Search Committees

A committee decides by unanimity whether to accept the current alternative, or to continue costly search. Alternatives are described by several distinct attributes. Each committee member privately assesses the quality of one attribute (her \Committee, Search, Specialization, Interdependent Values, Voting, Partisanship

Classification

In Classification learning, an algorithm is presented with a set of classified examples or ‘‘instances’’ from which it is expected to infer a way of classifying unseen instances into one of several ‘‘classes’’. Instances have a set of features or ‘‘attributes’’ whose values define that particular instance. Numeric prediction, or ‘‘regression,’’ is a variant of classification learning in which the class attribute is numeric rather than categorical. Classification learning is sometimes called supervised because the method operates under supervision by being provided with the actual outcome for each of the training instances. This contrasts with Data clustering (see entry Data Clustering), where the classes are not given, and with Association learning (see entry Association Learning), which seeks any association – not just one that predicts the class

The Interrelation between Audit Quality and Managerial Reporting Choices and Its Effects on Financial Reporting Quality

Two distinct lines of research have been dedicated to empirically testing how financial reporting quality (measured as the earnings response coefficient or ERC) is associated with management's choice of reporting bias and with audit quality. However, researchers have yet to consider how ERCs are affected by either the auditor's reaction to changes in the manager's reporting bias or the manager's reaction to changes in audit quality. Our study provides theoretical guidance on these interrelations and how changes in the manager's or the auditor's incentives affect both reporting bias and audit quality. Specifically, when the manager's cost (benefit) of reporting bias increases (decreases), we find that expected bias decreases, inducing the auditor to react by reducing audit quality. Because we also find that the association between expected audit quality and ERCs is always positive, changes in managerial incentives for biased reporting lead to a positive association between ERCs and expected reporting bias. When the cost of auditing decreases or the cost of auditor liability increases, we find that expected audit quality increases, inducing the manager to react by decreasing reporting bias. In this case, changes in the costs of audit quality lead to a negative association between ERCs and expected reporting bias. Finally, we demonstrate the impact of our theoretical findings by focusing on the empirical observations documented in the extant literature on managerial ownership and accounting expertise on the audit committee. In light of our framework, we provide new interpretations of these empirical observations and new predictions for future research

Ensemble Committees for Stock Return Classification and Prediction

This paper considers a portfolio trading strategy formulated by algorithms in the field of machine learning. The profitability of the strategy is measured by the algorithm's capability to consistently and accurately identify stock indices with positive or negative returns, and to generate a preferred portfolio allocation on the basis of a learned model. Stocks are characterized by time series data sets consisting of technical variables that reflect market conditions in a previous time interval, which are utilized produce binary classification decisions in subsequent intervals. The learned model is constructed as a committee of random forest classifiers, a non-linear support vector machine classifier, a relevance vector machine classifier, and a constituent ensemble of k-nearest neighbors classifiers. The Global Industry Classification Standard (GICS) is used to explore the ensemble model's efficacy within the context of various fields of investment including Energy, Materials, Financials, and Information Technology. Data from 2006 to 2012, inclusive, are considered, which are chosen for providing a range of market circumstances for evaluating the model. The model is observed to achieve an accuracy of approximately 70% when predicting stock price returns three months in advance.Comment: 15 pages, 4 figures, Neukom Institute Computational Undergraduate Research prize - second plac

Stochastic Dominance in Wheat Variety Development and Release Strategies

Variety development and release decisions involve tradeoffs between yields and characteristics valued by end-users, as well as uncertainties about agronomic, quality, and economic variables. In this study, methods are developed to determine the value of varieties to growers and end-users including the effects of variability in economic, agronomic, and quality variables. The application is to hard red spring (HRS) wheat, a class of wheat for which these tradeoffs and risks are particularly apparent. Results indicate two experimental varieties provide improvements in grower and end-user value, relative to incumbents. Stochastic dominance techniques and statistical tests are applied to determine efficient sets and robustness of the results. A risk-adjusted portfolio model, which simultaneously incorporates correlations between grower and end-use characteristics, is also developed to compare the portfolio value of varieties.end-user value, grower value, portfolio value, stochastic dominance, tradeoffs, variety development, wheat, Crop Production/Industries,

Do recruiters prefer applicants with similar skills? Evidence from a randomized natural experiment

In this paper we examine the potential existence of a similar-to-me effect in terms of skills between recruiters and applicants. Using evidence from entry exams to the Spanish Judiciary, where applicants are randomly assigned across evaluation committees, we find that committee members tend to be more demanding at those stages where they are more knowledgeable. As a result, applicants who excel in the same dimensions as recruiters are more likely to be hire

Does the National Institute for Health and Clinical Excellence take account of factors such as uncertainty and equity as well as incremental cost-effectiveness in commissioning health care services? A binary choice experiment

Background: NICE is an independent organisation responsible for providing national guidance on the promotion of good health and the prevention and treatment of ill health in England and Wales. One of NICE’s main roles is to produce national guidance on the use of health technologies within the NHS. Despite the Institute’s recent efforts to clarify the way in which its Appraisal Committees reach their recommendations concerning the use of health technologies, there remains ambiguity about how cost-effectiveness evidence is interpreted alongside other considerations such as the degree of clinical need within the patient population, and the degree of uncertainty surrounding cost-effectiveness estimates. Objective: To explore whether the NICE takes account of factors such as uncertainty and equity as well as incremental cost-effectiveness in commissioning health care services. Methods: A binary choice experiment was undertaken using NICE’s three Appraisal Committees. The experiment included five attributes: (1) Incremental cost-effectiveness (2) Degree of economic uncertainty (3) Age of the target population (4) Baseline health-related quality of life (5) Availability of other therapies A choice questionnaire detailing 18 scenarios was administered to NICE’s Appraisal Committees. For each scenario, respondents were asked to indicate whether they would recommend the intervention under consideration or not. The stated preference data obtained from respondents were analysed using a random effects logit regression model. Results: A response rate of 46% was obtained from the Appraisal Committees. The regression model suggests that increases in cost-effectiveness, economic uncertainty, and the availability of other therapies are associated with statistically significant reductions in the odds of adoption (p<0.05). The transition from a very low to a comparatively high level of health-related quality of life is also associated with a statistically significant reduction in the odds of a positive recommendation. Smaller changes in health-related quality of life, and the age of the target population are not associated with a statistically significant reduction in the odds of a positive recommendation. Analysis of revealed preference data indicates that the model is capable of distinguishing between those technologies which the Appraisal Committees would be highly likely to recommend, and those technologies which appear to be less attractive, although further external validation is warranted. Conclusion: The modelling suggests that cost-effectiveness, uncertainty and certain equity concerns influence the NICE Appraisal Committees’ recommendations on the use of health technologies. The modelling results appear to support Rawlins and Culyer’s notion of a probabilistic cost-effectiveness threshold approach; the "mythical" £30,000 per QALY gained threshold assumed within the literature is not supported by this stated preference modelling analysis