Mean–variance and mean–semivariance portfolio selection: a multivariate nonparametric approach

While univariate nonparametric estimation methods have been developed for estimating returns in mean-downside risk portfolio optimization, the problem of handling possible cross-correlations in a vector of asset returns has not been addressed in portfolio selection. We present a novel multivariate nonparametric portfolio optimization procedure using kernel-based estimators of the conditional mean and the conditional median. The method accounts for the covariance structure information from the full set of returns. We also provide two computational algorithms to implement the estimators. Via the analysis of 24 French stock market returns, we evaluate the in-sample and out-of-sample performance of both portfolio selection algorithms against optimal portfolios selected by classical and univariate nonparametric methods for three highly different time periods and different levels of expected return. By allowing for cross-correlations among returns, our results suggest that the proposed multivariate nonparametric method is a useful extension of standard univariate nonparametric portfolio selection approaches.     

Accelerators as start-up infrastructure for entrepreneurial clusters

ABSTRACT

Infrastructure is commonly conceptualized as a set of facilities that play a critical role in facilitating activities by individuals and organizations. Conventionally, infrastructure is tightly linked to publicly funded projects that facilitate access to key resources and enable diverse activities. Within entrepreneurial clusters research, infrastructure includes universities, research institutions and telecommunication technologies that facilitate entrepreneurial activities. These capital-intensive investments seek to facilitate start-ups emergence by aiding access to markets and development of ideas. Accelerators facilitate the same activities and have only recently been conceptualized as start-up infrastructure. This study builds upon this research stream by elaborating on how accelerators can play this meaningful role at the cluster level. Specifically, and by relying on the analysis of empirical evidence from three distinct studies, we uncover how accelerators provide tangible and intangible dimensions of start-up infrastructure to form a positively reinforcing cycle of entrepreneurial activities. Additionally, our findings allow us to push further the idea that start-up infrastructure development can be an endogenous process involving multiple actors within the cluster. Our empirical findings and the theoretical insights derived from them have meaningful implications for the aforementioned literature, as well as start-up practitioners and policymakers linked to the funding of entrepreneurial clusters.     

The Tragedy of the Commons and Inflation Bias in the Euro Area

Central bank credit has expanded dramatically in some of the Euro Area member countries since the beginning of the financial crisis. This paper makes two contributions to understand this stylized fact. First, we discuss a simple model of monetary policy that includes (i) a credit channel and (ii) a common pool problem in a monetary union. We illustrate that the interaction of the two elements leads to an inflation bias that is independent of the standard time-inconsistency bias. Secondly, we present an institutional analysis that is consistent with the view of fragmented monetary policy and empirical evidence that illustrates the heterogeneity of central bank credit expansion.     

Integrating Environmental,Social and Governance (ESG) Disclosure for a Sustainable Development: An Australian Study

Addressing environmental, social and governance (ESG) issues has become a critical part of business strategy. This article explores the extent of ESG reporting of metal and mining sector companies listed in the Australian Securities Exchange to determine the nature of ESG indicators in use in the sector. The current study argues that stakeholder engagement is the key to enhance company environmental policy and sustainable development. According to the results of this study, ESG reporting motives are highly influenced by reporting regulations. Given the diversity in reporting of ESG, comparability of ESG strategic performance is problematic. This study contributes towards developing an ESG disclosure index, which companies could use as a legitimacy tool that external stakeholders could use to reliably measure and compare the ESG performance of companies. It also reveals there is an increased demand for more empirical research on integration of sustainability into strategic planning process. Copyright © 2016 John Wiley & Sons, Ltd and ERP Environment     

Performance assessment of ensemble learning systems in financial data classification

Financial data classification plays an important role in investment and banking industry with the purpose to control default risk, improve cash and select the best customers. Ensemble learning and classification systems are becoming gradually more applied to classify financial data where outputs from different classification systems are combined. The objective of this research is to assess the relative performance of existing state‐of‐the‐art ensemble learning and classification systems with applications to corporate bankruptcy prediction and credit scoring. The considered ensemble systems include AdaBoost, LogitBoost, RUSBoost, subspace, and bagging ensemble system. The experimental results from three datasets: one is composed of quantitative attributes, one encompasses qualitative data, and another one combines both quantitative and qualitative attributes. By using ten‐fold cross‐validation method, the experimental results show that AdaBoost is effective in terms of low classification error, limited complexity, and short time processing of the data. In addition, the experimental results show that ensemble classification systems outperform existing models that were recently validated on the same databases. Therefore, ensemble classification system can be employed to increase the reliability and consistency of financial data classification task.     

