The impact of downside risk on risk-adjusted performance of mutual funds in the Euronext markets

Many performance measures, such as the classic Sharpe ratio have difficulty in evaluating the performance of mutual funds with skewed return distributions. Common causes for skewness are the use of options in the portfolio or superior market timing skills of the portfolio manager. In this article we examine to what extent downside risk and the upside potential ratio can be used to evaluate skewed return distributions. In order to accomplish this goal, we first show the relation between the risk preferences of the investor and the risk- adjusted performance measure. We conclude that it is difficult to interpret differences in the outcomes of risk-adjusted performance measures exclusively as differences in forecasting skills of portfolio managers. We illustrate this with an example of a simulation study of a protective put strategy. We show that the Sharpe ratio leads to incorrect conclusions in the case of protective put strategies. On the other hand, the upside potential ratio leads to correct conclusions. Finally, we apply downside risk and the upside potential ratio in the process of selecting a mutual fund from a sample of mutual funds in the Euronext stock markets. The rankings appear similar, which can be attributed to the absence of significant skewness in the sample. However, find that the remaining differences can be quite significant for individual fund managers, and that these differences can be attributed to skewness. Therefore, we prefer to use the UPR as an alternative to the Sharpe ratio, as it gives a more adequate evaluation of the use of options and forecasting skills.Downside risk, mutual funds, performance measurement, risk preference, asymmetric return distributions

Phase Diagram of Vertically Shaken Granular Matter

A shallow, vertically shaken granular bed in a quasi 2-D container is studied experimentally yielding a wider variety of phenomena than in any previous study: (1) bouncing bed, (2) undulations, (3) granular Leidenfrost effect, (4) convection rolls, and (5) granular gas. These phenomena and the transitions between them are characterized by dimensionless control parameters and combined in a full experimental phase diagram.Comment: 11 pages, 14 figures, submitted to "Physics of Fluids

Approximate density matrix functionals applied to hetero-atomic bond dissociation

A two-orbital two-electron diatomic model resembling LiH is used to investigate the differences between the exact L\"owdin-Shull and approximate Hartree-Fock-Bogoliubov and Baerends-Buijse density matrix functionals in the medium- to long-distance dissociation region. In case of homolytic dissociation (one electron on each atom), the approximate functionals fail to generate the correct energy due to a compromise between the Hartree-Fock component (which favors partial charge transfer) and the strong correlation component (which hampers charge transfer). The exact functional is able to generate the physically correct answer by enforcing the equi-charge distribution of the bonding and antibonding orbitals. Besides, the approximate functionals also have issues in correctly describing heterolytic dissociation (two electrons on one atom) due to the strong correlation component hampering charge transfer. In this work, we propose a new scheme in which the homolytic dissociation problem for approximate functionals is avoided by adding a Lagrange multiplier that enforces equi-charge distribution of the bonding and antibonding orbitals. The symmety based nature of the findings implies that they are most likely transferable to other cases in which one uses an approximate one-particle method in conjunction with a symmetrical particle-hole correction factor

The role of organic acid metabolites in geo-energy pipeline corrosion in a sulfate reducing bacteria environment

The dominant factors in Microbial Influenced Corrosion (MIC) are hard to determine because normally several individual species and their metabolites are involved, and, moreover, different metabolites may cause opposing effects. To address this problem, the effects of individual metabolites from different species should be elucidated when at the same time other metabolites are held constant. In this study, the role is investigated of simulated organic acid metabolites, namely, acetic and L–ascorbic acids, on corrosion of geo-energy pipelines (carbon steel) in a simulated Sulfate Reducing Bacteria (SRB) environment. The SRB environment is simulated using a calcium alginate biofilm, abiotic sulfide, CO(2), and NaCl brine. The electrochemical results show that both simulated organic acid metabolites accelerate corrosion in a simulated SRB environment. The results are further supported by electrochemical weight losses, kinetic corrosion activation parameters, multiple linear regression, ICP-OES, pH, and XRD. However, a comparison of electrochemical results with those published in the literature for a simulated SRB environment without acetic or L-ascorbic acid under similar experimental conditions shows that the presence of acetic in this study results in lower corrosion current densities while in presence of L-ascorbic acid results into higher corrosion current densities. This implies that acetic and L-ascorbic acids inhibit and accelerate corrosion, respectively. In addition, the results highlight that H(2)S is a key role of corrosion in the presence of organic acid. The results of this study are important new and novel information on the role of acetic and L-ascorbic acids in corrosion of geo-energy pipelines in the SRB environment

