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It is important that the medical profession play a significant role in critically evaluating the use of diagnostic procedures and therapies as they are introduced in the detection, management,

/pdf/acc-aha-2006-guidelines-for-the-management-of-patients-with-27qfh9fc7i.pdf

ACC/AHA 2006 Guidelines for the Management of Patients With Valvular Heart Disease

Research in experimental psychology suggests that, in violation of Bayes' rule, most people tend to "overreact" to unexpected and dramatic news events. This study of market efficiency investigates whether such behavior affects stock prices. The empirical evidence, based on CRSP monthly return data, is consistent with the overreaction hypothesis. Substantial weak form market inefficiencies are discovered. The results also shed new light on the January returns earned by prior "winners" and "losers." Portfolios of losers experience exceptionally large January returns as late as five years after portfolio formation. As ECONOMISTS INTERESTED IN both market behavior and the psychology of individual decision making, we have been struck by the similarity of two sets of empirical findings. Both classes of behavior can be characterized as displaying overreaction. This study was undertaken to investigate the possibility that these phenomena are related by more than just appearance. We begin by describing briefly the individual and market behavior that piqued our interest. The term overreaction carries with it an implicit comparison to some degree of reaction that is considered to be appropriate. What is an appropriate reaction? One class,,of tasks which have a well-established norm are probability revision problems for which Bayes' rule prescribes the correct reaction to new information. It has now been well-established that Bayes' rule is not an apt characterization of how individuals actually respond to new data (Kahneman et al. [14]). In revising their beliefs, individuals tend to overweight recent information and underweight prior (or base rate) data. People seem to make predictions according to a simple matching rule: "The predicted value is selected so that the standing of the case in the distribution of outcomes matches its standing in the distribution of impressions" (Kahneman and Tversky [14, p. 416]). This rule-of-thumb, an instance of what Kahneman and Tversky call the representativeness heuristic, violates the basic statistical principal that the extremeness of predictions must be moderated by considerations of predictability. Grether [12] has replicated this finding under incentive compatible conditions. There is also considerable evidence that the actual expectations of professional security analysts and economic forecasters display the same overreaction bias (for a review, see De Bondt [7]). One of the earliest observations about overreaction in markets was made by J. M. Keynes:"... day-to-day fluctuations in the profits of existing investments,

/pdf/does-the-stock-market-overreact-3jczd3xcx2.pdf

Does the Stock Market Overreact

Using daily stock returns: The case of event studies

Corporate disclosure is critical for the functioning of an efficient capital market. Firms provide disclosure through regulated financial reports, including the financial statements, footnotes, management discussion and analysis, and other regulatory filings. In addition, some firms engage in voluntary communication, such as management forecasts, analysts? presentations and conference calls, press releases, internet sites, and other corporate reports. Finally, there are disclosures about firms by information intermediaries, such as financial analysts, industry experts, and the financial press.

Information Asymmetry, Corporate Disclosure and the Capital Markets: A Review of the Empirical Disclosure Literature

This paper presents some empirical results of a study predicting corporate failure as evidenced by the event of bankruptcy. There have been a fair number of previous studies in this field of research; the more notable published contributions are Beaver [1966; 1968a; 1968b], Altman [1968; 1973], Altman and Lorris [1976], Altman and McGough [1974], Altman, Haldeman, and Narayanan [1977], Deakin [1972], Libby [1975], Blum [1974], Edmister [1972], Wilcox [1973], Moyer [1977], and Lev [1971]. Two unpublished papers by White and Turnbull [1975a; 1975b] and a paper by Santomero and Vinso [1977] are of particular interest as they appear to be the first studies which logically and systematically develop probabilistic estimates of failure. The present study is similar to the latter studies, in that the methodology is one of maximum likelihood estimation of the so-called conditional logit model. The data set used in this study is from the seventies (1970-76). I know of only three corporate failure research studies which have examined data from this period. One is a limited study by Altman and McGough [1974] in which only failed firms were drawn from the period 1970-73 and only one type of classification error (misclassification of failed firms) was analyzed. Moyer [1977] considered the period 1965-75, but the sample of bankrupt firms was unusually small (twenty-seven firms). The

Financial ratios and the probabilistic prediction of bankruptcy

At the turn of the century, ratio analysis was in its embryonic state. It began with the development of a single ratio, the current ratio,' for a single purpose-the evaluation of credit-worthiness. Today ratio analysis involves the use of several ratios by a variety of users-including credit lenders, credit-rating agencies, investors, and management.2 In spite of the ubiquity of ratios, little effort has been directed toward the formal empirical verification of their usefulness. The usefulness of ratios can only be tested with regard to some particular purpose. The purpose chosen here was the prediction of failure, since ratios are currently in widespread use as predictors of failure. This is not the only possible use of ratios but is a starting point from which to build an empirical verification of ratio analysis. "Failure" is defined as the inability of a firm to pay its financial obligations as they mature. Operationally, a firm is said to have failed when any of the following events have occurred: bankruptcy, bond default, an overdrawn bank account, or nonpayment of a preferred stock dividend.3 A "financial ratio" is a quotient of two numbers, where both num-

Financial Ratios As Predictors Of Failure

https://www.econ.kyoto-u.ac.jp/~hujii/myweb/GradSchool%202013%20AccA%20Barth%202001.pdf

The relevance of the value relevance literature for financial accounting standard setting: another view $

Relative valuation roles of equity book value and net income as a function of financial health

http://lib.cufe.edu.cn/upload_files/other/4_20140516103837_2.pdf

The information content of security prices

We develop a model that captures the distinct natures of and interactions between conditional and unconditional conservatism. Under unconditional conservatism, the book value of net assets is understated due to predetermined aspects of the accounting process. Under conditional conservatism, book value is written down under sufficiently adverse circumstances, but not up under favorable circumstances. The specification of earnings provided by the model yields hypotheses about how unconditional conservatism and other factors preempt conditional conservatism and so affect the asymmetric response of earnings to positive and negative share returns, both current and lagged, documented by Basu (1995, “Conservatism and the Asymmetric Timeliness of Earnings.” Ph.D. dissertation, University of Rochester’ 1997, “The Conservatism Principle and the Asymmetric Timeliness of Earnings.” Journal of Accounting and Economics 24, 3–37).

William H. Beaver

Papers

Financial Ratios As Predictors Of Failure

The relevance of the value relevance literature for financial accounting standard setting: another view $

Relative valuation roles of equity book value and net income as a function of financial health

The information content of security prices

Conditional and Unconditional Conservatism:Concepts and Modeling