The construction and optimization of a portfolio is a complex process that has been a historically active research area in finance. For portfolios with highly correlated assets, the performance of traditional risk-based asset allocations methods such as the mean-variance (MV) model is limited when numerous assets are correlated. A novel clustering-based asset allocation method, called Hierarchical Risk Parity (HRP), provides an opportunity to mitigate these limitations in portfolio construction. HRP utilizes the hierarchical relationships between the covariance of assets in a portfolio to determine weight distributions, eliminating the need for the inversion of the covariance matrix that is required by most traditional risk-based asset allocation methods. This research examines the viability of Hierarchical Risk Parity (HRP) method in portfolio construction of a US equity portfolio and compares the performances of HRP to traditional asset allocation methods exemplified by the mean-variance (MV) method. The results of this research show that the performance of the HRP method is comparable to the performance of the MV method. Given these findings, HRP provides an advantageous approach for portfolio construction in practical scenarios where correlated assets are present in the portfolios.

This paper explores how the medical expenditure risk affects the households’ portfolio choice across health status theoretically in a life cycle model and empirically using machine learning methods. Medical expenditure risk, as a background risk, has the potential to influence households’ financial decisions. A higher medical expenditure risk leads to a larger fluctuation and more uncertainty in households’ consumption and therefore utility. As a result, risk-free assets become more attractive. Our machine learning analysis provides evidence that aligns with the predictions of the theoretical life cycle model. Specifically, households with better health hold a larger proportion of stocks in their portfolios. Furthermore, when facing increased medical expenditure risk, households in good health demonstrate a greater willingness to invest in safe assets.

This paper uses China Household Income Project rural household survey data spanning 30 years (1988-2018) to study the impact of China’s industrialization on rural household income. In the semiparametric regression model, we use the rural households’ distance to the manufacturing hub to analyze the spillover effect of industrialization on their income and the spatial attenuation. The results show that the income gap between rural households in Guangdong and other provinces increased from 1988 to 2002, but continuously decreased from 2002 to 2018; the rural households’ distance to the Pearl River Delta or the Yangtze River Delta, the two international manufacturing hubs, has a significant impact on their income, which has been increasing from 1988 to 1995, but has been decreasing since the new century. On the contrary, the influence of the rural households’ distance to the provincial capital on their income has been increasing since the new century.

This study analyses the effects of piped water on household food consumption per capita by adopting inverse-probability-weighted regression adjustment and endogenous treatment effects approaches with data from the Cambodia Socio-Economic Survey carried out in 2013 and 2017. A complementary analysis of the effects on primary household income per working-age member is also conducted to give insights into the potential consequences. The study also conducts a robustness check by estimating the fixed effects of piped water utilising village panel data. The results suggest that households using piped water are likely to enjoy higher food consumption per capita, with a complementary finding demonstrating that the use of piped water is likely to increase household income per working-age member.

The study explores online consumers’ impulse buying intentions and behaviors on live streaming platforms. Unlike traditional shopping modes, the development of real-time video streaming provides online consumers with a distinct approach to interacting with live streamers and browsing online products in real-time, potentially causing their impulse buying intentions. To understand online consumers’ impulse buying intentions and behaviors, the paper establishes the research model based on the theory of planned behavior (TPB) model and analyses influencing factors from attitude, subject norm and perceived control aspects. Through the data analysis based on the partial least squares path modelling and variance-based structural equation modelling (PLS-SEM), the research results show that, three factors positively affect online consumers’ impulse buying intentions and lead to their final behaviors. Meanwhile, control variables, including gender, age, and income level, demonstrate insignificant effects across the model. Unlike existing literature, the current study displays the distinct features of live streaming platforms and discovers online consumers’ impulse buying intention based on the TPB model. The results are helpful for related scholars and departments to pay more attention to the live shopping environment and understand online consumers’ impulse buying issues.