An activity theory perspective on strategy : a case study in a medium-sized manufacturing firm

This research contributes to theory by clarifying underlying activities behind productivity growth strategies from an in depth case study in a medium-sized manufacturing firm as well as providing suggestions to policy makers and practitioners who work on SME development in the UK and Europe

Modelling and managing systemic risks in supply chains

A structured review of the supply chain and risk management literature supports an analysis of the sources and types of risks anticipated in supply chains and networks. We discuss alternative modelling approaches, such as Bayesian Belief Nets (BBN), System Dynamics, Fault and Event Trees, which are evaluated against the criteria characterizing systemic risks that emerge from the literature review. Finally, we briefly present an empirical pilot case study is conducted with a public sector organization in charge of a pharmaceutical distribution network to explore the feasibility of a BBN modelling approach

Combining density based dynamical correlation with a reduced density matrix strong correlation description

A combined density and first-order reduced-density-matrix (1RDM) functional method is proposed for the calculation of potential energy curves (PECs) of molecular multibond dissociation. Its 1RDM functional part, a pair density functional, efficiently approximates the ab initio pair density of the complete active space self-consistent-field (CASSCF) method. The corresponding approximate on top pair density {\Pi} is employed to correct for double counting a correlation functional of density functional theory (DFT). The proposed ELS-DM{\Pi}DFT method with the extended L\"owdin-Shull (ELS) 1RDM functional with dispersion and multibond (DM) corrections augmented with the {\Pi}DFT functional closely reproduces PECs of multibond dissociation in the paradigmatic N_2 , H_2O, and H_2CO molecules calculated with the recently proposed CAS{\Pi}DFT (CASSCF augmented with a {\Pi} based scaled DFT correlation correction) method. Furthermore, with the additional M-correction, ELS-DM{\Pi}DFT+M reproduces well the benchmark PEC of the N_2 molecule by Lie and Clementi

Personalized pancreatic cancer management : a systematic review of how machine learning is supporting decision-making

This review critically analyzes how machine learning is being utilized to support clinical decision-making in the management of potentially resectable pancreatic cancer. Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, electronic searches of MEDLINE, Embase, PubMed and Cochrane Database were undertaken. Studies were assessed using the Checklist for critical Appraisal and data extraction for systematic Reviews of prediction Modeling Studies (CHARMS) checklist. In total 89,959 citations were retrieved. Six studies met the inclusion criteria. Three studies were Markov decision-analysis models comparing neoadjuvant therapy versus upfront surgery. Three studies predicted survival time using Bayesian modeling (n = 1), Artificial Neural Network (n = 1), and one study explored machine learning algorithms including: Bayesian Network, decision trees, nearest neighbor, and Artificial Neural Networks. The main methodological issues identified were: limited data sources which limits generalizability and potentiates bias, lack of external validation, and the need for transparency in methods of internal validation, consecutive sampling, and selection of candidate predictors. The future direction of research relies on expanding our view of the multidisciplinary team to include professionals from computing and data science with algorithms developed in conjunction with clinicians and viewed as aids, not replacement, to traditional clinical decision making

COVID: how incorrect assumptions and poor foresight hampered the UK's pandemic preparedness

Matt Hancock, the former health secretary, has told the recently opened COVID-19 Inquiry that the UK's pandemic planning was "completely wrong". According to Hancock, the doctrine was "to plan for the consequences of a disaster" rather than stopping or containing the virus in the first place. While there is truth in this claim, it doesn’t give us the whole picture. Hancock was repeatedly asked during his appearance about something called Exercise Cygnus. In 2016, the UK government engaged in a series of exercises including Cygnus to assess their preparedness and response to a pandemic outbreak of influenza. As the global scale of the COVID pandemic was starting to become apparent in the first half of February 2020, the UK applied the lessons from these exercises to plan for a wide range of scenarios. Based on the scientific evidence available at that time, they anticipated that a "reasonable worst-case scenario" could involve up to 80% of the UK population being infected (with only 50% of those infected showing symptoms). However, it was hoped that the majority of cases would have relatively mild disease. This information was contained in planning assumptions labelled "officially sensitive" that were shared between a range of healthcare authorities and that I had access to at the time. Some of the figures were also published in the media