The blended approach to real estate allocations: performance implications of combining an exposure to German Spezialfonds with Global Listed Real Estate Securities

This paper seeks to increase the understanding of the performance implications for investors who choose to combine an unlisted real estate portfolio (in this case German Spezialfonds) with a (global) listed real estate element. We call this a “blended” approach to real estate allocations. For the avoidance of doubt, in this paper we are dealing purely with real estate equity (listed and unlisted) allocations, and do not incorporate real estate debt (listed or unlisted) or direct property into the process. A previous paper (Moss and Farrelly 2014) showed the benefits of the blended approach as it applied to UK Defined Contribution Pension Schemes. The catalyst for this paper has been the recent attention focused on German pension fund allocations, which have a relatively low (real estate) equity content, and a high bond content. We have used the MSCI Spezialfonds Index as a proxy for domestic German institutional real estate allocations, and the EPRA Global Developed Index as a proxy for a global listed real estate allocation. We also examine whether a rules based trading strategy, in this case Trend Following, can improve the risk adjusted returns above those of a simple buy and hold strategy for our sample period 2004-2015. Our findings are that by blending a 30% global listed portfolio with a 70% allocation (as opposed to a typical 100% weighting) to Spezialfonds, the real estate allocation returns increase from 2.88% p.a. to 5.42% pa. Volatility increases, but only to 6.53%., but there is a noticeable impact on maximum drawdown which increases to 19.4%. By using a Trend Following strategy raw returns are improved from 2.88% to 6.94% p.a. , The Sharpe Ratio increases from 1.05 to 1.49 and the Maximum Drawdown ratio is now only 1.83% compared to 19.4% using a buy and hold strategy . Finally, adding this (9%) real estate allocation to a mixed asset portfolio allocation typical for German pension funds there is an improvement in both the raw return (from 7.66% to 8.28%) and the Sharpe Ratio (from 0.91 to 0.98).

Combining 3D and 2D for less constrained periocular recognition

Periocular recognition has recently become an active topic in biometrics. Typically it uses 2D image data of the periocular region. This paper is the first description of combining 3D shape structure with 2D texture. A simple and effective technique using iterative closest point (ICP) was applied for 3D periocular region matching. It proved its strength for relatively unconstrained eye region capture, and does not require any training. Local binary patterns (LBP) were applied for 2D image based periocular matching. The two modalities were combined at the score-level. This approach was evaluated using the Bosphorus 3D face database, which contains large variations in facial expressions, head poses and occlusions. The rank-1 accuracy achieved from the 3D data (80%) was better than that for 2D (58%), and the best accuracy (83%) was achieved by fusing the two types of data. This suggests that significant improvements to periocular recognition systems could be achieved using the 3D structure information that is now available from small and inexpensive sensors.

Sparse density estimation on multinomial manifold combining local component analysis

A new sparse kernel density estimator is introduced based on the minimum integrated square error criterion combining local component analysis for the finite mixture model. We start with a Parzen window estimator which has the Gaussian kernels with a common covariance matrix, the local component analysis is initially applied to find the covariance matrix using expectation maximization algorithm. Since the constraint on the mixing coefficients of a finite mixture model is on the multinomial manifold, we then use the well-known Riemannian trust-region algorithm to find the set of sparse mixing coefficients. The first and second order Riemannian geometry of the multinomial manifold are utilized in the Riemannian trust-region algorithm. Numerical examples are employed to demonstrate that the proposed approach is effective in constructing sparse kernel density estimators with competitive accuracy to existing kernel density estimators.

Bayesian network classifiers: Beyond classification accuracy

This work proposes and discusses an approach for inducing Bayesian classifiers aimed at balancing the tradeoff between the precise probability estimates produced by time consuming unrestricted Bayesian networks and the computational efficiency of Naive Bayes (NB) classifiers. The proposed approach is based on the fundamental principles of the Heuristic Search Bayesian network learning. The Markov Blanket concept, as well as a proposed ""approximate Markov Blanket"" are used to reduce the number of nodes that form the Bayesian network to be induced from data. Consequently, the usually high computational cost of the heuristic search learning algorithms can be lessened, while Bayesian network structures better than NB can be achieved. The resulting algorithms, called DMBC (Dynamic Markov Blanket Classifier) and A-DMBC (Approximate DMBC), are empirically assessed in twelve domains that illustrate scenarios of particular interest. The obtained results are compared with NB and Tree Augmented Network (TAN) classifiers, and confinn that both proposed algorithms can provide good classification accuracies and better probability estimates than NB and TAN, while being more computationally efficient than the widely used K2 Algorithm.

