimality instances involving up to 50 requests. © 2015 Wiley Periodicals, Inc. NETWORKS, 2015

Project portfolios for the annual maintenance of infrastructure assets may contain dozens of projects which are selected out of hundreds of candidate projects. In the selection of these projects, it is necessary to account for multiple evaluation criteria, project interdependencies, and uncertainties about project performance as well as financial and other relevant constraints. In this paper, we report how Robust Portfolio Modeling (RPM) has been used repeatedly at the Finnish Transport Agency (FTA) for bridge maintenance programming. At FTA, project selection decisions are guided by the RPM's Core Index values which are derived from portfolio-level computations and reflect incomplete information about the relative importance of evaluation criteria. To-date, this application has been rerun with fresh data for six consecutive years. By drawing on experiences from this application, we discuss preconditions for the successful use of RPM or other methods of Portfolio Decision Analysis in comparable settings. We also develop an approximative algorithm for computing non-dominated portfolios in large project selection problems. We report a repeated application of RPM to bridge maintenance project selection.We identify general features that contributed to the success of this application.We develop an algorithm for solving non-dominated portfolios in large RPM problems.

We consider the discretization and solution of eigenvalue problems of elliptic operators with random coefficients. For solving the resulting systems of equations we present a new and efficient spectral inverse iteration based on the stochastic Galerkin approach with respect to a polynomial chaos basis. The curse of dimensionality inherent in normalization over parameter spaces is avoided by a solution of a non-linear system of equations defining the Galerkin coefficients. For reference we also present an algorithm for adaptive stochastic collocation. Functionality of the algorithms is demonstrated by applying them on four examples of a given model problem. Convergence of the Galerkin-based method is analyzed and the results are tested against the collocated reference solutions and theoretical predictions.

We consider two complementary aspects of mathematical skills, i.e. procedural fluency and conceptual understanding, from a point of view that is related to modern e-learning environments and computer-based assessment. Pedagogical background of teaching mathematics is discussed, and it is proposed that the traditional book medium has determined much of its historical development, including the classical style of presenting mathematical knowledge. Information technology is likely to be an emerging game changer in learning and teaching of mathematics, and we argue that the potential of e-learning platforms extends beyond simple drill exercises to complex problems (having several`right¨solutions) that are expected to improve conceptual understanding. We discuss these ideas referring two experiences and observations from a STACK-based automatic assessment system at Aalto University since 2006. The article is concluded by visions for the future development involving, e.g. learning analytics based on students' answer data.

The dial-a-ride problem (DARP) involves the dispatching of a fleet of vehicles to transport customers requesting service and is one of the most challenging tasks of combinatorial optimization. We study the DARP as a constraint satisfaction problem, where the goal is to find a feasible solution with respect to the time, capacity, and precedence constraints, or to prove infeasibility. The main contribution of our work is a new robust method for this problem formulation. The algorithm is based on a dynamic subroutine that finds for any set of customers a maximum cluster, that is, a maximal set of customers that can be served by a single vehicle. The performance of the algorithm is analyzed and evaluated by means of computational experiments, justifying the efficiency of the solution method.

Business-to-business (B2B) and business-to-consumer (B2C) markets differ in many ways as documented in the contemporary marketing literature. However, many behavioral characteristics of human beings – particularly those related to judgment and decision-making – are present across diverse contexts. From this insight, we derive a proposition: many behavioral price concepts developed in the past B2C behavioral price research may be applicable in B2B context as well. The objective of this paper is to examine this proposition through analyzing the existing evidence on five important behavioral price concepts: reference price, price thresholds, acceptable price range, price as an indicator of quality, and the price–perceived value model. At a more general level, the objective of this paper is to demonstrate the importance of recognizing how buyers' responses to prices and price information differ from the traditional assumptions about such behaviors in B2B marketing literature. The results provide strong evidence for the applicability of the reference price concept in B2B markets. The price–perceived value model is widely applied in B2B pricing, although in narrow form. Use of price as an indicator of quality also receives some support. For price thresholds and acceptable price range little research activity exists in B2B domain. Overall, while there has been some behavioral price research specifically in a B2B context, nevertheless it is comparatively sparse, and for some concepts virtually non-existent. We end the paper with a call that more behavioral price research is needed as such research has potential to help business marketing managers make more effective pricing decisions.

