Search

We estimate a narrative factor pricing model from news text of The Wall Street Journal. Our empirical method integrates topic modeling (LDA), latent factor analysis (IPCA), and variable selection (group lasso). Narrative factors achieve higher out-of-sample Sharpe ratios and smaller pricing errors than standard characteristic-based factor models and predict future investment opportunities in a manner consistent with the ICAPM. We derive an interpretation of the estimated risk factors from narratives in the underlying article text. Authors have furnished an Internet Appendix , which is available on the Oxford University Press Web site next to the link to the final published paper online [ABSTRACT FROM AUTHOR]

Abstract.Penalized regression methods, particularly LASSO, require the irrepresentable condition for variable selection consistency. This condition places an upper bound on the magnitudes of correlation between the signals and the rest of the covariates under consideration. Chudik, Kapetanios, and Pesaran (2018) proposed an alternative procedure called the one covariate at a time multiple testing (OCMT) that does not impose restrictions on the magnitude of these correlations. However, it requires that the number of covariates correlated with the signals grows at a rate less than the square root of the number of observations, denoted by T. Notably, the required conditions for both LASSO and OCMT can be challenged when possibly all the covariates under consideration are correlated with the signals. In this article, we follow the ideas from latent factor literature to adapt OCMT to allow for such scenarios. We refer to our proposed method as generalized one covariate at a time multiple testing (GOCMT). We establish that GOCMT selects a model that contains all the signals and none of the noise variables asymptotically. We also show that the least squares estimator of the post GOCMT selected model is T-consistent. The proposed method demonstrates promising finite-sample performance in our Monte Carlo experiments. An empirical application in risk factor selection within asset pricing further underscores the utility of our method. [ABSTRACT FROM AUTHOR]

The study inspects the status of school governance and school outcome at primary school level and set up roadmap for all the stakeholders to achieve the mandate of Goal 4 in SDGs within 2030, especially in the context of rural India. The status of school governance and school outcome are assessed under 4 dimensions and 16 parameters through a survey of 21 rural primary schools from rural Maharashtra. Two different indices have been constructed for school outcomes and school governance using multi-stage principal component analysis. Public and private-aided schools are compared according to the degree of accountability and transparency. It has been realized that there is an absolute need for strong school governance at ground level which is very poor across public schools in rural India. The study followed the Worldwide Governance Indicators (WGI) project and ASER (2014) and considered these baselines to find the present status of school governance and school outcome for the present study. [ABSTRACT FROM AUTHOR]

We propose a novel estimation approach for the covariance matrix based on the l 1-regularized approximate factor model (AFM). Our sparse approximate factor (SAF) covariance estimator allows for the existence of weak factors and hence relaxes the pervasiveness assumption generally adopted for the standard AFM. We prove the consistency of the covariance matrix estimator under the Frobenius norm as well as the consistency of the factor loadings and the factors. Our Monte Carlo simulations reveal that the SAF covariance estimator has superior properties in finite samples for low and high dimensions and different designs of the covariance matrix. Moreover, in an out-of-sample portfolio forecasting application, the estimator uniformly outperforms alternative portfolio strategies based on alternative covariance estimation approaches and modeling strategies including the 1 / N -strategy. [ABSTRACT FROM AUTHOR]

This article studies new tests for the number of latent factors in a large cross-sectional factor model with small time dimension. These tests are based on the eigenvalues of variance–covariance matrices of (possibly weighted) asset returns and rely on either an assumption of spherical errors, or instrumental variables for factor betas. We establish the asymptotic distributional results using expansion theorems based on perturbation theory for symmetric matrices. Our framework accommodates semi-strong factors in the systematic components. We propose a novel statistical test for weak factors against strong or semi-strong factors. We provide an empirical application to U.S. equity data. Evidence for a different number of latent factors according to market downturns and market upturns is statistically ambiguous in the considered subperiods. In particular, our results contradict the common wisdom of a single-factor model in bear markets. [ABSTRACT FROM AUTHOR]