The paper is motivated by the growth of the electronic payments system and its relevance in enhancing the banking industry's earnings. Consequently, the paper examines the causal relationship between the electronic payments system and the banking industry's returns in Nigeria. The paper offers some important contributions to the literature involving the use of an approach that allows for data-driven identification of the change points in the electronic payments system and the banking industry's returns nexus. The paper discovered three important findings. First, the causal relationship between the electronic payments system and the banking industry's returns in Nigeria changes with time changes. Second, there is evidence of causality between the electronic payments system and returns on assets and equity, however, the causality was more evident under the recursive window. Third, the causal relationship was more evident in some specific periods such as 2020Q4, all quarters of 2015 to 2016, and 2020 to the end of 2023. The paper recommends that policymakers should revisit and reinvigorate the specific cashless policies instituted in 2012-2016 that aided higher returns to the banking industry. Similarly, the Central Bank of Nigeria and other banking industry players should intensify efforts to encourage the use of electronic payments due to their usefulness and ability to enhance the banking industry's earnings.

Under the background of digital economy, global tax governance has become one of the important topics in today's world development and change. In recent years, new challenges, such as tax base erosion and profit transfer, and the imbalance of cross-border tax source division, have become prominent, forcing the reform of international tax rules and the reshaping of international tax order. Based on this, the article first deeply digs into the scale and cross-border characteristics of the spatial-temporal aggregation of the digital economy, and comprehensively evaluates its impact on the global tax governance pattern. After that, the article combs out the realistic picture and challenges of the current digital tax reform from the aspects of theoretical basis, basic principles and governance mechanism. Finally, based on China's basic national conditions, the article puts forward some institutional ideas and policy suggestions, with a view to improving China's participation, voice and influence in global tax governance, and also enabling governments, enterprises and residents of various countries to enjoy the fruits of world economic development fairly and promote the construction of Community of Shared Future for Mankind.

The recent interest rate policy decisions of the South African Reserve Bank have been criticized significantly by left-leaning political parties and civic society organisations for being anti-poor, anti-labour, and pro-capital because of their implications for household debt. Existing literature has established that interest rates and house prices are insignificant determinants of household debt dynamics in South Africa. Taking advantage of additional data for the period 2013-2022, and contrary to the previous studies, the paper maintains that house prices and the central bank policy rate play a crucial role in household debt dynamics. Applying a Markov Switching regression to quarterly data for the period 1981Q1 to 2022Q1, the paper finds that house prices and the policy rate have a significant influence on household debt dynamics. It establishes the existence of a ‘house price boom, low policy rate burden’ regime and a ‘high policy rate burden, low house price’ regime. The coexistence of the ‘house price boom’ and the ‘low policy rate’ explains the debt euphoria characterised by significant household leveraging.

Non-labor income is a crucial factor influencing time allocation, and prior studies have primarily concentrated on the linear association between non-labor income and labor hours. Utilizing micro-survey data from the CFPS in 2018 and 2020 and employing the panel threshold model, this paper empirically identifies a double threshold with the wage rate as the threshold variable. This finding reveals a non-linear relationship between non-labor income and labor hours. The two thresholds categorize the relationship into three intervals. In the first interval, non-labor income significantly promotes labor hours, while in the second and third intervals, non-labor income significantly decreases labor hours, exhibiting slightly varying degrees of influence. In general, the relationship between non-labor income and labor hours demonstrates an irregular inverted U-shaped pattern. Upon dividing the workers in the sample into three groups based on the two thresholds, it is observed that wage rates exhibit a positive correlation with non-labor income.

Banks operating within a free economic environment and with constructive regulatory frameworks can function with enhanced efficiency, augmenting their profitability. This study aimed to examine the influence of economic freedom and financial restrictions on banks' profitability within the Organization of Islamic Cooperation member nations. The study used a sample of 1453 banks, collecting panel data spanning 13 years. The System-GMM methodology was justified based on the enduring characteristics of the data and the profitability metrics. The dependent variables, namely the Net Income Margin, Return on Average Assets, and Return on Average Equity were individually examined. The research findings indicate that most economic freedom indicators, except for investment freedom, harm bank profitability. The influence of the rule of law on profitability indicators is mostly adverse, although regulatory quality within the financial system has a comparatively favorable effect on profitability. Furthermore, bank-specific performance indicators had a negative impact on profitability, except for bank size. Moreover, country-specific variables substantially impact banks' profitability, including inflation, tax, and interest rates. The presence of Islamic banking has been shown to substantially impact the profitability performance of banks within the Organization of Islamic Cooperation. OIC banks' profitability is adversely impacted by the ongoing health crises, including the Covid-19 pandemic.

