[The quality of capacity planning significantly affects firm profitability, particularly for firms in service industries. In practice, firms use product cost data to infer the expected cost of under- and over-stocking capacity and to determine installed capacity. Theory shows that this is not optimal practice. In light of the informational and computational complexities associated with the optimal theoretical formulation, the use of product cost may be justified as a heuristic. For a multi-product, multi-resource firm, we use simulations to investigate the efficiency of four cost-based decision rules in determining the expected cost of under- and over-stocking capacity. Results indicate surprisingly high performance levels, relative to a benchmark solution. The performance of the product-based planning rule deteriorates as products increasingly share capacity resources. The opposite is true for resource-focused rules. There appears to be significant value from identifying mechanisms to balance installed capacity across resources.]
In this article, the authors provide a unified approach to joint cost allocation for situations where allocation is needed. First, with the help of an illustration, the apparent weakness of Moriarity's scheme is discussed. Later, certain desirable properties of Louderback's method are shown. Employing their "propensity to contribute" concept, the authors utilize the desirable aspects of both the Moriarity and the Louderback schemes to come up with a model which is shown to be in the core. With game-theoretic concepts, a "modified Shapley Value" to allocate the joint cost is provided.
The alternative versions of the t ‐test found in event studies result from different weighting schemes for abnormal returns, different abnormal return models, and different correlational structures among abnormal returns. In the presence of dependencies among abnormal returns, the generalized least squares t ‐tests are much more sensitive to the mis‐specifications in the abnormal return model than are the nongeneralized t ‐tests. Therefore, when analyzing contemporaneous returns, particularly with samples exhibiting a large industry concentration, a nongeneralized t ‐test should be preferred to a generalized least squares t ‐test because of the dependencies that may exist. Because the generalized least squares t ‐tests are highly sensitive to errors in specifying an appropriate abnormal return model, a portfolio time‐series ordinary least squares regression should be preferred to a generalized least squares regression even when the variance or covariance matrix of abnormal returns can be estimated with a high degree of reliability. In testing for the mean effects, the concern for event period variance increases seems to be unwarranted, and the variance estimators using event period data are inefficient and biased. The issue is not whether variance increases in an event period, but which variance is to be used: that of the event period or of the nonevent period? Answers to such questions are presented in this paper. Résumé. Les différentes versions possibles du test t que l'on trouve dans les études d'événements résultent de différents systèmes de pondération des rendements anormaux, de différents modèles de rendements anormaux et de différentes structures de corrélation des rendements anormaux. Lorsqu'il existe une dépendance entre les rendements anormaux, les tests t généralisés des moindres carrés sont beaucoup plus sensibles aux défauts de construction du modèle des rendements anormaux que ne le sont les tests t non généralisés. C'est pourquoi lorsqu'on analyse des rendements simultanés, en particulier si l'échantillon présente une forte concentration industrielle, le test t non généralisé est préférable au test t généralisé des moindres carrés, compte tenu des dépendances qui peuvent exister. Les tests t généralisés des moindres carrés étant très sensibles aux défauts de construction du modèle approprié de rendements anormaux, l'application de la méthode classique des moindres carrés à une série chronologique relative à un portefeuille est préférable à la régression généralisée des moindres carrés, même s'il est possible d'estimer avec un degré élevé de fiabilité la matrice de variance ou de covariance des rendements anormaux. Dans le test des effets moyens, la préoccupation relative aux augmentations de la variance de la période d'événements semble être injustifiée, et les estimateurs de la variance fondés sur les données de la période d'événements sont inefficients et biaisés. Il ne s'agit pas de déterminer si la variance augmente pendant la période d'événements, mais quelle variance doit être utilisée: celle de la période d'événements ou une autre. Les auteurs répondent à ces questions.
