Conditional variance forecasts for long-term stock returns

In this paper, we apply machine learning to forecast the conditional variance of long-term stock returns measured in excess of different benchmarks, considering the short- and long-term interest rate, the earnings-by-price ratio, and the inflation rate. In particular, we apply in a two-step procedure a fully nonparametric local-linear smoother and choose the set of covariates as well as the smoothing parameters via cross-validation. We find that volatility forecastability is much less important at longer horizons regardless of the chosen model and that the homoscedastic historical average of the squared return prediction errors gives an adequate approximation of the unobserved realised conditional variance for both the one-year and five-year horizon

Social Enterprise for Assistive Technologies: An operational review of Solve Disability Solutions

Executive Summary This report details an investigation by RMIT researchers into the knowledge capture and knowledge sharing procedures used by Solve Disability Solutions (Solve) which aimed to facilitate their operations and better support their volunteer network. Solve offers assistive technology solutions to clients with disabilities, chronic disease and age-related conditions to improve quality and life and enhance mobility and independence. Solve’s client support activities are delivered by a small team of Occupational Therapists (OTs) working in collaboration with a large volunteer network; many of whom are retired engineers and fabricators, with specific and relevant knowledge and skill sets. These volunteers work from their own premises to collaboratively design, develop and prototype and fabricate Assistive Technology enabling solutions for Solve clients. This system enables Solve to benefit from access to a large network of expertise, but it is possibly vulnerable in regard to critical knowledge and expertise being held outside the organisation, mostly within an ageing volunteer network. Whilst rudimentary knowledge capture procedures are in place to record project outcomes, it is difficult to accurately capture the full technical specification of complex solutions and many volunteers do not fully complete the process, due to a variety of reasons as detailed within this report

Less is more: increasing retirement gains by using an upside terminal wealth constraint

We solve a portfolio selection problem of an investor with a deterministic savings plan who aims to have a target wealth value at retirement. The investor is an expected power utility-maximizer. The target wealth value is the maximum wealth that the investor can have at retirement. By constraining the investor to have no more than the target wealth at retirement, we find that the lower quantiles of the terminal wealth distribution increase, so the risk of poor financial outcomes is reduced. The drawback of the optimal strategy is that the possibility of gains above the target wealth are eliminated

Identification of small-molecule inhibitors of the antiapoptotic protein myeloid cell leukaemia-1 (Mcl-1)

Protein–protein interactions (PPIs) control many cellular processes in cancer and tumour growth. Of significant interest is the role PPIs play in regulating apoptosis. The overexpression of the antiapoptosis regulating Bcl-2 family of proteins is commonly observed in several cancers, leading to resistance towards both radiation and chemotherapies. From this family, myeloid cell leukemia-1 (Mcl-1) has proven the most difficult to target, and one of the leading causes of treatment resistance. Exploiting the selective PPI between the apoptosis-regulating protein Noxa and Mcl-1, utilising a fluorescence polarization assay, we have identified four small molecules with the ability to modulate Mcl-1. The identified compounds were computationally modelled and docked against the Mcl-1 binding interface to obtain structural information about their binding sites allowing for future analogue design. When examined for their activity towards pancreatic cell lines that overexpress Mcl-1 (MiaPaCa-2 and BxPC-3), the identified compounds demonstrated growth inhibition, suggesting effective Mcl-1 modulation

ArMedEa project: archaeology of medieval earthquakes in Europe (1000-1550 AD). First research activities

This paper introduces the research of the Armedea project. Armedea (Archaeology of medieval earthquakes in Europe, 1000-1550 AD) is a medieval archaeology project undertaken at the Department of Archaeology of Durham University which analyses archaeological evidence related to late medieval seismic-affected contexts at a European scale. This project is therefore focused on both earthquake effects on archaeological sites, their standing buildings and environment, and the archaeological evidence that reveals the response of medieval societies in terms of risk reduction, protection and resilience. A first preview of GIS analysis of seismic activity impact on medieval societies and fieldwork activities carried out in Italy, Cyprus and Azores (Portugal) is presented here. This research is supported by a Marie Curie Intra European Fellowship within the 7th European Community Framework Programme

Inter-laboratory analytical improvement of succinylacetone and nitisinone quantification from dried blood spot samples

