The focus of this study is the role of default risk in capital market theory. The impact of default risk on the value of securities has been a major concern of investors and academics alike. Several authors have examined the relationship between bond ratings, the probability of default, and security value [5, 12]. In this context, the ability to avoid or reduce expected bankruptcy costs and thereby increase value has been suggested as a reason for mergers and consolidations [16, 18]. In other studies, models have been developed for predicting ratings [17, 20, 21, 28], for predicting bankruptcy using accounting and other financial variables [1, 6, 7], and for approximating default premiums in the credit markets [22]. Finally a question which has received considerable attention is the effect of bankruptcy on a company's cost of capital. When bankruptcy is possible and there exists a positive bankruptcy transaction cost, it has been argued that there is an optimal capital structure [24, 26].

The article presents information on content of general price level earnings. The criticism of the use of a binomial test in which the author compare the number of cases in which the General Price Level (GPL) correlation coefficients exceed the historical cost (HC) coefficients, are correct. The problem with the test is that the observations are not independent. The results clearly indicate that, in the tests that we conducted, GPL earnings outperfouued HC earnings. The authors present results for the difference between GPL correlations and HC correlations. Based on the results in this table, the conclusions of this paper are criticized. The test is performed for three earnings ratios-net income to common equity, net income to market value of common equity and net income plus depreciation to market value of common equity. In addition, the test is performed for three portfolio sizes-individual securities, portfolios of five securities and portfolios often securities. The results for two out of the three earnings definitions, reveal that GPL earnings outperform HC earnings at the five percent significance level.

This paper examines the price effect of institutional stock trading, using a unique data set that reports the transactions (large and small) of 37 large institutional money management firms. The direction of each trade and the identity of the management firm behind each trade are known. Although institutional trades are associated with some price pressure, we find that the average effect is small. There is also a marked asymmetry between the price impact of buys versus sells. We relate our findings to various hypotheses on the elasticity of demand for stocks, the cost of executing transactions, and the determinants of market impact. Although market capitalization and relative trade size influence the market impact of a trade, the dominant influence is the identity of the money manager behind the trade.

Recent literature in accounting, finance, and economics often assumes that information can be processed efficiently. Among the outputs of the processing activity are the presumably appropriate assessments of the underlying probability distributions for all important variables, and a good deal of the recent research assumes that observable realizations of the variables are drawn from these distributions. This paper provides evidence concerning the ability of selected individuals, namely security analysts, to provide estimates of earnings per share after presumably processing the available information. Several aspects of the quality of analyst forecasts are examined. The study indicated, as expected, that analysts' forecasts become more accurate as the reporting date is approached. Furthermore, the predictions of changes in earnings per share data contain no significant systematic bias. However, the authors do not find sufficient support for the expected decline in forecast variability among analysts as the reporting date is approached.

We compare execution costs (market impact plus commission) on the New York Stock Exchange (NYSE) and Nasdaq for institutional investors. The differences in cost generally conform to each market's area of specialization. Controlling for firm size, trade size, and the money management firm's identity, costs are lower on Nasdaq for trades in comparatively smaller firms, while costs for trading the larger stocks are lower on NYSE. The cost differences estimated from a regression model are, however, sensitive to the choice of time period.

The authors compare execution costs (market impact plus commission) on the New York Stock Exchange (NYSE) and Nasdaq for institutional investors. The differences in cost generally conform to each market's area of specialization. Controlling for firm size, trade size, and the money management firm's identity, costs are lower on Nasdaq for trades in comparatively smaller firms, while costs for trading the larger stocks are lower on NYSE. The cost differences estimated from a regression model are, however, sensitive to the choice of time period.

We compare execution costs (market impact plus commission) on the New York Stock Exchange (NYSE) and Nasdaq for institutional investors. The differences in cost generally conform to each market's area of specialization. Controlling for firm size, trade size, and the money management firm's identity, costs are lower on Nasdaq for trades in comparatively smaller firms, while costs for trading the larger stocks are lower on NYSE. The cost differences estimated from a regression model are, however, sensitive to the choice of time period.

All trades executed by thirty-seven large investment management firms from July 1986 to December 1988 are used to study the price impact and execution cost of the entire sequence('package') of trades that the authors interpret as an order. The authors find that market impact and trading cost are related to firm capitalization, relative package size, and, most importantly, to the identity of the management firm behind the trade. Money managers with high demands for immediacy tend to be associated with larger market impact.