Insider Trading in the OTC Market

In this paper, we examine the profitability of insider trading in firms whose securities trade in the OTC/NASDAQ market. Although the evidence suggests timing and forecasting ability on the part of insiders, high transaction costs (especially bid-ask spreads) appear to eliminate the potential for positive abnormal returns from active trading. By implication, outside investors who mimic the trading of insiders are also precluded from earning abnormal profits. In addition, we provide evidence on the determinants of insiders' profits. The data suggest that insiders closer to the firm trade on more valuable information than insiders removed from the firm.     

SMOKE-FREE LAWS AND EMPLOYEE TURNOVER

This study examines how smoke-free laws influence turnover among restaurant workers. The study uses a unique data set of payroll records of a franchisee of a national full-service restaurant chain operating 23 restaurants in the state of Arizona, a state where several communities have adopted smoke-free laws. Municipal smoke-free laws did not, on average, have a statistically significant effect on the probability of employee separation in the years after implementation. These results suggest that training costs associated with employee turnover would not rise for full-service restaurants in municipalities that adopt smoke-free laws. ( JEL I18, J63)     

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)     

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.