Background: Nitisinone is used to treat hereditary tyrosinemia type 1 (HT-1) by preventing accumulation of toxic metabolites, including succinylacetone (SA). Accurate quantification of SA during newborn screening is essential, as is quantification of both SA and nitisinone for disease monitoring and optimization of treatment. Analysis of dried blood spots (DBS) rather than plasma samples is a convenient method, but interlaboratory differences and comparability of DBS to serum/plasma may be issues to consider. Methods: Eight laboratories with experience in newborn screening and/or monitoring of patients with HT-1 across Europe participated in this study to assess variability and improve SA and nitisinone concentration measurements from DBS by liquid chromatography-tandem mass spectrometry (LC-MS/MS). Quantification of nitisinone from both DBS and plasma was performed to assess sample comparability. In addition, efforts to harmonize laboratoryprocedures of SA and nitisinone quantifications during 5 rounds of analysis are described. Results: Nitisinone levels measured from DBS and plasma strongly correlated (R2 = 0.93). Due to partitioning of nitisinone to the plasma, levels were higher in plasma by a factor of 2.34. In the initial assessment of laboratory performance, all had linear calibrations of SA and nitisinone although there was large inter-laboratory variability in actual concentration measurements. Subsequent analytical rounds demonstrated markedly improved spread and precision over previous rounds, an outcome confirmed in a final re-test round. Conclusion: The study provides guidance for the determination of nitisinone and SA from DBS and the interpretation of results in the clinic. Interlaboratory analytical harmonization was demonstrated through calibration improvements.SCOPUS: ar.kinfo:eu-repo/semantics/publishe

Introducing SpatialGridBuilder: A new system for creating geo-coded datasets

Researchers in the conflict research community have become increasingly aware that we can no longer depend on state-aggregated data. Numerous factors at the substate level affect the nature of human interactions, so if we really want to understand conflict, we need to find more appropriate units of analysis. However, while many conflict researchers have realized this, actually taking the next step and performing data analysis on spatial data grids has remained a rather elusive goal for many because of the difficulty of learning the new techniques to perform such analyses. This paper introduces SpatialGridBuilder, a new, freely available, open-source system with the goal of empowering conflict researchers with no background in GIS methods to start their own spatial analyses. SpatialGridBuilder allows the researcher to: (a) create entirely new spatial datasets, based on the needs of their own research; (b) import their own spatial data; (c) easily add a range of important variables to the datasets, including commonly used conflict variables, plus new variables that have not been presented before; and (d) visualize graphical renderings of this data. Having done this, SpatialGridBuilder will then export the dataset for the researcher to analyse using conventional statistical methods. This article introduces the new program, and demonstrates how it can be used to set up such a statistical analysis. It also shows how different results can be achieved by building grids of different resolutions, thereby encouraging researchers to choose grid resolutions appropriate to their research questions and data. The article also introduces a novel means of determining infrastructure complexity, using Google maps

Human helminth therapy to treat inflammatory disorders - where do we stand?

Parasitic helminths have evolved together with the mammalian immune system over many millennia and as such they have become remarkably efficient modulators in order to promote their own survival. Their ability to alter and/or suppress immune responses could be beneficial to the host by helping control excessive inflammatory responses and animal models and pre-clinical trials have all suggested a beneficial effect of helminth infections on inflammatory bowel conditions, MS, asthma and atopy. Thus, helminth therapy has been suggested as a possible treatment method for autoimmune and other inflammatory disorders in humans

Tail Dependence Measure for Examining Financial Extreme Co-movements

Modeling and forecasting extreme co-movements in financial market is important for conducting stress test in risk management. Asymptotic independence and asymptotic dependence behave drastically different in modeling such co-movements. For example, the impact of extreme events is usually overestimated whenever asymptotic dependence is wrongly assumed. On the other hand, the impact is seriously underestimated whenever the data is misspecified as asymptotic independent. Therefore, distinguishing between asymptotic independence/dependence scenarios is very informative for any decision-making and especially in risk management. We investigate the properties of the limiting conditional Kendall’s tau which can be used to detect the presence of asymptotic independence/dependence. We also propose nonparametric estimation for this new measure and derive its asymptotic limit. A simulation study shows good performances of the new measure and its combination with the coefficient of tail dependence proposed by Ledford and Tawn (1996, 1997). Finally, applications to financial and insurance data are provided