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Risk‐adjustment is designed to predict healthcare costs to align capitated payments with an individual's expected healthcare costs. This can have the consequence of reducing overpayments and incentives to under treat or reject high cost individuals. This paper seeks to review recent studies presenting risk‐adjustment models.

This paper presents a brief discussion of two commonly reported statistics used for evaluating the accuracy of risk adjustment models and concludes with recommendations for increasing the predictive accuracy and usefulness of risk‐adjustment models in the context of predicting future healthcare costs.

Over the last decade, many advances in risk‐adjustment methodology have been made. There has been a focus on the part of researchers to transition away from including only demographic data in their risk‐adjustment models to incorporating patient data that are more predictive of healthcare costs. This transition has resulted in more accurate risk‐adjustment models and models that can better identify high cost patients with chronic medical conditions.

The paper shows that the transition has resulted in more accurate risk‐adjustment models and models that can better identify high cost patients with chronic medical conditions.

The purpose of this paper is to evaluate the predictive capacity of market risk estimation models in times of financial crises.

For this, value-at-risk (VaR) valuation models applied to the daily returns of portfolios composed of stock indexes of developed and emerging countries were tested. The Historical Simulation VaR model, multivariate ARCH models (BEKK, VECH and constant conditional correlation), artificial neural networks and copula functions were tested. The data sample refers to the periods of two international financial crises, the Asian Crisis of 1997, and the US Sub Prime Crisis of 2008.

The results pointed out that the multivariate ARCH models (VECH and BEKK) and Copula-Clayton had similar performance, with good adjustments in 100 percent of the tests. It was not possible to perceive significant differences between the adjustments for developed and emerging countries and of the crisis and normal periods, which was different to what was expected.

Previous studies focus on the estimation of VaR by a group of models. One of the contributions of this paper is to use several forms of estimation.

The purpose of this paper is to argue that Bernoulli's “utility function solution to the St Petersburg paradox” was wrong and to find a new method to solve the paradox.

This goal is attained through two ways: using Bernoulli's and Kramer's utility function to construct new paradoxes; and designing and implementing a new St Petersburg game which does not carry the effect of diminishing marginal utility.

In this paper, the author finds that Bernoulli's “utility function solution to the St Petersburg paradox” was wrong, and also finds a new model to solve the paradox, which is also a brand‐new model of estimates under uncertainty.

Bernoulli put forward the diminishing marginal utility of currency and thus accordingly provided the utility function solution to solve the paradox. This paper indicates that the Bernoulli's utility function solution does not work. Thus, further research needs to be taken in several aspects: is the diminishing marginal utility of currency tenable? Does the marginal utility of currency decrease monotonically? Are concave utility functions represented by negative index functions which are widely used in theoretical study reasonable?

The paper proposes a brand‐new possible research idea and direction for economic theoretical researches based on uncertainty.

This paper proved the untenability of the utility function solution to solve the St Petersburg paradox for the first time and proposed the pioneering “risk adjustment model” of estimates under uncertainty.

The purpose of this study is to examine the capital budgeting strategies that are used by foreign subsidiaries of U.S.‐based multinational enterprises. While the results indicated a preference for sophisticated capital budgeting techniques as the primary method of analysis, the actual use of sophisticated capital budgeting techniques by foreign managers may not be as widespread as expected by financial theorists. Although it was found that certain environmental and company‐specific factors influenced the level of sophistication of capital budgeting practices used by U.S. foreign subsidiaries, the associations were small and had only minor explanatory significance. The results showed that foreign subsidiaries exposed to high levels of political and financial risk tended to use sophisticated capital budgeting strategies. Subsidiaries characterized by high levels of financial leverage and high cost of capital requirements also employed advanced capital budgeting strategies. Multinational enterprises (MNEs) have many options available to them in terms of how they manage their foreign subsidiaries. Traditionally, most major policy decisions were made at the parent firm's headquarter office while foreign subsidiaries had few opportunities to influence major corporate decisions. Today, more companies are using a flexible approach which involves setting strategic goals at the home office and allowing local managers to implement their own specific policies. An important question in this study involved determining how effective local foreign managers were in implementing their capital budgeting processes. As U.S.‐based MNEs continue to expand their operations abroad, there is an increased need to examine which financial decision models are actually used by subsidiary managers to deal with the increased complexity of investing in foreign countries. Unlike traditional capital budgeting analysis, international analysis is a considerably more complex process. These complexities occur for a number of reasons including complicated cash flows estimates, changes in foreign exchange rates, different accounting systems, potential for blocked funds, and political risk considerations. These factors are rarely experienced by traditionally domestic U.S. firms. To maintain a competitive edge, MNEs must continue to use the most efficient approaches available to them. This study provides a detailed analysis of the capital budgeting practices that are actually being used by foreign subsidiaries of U.S.‐based MNEs. The paper is organized in the following manner. Section I provides a brief overview of the theoretical and practical issues of international capital budgeting analysis. Section II focuses on the areas of data collection, questionnaire design, and environment‐specific and company‐specific factors. Section III discusses usage of capital budgeting techniques, adjustment and assessment of project risk, and factors influencing capital budgeting policies. The final section presents some findings from this study.

This study examines the value relevance of recognised and disclosed revaluations of land and buildings for a large sample of Australian firms from 1993 through 1997. In contrast to prior research, we control for risk and cyclical effects and find no difference between recognised and disclosed revaluations, using yearly‐cross‐sectional and pooled regressions and using both market and non‐market dependent variables. We also find only weak evidence that revaluations of recognised and disclosed land and buildings are value relevant.