A differences-in-differences (DID) model and financial data from Chinese listed firms from 2011 to 2019 are used to empirically investigate the effects of the digital economy on corporate financial asset holdings using the "Broadband China" policy as a quasi-natural experiment. After robustness testing and heterogeneity effects are disposed of, the digital economy dramatically boosts corporate financial asset holdings. According to the channel analysis, the digital economy can reduce corporate financing constraints and boost corporate financial asset holdings, notably based on speculative demand. Heterogeneity analysis shows that the digital economy has a greater impact on corporate financial asset holdings for firms with higher market competition, small-scale firms with a short-listed age, and firms in the western region. This paper provides policy guidance for enterprises returning to the real economy.

This paper provides a thorough review of the shifting landscape of economic analysis, spotlighting recent trends and predicting future paths. While traditional economic models remain key for interpreting economic activity, they are being supplemented by fresh methods and cross-disciplinary viewpoints. The increased attention to inequality studies, using advanced statistical techniques and unique data sources, underscores the growing emphasis on fairness and distribution within economic analysis. The incorporation of behavioral elements into economic models also expands our comprehension of economic decision-making and market results. Notably, the emergence of computational economics-integrating artificial intelligence (AI), big data, and machine learning into economic scrutiny-represents a major development. Often referred to as ’smart economics,’ this field employs technology to formulate, address complex economic dilemmas, and perceive economic activity in unconventional ways. Yet, the application of AI and machine learning in economics introduces new hurdles around data privacy, algorithmic bias, and the transparency of model outcomes. The impact of the digital revolution on economic analysis is significant, as the advent of computational economics and the surge of big data are transforming research techniques and policy implications. Concurrently, the advent of the circular economy indicates a radical shift in our perspective on economic sustainability, carrying considerable implications for environmental policy and business tactics.  In the future, it’s anticipated that these trends will further modify the realm of economic analysis, with AI and machine learning integration, emphasis on sustainability and fairness, and the influence of big data becoming more pronounced. As these changes take place, it’s imperative for researchers, policymakers, and practitioners to remain adaptable and flexible, prepared to capitalize on the opportunities and tackle the challenges these trends present.

According to economic theory and common sense, the issuance of consumption coupons on one hand stimulated the increasing of residents' consumption, but on the other pushed up the price level. In this paper, using the theory of mechanism design, the game model was built to analyze the relationship between the effect of consumer coupons during the epidemic and the number of merchants participated. The results show that the smaller the merchants’ number, the weaker the coupons’ effect, vice versa. Then, using KNN regression method, combined with the monthly year-on-year data of CPI from 1998 to 2020, this paper analyzes the changes of CPI forecast value and actual value in the first 10 months of 2020, finding that the actual value of CPI is higher than the predicted one during the first two months of 2020, but lower from March 2020 on, which further verifies the conclusion of game analysis.

Reckless behavior by business leaders can be a systemic risk for individual firms and the economies in which the firms exist. We propose that a synthesis of behavioral economics, in particular prospect theory, and the study of psychopathy may help researchers better understand why some business leaders engage in high-risk criminal activity. We propose that psychopathy is associated with an abnormal response to negative consequences. Where traditional Prospect Theory proposes that people are loss avoidant, we propose that people high on the trait of psychopathy may be more motivated by gains and will be risk seeking in high reward/high risk situations for which most people will avoid risk. We propose two empirical study designs that may be used to test the framework in the future.

Applying the network analysis’ tools and a large dataset over 2000-2017, this paper provides insights to the patterns and structure of global and regional migration and remittances as well as the connection between these two phenomena inside a complex network. We reveal that even though the structure of migration and remittance networks is slightly altering, the major changes have only occurred in the intensive margin of the networks. In addition, there is a deepening intra-regional trend regarding the structure of migration communities. By contrast, we observe an instability in the structure of migration and remittance communities formed around the U.S.

This research investigates the determinants of women's empowerment within households in a developing country. The investigation specifically focuses on the impact of employment and fertility, considering constraints imposed by family rules that play a pivotal role in shaping women's empowerment. The theoretical framework outlined in this study posits a simplistic model demonstrating that, within the prevailing family structure and household composition, earned income and fertility may bear significant relevance. Utilizing data extracted from the Pakistan Social and Living Standards Measurement Survey, we engage in estimating the level of female empowerment, operationalized as the extent of female authority over household expenditures. Notably, our empirical approach accounts for the endogeneity of employment and fertility through a two-stage estimation process. This involves leveraging information on family rules—representative of a family's cultural norms—pertaining to work and childbearing. Our empirical findings affirm the viability of family-specific rules as instruments to estimate and subsequently predict employment and fertility. Importantly, the evidence gleaned underscores the critical role of selecting valid threat options in theoretical analyses of women's empowerment.