Purpose: In the past 25 years, startup ecosystem has grown significantly. Prior research tended to focus more on quantitative analysis and very limited work is available specifically in campus startup ecosystem. This study aims to map the intellectual structure of research on campus startup ecosystem through comprehensive bibliometric analysis. Design/methodology/approach: A selection of 1157 studies from the Scopus Database, published between 1998 and 2023, is examined. The analysis incorporates various analysis such as citation, bibliographic coupling, cluster, keyword, and three-field analysis using VOS viewer and Biblioshiny software package. Findings: The study shows a notable growth in publication during the study period, resulting in findings of the impactful authors and journals, current and evolving themes and avenues for future research. The study identifies the paper "Organizational Endowments and the Performance of University Start-ups" as having highest citation and recognizes the "Entrepreneurship and Regional Development" as the most influential journal in the field. Additionally, the investigation reveals ten thematic clusters which are grouped based on study and similarity resulting in five major themes/clusters. The article concludes by suggesting potential directions for future research in the field of campus startup ecosystems, addressing identified gaps and highlighting opportunities for further growth and exploration. Future research: These findings highlight the importance of further exploration into campus startup ecosystems, especially in understanding how academic institutions can strengthen entrepreneurial initiatives through policy support, enhanced mentorship, and industry collaboration. Key areas for future research include interdisciplinary collaboration and global comparisons of campus startup ecosystems. Addressing these gaps will provide practical insights for academia, policymakers, and practitioners in fostering campus startup ecosystem. Originality/value: To the best of the authors' knowledge, this study is one of the comprehensive bibliometric analyses focusing specifically on the campus startup ecosystem, an area that has received relatively limited attention in prior research. While past studies often focused on broader entrepreneurial ecosystems or startup landscapes, this study delves into the unique dynamics of campus startups, emphasizing the pivotal role academic institutions play in fostering entrepreneurial ventures. [ABSTRACT FROM AUTHOR]

In today’s business environment, with organizations enhancing performance, maintaining a competitive edge, and fostering innovation, the efficient transfer of knowledge plays an increasingly pivotal role. As industries recognize the importance of and strive for more and more productive knowledge transfer, understanding the factors affecting this process becomes imperative. Manufacturing is considered a strategic sector, and well-performing knowledge transfer is necessary for higher innovation and competitiveness. To increase knowledge transfer performance, companies must have the awareness, ability, and opportunity to identify the main aspects that need operational improvement. Studies based on systematic literature review and mapping impact factors have shown that knowledge transfer is hypothetically affected by a vast variety of factors, which creates challenges for companies in selecting and applying these factors. Therefore, the two research questions of this study are – RQ1: What are the relationships between the knowledge transfer-influencing factors identified in the literature analysis and the dependent variables? RQ2: What is the weight of the statistically significant factors identified in the empirical study?

It is evident that relying solely on domestic financing cannot bridge the infrastructure gap in the Sub-Saharan African (SSA) region. Therefore, exploring alternative sources of financing becomes crucial to complement domestic efforts and address the infrastructure needs effectively.

Social exclusion and poverty constitute pressing issues in contemporary societies and significant obstacles to achieving sustainable social development. In contemporary Europe, they are recognised as one of the most severe social problems. Consequently, the pursuit of research in this domain appears entirely justified.

The milk processing sector in the Republic of Croatia faces numerous challenges. It is a distinctly bipolar structure, with some entities resembling the largest milk producers in the EU, while many small and medium-sized dairy farms struggle to remain competitive and achieve further progress. To formulate effective policy, it is important to differentiate between these types and address their key challenges.

This study analyzes education, training, and the youth labor market in central and eastern Europe.