In this paper, we study right half-plane harmonic mappingsf 0 and f, where f 0 is xed and f is such that its dilatation of a conformalautomorphism of the unit disk. We obtain a su cient condition for theconvolution of such mappings to be convex in the direction of the realaxis. The result of the paper is a generalization of the result of by Li andPonnusamy [11], which itself originates from a problem posed by Dor etal. in [7]. 1. IntroductionLet D = fz2C : jzj

This paper presents two new procedures for ranking and selection (R&S) problems where the best system designs are selected from a set of competing ones based on multiple performance measures evaluated through stochastic simulation. In the procedures, the performance measures are aggregated with a multi-attribute utility function, and incomplete preference information regarding the weights that reflect the relative importance of the measures is taken into account. A set of feasible weights is determined according to preference statements that are linear constraints on the weights given by a decision-maker. Non-dominated designs are selected using two dominance relations referred to as pairwise and absolute dominance based on estimates for the expected utilities of the designs over the feasible weights. The procedures allocate a limited number of simulation replications among the designs such that the probabilities of correctly selecting the pairwise and absolutely non-dominated designs are maximized. The new procedures offer ease of eliciting the weights compared with existing R&S procedures that aggregate the performance measures using unique weights. Moreover, computational advantages are provided over existing procedures that identify non-dominated designs based on the expected values of the performance measures. The new procedures allow to obtain a smaller number of non-dominated designs. They also identify these designs correctly with a higher probability or require a smaller number of replications for correct selection. Finally, the new procedures allocate a larger number of replications to the non-dominated designs that are therefore evaluated with greater accuracy. These computational advantages are illustrated through several numerical experiments.

This article utilizes agency theory to explain maverick buying in governmental agencies, that is, noncompliance to centrally negotiated frame agreements. Traditional agency theory assumes the agent to be an opportunistic self-interest seeker. A complementary view of agency problems portrays man as an honest, yet not fully competent, actor; both agents and principals may be burdened by “honest incompetence.” We apply both perspectives on principal-agent relationships to study maverick buying in government procurement and link agency problems to three governance mechanisms: monitoring, training, and guidance. We find that guidance and training help to reduce governmental employees’ noncompliance, but output monitoring does not. Our findings further indicate that maverick buying is related to goal incongruence and two different types of information asymmetry: Agency problems may arise not only because the agent has information the principal is not aware of but also because the principal may have information the agent is not aware of. Future research in public management using agency theory to study instances of hidden action could benefit from applying a similar dual lens to behaviors previously examined as purely opportunistic.

In conversational storytelling, the recipients are expected to show affiliation with the emotional stance displayed by the storytellers. We investigated emotional arousal-related autonomic nervous system responses in tellers and recipients of conversational stories. The data consist of 20 recordings of 45- to 60-minute dyadic conversations between female university and polytechnic students. Conversations were videotaped and analyzed by means of conversation analysis (CA), with a special emphasis on the verbal and nonverbal displays of affiliation in storytelling. Electrodermal activity in both participants was measured to estimate their arousal level. The results show that the verbal and nonverbal displays of affiliation decrease the storyteller’s but increase the recipient’s level of arousal. This means that the monitoring of the recipient actions in storytelling, shown by earlier CA studies, has a physiological correlate. We suggest that storytelling involves an emotional load, which the participants share physiologically in affiliative responses.

For numerical simulations of crowd dynamics in an evacuation we need a computationally light environment, such as the cellular automaton model (CA). By choosing the right model parameters, different types of crowd behavior and collective effects can be produced. But the CA does not answer why, when, and how these different behaviors and collective effects occur. In this article, we present a model, where we couple a spatial evacuation game to the CA. In the game, an agent chooses its strategy by observing its neighbors' strategies. The game matrix changes with the distance to the exit as the evacuation conditions develop. In the resulting model, an agent's strategy choice alters the parameters that govern its behavior in the CA. Thus, with our model, we are able to simulate how evacuation conditions affect the behavior of the crowd. Also, we show that some of the collective effects observed in evacuations are a result of the simple game the agents play.

We analyse the impact of supply uncertainty on newsvendor decisions. First, we derive a solution for a newsvendor facing stochastic supply yield, in addition to stochastic demand. While earlier research has considered independent uncertainties, we derive the optimal order quantity for interdependent demand and supply and provide a closed-form solution for a specific copula-based dependence structure. This allows us to give insights into how dependence impacts the newsvendor’s decision, profit and risk level. In addition to the theory, we present experimental results that show how difficult newsvendor decisions under supply uncertainty are for human subjects. In our experiment, the control group replicated a well-known newsvendor experiment, whereas the test group faced additional supply yield uncertainty. Comparison of these results shows that under low-profit condition, subjects are able to incorporate supply uncertainty quite well in their decisions. Under high-profit condition, the deviation from the o...