Building binary-tree-based multiclass classifiers using separability measures

Various popular machine learning techniques, like support vector machines, are originally conceived for the solution of two-class (binary) classification problems. However, a large number of real problems present more than two classes. A common approach to generalize binary learning techniques to solve problems with more than two classes, also known as multiclass classification problems, consists of hierarchically decomposing the multiclass problem into multiple binary sub-problems, whose outputs are combined to define the predicted class. This strategy results in a tree of binary classifiers, where each internal node corresponds to a binary classifier distinguishing two groups of classes and the leaf nodes correspond to the problem classes. This paper investigates how measures of the separability between classes can be employed in the construction of binary-tree-based multiclass classifiers, adapting the decompositions performed to each particular multiclass problem. (C) 2010 Elsevier B.V. All rights reserved.

A review on the combination of binary classifiers in multiclass problems

Several real problems involve the classification of data into categories or classes. Given a data set containing data whose classes are known, Machine Learning algorithms can be employed for the induction of a classifier able to predict the class of new data from the same domain, performing the desired discrimination. Some learning techniques are originally conceived for the solution of problems with only two classes, also named binary classification problems. However, many problems require the discrimination of examples into more than two categories or classes. This paper presents a survey on the main strategies for the generalization of binary classifiers to problems with more than two classes, known as multiclass classification problems. The focus is on strategies that decompose the original multiclass problem into multiple binary subtasks, whose outputs are combined to obtain the final prediction.

Comparing machine learning classifiers in potential distribution modelling

Species` potential distribution modelling consists of building a representation of the fundamental ecological requirements of a species from biotic and abiotic conditions where the species is known to occur. Such models can be valuable tools to understand the biogeography of species and to support the prediction of its presence/absence considering a particular environment scenario. This paper investigates the use of different supervised machine learning techniques to model the potential distribution of 35 plant species from Latin America. Each technique was able to extract a different representation of the relations between the environmental conditions and the distribution profile of the species. The experimental results highlight the good performance of random trees classifiers, indicating this particular technique as a promising candidate for modelling species` potential distribution. (C) 2010 Elsevier Ltd. All rights reserved.

Poly-bagging predictors for classification modelling for credit scoring

Credit scoring modelling comprises one of the leading formal tools for supporting the granting of credit. Its core objective consists of the generation of a score by means of which potential clients can be listed in the order of the probability of default. A critical factor is whether a credit scoring model is accurate enough in order to provide correct classification of the client as a good or bad payer. In this context the concept of bootstraping aggregating (bagging) arises. The basic idea is to generate multiple classifiers by obtaining the predicted values from the fitted models to several replicated datasets and then combining them into a single predictive classification in order to improve the classification accuracy. In this paper we propose a new bagging-type variant procedure, which we call poly-bagging, consisting of combining predictors over a succession of resamplings. The study is derived by credit scoring modelling. The proposed poly-bagging procedure was applied to some different artificial datasets and to a real granting of credit dataset up to three successions of resamplings. We observed better classification accuracy for the two-bagged and the three-bagged models for all considered setups. These results lead to a strong indication that the poly-bagging approach may promote improvement on the modelling performance measures, while keeping a flexible and straightforward bagging-type structure easy to implement. (C) 2011 Elsevier Ltd. All rights reserved.

A Road Following Approach Using Artificial Neural Networks Combinations

Navigation is a broad topic that has been receiving considerable attention from the mobile robotic community over the years. In order to execute autonomous driving in outdoor urban environments it is necessary to identify parts of the terrain that can be traversed and parts that should be avoided. This paper describes an analyses of terrain identification based on different visual information using a MLP artificial neural network and combining responses of many classifiers. Experimental tests using a vehicle and a video camera have been conducted in real scenarios to evaluate the proposed approach.

Combining structure and dynamics: non-denaturing high-pressure effect on lysozyme in solution

Small-angle X-ray scattering (SAXS) and elastic and quasi-elastic neutron scattering techniques were used to investigate the high-pressure-induced changes on interactions, the low-resolution structure and the dynamics of lysozyme in solution. SAXS data, analysed using a global-fit procedure based on a new approach for hydrated protein form factor description, indicate that lysozyme completely maintains its globular structure up to 1500 bar, but significant modi. cations in the protein-protein interaction potential occur at approximately 600-1000 bar. Moreover, the mass density of the protein hydration water shows a clear discontinuity within this pressure range. Neutron scattering experiments indicate that the global and the local lysozyme dynamics change at a similar threshold pressure. A clear evolution of the internal protein dynamics from diffusing to more localized motions has also been probed. Protein structure and dynamics results have then been discussed in the context of protein-water interface and hydration water dynamics. According to SAXS results, the new configuration of water in the first hydration layer induced by pressure is suggested to be at the origin of the observed local mobility changes.