The study investigates the financial well-being of older people in Poland, Spain and Denmark, with a particular focus on their ability to make ends meet. Using data from the SHARE survey to analyse retired individuals aged 65 to 79 years, it aims to identify the socio-economic factors that influence financial well-being among older people in these countries. In terms of methodology, it uses Light Gradient Boosting Machine algorithm and SHAP value calculations to predict the ability to make ends meet and determine the importance of 167 various features. The study concludes that household income and financial resources are the primary determinants of older people’s ability to make ends meet. The findings underscore the need for policymakers and practitioners the fields of ageing and economics to address specific challenges, such as housing costs in Denmark and food expenditure in Poland and Spain, to improve the financial well-being of older individuals.

The study aims to examine the relationship between Sustainable Development Goals (SDGs) knowledge, sustainability attitudes, sustainable entrepreneurship, and sustainable behaviors among students enrolled in business-related courses at Spanish universities. Employing the Knowledge-Attitude-Behavior (KAB) model, this study innovatively disaggregates the attitude variable into attitudes toward sustainability and sustainable entrepreneurship. The findings affirm the direct and indirect effects posited in the model, indicating that familiarity with SDGs positively impacts attitudes toward sustainability, subsequently influencing sustainable behaviors. Moreover, positive attitudes toward sustainable entrepreneurship also directly influence sustainable behaviors. Additionally, the study examines the moderating influence of receiving a scholarship; results indicate that attitudes toward sustainability and sustainable entrepreneurship significantly shape behavior, with the scholarship variable altering the relationship between attitudes toward entrepreneurship and behavior. These outcomes underscore the significance of enhancing SDGs awareness and nurturing favorable attitudes toward sustainability and sustainable entrepreneurship among university students to stimulate sustainable behaviors and advance SDGs objectives. [ABSTRACT FROM AUTHOR]

This study addresses the challenge of sovereign external debt sustainability by employing a cointegration test, machine-learning classifiers, and explainable models. Focusing on 22 middle-income countries during the period 2000-2021, our study aims to provide accurate insights into debt positions and capture the complex dynamics between a set of economic and fiscal indicators. Unlike conventional econometric methods, which categorize debt situations as either sustainable or unsustainable over specific periods and often have limitations in generalizing the influences of public policies on debt positions, our machine-learning approach reveals a more nuanced perspective. The results indicate that some countries have encountered episodes of debt unsustainability. These results underscore the substantial role of macroeconomic indicators in shaping a country’s financial position in conjunction with outstanding debt. Furthermore, our findings demonstrate that the impact of each feature varies based on its specific threshold, emphasizing the critical role of exchange rates in straining debt sustainability.

This study proposes an exploratory methodology for analyzing participation in Electronic Networks of Practice, focusing on a network of 500 specialists in breast cancer treatment as a case study. The research addresses a critical gap in understanding how conversational dynamics influence participation in professional forums. Conversation measures and participation measures reported in the literature are identified and categorized, and new ones are suggested. This study by correlation analysis reveals three key participation triggers: initiator features, timing of initiation, and feedback, while also identifying non-associated variables like conversation topic and expression. Clustering categorizes conversations into four types, each exhibiting participation patterns and attributes. Social network analysis further reveals the network’s structure and participants’ interrelations. These findings provide a comprehensive understanding of participation in Electronic Networks of Practice and show the methodology’s broader applicability in analyzing professional networks. The study’s insights are vital for practitioners and theorists by offering a perspective on fostering knowledge sharing.

The subject-matter and aim of this paper is to present the extent of poverty and economic and social exclusion in the countries of the European Union (EU-27). The specific aim is the comparative assessment and reduction of these processes between 2011 and 2022.