In order to develop our understanding of valuation models and so extend this to encompass the important area of performance measurement and its interpretation, it is essential to have a framework which will enable such developments to take place. This paper presents a theoretical model based on a certainty equivalent approach which enables the market risk of individual properties and portfolios to be assessed on an expectations basis. The data requirements for using the model are not onerous and with simple extensions it can be adapted to cope with changes in risk that occur when variations in the lease structure are anticipated. Understanding the influence of systematic or market risk is essential if our understanding of valuation is to improve. Systematic risk is the single most important factor which determines the premium which should be allowed to compensate for risk. This aspect has been largely ignored in the property literature with the result that risk premium figures are frequently assumed to be constant across all sectors and properties. This paper derives a model which attempts to overcome some of these problems. Due to data limitations empirical tests of the model cannot be regarded as conclusive. However, those tests that have been carried out suggest that the model could be used for estimating the required rate of return of both sectors and individual properties. It also has considerable potential in estimating growth expectations for groups of properties and can thus be used in the decision‐making process. Much, however, remains to be done.

The purpose of this paper is to identify and analyse the readmission risk prediction tools reported in the literature and their benefits when it comes to healthcare organisations and management.

Readmission risk prediction is a growing topic of interest with the aim of identifying patients in particular those suffering from chronic diseases such as congestive heart failure, chronic obstructive pulmonary disease and diabetes, who are at risk of readmission. Several models have been developed with different levels of predictive ability. A structured and extensive literature search of several databases was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-analysis strategy, and this yielded a total of 48,984 records.

Forty-three articles were selected for full-text and extensive review after following the screening process and according to the eligibility criteria. About 34 unique readmission risk prediction models were identified, in which their predictive ability ranged from poor to good (c statistic 0.5–0.86). Readmission rates ranged between 3.1 and 74.1% depending on the risk category. This review shows that readmission risk prediction is a complex process and is still relatively new as a concept and poorly understood. It confirms that readmission prediction models hold significant accuracy at identifying patients at higher risk for such an event within specific context.

Since most prediction models were developed for specific populations, conditions or hospital settings, the generalisability and transferability of the predictions across wider or other contexts may be difficult to achieve. Therefore, the value of prediction models remains limited to hospital management. Future research is indicated in this regard.

This review is the first to cover readmission risk prediction tools that have been published in the literature since 2011, thereby providing an assessment of the relevance of this crucial KPI to health organisations and managers.

On May 17, 2012, the social networking company Facebook Inc. fixes its initial public offering (IPO) price at $38.00 a share. Over the next couple of months, contrary to expectations raised by previous IPOs, the stock price crashes more than 50 per cent. Immediately, the question arises whether the issuer’s or the stock market’s pricing of the share are in line with the firm’s fundamentals. Thus, the purpose of this paper is to determine the company value in close proximity to the date of IPO.

As Facebook is an archetypal internet growth company, it is evaluated using the Schwartz/Moon model. This approach features significant advantages over traditional valuation models and more adequately captures the characteristics of growth companies.

As of September 30, 2012, the fundamental share value determined was $26.53, which exceeded the market price per share of $22.66 by 22.48 per cent, but was far less than the IPO stock price. The subsequent sensitivity analysis reveals the robustness of the result to key input parameters.

The results raise doubts about the IPO price of Facebook. Furthermore, this paper is of value from a more conceptual perspective in that an extended version of the Schwartz/Moon model is provided. Beyond extensions previously discussed in the subject-based literature, the authors include stochastic interest rates (as an additional source of uncertainty) and investigate their valuation effects.

The aim of this paper is the discussion and the dissemination of initiatives promoted by the Lombardy region for the construction of benchmarking systems between regional health structures of care utilizing administrative archives.

The paper focuses on relative effectiveness (specific effect of care on patients) in a benchmarking framework, considering the dimension of sentinel outcomes. From Lombardy Hospital Discharge Cards proxies of sentinel outcomes are identified, defined as “context indicators” useful for a benchmarking analysis.

First, the authors present outcomes and covariates at different levels (patient and healthcare structure) extracted from the Lombardy Hospital Discharge Cards for a benchmarking analysis. Second, empirical results show a consistent quota of outcome variability between structures of care and weak agreement between estimated rankings for context indicators. Finally, a slicing approach is suggested in order to apply an equitable comparison among healthcare structures.

The paper provides regional stakeholders with practical implications regarding available strategies (outcomes, statistical methodology, risk adjustment) for consistent processes of evaluation, in a benchmarking framework, based on existing regional administrative data.

After having presented available information contained in regional archives for a benchmark analysis, empirical results were discussed about context indicators, presenting indications and strategies for a refinement of the approach. From a methodological point of view, the utilization of multilevel models (improving methodological strategies adopted by international agencies) in large administrative databases is proposed.

Report cards, performance evaluations, and quality assessments continue to penetrate the lexicon of the healthcare sector. The value of report cards is typically couched as enhancing consumerism among patients, increasing accountability among healthcare providers, and more broadly increasing the transparency of healthcare information. This paper discusses the potential benefits and pitfalls of these performance assessments.

This paper briefly reviews empirical evidence regarding the impact of report cards for healthcare providers and synthesizes the role and limitations of these performance measures into distinct evaluation criteria. The rapid proliferation of report cards for healthcare providers suggests a growing need to develop mechanisms and tools to evaluate their impact. The risks associated with utilizing report cards for provider oversight include the deleterious impact on vulnerable populations and a failure to accurately measure quality of care. The capacity to create report cards should not be the sole criterion to develop and utilize report cards to evaluate healthcare providers. Rather, careful consideration of the benefits and risks should accompany the implementation and utilization of report cards into regulatory processes. This report proposes an evaluation checklist by which to assess the role of report cards in a given healthcare context.