Firm heterogeneity, productivity and spatially bounded knowledge externalities

In this paper, we argue that conceptually disentangling the ‘context versus composition’ aspects of regional growth is a multilevel issue. By applying multilevel models (also called random-effects models), we show (1) the importance of considering firm-specific characteristics simultaneously with region-specific characteristics, as we find that a large part of what is traditionally assigned to the impact of the region should be assigned to firm-specific characteristics and (2) that existing single-level methodologies can be problematic, as they are vulnerable to the charge of estimating significance levels that are too liberally assigned and promote exaggerations. This is illustrated empirically by showing that single-level approaches would lead to the conclusion that innovation spillovers are highly significant in a setting of Dutch urban growth differentials, while multilevel analyses shows less liberally assigned significance levels. We conclude that multilevel-effect models better fit research questions that combine firm and spatial characteristics simultaneously, especially because they allow firm-specific characteristics to be differently linked to their regional contexts.     

Testing the Influence of Substitute Sites in Nature Valuation by Using Spatial Discounting Factors

This paper investigates the effect of nearby nature substitute sites on preferences for nature restoration. Contrary to prior studies, we use a respondent-centric approach to control for substitute sites. We assess each respondent-specific spatial context by computing densities of nature substitute sites within various ranges from each respondent’s home. This approach considers the use and non-use values of nature together. Data from three similar discrete choice experiments carried out in Flanders (Belgium) are compared. Different spatial discounting factors are tested to explore how the substitution effect behaves with regard to distance. Latent class analyses are performed to account for preference heterogeneity among respondents. We observe divergent behaviours across groups of respondents. The “distance-to-substitutes” affects how respondents gauge substitute sites. We find a significant influence of the squared average buffer distance but this effect varies in sign across case studies and classes of respondents. Our results demonstrate that individual-specific GIS data can significantly improve the representation of the spatial context and the transferability of value functions. However, the roles played by preference heterogeneity and nature perception on respondents’ capacity to value nature still deserves further attention in future research.     

Wheat variety yield data: Do commercial and public performance tests provide the same information?

Wheat yields from reported performance test results are of economic importance to wheat producers, since their profits depend on selecting the optimal variety for their location. However, our data shows differences in absolute and relative wheat yields between commercial and public wheat breeding program's performance test data in Kansas. Newly available data are used to test if the difference in yields arose from potential selectivity bias, and to determine the contribution of private and public wheat breeding programs to varietal yield improvement during 2007–2012. Both Heckman selection models and multiple regression showed no statistical evidence of the potential presence of selectivity bias rather, managerial practices, agronomic conditions, field location, and inherent genetic traits of the seed variety were identify as the source of yield differences.     

Life Expectancy Heterogeneity and the Political Support for Collective Annuities

Individuals, differing in productivity and life expectancy, vote over the size and type of a collective annuity. Its type is represented by the fraction of the contributive (Bismarckian) component (based on the worker's past earnings) as opposed to the non‐contributive (Beveridgean) part (based on average contribution). The equilibrium collective annuity is either a large mostly Bismarckian program, a smaller pure Beveridgean one (in accordance with empirical evidence), or nil. A larger correlation between longevity and productivity, or a larger average life expectancy, both make the equilibrium collective annuity program more Beveridgean, although at the expense of its size.     

Rivaroxaban for non-valvular atrial fibrillation and venous thromboembolism in the Netherlands: a real-world data based cost-effectiveness analysis

Background: Non-vitamin K antagonist oral anticoagulants (NOACs) have been included in international guidelines as important alternatives to vitamin K antagonists (VKAs) for the treatment of venous thromboembolism (VTE) and stroke prevention in non-valvular atrial fibrillation (NVAF). Meanwhile, in the Netherlands, NOACs are widely used next to VKAs. The objective of this study is to estimate the cost-effectiveness of treatment with rivaroxaban compared to VKAs in NVAF and VTE patients in the Netherlands, using data from international prospective observational phase IV studies.

Methods: Two models were developed to represent NVAF and VTE patients, populated with patients from the XANTUS (NCT01606995) and XALIA (NCT01619007) international prospective observational studies. The 1-year cost-effectiveness of rivaroxaban use, compared to VKAs, was explored in a population consisting of NVAF and VTE patients (base case) as well as for four scenarios with sub-populations: NVAF patients only, VTE patients only, NVAF patients with unstable international normalized ratio (INR), and NVAF patients using an INR self-measuring device.

Results: In the base case, rivaroxaban saved €72,350 and gained 21 quality-adjusted life-years (QALYs) in a simulation of 2,000 patients over the use of VKAs. Ergo, rivaroxaban was dominant over VKAs. The probabilistic sensitivity analysis showed a probability of 85% for rivaroxaban being dominant and 100% at a willingness-to-pay threshold of €20,000/QALY. Rivaroxaban appeared to be dominant in all scenarios as well, except for the NVAF-patients-only scenario where the incremental cost-effectiveness ratio (ICER) was €157/QALY.

Conclusions: In patients with NVAF or VTE, rivaroxaban treatment is likely to be cost-effective and a potentially cost-saving alternative to VKA in the Netherlands.