TECHNICAL NOTE : THE VARIABLE FINANCIAL INDICATOR IRR AND THE CONSTANT ECONOMIC INDICATOR NPV

The IRR (internal rate of return) and the NPV (net present value) are the two most common and important indicators in investment decisions. These two indicators, however, have intrinsic differences between one another. The IRR is a financial indicator and the NPV an economic indicator of a capital investment. The former gives the private investor's point of view and the latter the society's point of view. The value of ERR varies with the change of Financial arrangement of an investment. The NPV, however, does not but remains constant no matter how the financial arrangement changes. This paper uses an illustrative example to show their intrinsic differences and then describes a mathematical proof to substantiate the different natures of IRR and NPV.     

Inferring Transactions from Financial Statements*

In this paper, we embed the double entry accounting structure in a simple belief revision (estimation) problem. We ask the following question: Presented with a set of financial statements (and priors), what is the reader's “best guess” of the underlying transactions that generated these statements? Two properties of accounting information facilitate a particularly simple closed form solution to this estimation problem. First, accounting information is the outcome of a linear aggregation process. Second, the aggregation rule is double entry.     

Common Divisors,Payout Persistence,and Return Predictability

In the finance and accounting literature, the use of a common divisor in the dependent and independent variables of ordinary least‐squares regressions is commonplace. What goes less recognized, however, is that their use induces spurious correlation between the regression variables and invalidates standard testing procedures. This paper analyses the common divisor problem by outlining analytical results concerning the expected R² and providing a simulation procedure that generates test statistics from which critical values can be drawn. To illustrate the procedure, we re‐investigate payout yield return predictability findings that have appeared in the literature and show that the results are spurious.     

THE DETERMINANTS OF HAZARDOUS WASTE DISPOSAL CHOICE: AN EMPIRICAL ANALYSIS OF HALOGENATED SOLVENT WASTE SHIPMENTS

Disposal of hazardous waste is more complex than simply choosing the option with the lowest price: Legislation may hold a waste generator responsible for cleaning up a badly contaminated waste disposal site if the operator of that site goes bankrupt. This paper estimates conditional logit models of a generator's choice of waste management facility (TSDF) for shipments of halogenated solvent waste originating in California in 1995. The probability that a facility is selected as the destination of a shipment depends on the cost of shipping to and disposal at the facility, on existing contamination at the site, and on the track record of the facility, suggesting that generators do seem to balance current disposal costs with the likelihood of future liability. There is no evidence that generators prefer facilities owned by publicly traded firms, but when generators do choose TSDFs belonging to a publicly traded company, they prefer wealthier companies. (JEL Q21)     

A Comment on Establishing the Fair Value of Consideration Given in an Acquisition

The distinction between offer date and acquisition date is important when determining the fair value of shares offered as consideration in an acquisition or takeover. Standards on business combinations have required this to be determined at acquisition date, certainly since 2001, even though earlier opinions such as APB Opinion 16 had allowed the use of offer date. Despite this, Lonergan (2004) continued to favour the use of offer date, using the Wesfarmers takeover of Howard Smith in 2001 as support, and claiming that acquisitions are essentially locked-in from the offer date. But this is generally not the case. This paper shows that acquisition date is correct when conditions change during a takeover.     

THE STRAIGHT STORY ABOUT THE GREENHOUSE EFFECT

During the hot, dry summer of 1988, nearly every newspaper, magazine, and television and radio station in the United States discussed the hot weather and its possible connection with the "greenhouse effect." This public discussion was needed to alert many people to an important topic, but it also led to many misunderstandings about the physical and technical facts and studies involved in considering future climate heating. Some of these misunderstandings originate with the media, some result from the manner in which climate modelers make heating calculations, and a few result from words having meanings that are slightly different to scientists than they are to the general public. The most basic facts that one must understand are as follows, (i) Theory foreseeing a rapid climate heating during the coming decades is based on some of the best understood features of the atmosphere, (ii) The average surface temperature of the earth has increased during the past 120 years, but this increase has been far from uniform and does not prove or disprove that a climate heating is under way. (Hi) The technology is available to begin decreasing rapidly the emission of infrared-trapping gases.     

Forecasts of earnings per share: Possible sources of analyst superiority and bias*

Abstract. Previous research has shown that analysts' forecasts of quarterly earnings per share (EPS) are more accurate than those of accepted time-series models. In addition, some previous research suggests that, on average, analysts' forecasts tend to be optimistic (i.e., biased). Two explanations for analysts' superiority have been proposed: (1) analysts use more recent information than can time-series models and (2) analysts use forecast-relevant information not included in the time-series of past earnings. This paper provides evidence on a third potential source of analyst superiority: the possibility that humans can use past earnings data to predict future earnings more accurately than can mechanical time-series models. We find that human judges do no worse than accepted time-series models when both use the same information set: namely, the series of past EPS figures. To date, little or no research has attempted to determine why analyst bias might exist. Still, some possible reasons have been forwarded. First, pessimistic forecasts or reports may hinder future efforts of the analyst or the analyst's employer to obtain information from the company being analyzed. Second, forecast data bases may suffer a selection bias if analysts tend to stop following those firms that they perceive as performing poorly. This study proposes, and provides evidence regarding, a third possible explanation for analyst bias: the use of judgmental heuristics by analysts. Many studies have shown that human predictions are often biased because of the use of such heuristics. We present evidence that suggests this may be the case for analysts' forecasts of earnings per share. Résumé. De précédents travaux de recherche ont démontré que les prévisions des analystes relatives au bénéfice par action (BPA) trimestriel sont plus exactes que celles que permettent d'obtenir les modèles reconnus basés sur les séries chronologiques. De plus, les résultats de certains travaux de recherche laissent croire qu'en moyenne, les prévisions des analystes tendent à être optimistes (c'est-à-dire biaisées). Deux explications à cette supériorité ont été proposées: 1) l'information que les analystes utilisent est plus récente que celles utilisées dans les modèles fondés sur les séries chronologiques et 2) les analystes utilisent de l'information pertinente aux prévisions qui ne figure pas dans les séries chronologiques relatives aux bénéfices passes. Les auteurs attribuent à un troisième facteur potentiel cette supériorité: la possibilité pour les humains d'utiliser les données relatives aux bénéfices passés pour prédire les bénéfices futurs de façon plus précise que ne le peuvent les modèles fondés sur les séries chronologiques. Ils en viennent à la conclusion que les humains obtiennent des résultats tout aussi efficaces que les modèles chronologiques reconnus lorsqu'ils utilisent un jeu de renseignements identique, soit les données historiques relatives au BPA. Jusqu'à maintenant, peu de chercheurs, sinon aucun, ont tenté de déterminer à quoi tiendrait l'existence d'un biais chez l'analyste. Malgré tout, certaines explications possibles ont été proposées. Premièrement, les prévisions ou les rapports pessimistes peuvent faire obstacle aux efforts futurs de l'analyste ou de son employeur pour obtenir de l'information de la société faisant l'objet de l'analyse. Deuxièmement, les bases de données servant à la prévision peuvent être entachées d'un biais de sélection si les analystes ont tendance à cesser de suivre les entreprises qui leur semblent afficher une piètre performance. Les auteurs proposent et attestent une troisième explication possible du biais de l'analyste: l'utilisation de méthodes heuristiques fondées sur le jugement. De nombreuses études ont démontré que les prédictions humaines sont souvent biaisées par suite de l'utilisation de ces méthodes heuristiques. Les auteurs apportent des arguments qui permettent de croire que ce pourrait être le cas des prévisions des analystes du bénéfice par action.