Caveat Emptor: Does Bitcoin Improve Portfolio Diversification?

Bitcoin is an unregulated digital currency originally introduced in 2008 without legal tender status. Based on a decentralized peer-to-peer network to confirm transactions and generate a limited amount of new bitcoins, it functions without the backing of a central bank or any other monitoring authority. In recent years, Bitcoin has seen increasing media coverage and trading volume, as well as major capital gains and losses in a high volatility environment. Interestingly, an analysis of Bitcoin returns shows remarkably low correlations with traditional investment assets such as other currencies, stocks, bonds or commodities such as gold or oil. In this paper, we shed light on the impact an investment in Bitcoin can have on an already well-diversified investment portfolio. Due to the non-normal nature of Bitcoin returns, we do not propose the classic mean-variance approach, but adopt a Conditional Value-at-Risk framework that does not require asset returns to be normally distributed. Our results indicate that Bitcoin should be included in optimal portfolios. Even though an investment in Bitcoin increases the CVaR of a portfolio, this additional risk is overcompensated by high returns leading to better return-risk ratios

Re-Mapping Credit Ratings

Rating agencies report ordinal ratings in discrete classes. We question the market’s implicit assumption that agencies define their classes on identical scales, e.g., that AAA by Standard & Poor’s is equivalent to Aaa by Moody’s. To this end, we develop a non-parametric method to estimate the relation between rating scales for pairs of raters. For every rating class of one rater this, scale relation identifies the extent to which it corresponds to any rating class of another rater, and hence enables a rating-class specific re-mapping of one agency’s ratings to another’s. Our method is based purely on ordinal co-ratings to obviate error-prone estimation of default probabilities and the disputable assumptions involved in treating ratings as metric data. It estimates all rating classes’ relations from a pair of raters jointly, and thus exploits the information content from ordinality. We find evidence against the presumption of identical scales for the three major rating agencies Fitch, Moody’s and Standard & Poor’s, provide the relations of their rating classes and illustrate the importance of correcting for scale relations in benchmarking

Re-Mapping Credit Ratings

Rating agencies report ordinal ratings in discrete classes. We question the market’s implicit assumption that agencies define their classes on identical scales, e.g., that AAA by Standard & Poor’s is equivalent to Aaa by Moody’s. To this end, we develop a non-parametric method to estimate the relation between rating scales for pairs of raters. For every rating class of one rater this, scale relation identifies the extent to which it corresponds to any rating class of another rater, and hence enables a rating-class specific re-mapping of one agency’s ratings to another’s. Our method is based purely on ordinal co-ratings to obviate error-prone estimation of default probabilities and the disputable assumptions involved in treating ratings as metric data. It estimates all rating classes’ relations from a pair of raters jointly, and thus exploits the information content from ordinality. We find evidence against the presumption of identical scales for the three major rating agencies Fitch, Moody’s and Standard & Poor’s, provide the relations of their rating classes and illustrate the importance of correcting for scale relations in benchmarking

Presentation and formatting of laboratory results: a narrative review on behalf of the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) Working Group "postanalytical phase" (WG-POST).

In laboratory medicine, much effort has been put into analytical quality in the past decades, making this medical profession one of the most standardized with the lowest rates of error. However, even the best analytical quality cannot compensate for errors or low quality in the pre or postanalytical phase of the total testing process. Guidelines for data reporting focus solely on defined data elements, which have to be provided alongside the analytical test results. No guidelines on how to format laboratory reports exist. The habit of reporting as much diagnostic data as possible, including supplemental information, may lead to an information overload. Considering the multiple tasks physicians have to do simultaneously, unfiltered data presentation may contribute to patient risk, as important information may be overlooked, or juxtaposition errors may occur. As laboratories should aim to answer clinical questions, rather than providing sole analytical results, optimizing formatting options may help improve the effectiveness and efficiency of medical decision-making. In this narrative review, we focus on the underappreciated topic of laboratory result reporting. We present published literature, focusing on the impact of laboratory result report formatting on medical decisions as well as approaches, potential benefits, and limitations for alternative report formats. We discuss influencing variables such as, for example, the type of patient (e.g. acute versus chronic), the medical specialty of the recipient of the report, the display of reference intervals, the medium or platform on which the laboratory report is presented (printed paper, within electronic health record systems, on handheld devices, etc.), the context in which the report is viewed in, and difficulties in formatting single versus cumulative reports. Evidence on this topic, especially experimental studies, is scarce. When considering the medical impact, it is of utmost importance that laboratories focus not only on the analytical aspects but on the total testing process. The achievement of high analytical quality may be of minor value if essential results get lost in overload or scattering of information by using a non-formatted tabular design. More experimental studies to define guidelines and to standardize effective and efficient reporting are most definitely needed