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Fault-related monitoring variables selection is a process of obtaining a subset of variables from the original set, which is of great significance for reducing information redundancy and improving the performance of the fault diagnosis models. This paper aims to propose a novel variables selection approach based on complex networks.

Firstly, a dual-layer correlation networks (DlCN) which consists of mechanism-oriented correlation sub-network (MoCSN) and data-oriented correlation sub-network (DoCSN) is constructed. Secondly, an algorithm for identifying critical fault-related monitoring variables based on dual correlations is introduced. In the algorithm, the topological attributes of the MoCSN and correlation threshold of the DoCSN are used successively.

In the experiments of vertical elevator fault diagnosis, the critical fault-related monitoring variables selected by the DlCN-based approach is more effective than the traditional approaches. It indicates that fusion mechanism-oriented correlation can enhance the comprehensiveness of variable correlation analysis. Moreover, the approach has been proved to be adaptable to different fault diagnosis models.

In the DlCN-based variables selection approach, the mechanism-oriented correlation and data-oriented correlation are comprehensively considered. It improves the precision of variables selection. Meanwhile, it is an unsupervised and model-agnostic approach which addresses the shortcomings of some conventional approaches that require data labels and have insufficient adaptability for fault diagnosis models.

As blockchain-based virtual worlds gain prominence within the emerging metaverse and Web3, numerous global companies and investors are buying purely virtual land to explore new business potentials and capitalize on digital assets. Given the similarities to physical real estate, this study examines the dynamics of the secondary market for virtual land and relates its returns to those of physical real estate.

Using transaction-level data from a prominent virtual land platform, the authors construct a virtual land market index based on repeat sales index methodology from traditional real estate studies. Wavelet coherence analysis is employed to examine the dynamic correlation between virtual land and various physical real estate market returns. The determinants of this correlation are estimated using stepwise regression analysis. A portfolio analysis explores the implications of adding virtual land to traditional asset portfolios.

The correlation between virtual and physical real estate market returns is generally low, reaching its lowest during the Covid-19 lockdowns from 2020 to 2022. It spikes during acute economic turmoil such as the initial Covid-19 outbreak or interest rate change announcements. The correlation is primarily driven by consumer and economic climate, the price of the virtual economy token and investor attention. Portfolio analysis indicates that virtual land can enhance risk-adjusted returns within a traditional portfolio, particularly when added to a commercial real estate portfolio.

This study examines a single virtual land market, despite it being the oldest and one of the largest. Given the rapidly evolving nature of virtual worlds, it is crucial to further test the results and include new virtual land platforms as they emerge.

The findings provide actionable insights on portfolio implications for investors seeking alternative real-estate-like assets in the digital space. Additionally, this study offers strategic guidance for entering the metaverse, including a comprehensive overview of established virtual presences.

With the advancing digitization of real estate markets, this study is the first to explore the correlation between market returns of virtual land in the metaverse and traditional physical real estate. The findings provide valuable empirical insights for investors, policymakers, entrepreneurs and companies interested in the intersection of digital and traditional property markets.

Understanding correlations between stock and direct real estate returns, which is the key factor that determines diversification benefits in a portfolio, helps formulate and implement better investors' asset allocation and risk management strategies. The past studies find that direct real estate returns have a low unconditionally (long‐run) correlation with the returns of equities. However, assuming that such correlation is constant throughout all periods is implausible. The purpose of this study is to test the time‐varying correlations of returns between general stocks and direct real estate.

This study uses the dynamic conditional correlation (DCC) model, which is a simplified version of the multivariate generalised autoregressive conditional heteroskedasticity (GARCH) model, proposed by Engle to test the time‐varying correlations between stock and direct real estate returns in six markets, which include the USA, the UK, Ireland, Australia, Hong Kong and Singapore.

The empirical results show significant time‐varying effects in the conditional covariance between stock returns and direct real estate returns. The results vary across different real estate sub‐sectors, and across different countries. It is observed that the conditional covariance increases in the boom markets, but becomes weaker in the post‐crisis periods. The authors observed significant jumps in the conditional covariance between the two asset markets in Singapore and Hong Kong in the post‐1977 Asian Financial crisis periods and in the post‐2007 US Sub‐prime crisis periods.

