Imperfect predictability and mutual fund dynamics. How managers use predictors in changing systematic risk.

Suppose a fund manager uses predictors in changing port-folio allocations over time. How does predictability translate into portfolio decisions? To answer this question we derive a new model within the Bayesian framework, where managers are assumed to modulate the systematic risk in part by observing how the benchmark returns are related to some set of imperfect predictors, and in part on the basis of their own information set. In this portfolio allocation process, managers concern themselves with the potential benefits arising from the market timing generated by benchmark predictors and by private information. In doing this, we impose a structure on fund returns, betas, and bench-mark returns that help to analyse how managers really use predictors in changing investments over time. The main findings of our empirical work are that beta dynamics are significantly affected by economic variables, even though managers do not care about bench-mark sensitivities towards the predictors in choosing their instrument exposure, and that persistence and leverage effects play a key role as well. Conditional market timing is virtually absent, if not negative, over the period 1990-2005. However such anomalous negative timing ability is offset by the leverage effect, which in turn leads to an increase in mutual fund extra performance. JEL Classification: C11, C13, G12, G13Bayesian analysis, conditional asset pricing models, Equity mutual funds, time-varying beta

Electronic thermal conductivity of disordered metals

We calculate the thermal conductivity of interacting electrons in disordered metals. In our analysis we point out that the interaction affects thermal transport through two distinct mechanims, associated with quantum interference corrections and energy exchange of the quasi particles with the electromagnetic environment, respectively. The latter is seen to lead to a violation of the Wiedemann-Franz law. Our theory predicts a strong enhancement of the Lorenz ratio $\kappa /\sigma T$ over the value which is predicted by the Wiedemann-Franz law, when the electrons encounter a large environmental impedance.Comment: 4 page

The effects of local systems on the international de-localisation of production.The case of made in Italy

The paper examines the fragmentation of production from the view-point of industrialised countries. From this perspective, the following questions are addressed: how do local systems evolve in the process of de-localisation of productions? Which are the short term and long term effects to be expected? Can we interpret these processes under the light of changing specialization of economic systems, necessarily associated with gains from trade? Evidence is provided on the internationalization of manufacturing activities that are commonly identified as “made in Italy”, with specific reference to the textile and footwear industries. The focus will be on the re-organization of economic activities at the level of local systems specialized in these industries, rather than on individual firms; on the whole set of international operations involved in this process, regardless of the legal form adopted (FDIs, import-export, cooperative agreements and licensing); and on how changes in the international organisation of production in these industries are associated with changes in the economic performances within these industries as well as in related sectors, such as service industries.Foreign Direct Investments,Import-export cooperative agreements, Licensing.

Rules of Thumb for Banking Crises in Emerging Markets

This paper employs a recent statistical algorithm (CRAGGING) in order to build an early warning model for banking crises in emerging markets. We perturb our data set many times and create “artificial” samples from which we estimated our model, so that, by construction, it is flexible enough to be applied to new data for out-of-sample prediction. We find that, out of a large number (540) of candidate explanatory variables, from macroeconomic to balance sheet indicators of the countries’ financial sector, we can accurately predict banking crises by just a handful of variables. Using data over the period from 1980 to 2010, the model identifies two basic types of banking crises in emerging markets: a “Latin American type”, resulting from the combination of a (past) credit boom, a flight from domestic assets, and high levels of interest rates on deposits; and an “Asian type”, which is characterized by an investment boom financed by banks’ foreign debt. We compare our model to other models obtained using more traditional techniques, a Stepwise Logit, a Classification Tree, and an “Average” model, and we find that our model strongly dominates the others in terms of out-of-sample predictive power

