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The purpose of this paper is to highlight not only the pre‐closing, but also the continuing due diligence required of all parties in private placement life insurance or annuities invested in hedge funds.

This technical paper describes the due diligence process for these high‐end offerings, illustrating key concerns through case studies.

Recent events have caused the advantages, disadvantages and timing issues to crystallize, together with what issues require pre‐closing and post‐closing due diligence.

This paper provides timely guidance from experts approaching the issues from multiple disciplines – law, accounting, and financial analysis – to yield a comprehensive analysis of the position of all parties to the transaction.

Motivated by empirical features that characterize cryptocurrency volatility data, the authors develop a forecasting strategy that can account for both model uncertainty and heteroskedasticity of unknown form. The theoretical investigation establishes the asymptotic optimality of the proposed heteroskedastic model averaging heterogeneous autoregressive (H-MAHAR) estimator under mild conditions. The authors additionally examine the convergence rate of the estimated weights of the proposed H-MAHAR estimator. This analysis sheds new light on the asymptotic properties of the least squares model averaging estimator under alternative complicated data generating processes (DGPs). To examine the performance of the H-MAHAR estimator, the authors conduct an out-of-sample forecasting application involving 22 different cryptocurrency assets. The results emphasize the importance of accounting for both model uncertainty and heteroskedasticity in practice.

Gives a bibliographical review of the finite element methods (FEMs) applied for the linear and nonlinear, static and dynamic analyses of basic structural elements from the theoretical as well as practical points of view. The range of applications of FEMs in this area is wide and cannot be presented in a single paper; therefore aims to give the reader an encyclopaedic view on the subject. The bibliography at the end of the paper contains 2,025 references to papers, conference proceedings and theses/dissertations dealing with the analysis of beams, columns, rods, bars, cables, discs, blades, shafts, membranes, plates and shells that were published in 1992‐1995.

Wages have been spreading out across workers over time – or in other words, the 90th/50th wage ratio has risen over time. A key question is, has the productivity distribution also spread out across worker skill levels over time? Using our calculations of productivity by skill level for the United States, we show that the distributions of both wages and productivity have spread out over time, as the right tail lengthens for both. We add Organization for Economic Co-Operation and Development (OECD) countries, showing that the wage–productivity correlation exists, such that gains in aggregate productivity, or GDP per person, have resulted in higher wages for workers at the top and bottom of the wage distribution. However, across countries, those workers in the upper-income ranks have seen their wages rise the most over time. The most likely international factor explaining these wage increases is the skill-biased technological change of the digital revolution. The new artificial intelligence (AI) revolution that has just begun seems to be having similar skill-biased effects on wages. But this current AI, called “supervised learning,” is relatively similar to past technological change. The AI of the distant future will be “unsupervised learning,” and it could eventually have an effect on the jobs of the most highly skilled.

Electrical capacitance tomography (ECT) and electrical resistance tomography (ERT) are promising techniques for multiphase flow measurement due to their high speed, low cost, non-invasive and visualization features. There are two major difficulties in image reconstruction for ECT and ERT: the “soft-field”effect, and the ill-posedness of the inverse problem, which includes two problems: under-determined problem and the solution is not stable, i.e. is very sensitive to measurement errors and noise. This paper aims to summarize and evaluate various reconstruction algorithms which have been studied and developed in the word for many years and to provide reference for further research and application.

In the past 10 years, various image reconstruction algorithms have been developed to deal with these problems, including in the field of industrial multi-phase flow measurement and biological medical diagnosis.

This paper reviews existing image reconstruction algorithms and the new algorithms proposed by the authors for electrical capacitance tomography and electrical resistance tomography in multi-phase flow measurement and biological medical diagnosis.

The authors systematically summarize and evaluate various reconstruction algorithms which have been studied and developed in the word for many years and to provide valuable reference for practical applications.

This study examined subjective numeracy and its relationship with accounting judgements on probability issues.

A subjective numeracy scale (SNS) questionnaire was distributed to 231 accounting students to measure self-evaluated numeracy. Modified Bayesian reasoning tasks were applied in an accounting-related probability estimation, manipulating presentation formats.

The study revealed a positive relationship between self-evaluated numeracy and performance in accounting probability estimation. The findings suggest that switching the format of probability expressions from percentages to frequencies can improve the performance of participants with low self-evaluated numeracy.

Adding objective numeracy measurements could enhance results. Future numeracy research could add objective numeracy items and assess whether this influences participants' self-perceived numeracy. Based on this sample population of accounting students, the findings may not apply to large populations of accounting-information users.

Investors' ability to exercise sound judgement depends on the accuracy of their probability estimations. Manipulating the format of probability expressions can improve probability estimation performance in investors with low self-evaluated numeracy.

This study identified a significant performance gap among participants in performing accounting probability estimations: those with high self-evaluated numeracy performed better than those with low self-evaluated numeracy. The authors also explored a method other than additional training to improve participants' performance on probability estimation tasks and discovered that frequency formats enhanced the performance of participants with low self-evaluated numeracy.

This study caries a survey approach using the expert's interview and literature to select the important criteria to select and evaluate the third-party reverse logistics providers (3PRLPs) in manufacturing companies. In total, 16 criteria are selected to evaluate 3PRLPs, and these criteria are classified on the basis of three main elements of sustainable growth, including economic, social and environmental development. Therefore, a hybrid decision-making approach is utilized to evaluate and rank the 3PRLPs in manufacturing companies.