The past studies find that direct real estate returns have a low unconditionally (long‐run) correlation with the returns of equities. However, assuming that such correlation is constant throughout all periods is implausible. This study fills in the gap by using the dynamic conditional correlation models to allow for time‐varying effects in the correlations between stock and real estate returns.

The purpose of this paper is to reveal the multi‐scale relation between power law distribution and correlation of stock returns and to figure out the determinants underlying capital markets.

The multi‐scale relation between power law distribution and correlation is investigated by comparing the original series with the special series. The eliminating intraday trend series approach developed by Liu et al. is utilized to analyze the effects of power law decay change on correlation properties, and shuffling series originated by Viswanathan et al. for the impacts of special type of correlation on power‐law distribution.

It is found that the accelerating decay of power law has an insignificant effect on correlation properties of returns and the empirical results indicate that time scale may also be an important factor maintaining power law property of returns besides correlation. When time scale is under critical point, the effects of correlation are crucial, and the correlation of nonlinear long‐range presents the strongest influence. However, for time scale beyond critical point, the impact of correlation begins to diminish or even finally disappear and then the power law property shows complete dependence on time scale.

The 5‐min high frequency data of the Shanghai market as the empirical benchmark is insufficient to depict the relation over the entire time scale in the Chinese stock market.

The paper identifies the determinants of market dynamics to apply them to risk management through analysis of multi‐scale relations, and supports endeavors to introduce time parameter into further risk measures and control.

The paper provides the empirical evidence that time scale is one of the key determinants of market dynamics by analyzing the multi‐scale relation between power law distribution and correlation.

This paper develops visual aids for the understanding of two asset portfolio mathematics. Specifically, visual aids are utilized in teaching portfolio variance and correlation coefficient concepts. The presentation is simple, yet powerful, and is useful for an audience with varying levels of statistical sophistication. Consequently, the visual aids can replace or complement standard presentations of basic portfolio theory.

Refers to previous research on deciding the balance between equities and bonds in investment portfolios and puts forward a model based on a single period correlation to predict future stock‐bond correlations from past interest and growth rates. Explains the concepts involved and uses 1948‐2000 US data to test it. Shows that the model predicts stock‐bond correlation significantly better than the traditional method of extrapolating from past correlations; and relates this to the theory of loanable funds. Concludes that high interest rates and high growth lead to higher correlations between stocks and bonds and calls for further research.

The purpose of this study is to analyze the relationship between lean manufacturing and corporate environmental performance. Scholarly literature has extensively addressed the relationship between those two areas but empirical papers present mixed and inconsistent results, calling for further analysis to establish a clearer understanding of the actual relationship and to identify the causes of conflicting findings across studies. Given the importance of sustainable practices in the current business landscape, this paper aims to provide a comprehensive overview of this relationship through a meta-analysis of previous research, with a focus on integrating quantitative findings to shed light on the potential impact of lean manufacturing on environmental performance and report its intensity with Pearson’s correlation coefficient.

This paper analyzes the data from 29 primary studies published between 2001 and 2022 that have empirically measured the relationship between lean manufacturing and corporate environmental performance and that have been identified in the Web of Science and SCOPUS databases through an exhaustive review of the literature. To integrate previous empirical results and evaluate the evidence for the lean manufacturing’s influence on environmental performance, a meta-analytic methodology was adopted through the Hedges–Olkin random effect approach, based on correlations.

Main findings support the notion that a significant, positive, rather moderated, relationship exists between lean manufacturing and environmental performance, with an overall correlation coefficient r¯ = 0.25. The result seems robust, as several tests confirm that publication bias is unlikely. Overall, various lean practices are correlated to varying degrees with different measures of environmental performance. The environmental efficiency of external lean practices is higher than that observed for internal practices and quality management has a more pronounced effect than other internal lean practices. The 2008 global crisis did not change these relationships and the impact of lean manufacturing on environmental performance does not seem to be moderated by the degree of environmental awareness of the country where applied.

The results conclude that a significant, positive relationship exists between lean manufacturing and environmental performance (r¯ = 0.25). This study tests certain factors that exhibit varying effect sizes and moderate the overall outcome, highlighting that the environmental efficiency of external lean practices is higher than that observed for internal practices. In addition, it suggests a more pronounced effect of quality management among other internal lean practices.