Anatomy of a Sovereign Debt Crisis: CDS Spreads and Real-Time Macroeconomic Data

We construct a unique and comprehensive data set of 19 real-time daily macroeconomic indicators for 11 Eurozone countries, for the 5/11/2009{4/25/2013 period. We use this new data set to characterize the time-varying dependence of the cross-section of sovereign credit default swap (CDS) spreads on country-specific macro indicators. We employ daily Fama-MacBeth type cross-sectional regressions to produce time-series of macro-sensitivities, which are then used to identify risk regimes and forecast future equity market volatility. We document pronounced time-variation in the macro-sensitivities, consistent with the notion that market participants focused on very different macro indicators at the different times of the crisis. Second, we identify three distinct crisis risk regimes, based on the general level of CDS spreads, the macro-sensitivities, and the GIPSI connotation. Third, we document the predictive power of the macro-sensitivities for future option-implied equity market volatility, consistent with the notion that expected future risk aversion is an important driver of how CDS spreads impound macro information.JRC.B.1-Finance and Econom

A LC-QTOF Method for the Determination of PEGDE Residues in Dermal Fillers

Hyaluronic acid is one of the most important ingredients in dermal fillers, where it is often cross-linked to gain more favorable rheological properties and to improve the implant duration. Poly(ethylene glycol) diglycidyl ether (PEGDE) has been recently introduced as a crosslinker because of its very similar chemical reactivity with the most-used crosslinker BDDE, while giving special rheological properties. Monitoring the amount of the crosslinker residues in the final device is always necessary, but in the case of PEGDE, no methods are available in literature. Here, we present an HPLC-QTOF method, validated according to the guidelines of the International Council on Harmonization, which enables the efficient routine examination of the PEGDE content in HA hydrogels

Towards a Framework for a New Research Ecosystem

A major gap exists between the conceptual suggestion of how much a nation should invest in science, innovation, and technology, and the practical implementation of what is done. We identify 4 critical challenges that must be address in order to develop an environment conducive to collaboration across organizations and governments, while also preserving commercial rewards for investors and innovators, in order to move towards a new Research Ecosystem.Comment: 20 pages, 1 table, 2 figure

The Role of Attitudes Toward Medication and Treatment Adherence in the Clinical Response to LAIs: Findings From the STAR Network Depot Study

Background: Long-acting injectable (LAI) antipsychotics are efficacious in managing psychotic symptoms in people affected by severe mental disorders, such as schizophrenia and bipolar disorder. The present study aimed to investigate whether attitude toward treatment and treatment adherence represent predictors of symptoms changes over time. Methods: The STAR Network \u201cDepot Study\u201d was a naturalistic, multicenter, observational, prospective study that enrolled people initiating a LAI without restrictions on diagnosis, clinical severity or setting. Participants from 32 Italian centers were assessed at three time points: baseline, 6-month, and 12-month follow-up. Psychopathological symptoms, attitude toward medication and treatment adherence were measured using the Brief Psychiatric Rating Scale (BPRS), the Drug Attitude Inventory (DAI-10) and the Kemp's 7-point scale, respectively. Linear mixed-effects models were used to evaluate whether attitude toward medication and treatment adherence independently predicted symptoms changes over time. Analyses were conducted on the overall sample and then stratified according to the baseline severity (BPRS < 41 or BPRS 65 41). Results: We included 461 participants of which 276 were males. The majority of participants had received a primary diagnosis of a schizophrenia spectrum disorder (71.80%) and initiated a treatment with a second-generation LAI (69.63%). BPRS, DAI-10, and Kemp's scale scores improved over time. Six linear regressions\u2014conducted considering the outcome and predictors at baseline, 6-month, and 12-month follow-up independently\u2014showed that both DAI-10 and Kemp's scale negatively associated with BPRS scores at the three considered time points. Linear mixed-effects models conducted on the overall sample did not show any significant association between attitude toward medication or treatment adherence and changes in psychiatric symptoms over time. However, after stratification according to baseline severity, we found that both DAI-10 and Kemp's scale negatively predicted changes in BPRS scores at 12-month follow-up regardless of baseline severity. The association at 6-month follow-up was confirmed only in the group with moderate or severe symptoms at baseline. Conclusion: Our findings corroborate the importance of improving the quality of relationship between clinicians and patients. Shared decision making and thorough discussions about benefits and side effects may improve the outcome in patients with severe mental disorders