This paper analyzes the incentive effects of alternative legal systems on the auditor's decision making process. The first system, termed strict liability, holds the auditor liable whenever there is a loss, and the second system, termed negligence, holds the auditor liable for losses arising from the auditor's deviation from a prescribed due care standard. The auditor is assumed to have ex ante limited knowledge of the financial state of the client and the standard setting process. He revises his beliefs on the client's financial state based on a costless signal that may be interpreted as an internal audit report. The analysis shows that signal “quality” and auditor's perception of the client's financial state jointly determine the level of liability required to induce the auditor to adopt a socially optimal due care level. As in Simon's (1982) Model, negligence with supplementary insurance is preferred to strict liability. Further, negligence is shown to operate with less information than strict liability, in contrast to models such as Green (1978) and Shavell (1978). Résumé. Cet article analyse les effets incitatifs de systèmes juridiques alternatifs sur le processus décisionnel du vérificateur. Le premier système, dit de responsabilité stricte, considère le vérificateur responsable à chaque fois qu'il y a une perte, et le deuxième système, dit de négligence, considère le vérificateur responsable des pertes résultant de sa déviance d'une norme prescrite. Le vérificateur est supposé posséder ex ante une connaissance limitée de la situation financière du client ainsi que du processus de normalisation. Il révise ses opinions portant sur la situation financière du client à la lumière d'un signal sans frais qui peut être assimilé à un rapport de vérification interne. L'analyse montre qu'un signal «qualité» ainsi que la perception du vérificateur quant à la situation financière du client déterminent conjointement le niveau de responsabilité requis pour inciter le vérificateur à retenir un niveau de soin raisonnable socialement optimal. De même que le modèle de Simon (1982), le système de négligence avec assurances additionnelles est préféré à la responsabilité stricte. De plus, il est montré que le système de négligence fonctionne avec moins d'information que le système de responsabilité stricte, par opposition à d'autres modèles tels Green (1978) et Shavell (1978).
OLS regression ignores both heteroscedasticity and cross‐correlations of abnormal returns; therefore, tests of regression coefficients are weak and biased. A Portfolio OLS (POLS) regression accounts for correlations and ensures unbiasedness of tests, but does not improve their power. We propose Portfolio Weighted Least Squares (PWLS) and Portfolio Constant Correlation Model (PCCM) regressions to improve the power. Both utilize the heteroscedasticity of abnormal returns in estimating the coefficients; PWLS ignores the correlations, while PCCM uses intra‐and inter‐industry correlations. Simulation results show that both lead to more powerful tests of regression coefficients than POLS.
OLS regression ignores both heteroscedasticity and cross-correlations of abnormal returns; therefore, tests of regression coefficients are weak and biased. A Portfolio OLS (POLS) regression accounts for correlations and ensures unbiasedness of tests, but does not improve their power. We propose Portfolio Weighted Least Squares (PWLS) and Portfolio Constant Correlation Model (PCCM) regressions to improve the power. Both utilize the heteroscedasticity of abnormal returns in estimating the coefficients; PWLS ignores the correlations, while PCCM uses intra-and inter-industry correlations. Simulation results show that both lead to more powerful tests of regression coefficients than POLS.
The quality of capacity planning significantly affects firm profitability, particularly for firms in service industries. In practice, firms use product cost data to infer the expected cost of under- and over-stocking capacity and to determine installed capacity. Theory shows that this is not optimal practice. In light of the informational and computational complexities associated with the optimal theoretical formulation, the use of product cost may be justified as a heuristic. For a multi-product, multi-resource firm, we use simulations to investigate the efficiency of four cost-based decision rules in determining the expected cost of under- and over-stocking capacity. Results indicate surprisingly high performance levels, relative to a benchmark solution. The performance of the product-based planning rule deteriorates as products increasingly share capacity resources. The opposite is true for resource-focused rules. There appears to be significant value from identifying mechanisms to balance installed capacity across resources.
This paper examines the relationship between the level of short interest and stock returns in the Nasdaq market from June 1988 through December 1994. We find that heavily shorted firms experience significant negative abnormal returns ranging from −0.76 to −1.13 percent per month after controlling for the market, size, book‐to‐market, and momentum factors. These negative returns increase with the level of short interest, indicating that a higher level of short interest is a stronger bearish signal. We find that heavily shorted firms are more likely to be delisted compared to their size, book‐to‐market, and momentum matched control firms.