This study provides companies with an opportunity to align their operational strategies with environmental sustainability goals. Understanding that various lean practices exhibit diverse levels of correlation with multiple measures of environmental performance, decision-makers can prioritize their efforts and apply the lean practices that have a stronger effect on the desired environmental outcomes to improve their environmental impact. Conversely, managers are aware that certain lean practices have a week relationship with some environmental performance so they can avoid overestimating environmental benefits of lean manufacturing. Finally, results underscore the importance of organizational commitment to environmental sustainability.

It is, to the best of the authors’ knowledge, the first meta-analytic study to investigate the strength of the association between lean manufacturing and environmental performance and to test whether various lean practices are correlated to different measures of environmental performance. It fills this gap in the literature and therefore it represents a valuable contribution to the field. In addition, this paper explores certain factors that moderate the overall outcome.

This study aims to investigate the impact of the 2022 collapse of the Terra-Luna ecosystem on volatility correlations among digital assets, including U.S. Terra, Luna, Bitcoin, Ether, a Decentralized Finance index and U.S.-sourced conventional assets stocks, bonds, oil, gold and the dollar index. The primary research question addresses whether correlations increased between digital and conventional assets during the collapse.

A dynamic conditional correlation generalized autoregressive conditional heteroskedasticity model was used to examine changes in volatility correlations during the market crash. Specifically, a data set of 1,442 close prices from 30-minute interval candles of digital and conventional asset prices are considered to provide a granular view of market dynamics during the sample period from January 3rd, 2022, to May 31st, 2022, including the crash event.

While the dynamic conditional correlation plots of the model indicate increased volatility, the results do not offer sufficient evidence to confirm an increase in correlations between digital and conventional assets during the Terra-Luna downfall. Furthermore, the authors confirm Bitcoin’s role as a diversifier with oil and observe the dollar index maintaining a negative correlation with Bitcoin during the crash, supporting Bitcoin’s function as a hedge against the U.S. dollar. However, the findings during the crash diverge from previous studies, reflecting shifts in correlation patterns in broader market downturns. Specifically, the authors identify the need for adaptive capital allocation strategies, as gold’s oscillation during the period suggests it may not serve as an effective hedge during black swan events.

The findings provide insights for investors, financial institutions and regulators to improve risk management, portfolio diversification, trading strategies and the formulation of consumer protection regulations. In addition, the results underscore the challenges of mitigating risks beyond regulatory measures and emphasize the importance of exercising caution for investors.

This study addresses the research gap in changes between conventional and digital asset volatility correlations during collapses in the digital asset space.

This paper aims to provide a reference for the development of digital transformation from the perspective of manufacturing process links.

This paper applies canonical correlation analysis based on digital technology patents in the key links of manufacturing industries (product design, procurement, product manufacturing, warehousing and transportation, and wholesale and retail) and the related indicators of economic benefits of regions in China.

(1) The degree of digitalization of manufacturing process links is significantly correlated with economic benefits. (2) The improvement of the degree of digitalization in the “product design” link, the “warehousing and transportation” link, the “product manufacturing” link and the “wholesale and retail” link has significant impacts on the economic benefits of manufacturing industry. (3) The digital degree of the “procurement” link has no obvious influence on the economic benefits of manufacturing industry.

The research results can provide reference for the formulation and implementation of micro policies. The strategy of improving the level of digital transformation of key links of manufacturing industry is put forward to better promote both the digital transformation of manufacturing industry and economic development.

This paper innovatively studies the relationship between digitalization of manufacturing process links and economic benefits. The findings can provide theoretical and empirical support for the digital transformation of China's manufacturing industry and high-quality development of economy.

This chapter investigates the correlation dynamics in the equity markets of 13 Asia-Pacific countries, Europe and the US using the asymmetric dynamic conditional correlation GARCH model (AG-DCC-GARCH) introduced by Cappiello, Engle, and Sheppard (2006). We find significant variation in correlation between markets through time. Stocks exhibit asymmetries in conditional correlations in addition to conditional volatility. Yet asymmetry is less apparent in less integrated markets. The Asian crisis acts as a structural break, with correlations increasing markedly between crisis countries during this period though the bear market in the early 2000s is a more significant event for correlations with developed markets. Our findings also provide further evidence consistent with increasing global market integration. The documented asymmetries and correlation dynamics have important implications for international portfolio diversification and asset allocation.