Pulmonary flow loop

Pulmonary flow loops display characteristic patterns and give clues to the underlying pathology of airway or parenchymal lung disease.     

Pulmonary flow loop

Pulmonary flow loops display characteric patterns which may indicate the underlying pathology of airway or parenchymal lung disease.     

Nonparametric event study tests

This paper provides the first documentation of the power and specification of the generalized sign test, which is based on the percentage of positive abnormal returns in an estimation period. In simulations using daily stock return data, the generalized sign test is well specified with both exchange listed and NASDAQ stocks. A rank test is more powerful under ideal conditions. However, the rank test is more sensitive to increases in the length of the event window, to increases in return variance, and to thin trading. The generalized sign test is a viable alternative to the rank test under these conditions.     

Specification tests in hedonic models

Specification testing can be an effective method for addressing the considerable econometric problems present in hedonic models. In this article, we suggest the use of three different Hausman-type specification tests—tests for the hedonic price equation, each preference equation, and a system of equations—as a way of isolating the sources of misspecification in a hedonic model. Using a national data sample as an example, we illustrate the use of these tests to guide model specification in a simultaneous setting.     

The tests of a prince

When a CEO takes office, stakeholders dissect his or her intellectual, physical, and emotional capacities as they try to gauge whether the new leader will help them fulfill their aspirations and protect them from trouble. For the heir to a family business, the challenge of turning stakeholders into followers is particularly thorny: He or she must manage many constituencies--family members, directors, senior executives, investors, trade unions--that may not be convinced the successor has earned the right to hold the top spot. Making matters worse, says Lansberg, a family business expert, corporate scions usually ignore or greatly underestimate stakeholders. They don't realize that, particularly after they are formally anointed as CEOs, they must establish their credibility with and authority over these spheres of influence. Smart CEOs understand that their success depends on how well they respond to the iterative testing process that stakeholders use to make judgments about would-be leaders. This article offers a road map for managing the four kinds of tests that constitute iterative testing: Qualifying tests are assessments based on criteria--such as formal education, work experience, and professional awards--that executives can cite as evidence of suitability for the top job. Self-imposed tests are expectations that leaders themselves set and against which they assume stakeholders will measure their performance. Circumstantial tests are unplanned challenges or crises, during which stakeholders can observe the leader coping with the unexpected. And political tests are challenges from rivals who want to enhance their own influence, often by undermining the leader.     

Dementia screening using computerized tests

The preclinical phase of dementia usually precedes the clinical diagnosis by many years. Early detection of dementing conditions during this preclinical phase may provide opportunities for treatments that may slow or mitigate progression. Conventional assessment tools usually can only detect dementia when the symptoms are overt and the disease is well-established. Computerized neurocognitive screening tools hold promise for diagnosing dementia in its early phase. The use, performance and development of several computerized screening tools to diagnose and monitor patients with pre-dementias and dementia are reviewed. The ability to accurately assess the presence of dementia clearly has direct relevance to insurance risk assessment and risk management. As new treatments appear, their role in clinical management of dementia patients will increase as well. In a future issue, the differential diagnosis of dementias related to the findings on these screening tools will be reviewed.     

Liquidity biases in asset pricing tests

Microstructure noise in security prices biases the results of empirical asset pricing specifications, particularly when security-level explanatory variables are cross-sectionally correlated with the amount of noise. We focus on tests of whether measures of illiquidity, which are likely to be correlated with the noise, are priced in the cross-section of stock returns, and show a significant upward bias in estimated return premiums for an array of illiquidity measures in Center for Research in Security Prices (CRSP) monthly return data. The upward bias is larger when illiquid securities are included in the sample, but persists even for NYSE/Amex stocks after decimalization. We introduce a methodological correction to eliminate the biases that simply involves weighted least squares (WLS) rather than ordinary least squares (OLS) estimation, and find evidence of smaller, but still significant, return premiums for illiquidity after implementing the correction.     

Exact distribution-free tests of mean-variance efficiency

This paper develops exact distribution-free tests of unconditional mean-variance efficiency. These new tests allow for unknown forms of non-normalities, conditional heteroskedasticity, and other non-linear temporal dependencies among the absolute values of the error terms in the asset pricing model. Exactness here rests on the assumption that the joint temporal error density is symmetric around zero. This still leaves open the possibility of return distribution asymmetry via coskewness with the benchmark portfolio. A simulation study shows that the new tests have very good power relative to that of many commonly used tests. The inference procedures developed are further illustrated by tests of the mean-variance efficiency of a market index using a 42-year sample of monthly returns on ten U.S. equity portfolios.     

Nonparametric rank tests for event studies

Because stock prices are not normally distributed, the power of nonparametric rank tests dominate parametric tests in event study analyses of abnormal returns on a single day. However, problems arise in the application of nonparametric tests to multiple day analyses of cumulative abnormal returns (CARs) that have caused researchers to normally rely upon parametric tests. In an effort to overcome this shortfall, this paper proposes a generalized rank (GRANK) testing procedure that can be used on both single day and cumulative abnormal returns. Asymptotic distributions of the associated test statistics are derived, and their empirical properties are studied with simulations of CRSP returns. The results show that the proposed GRANK procedure outperforms previous rank tests of CARs and is robust to abnormal return serial correlation and event-induced volatility. Moreover, the GRANK procedure exhibits superior empirical power relative to popular parametric tests.     