We investigate the impact of China’s outward foreign direct investment (OFDI) on the export sophistication of countries participating in the Belt and Road Initiative (BRI). Through an examination of data spanning from 2010 to 2015, our findings reveal that China’s OFDI significantly enhances export sophistication in host countries, with a more pronounced effect observed in relatively poor BRI nations. However, the positive correlation between China’s OFDI and export sophistication diminishes in host countries characterized by elevated productivity levels. Utilizing a structural equation model, we identify host country productivity as a crucial mediating factor. The indirect technology spillover effects of China’s OFDI, facilitated through increased productivity, account for approximately 39% of their overall impact on export sophistication. Furthermore, we delineate threshold effects of productivity for China’s OFDI, demonstrating substantial positive effects in host countries with a GDP per capita below the first threshold (approximately US$1,141) or within the range of the first and second thresholds (around US$7,115). Upon consideration of these thresholds, distinctions in the effects of China’s OFDI between BRI and non-BRI host countries diminish. Our analysis underscores that the favorable influence of China’s OFDI extends beyond the BRI, providing benefits to low and middle-income countries globally. [ABSTRACT FROM AUTHOR]

This paper employs a new framework based on internationally available indicators to conduct a comparative analysis of electricity generation among Latin American and Caribbean (LAC) countries, using data from 2021, the most recent available. Two multivariate techniques, cluster analysis and factor analysis, are used in this study. In recent years, electricity generation in the region has increased significantly, largely driven by the economic growth of these nations. A notable trend is the rising contribution of renewable energy sources, which accounted for 55 % of total generation in 2021, thanks to the region's exceptional climatic and geographic conditions. However, the absence of overarching supranational policies has led to significant disparities in energy strategies across these countries, both in design and implementation. Among the 20 LAC countries studied, 35 % do not reach a 50 % share of sustainable electricity generation. Understanding how these countries group regarding sustainable electricity generation is essential for identifying their key characteristics. The analysis yields three fundamental conclusions: first, the selected indicators are highly effective in achieving the study's objectives; second, the principal differences between the identified groups are largely influenced by each country's level of development and reliance on renewable energy sources; and third, from a policy perspective, there is a clear need for a supranational framework to support the national efforts and strategies being independently pursued by these countries, particularly regarding the design and implementation of energy policies. This paper contributes to bridging the gap in the international academic literature concerning sustainable electricity generation in LAC region, and it aligns with studies focused on the analysis of renewable energy sources and sustainable development.

The purpose of this article is a comparative analysis of regional and temporal labor market differentiation. In the study, the following voivodeships were taken as comparative objects. In accordance, following the administrative division, the voivodeships were adopted, while the research period was taken as the years 2011–2021. To study the differentiation of the voivodeship labor markets, the classical method of share shifts and the analysis of share shifts were used. The number of employed people and the registered unemployed by gender were chosen as the measures of differentiation on which the study was based. The results of the analyses made it possible to examine changes in the structure and assess the development of regional labor markets over time.

Due to the variety of corporate risks in turmoil markets and the consequent financial distress especially in COVID-19 time, this paper investigates corporate resilience and compares different types of resilience that can be potential sources of heterogeneity in firms’ implied rate of return. Specifically, the novelty is not only to quantify firms’ financial resilience but also to compare it with workplace resilience which matters more in the COVID-19 era. The study prepares several pieces of evidence of the necessity and insufficiency of these two main types of resilience by comparing earnings expectations and implied discount rates of high- and low-resilience firms. Particularly, results present evidence of the possible amplification of workplace resilience by the financial status of firms in the COVID-19 era. The paper proposes a novel composite-financial resilience index as a potential measure for disaster risk that significantly and persistently reveals low-resilience characteristics of firms and resilience-heterogeneity in implied discount rates. [ABSTRACT FROM AUTHOR]

Our confidence set quantifies the statistical uncertainty from data‐driven group assignments in grouped panel models. It covers the true group memberships jointly for all units with pre‐specified probability and is constructed by inverting many simultaneous unit‐specific one‐sided tests for group membership. We justify our approach under N,T → ∞ asymptotics using tools from high‐dimensional statistics, some of which we extend in this paper. We provide Monte Carlo evidence that the confidence set has adequate coverage in finite samples. An empirical application illustrates the use of our confidence set. [ABSTRACT FROM AUTHOR]