This paper proposes a new decision-making approach using the projection model and entropy method under the interval-valued intuitionistic fuzzy set to assess 3PRLPs based on sustainability perspectives. A survey approach using the literature review and experts' interview is conducted to select the important criteria to select and evaluate 3PRLPs in manufacturing companies. To assess the criteria weight, the entropy method is used. Further, the projection model is applied to prioritize the 3PRLPs option. Sensitivity analysis and comparison process are performed in order to test and validate the developed method.

The presented methodology uses the benefits to determine the former for measuring the parameters considered and the latter for rating the 3PRLPs alternatives. A case study is taken to 3PRLPs in the manufacturing industry to illustrate the efficiency of the introduced hybrid method. The findings of this study indicate that when facing uncertainties of input and qualitative data, the proposed solution delivers more viable performance and therefore is suitable for wider uses.

The conception of the circular economy (CE) comes from the last 4 decades, and in recent years, tremendous attention has been carried out on this concept, partially because of the availability of natural resources in the world and changes in consumption behaviour of developed and developing nations. Remarkably, the sustainable supply chain management concepts are established parallel to the CE foundations, grown in industrial practice and ecology literature for a long time. In fact, to reduce the environmental concerns, sustainable supply chain management seeks to diminish the materials' flow and minimize the unintentional harmful consequences of consumption and production processes. Customers and governments are becoming increasingly aware of the environmental sustainability in the CE era, which allows businesses to concentrate more resources on reverse logistics (RLs). However, most manufacturing enterprises have been inspired to outsource their RL operations to competent 3PRLPs due to limited resources and technological limitations. In RL outsourcing practices, the selection of the best 3PRLP is helpfully valuable due to its potential to increase the economic viability of enterprises and boost their long-term growth.

Using a GED-GARCH model to estimate monthly data from January 1990 to February 2022, we test whether gold acts as a hedge or safe haven asset in 10 countries. With a downturn of the stock market, gold can be viewed as a hedge and safe haven asset in the G7 countries. In the case of inflation, gold acts as a hedge and safe haven asset in the United States, United Kingdom, Canada, China, and Indonesia. For currency depreciation, oil price shock, economic policy uncertainty, and US volatility spillover, evidence finds that gold acts as a hedge and safe haven for all countries.

It is well known that the selection of initial cluster centers can significantly affect K-means clustering results. The purpose of this paper is to propose an improved, efficient methodology to handle such a challenge.

According to the fact that the inner-class distance among samples within the same cluster is supposed to be smaller than the inter-class distance among clusters, the algorithm will dynamically adjust initial cluster centers that are randomly selected. Consequently, such adjusted initial cluster centers will be highly representative in the sense that they are distributed among as many samples as possible. As a result, local optima that are common in K-means clustering can then be effectively reduced. In addition, the algorithm is able to obtain all initial cluster centers simultaneously (instead of one center at a time) during the dynamic adjustment.

Experimental results demonstrate that the proposed algorithm greatly improves the accuracy of traditional K-means clustering results and, in a more efficient manner.

The authors presented in this paper an efficient algorithm, which is able to dynamically adjust initial cluster centers that are randomly selected. The adjusted centers are highly representative, i.e. they are distributed among as many samples as possible. As a result, local optima that are common in K-means clustering can be effectively reduced so that the authors can achieve an improved clustering accuracy. In addition, the algorithm is a cost-efficient one and the enhanced clustering accuracy can be obtained in a more efficient manner compared with traditional K-means algorithm.

Board faultlines, which reflect intrinsic divisions of board members into relatively homogeneous subgroups, are associated with poor firm performance. This paper aims to extend the existing board faultline research by examining how acquisition deal size moderates the negative implications of board faultlines.

This paper uses a sample of acquisitions and a quantitative research approach to conduct statistical analysis.

Using a sample of acquisitions announced between 2007 and 2016, this paper finds evidence suggesting that strong faultlines are associated with poorer acquisition outcomes in the long-term, but not in the short term. Further, this paper finds that the effect of faultline strength on long-term acquisition outcomes is weaker for larger acquisition deals than smaller acquisition deals. The findings are consistent with deal size moderating the relation between faultlines and acquisition outcomes.

This paper addresses possible endogeneity through firm fixed effects and instrumental variable analysis. Although this paper provides evidence on the moderating role of deal size in the context of faultlines, future research could examine the role of additional moderators, such as pro-diversity, trust, board leadership and board and task characteristics.

The findings suggest that boards need to be aware of situations where the negative effects of faultlines are more likely to come to the fore. For example, faultlines are more likely to play a role in more routine, obscure monitoring than for high-profile strategic decisions.

The study is multidisciplinary as it draws on the management, organizational behaviour and psychology and finance literature. It contributes to the developing literature on faultlines in several important ways. First, this paper supports their view that faultlines have adverse effects on board performance by showing that faultlines negatively impact discrete strategic investment decisions. Second, this paper provides evidence that deals size moderates the faultline-acquisition performance relation, indicating that the role of faultlines is contextual. Third, this paper finds evidence that suggests investors do not factor in board faultlines when responding to acquisition announcements.