Multivariate tests of the zero-beta CAPM

A ‘cross-sectional regression test’ (CSRT) of the CAPM is developed and its connection to the Hotelling T² test of multivariate statistical analysis is explored. Algebraic relations between the CSRT, the likehood ratio test and the Langrange multiplier test are derived and a useful small-sample bound on the distribution function of the CSRT is obtained. An application of the CSRT suggests that the CRSP equally-weighted index is inefficient, but that the inefficiency is not explained by a firm size-effect from February to December.     

Cross-sectional tests of deterministic volatility functions

We study the cross-sectional performance of option pricing models in which the volatility of the underlying stock is a deterministic function of the stock price and time. For each date in our sample of FTSE 100 index option prices, we fit an implied binomial tree to the panel of all European style options with different strike prices and maturities and then examine how well this model prices a corresponding panel of American style options. We find that the implied binomial tree model performs no better than an ad-hoc procedure of smoothing Black–Scholes implied volatilities across strike prices and maturities. Our cross-sectional results complement the time-series findings of Dumas et al. [J. Finance 53 (1998) 2059].     

Efficient tests of stock return predictability

Conventional tests of the predictability of stock returns could be invalid, that is reject the null too frequently, when the predictor variable is persistent and its innovations are highly correlated with returns. We develop a pretest to determine whether the conventional t-test leads to invalid inference and an efficient test of predictability that corrects this problem. Although the conventional t-test is invalid for the dividend–price and smoothed earnings–price ratios, our test finds evidence for predictability. We also find evidence for predictability with the short rate and the long-short yield spread, for which the conventional t-test leads to valid inference.     

Asymmetric timeliness tests of accounting conservatism

Recent accounting research employs an asymmetric timeliness measure to test the hypothesis that reported accounting earnings are “conservative.” This research design regresses earnings on stock returns to examine whether “bad” news is incorporated into earnings on a more timely basis than “good” news. We identify properties of the asymmetric timeliness estimation procedure that will result in biases in the test statistics except under very restrictive conditions that are rarely met in typical empirical settings. Using data series that are devoid of asymmetric timeliness in reported earnings, we show how these biases result in evidence consistent with conservatism. We conclude that the biased test statistics inherent in the asymmetric timeliness research design preclude using this method to measure conservatism; that these biases are irresolvable as they originate in the test’s specification; and that studies employing asymmetric timeliness tests cannot be interpreted as providing evidence of conservatism.

Bootstrap refinements in tests of microstructure frictions

Bootstrapping is often used as a substitute for asymptotic distributions when the latter are not available. Recent developments in the theory of the bootstrap show that combining the bootstrap with a known asymptotic distribution yields inferences that improve on those drawn from asymptotic distribution theory or bootstrapping alone. We review the key to obtaining the improvement and compare asymptotic and bootstrap inferences of three variance ratio tests used in microstructure research. The more precise bootstrap inferences lead to conclusions that differ from those found in extant research on transitory volatility. Asymptotic tests are biased toward rejection, and bootstrap and asymptotic critical values are not generally close to each other. These findings suggest that the more precise bootstrap inferences should be used in future applications of these tests, as well as in various other empirical applications where intradaily or other high frequency data are modeled using vector autoregressions     

Empirical tests of the float-adjusted return model

This paper implements empirical tests of the recently proposed float-adjusted return model by using Chinese stock-market data. The results show that variation in free float can explain cross-sectional variation in asset returns by about 6.7% annually, after we control for market risk, size, and book-to-market equity. In addition, we also find that size and book-to-market equity help explain cross-sectional variations in returns even after controlling for free float.     

Systematic stress tests with entropic plausibility constraints

Stress tests with handpicked scenarios might misrepresent risks either because dangerous scenarios are not considered or because the scenarios considered are too implausible. To overcome these two pitfalls we propose a systematic search for the worst case within a relative entropy ball of sufficiently plausible scenarios. For this purpose we use mixed scenarios, which are risk factor distributions rather than realisations. A Maximum Loss theorem explicitly gives the worst case distribution. The method is illustrated in a number of example applications: linear and quadratic portfolios, stressed default probabilities, stressed correlations, macroeconomic stress tests.     

Potential performance and tests of portfolio efficiency

The potential performance of an asset set may be obtained by choosing the portfolio proportions to maximize the Sharpe (1966) performance measure. If a portfolio has a Sharpe measure equivalent to the potential performance of the underlying set of assets, then it is efficient. Multivariate statistical procedures for comparing potential performance and testing portfolio efficiency are developed and then evaluated using simulations. Two likelihood ratio statistics are then used to compare stock and bond indices against sets of 20 and 40 portfolios. The procedures are also compared to the Gibbons (1982) methodology for testing financial models.     

A simple approach to standardized-residuals-based higher-moment tests

We propose a new approach to the higher-moment tests for evaluating the standardized error distribution hypothesis of a conditional mean-and-variance model (such as a GARCH-type model). Our key idea is to purge the effect of estimating the conditional mean-and-variance parameters on the estimated higher moments by suitably using the first and second moments of the standardized residuals. The resulting higher-moment tests have a simple invariant form for various conditional mean-and-variance models, and are also applicable to the symmetry or independence hypothesis that does not involve a complete standardized error distribution. Thus, our tests are simple and flexible. Using our approach, we establish a class of skewness–kurtosis tests, characteristic-function-based moment tests, and Value-at-Risk tests for exploring the standardized error distribution and higher-order dependence structures. We also conduct a simulation to show the validity of our approach in purging the estimation effect, and provide an empirical example to show the usefulness of our tests in exploring conditional non-normality.