Cross-Industry Product Diversification and Contagion in Risk and Return: The Case of Bank-Insurance and Insurance-Bank Takeovers

We investigate the impact of domestic/international bancassurance deals on the risk-return profiles of announcing and non-announcing banks and insurers within a GARCH model. Bank-insurance deals produce intra- and inter-industry contagion in both risk and return, with larger deals producing greater contagion. Bidder banks and peers experience positive abnormal returns, with the effects on insurer peers being stronger than those on bank peers. Insurance-bank deals produce insignificant excess returns for bidder and peer insurers and positive valuations for peer banks. Following the deal, the bank bidders’ idiosyncratic (systematic) risk falls (increases), while insurance bidders exhibit a lower systematic risk and maintain their idiosyncratic risk.

Diversification, size and risk: The case of bank acquisitions of nonbank financial firms

We investigate the risk effects of bank acquisitions of insurance companies and securities firms between 1991 and 2012 using a newly constructed dataset of M&A deals. We examine risk changes before and after deal announcements by decomposing risk into systematic and idiosyncratic components. Subsequently, we investigate the relationship between risk and diversification by modelling the determinants of risks. We find that bank combinations with securities firms yield higher risks than combinations with insurance companies. Bank size is an important and consistent determinant of risk whereas diversification is not. Our results inform the continuing debate on diversification versus functional separation of bank activities.

Prediction of breast cancer risk based on profiling with common genetic variants

BACKGROUND: Data for multiple common susceptibility alleles for breast cancer may be combined to identify women at different levels of breast cancer risk. Such stratification could guide preventive and screening strategies. However, empirical evidence for genetic risk stratification is lacking. METHODS: We investigated the value of using 77 breast cancer-associated single nucleotide polymorphisms (SNPs) for risk stratification, in a study of 33 673 breast cancer cases and 33 381 control women of European origin. We tested all possible pair-wise multiplicative interactions and constructed a 77-SNP polygenic risk score (PRS) for breast cancer overall and by estrogen receptor (ER) status. Absolute risks of breast cancer by PRS were derived from relative risk estimates and UK incidence and mortality rates. RESULTS: There was no strong evidence for departure from a multiplicative model for any SNP pair. Women in the highest 1% of the PRS had a three-fold increased risk of developing breast cancer compared with women in the middle quintile (odds ratio [OR] = 3.36, 95% confidence interval [CI] = 2.95 to 3.83). The ORs for ER-positive and ER-negative disease were 3.73 (95% CI = 3.24 to 4.30) and 2.80 (95% CI = 2.26 to 3.46), respectively. Lifetime risk of breast cancer for women in the lowest and highest quintiles of the PRS were 5.2% and 16.6% for a woman without family history, and 8.6% and 24.4% for a woman with a first-degree family history of breast cancer. CONCLUSIONS: The PRS stratifies breast cancer risk in women both with and without a family history of breast cancer. The observed level of risk discrimination could inform targeted screening and prevention strategies. Further discrimination may be achievable through combining the PRS with lifestyle/environmental factors, although these were not considered in this report.

A novel framework for predicting patients at risk of readmission

Uncertainty in decision-making for patients’ risk of re-admission arises due to non-uniform data and lack of knowledge in health system variables. The knowledge of the impact of risk factors will provide clinicians better decision-making and in reducing the number of patients admitted to the hospital. Traditional approaches are not capable to account for the uncertain nature of risk of hospital re-admissions. More problems arise due to large amount of uncertain information. Patients can be at high, medium or low risk of re-admission, and these strata have ill-defined boundaries. We believe that our model that adapts fuzzy regression method will start a novel approach to handle uncertain data, uncertain relationships between health system variables and the risk of re-admission. Because of nature of ill-defined boundaries of risk bands, this approach does allow the clinicians to target individuals at boundaries. Targeting individuals at boundaries and providing them proper care may provide some ability to move patients from high risk to low risk band. In developing this algorithm, we aimed to help potential users to assess the patients for various risk score thresholds and avoid readmission of high risk patients with proper interventions. A model for predicting patients at high risk of re-admission will enable interventions to be targeted before costs have been incurred and health status have deteriorated. A risk score cut off level would flag patients and result in net savings where intervention costs are much higher per patient. Preventing hospital re-admissions is important for patients, and our algorithm may also impact hospital income.

Chancen und herausforderungen der rückversicherung in der gesetzlichen krankenversicherung (Chances and challenges of reinsurance in the social health scheme)

In 2009 a so-called morbidity orientated risk structure equalization scheme was installed for the German statutory health insurance in order to minimize structural differences between different providers with respect to revenue and expenditures. Even with this mechanism some risks to the individual health insurance providers remain. Reinsurance could be a way to mitigate these risks, but so far only very few contracts have been signed. Moreover the existing reinsurance contracts only focus on the periphery of the statutory health insurance system such as travel health insurance. In this article we therefore analyse existing risks for individual health insurance providers and evaluate their (re-)insurability. Hereafter the potential for reinsurance solutions in the German statutory health insurance itself as well as in newer forms of healthcare provision (e.g. integrated health care and managed care) is discussed. We find that reinsurance may be a reasonable solution for many of the risks in the statutory health insurance scheme. But as research in this area is very young further analysis of the nature of risks is necessary.

The Role of Emotion in Global Warming Policy Support and Opposition

Prior research has found that affect and affective imagery strongly influence public support for global warming. This article extends this literature by exploring the separate influence of discrete emotions. Utilizing a nationally representative survey in the United States, this study found that discrete emotions were stronger predictors of global warming policy support than cultural worldviews, negative affect, image associations, or sociodemographic variables. In particular, worry, interest, and hope were strongly associated with increased policy support. The results contribute to experiential theories of risk information processing and suggest that discrete emotions play a significant role in public support for climate change policy. Implications for climate change communication are also discussed.