Pseudomonas aeruginosa is a common pathogen, and its presence in medical environments and water bodies has attracted widespread attention. Traditional detection methods are usually time-consuming and cumbersome, so it is necessary to develop a rapid and sensitive detection technology. DNase can specifically recognize and cut DNA molecules complementary to its substrate sequence. The researchers took advantage of this property to design various DNase-based sensors for detecting the presence of Pseudomonas aeruginosa. These sensors usually use DNase as a recognition element to identify target strains by hybridizing with specific DNA sequences. When the target strain is present, DNase is activated and begins to catalyze the cleavage reaction, producing a detectable signal. This DNase-based sensor has the advantages of rapidity, high sensitivity, and high specificity. In addition, the researchers also explored combining DNase with nanomaterials, fluorescent dyes, etc. to further improve the performance of the sensor. These improvements have improved the detection ability of the sensor in complex samples, laying the foundation for practical applications. With the continuous improvement of technology, these sensors are expected to be widely used in medical, environmental monitoring and other fields, and provide more efficient and convenient solutions for bacterial detection. This study reviewed the research progress of DNase-based sensors for the rapid detection of Pseudomonas aeruginosa.

Metadherin (MTDH), also known as LYRIC or AEG-1, is an oncogene that enhances tumor progression, metastasis, drug resistance, and immune escape in various cancers by modulating multiple oncogenic pathways, including NF-κB, PI3K/AKT, Wnt/β-catenin, MAPK, and AMPK. Due to the unknown of the complete structure of MTDH, the deep mechanisms of MTDH and selective inhibitors targeting MTDH remain to be explored. The Protein-Protein interaction (PPI) with the Staphylococcal nuclease domain containing 1 (SND1) is a crucial mechanism underlying the function of MTDH. Current studies have demonstrated that inhibitors, including antisense oligonucleotides, peptides, and small molecules targeting MTDH or MTDH-SND1 interactions, provide novel strategies to inhibit the oncogenetic effects of MTDH. This review summarizes and discusses the structure, function, and regulation of MTDH in cancers, providing the potential therapeutic perspectives of MTDH or MTDH-SND1 PPI for drug discovery.

The long-term extensive economic development has caused China's resource and environmental problems, especially the resource misallocation. The way China prioritises its limited resources is being significantly impacted by the rise of the digital economy and the interconnectedness of new technologies and the real economy. This paper quantitatively examines the linear and nonlinear impacts and mechanisms of digital development represented by internet development. With a series of empirical tests, we found that the internet development has significantly inhibited the resources misallocation, and the conclusion is still valid in the robustness test with internet popularization and internet infrastructure as the core explanatory variables. In addition to the marketization, internet development can further inhibit resource misallocation by promoting financial development, openness, urbanization and industrial structure. The findings of threshold regression suggest that the inhibitory effect of internet growth on resource misallocation becomes more visible as the degree of financial development and industrial structure increases; with the higher degree of urbanisation and marketization, although the internet development has always played an inhibitory role on resource mismatch, the inhibitory effect first increases and then decreases; with the improvement of openness, the hindering impact of internet growth on resource mismatch becomes more visible as the degree of financial development and industrial structure increases.

The very nature of work, labor market composition, and skill requirements are severely affected by technology-driven transformations currently underway with the 4^th industrial revolution (4IR). The effect of 4IR technologies on technical and vocational education and training (TVET) and the contribution of TVET to the labor market is explicit. The paper presents the findings of a study that investigated whether TVET institutions are prepared to face the challenges of 4IR. The data for the study were collected by surveying 428 teachers from 15 countries in Asia and the Pacific engaged in science, technology, engineering, and mathematics (STEM) fields at the tertiary level. The findings of our study undoubtedly bring greater attention to the initiatives of TVET in these countries in preparing the workforce for job roles necessitated in the 4IR. The findings can also be used to frame policy recommendations for the growth of economies.