Geochronological data from the Faxinal coal succession, southern Parana Basin, Brazil: A preliminary approach combining radiometric U-Pb dating and palynostratigraphy

A radiometric zircon age of 285.4 +/- 8.6 Ma (IDTIMS U-Pb) is reported from a tonstein layer interbedded with coal seams in the Faxinal coalfield, Rio Grande do Sul, Brazil. Calibration of palynostratigraphic data with the absolute age shows that the coal depositional interval in the southern Parana Basin is constrained to the Sakmarian. Consequently, the basal Gondwana sequence in the southern part of the basin should lie at the Carboniferous-Permian boundary, not within the Sakmarian as previously considered. The new results are significant for correlations between the Parana Basin and the Argentinian Paganzo Basin (302 +/- 6 Ma and 288 +/- 7 Ma) and with the Karoo Basin, specifically with the top of the Dwyka Tillite (302 +/- 3 Ma and 299.2 +/- 3.2 Ma) and the lowermost Ecca Group (288 +/- 3 Ma and 289.6 +/- 3.8 Ma). The evidence signifies widespread latest Carboniferous volcanic activity in western Gondwana. (C) 2007 Elsevier Ltd. All rights reserved.

The interaction between atoms of Au and Cu with clean Si(111) surface: A study combining synchrotron radiation grazing incidence X-ray fluorescence analysis and theoretical calculations

In order to evaluate the interactions between Au/Cu atoms and clean Si(l 11) surface, we used synchrotron radiation grazing incidence X-ray fluorescence analysis and theoretical calculations. Optimized geometries and energies on different adsorption sites indicate that the binding energies at different adsorption sites are high, suggesting a strong interaction between metal atom and silicon surface. The Au atom showed higher interaction than Cu atom. The theoretical and experimental data showed good agreement. Crown Copyright (C) 2009 Published by Elsevier B.V. All rights reserved.

The Passionate Combining Entrepreneurs

Entrepreneurs are portrayed as salient drivers of regional development and for a number of years nascent entrepreneurs have been studied in a large number of countries as part of the Global Entrepreneurship Monitor project and the Panel Study of Entrepreneurial Dynamics. Scholars have devoted much effort to investigating factors that determine how individuals engage in entrepreneurial activities, with most of the discussion limited to business start-ups. However, since this type of project does not follow identical nascent entrepreneurs over time, limited knowledge exists about their development and whether they stay in this nascent phase for a long time. In practice, it is common for entrepreneurs to run a business and at the same time work in wage work, so-called combining entrepreneurs. In Sweden, almost half of all business owners combine wage work with a business. However, not all combining entrepreneurs will eventually decide to leave the wage work and invest fully in the business. Consequently, much research has focused on the first step of entering entrepreneurship full time, but less has focused on the second step, the transition from the combining phase to full-time self-employment. The aim of this thesis is therefore to contribute to the theory of entrepreneurship by gaining a deeper understanding of combining entrepreneurs and their motives and intentions.   In the context of combining entrepreneurs, the theory of identity, resources and choice overload has been used to examine how entrepreneurs’ age (when starting the business), entrepreneurial tenure (the length of engagement in the side-business), hours spent (weekly involvement in the side-business), involvement in entrepreneurial teams (leading the business with one or more partners) and involvement in networks (business networks) influence their passion for engaging in entrepreneurship while sustaining wage work. Different categories of combining entrepreneurs and their intentions have also been examined.   A survey was administered to 1457 entrepreneurs within the creative sector in two counties in Sweden (Gävleborgs County and Jämtlands County). Since there were no separate mailing lists to only combining entrepreneurs, the survey was sent to all entrepreneurs within the chosen industry and counties. The total response rate was 33.5 percent and of them 57.6 percent combined, yielding 262 combining entrepreneurs who answered the questionnaire. The survey was then followed up with eight focus group interviews and two single interviews to validate the answers from the questionnaire.   The results indicate three types of combining entrepreneurs: nascent – with the intention to leave the combining phase for a transition into full-time self-employment, lifestyle – with the intention to stay in the combining phase, and occasional – with the intention to leave the combining phase for full-time wage work and close down the business. Transitioning fully to self-employment increases with the individual’s age. Also, a positive interactive effect exists with involvement in entrepreneurial networks. The results also indicate that the ability to work with something one is passionate about is the top motive for combining wage work with a side-business. Passion is also more likely to be the main motive behind the combining form among individuals who are older at business start-up, but passion is less likely to be the main motive behind the combining form among individuals who spend more time on the business. The longer the individual has had the side-business, the less likely passion is the main motive behind the combining form, and passion is less likely to be the main motive among those who are part of an entrepreneurial team.