COMPARING PPP WITH TRADITIONAL PROCUREMENT: THE CASE OF SCHOOLS PROCUREMENT IN IRELAND

Advocates of public‐private partnerships (PPPs) argue that they can deliver public infrastructure more efficiently than traditional procurement through timelier completion and superior value for money. Despite these claims comparative analysis of the performance of both procurement methods has received scant attention in the PPP literature to date. This paper addresses this issue by providing an in‐depth, case‐based comparison of PPP versus traditional procurement in the schools sector in Ireland. Through detailed semi‐structured interviews with key stakeholders and an examination of the available documentation, we assess whether the key objectives of using PPP have been achieved. Overall, we find no evidence that PPP leads to faster delivery of infrastructure when the overall procurement process from contract notice to delivery is accounted for. In addition, we find only limited evidence to suggest that PPP results in better value for money.     

Some issues in blockchain for accounting and the supply chain,with an application of distributed databases to virtual organizations

This paper reviews some recent blockchain‐based applications for information capture, distribution and preservation. As part of that review, this paper examines two key concerns with current blockchain designs for accounting and supply chain transactions: data independence and multiple semantic models for the same information distribution problem. Blockchain applications typically integrate database, application and presentation tiers all in the same ledger. This results in a general inability to query information in the ledger and other concerns. Further, since most applications appear to be private blockchain applications, there is a concern of agents needing to accommodate multiple blockchains depending on who their trading partners are and what they request. Finally, this paper uses a distributed database to design a ‘blockchain‐like’ system for virtual organizations.     

A multivariate distance nonlinear causality test based on partial distance correlation: a machine learning application to energy futures

This paper proposes a multivariate distance nonlinear causality test (MDNC) using the partial distance correlation in a time series framework. Partial distance correlation as an extension of the Brownian distance correlation calculates the distance correlation between random vectors X and Y controlling for a random vector Z. Our test can detect nonlinear lagged relationships between time series, and when integrated with machine learning methods it can improve the forecasting power. We apply our method as a feature selection procedure and combine it with the support vector machine and random forests algorithms to study the forecast of the main energy financial time series (oil, coal, and natural gas futures). It shows substantial improvement in forecasting the fuel energy time series in comparison to the classical Granger causality method in time series.     

我国物流业转型升级研究综述

中国经济已经到了只有转型升级才能持续健康发展的关键阶段,产业转型升级在国家政策文件和研究文献中出现的词频较高。文章以我国物流业为研究对象,从现有涉及到物流业转型升级方面的文献挖掘有效信息,梳理我国物流业转型升级研究的历史脉络,从供应链与价值链、产业集群、现代科学技术应用和产业联动四个视角对现有的物流业转型升级文献进行了总结和归纳,并从物流业转型升级的动力研究、阶段性划分研究和演进规律研究三个方面指出了现有文献的研究不足和以后可能的研究方向。其中,供应链与价值链视角侧重于物流企业的纵向角度,产业集群视角侧重于物流企业的横向角度。文章对于物流企业战略设计、政府政策的制定和理论研究具有一定的参考作用。     

Machine Learning and Expert Judgement: Analyzing Emerging Topics in Accounting and Finance Research in the Asia–Pacific

In this paper, we focus on the question to what extent machine learning (ML) tools can be used to support systematic literature reviews. We apply a ML approach for topic detection to analyze emerging topics in the literature—our context is accounting and finance research in the Asia–Pacific region. To evaluate the robustness of the approach, we compare findings from the automated ML approach with the results from a manual analysis of the literature. The automated approach uses a keyword algorithm detection mechanism whereby the manual analysis uses common techniques for qualitative data analysis, that is, triangulation between researchers (expert judgement). From our paper, we conclude that both methods have strengths and weaknesses. The automated analysis works well for large corpora of text and provides a very standardized and non-biased way of analyzing the literature. However, the human researcher is potentially better equipped to evaluate current issues and future trends in the literature. Overall, the best results might be achieved when a variety of tools are used together.     

Local opinions on trophy hunting in Kyrgyzstan

As traditional international trophy hunting destinations are becoming less accessible due to hunting restrictions and regulations, new destinations are entering the scene, such as the Republic of Kyrgyzstan, located in Central Asia. Kyrgyzstan has grown to be one of the top destinations for international trophy hunting of argali Ovis ammon and ibex Capra sibirica, both of which are in danger of extinction. Empirically, the article draws on a case study from the largest region in Kyrgyzstan, At-Bashy, and 395 questionnaires with local inhabitants from 5 villages, and 1 interview with an international trophy hunting tour operator. In this article, the impacts of trophy hunting as a tourism practice in a rural context is discussed in terms of its sustainability and through the opinions of the local inhabitants. In sum, the negative impacts of trophy hunting in At-Bashy seem to overrule the positive ones, and in its current form it is not sustainable. The local inhabitants report about a decrease in argali and ibex during the last years; they receive basically no economic benefits from hunting tourism; and not surprisingly, 70% of the population rejects the further development of the industry in its current shape.     

Time Inhomogeneous Multiple Volatility Modeling

Price variations at speculative markets exhibit positive autocorrelationand cross correlation. Due to large parameter spaces necessaryfor joint modeling of variances and covariances, multivariateparametric volatility models become easily intractable in practice.We propose an adaptive procedure that identifies periods ofsecond-order homogeneity for each moment in time. To overcomethe high dimensionality of the problem we transform the multivariateseries into a set of univariate processes. We discuss thoroughlythe implementation of the adaptive technique. Theoretical andMonte Carlo results are given. We provide two applications ofthe new method. For a bivariate exchange rate series we comparethe multivariate GARCH approach with our method and find thelatter to be more in line with the underlying assumption ofindependently distributed innovations. Analyzing a 23-dimensionalvector of asset returns we underscore the case for adaptivemodeling in high-dimensional systems.     

The Impact of Institutional Differences on Derivatives Usage: a Comparative Study of US and Dutch Firms

This paper examines the influence of institutional differences on corporate risk management practices in the USA and the Netherlands. We compare results to surveys in each country using a strategy that corrects for differences over industry and size classes across the Dutch and US samples. We document several differences in the firms’ uses and attitudes towards derivatives and attempt to attribute them to the differences in the institutional environments between the USA and the Netherlands. We find that institutional differences appear to have an important impact on risk management practices and derivatives use across US and Dutch firms.     

State-owned enterprises going public The case of China

Public listing is a key reform measure for large state‐owned enterprises (SOEs) in China. We find evidence that public listing lowers state ownership significantly, lessens firms’ reliance on debt finance, and allows firms to increase capital expenditure, at least temporarily. We also find that ownership structure affects post‐listing performance. However, we find no statistical evidence of a positive effect of public listing on firms’ profitability. We suggest alternative interpretations of the last finding.