Thought leadership in investment banking: The beginning of a new era

The paper argues that although considerations such as industry consolidation, the role of lending and the effects of technology are still important in shaping the future of investment banking, sustainable competitive advantage necessitates that emphasis is also placed on thought leadership. This entails quite a radical change in terms of the way in which the industry approaches the problem of competition and requires emphasis on value creation for all the constituent parts of the industry: corporations, investors, the banks themselves and research departments. Accordingly, the paper provides a rationale for this change and provides inter alia a range of examples to illustrate how thought leadership could lead to a fundamental change in the future of the investment banking industry.     

A play-the-winner-type urn design with reduced variability

We propose a new adaptive allocation rule, the drop-the-loser, that randomizes subjects in the course of a trial comparing treatments with dichotomous outcomes. The rule tends to assign more patients to better treatments with the same limiting proportion as the randomized play-the-winner rule. The new design has significantly less variable allocation proportion than the randomized play-the-winner rule. Decrease in variability translates into a gain in statistical power. For some values of success probabilities the drop-the-loser rule has a double advantage over conventional equal allocation in that it has better power and assigns more subjects to the better treatment. Acknowledgments. I thank Stephen Durham, the associate editor, and the referees for their helpful suggestions.     

Earnings management and abnormal returns: Evidence from the 1970–1972 Price Control Regulations

We examine the association between abnormal returns and earnings management in the context of price control regulations to test the construct validity of the earnings management model. Abnormal returns are used as a market–based measure, and discretionary accruals are employed to measure earnings management. Our results support the hypotheses that (1) price control regulations affect firms' security prices negatively, (2) firms make income–decreasing discretionary accruals to increase the likelihood of price increase approval, and (3) firms that are affected most negatively by the regulations manage earnings more aggressively. We conclude that the earnings management model we use in this study is capable of predicting opportunistic discretionary accruals.     

Testing Rival Theories of Budgetary Decision–making in the US States

This article tests three common budgetary decision–making theories in the US states. Pooled time series cross–section analysis is used from 1960 to 1996 to test the garbage can theory and incrementalism, and from 1989 to 1996 to test various theories of rational budgeting. The results demonstrate that there was some support for all three theories in terms of their impact on reducing state budget outputs. Rational budgeting reduces expenditures in aggregate, incrementalism has relatively low explanatory power below the aggregates, and garbage can budgeting is more prevalent in functional areas than for government as a whole. These findings imply that the future research agenda on budgetary decision–making theories should focus more on a system–wide approach, which takes into account many of the characteristics of all three rival models of decision–making, rather than exclusively focusing on each one singly.     

Density estimation in the uniform deconvolution model

We consider the problem of estimating a probability density function based on data that are corrupted by noise from a uniform distribution. The (nonparametric) maximum likelihood estimator for the corresponding distribution function is well defined. For the density function this is not the case. We study two nonparametric estimators for this density. The first is a type of kernel density estimate based on the empirical distribution function of the observable data. The second is a kernel density estimate based on the MLE of the distribution function of the unobservable (uncorrupted) data.