Structural properties of the currency market were examined with the use of topological networks. Relationships between currencies were analyzed by constructing minimal spanning trees (MSTs). The dissimilarities between time series of currency returns were measured in various ways: by applying Euclidean distance, Pearson's linear correlation coefficient, Spearman's rank correlation coefficient, Kendall's coefficient, partial correlation, dynamic time warping measure, and Kullback–Leibler relative entropy. For the constructed MSTs, their topological characteristics were analyzed and conclusions were drawn regarding the influence of the dissimilarity measure used. It turned out that the strength of most types of correlations was highly dependent on the choice of the numeraire currency, while partial correlations were invariant in this respect. It can be stated that a network built on the basis of partial correlations provides a more adequate illustration of pairwise relationships in the foreign exchange market. The data for quotations of 37 of the most important world currencies and four precious metals in the period from 1 January 2019 to 31 December 2022 were used. The outbreak of the COVID-19 pandemic in 2020 and Russia's invasion of Ukraine in 2022 triggered changes in the topology of the currency network. As a result of these crises, the average distances between tree nodes decreased and the centralization of graphs increased. Our results confirm that currencies are often pegged to other currencies due to countries' geographic locations and economic ties. The detected structures can be useful in descriptions of the currency market, can help in constructing a stable portfolio of the foreign exchange rates, and can be a valuable tool in searching for economic factors influencing specific groups of countries. [ABSTRACT FROM AUTHOR]

Ethical consumerism is one of this century's retail success stories and has continued to grow despite the global pandemic and economic contraction. Consumers worldwide are increasingly putting their money where their values are in purchases of food and drink, clothing, housing, and financial and other services. Understanding these consumers is key to competing successfully in markets for ethically sourced products and presents opportunities for reducing poverty in developing countries where food and agricultural production is the dominant economic activity, such as small island developing states (SIDS) in the Pacific. While ethical consumerism is an expanding research field, findings rarely are analysed in detail for market development. Rarer still is segmentation in terms of product attributes aimed explicitly at poverty reduction in slow-growth, emerging economies like those of the Pacific. Here we report a latent class cluster analysis of survey responses by ethically conscious Australian consumers (n = 637) associated with Oxfam, the development charity. Four distinct consumer segments were identified with implications for Pacific producers. Product origin and 'the story behind the product' were particularly important to these consumers. The findings provide practical insights to agricultural stakeholders in the Pacific and those similarly situated around the globe that are in need of new economic opportunities. [ABSTRACT FROM AUTHOR]

We introduce a new dynamic clustering method for multivariate panel data characterized by time-variation in cluster locations and shapes, cluster compositions, and possibly the number of clusters. To avoid overly frequent cluster switching (flickering), we extend standard cross-sectional clustering techniques with a penalty that shrinks observations toward the current center of their previous cluster assignment. This links consecutive cross-sections in the panel together, substantially reduces flickering, and enhances the economic interpretability of the outcome. We choose the shrinkage parameter in a data-driven way and study its misclassification properties theoretically as well as in several challenging simulation settings. The method is illustrated using a multivariate panel of four accounting ratios for 28 large European insurance firms between 2010 and 2020. [ABSTRACT FROM AUTHOR]

This study examines whether there is convergence in military expenditures among NATO countries by using Philips and Sul (Econometrica 75:1771–1855, 2007) convergence methodology. We obtained the primary dataset of 27 NATO member countries between 2009 and 2021 for their military expenditures to accomplish this. The results show no convergence between the military expenditures of NATO member states. In addition, although these 27 member countries are divergent, the analysis also identified three convergent clubs among NATO members with exciting patterns and trends in their military expenditures. [ABSTRACT FROM AUTHOR]

This study aims to fill the gap in estimating the Green Country Brand Strength Index and its convergence among EU countries and Ukraine (as a potential EU candidate) from 2006 to 2020. Drawing upon data from the World Data Bank, Eurostat, and The Food and Agriculture Organization, the study employs various analytical tools including principal component analysis, the global Malmquist‒Luenberger productivity index, the entropy method, and σ- and β-convergence theories. The findings suggest a notable increase in the green brand values of the analyzed countries from 2006 to 2020, with a significant expansion of the measurement scale. Notably, France, Germany, Spain, the Netherlands, and Sweden emerged as leaders in the Green Country Brand Strength Index by 2020. Furthermore, the study uncovers convergent policies among EU countries aimed at fostering green brands aligned with the Sustainable Development Goals, as evidenced by a decline in σ- and β-convergences. This research provides valuable insights into the dynamics of green branding and policy convergence within the EU and prospective candidate countries. It introduces a distinctive approach to assessing and analyzing green brand strength, emphasizing its critical importance to the formulation of sustainable development policies and strategies.

The digitalization of the economy and society in the context of Industry 4.0 affects the labor market. New jobs are being created with new personnel requirements. At the same time, digitalization processes affect countries and sectors of the economy in different ways. The number of employed is declining in some sectors and increasing in others. The ratio of people employed in different industries also differs from country to country. As the digitalization of production relations intensifies, transformation processes in the labor market will increase. In the context of the actualization of digital changes in modern society, the aim of the article is to analyze the labor market under the digitalization of economic relations. The World Development Indicators (WDI) and Digital Intelligence Index (DII) were used to realize the purpose of the study. The study covers 89 countries for 2019. Correlation analysis, standardization, cluster analysis and analysis of variance were used as methods in the research. As a result of the study, the following conclusions were obtained. A strong relationship was found between Gross Domestic Product (GDP) and DII for the whole sample. The cluster analysis process found that the variables form three clusters with values of most indicators by group: at the high, the average and the low levels. Plots of mean and confidence intervals for the variables in each cluster were also plotted. Correlation coefficients were calculated for each cluster. It was found that each group has its own internal patterns.

An analysis of the similarity of countries’ economic developments can provide an important premise for both the assessment of current international economic relations and the long-term prediction of their economic development potential.

The article presents students’ expectations regarding changes in the housing market after the COVID-19 pandemic. The need to switch to remote teaching was very often associated with the need to change the place of residence or return to the family home of students. This was related, directly or indirectly, to their economic situation, which deteriorated due to the loss of their job or the need to change jobs. When preparing the research tool, the focus was therefore on the subjective expectations of students regarding rental conditions after the pandemic, regarding not only rental costs, but also changes in lease agreements. During the survey period, respondents were already studying remotely, so it can be assumed that they have already developed some strategies and tactics regarding the issues covered by the survey. The article aims to signal and illustrate with available data the diversity of expectations regarding fees and provisions in lease agreements, taking into account the demographic and economic characteristics of respondents. The aim of this study was not only to demonstrate the diversity of expectations of changes in residence, rental conditions, or the amount of fees caused by the pandemic, taking into account the employment and gender of the respondent, but also taking into account their activity in the labour market. The analysis of the relationship between the expectations declared by respondents and their demographic characteristics was carried out using a correlation analysis. The analysis made it possible to verify the research hypotheses on the existence of correlations between the respondents’ characteristics and their (1) assessments of changes in the place of residence, (2) expectations regarding provisions in rental contracts, and (3) changes in the level of housing fees caused by the pandemic situation.

As the world rapidly evolves, digitalisation continues, and artificial intelligence is used, it is crucial to understand how education systems are preparing future generations to succeed in this very dynamic environment where there is a tremendous demand for skilled workers.

The study examined the nexus between financial inclusion and economic growth, poverty, and inequality in Fragile and Post-Conflict African economies based on data sourced from 2004 to 2021. The study employed the system GMM and dynamic threshold model within the context of the Tunnel and Joneses effects to analyze the data generating set. The study noted that financial inclusivity enhances growth, and reduces poverty and inequality in the studied economies. The results of robustness tests reveal that our model is appropriate, this suggests the validity of the Tunnel effect in the studied economies. The threshold dynamic result suggests that the optimal influence of financial inclusion on economic growth, poverty, and inequality is at 42% for the studied economies. The study offered some policy implications.