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This paper investigates methodological issues and limited coverage of providers' environmental scores, which are increasingly employed by investors, financial institutions and policymakers for corporate environmental assessment. The contribution of the paper is twofold. First, regression analysis shows a substantial heterogeneity among the environmental scores of seven providers in the reliance on raw data. However, as some variables are found meaningful across providers, the request to enhance disclosure should focus on such variables. The heterogeneity of the unexplained component of the regression across providers can be arguably referred to as judgemental factors and underlines the providers' different focus on financial risk or environmental impact. Second, we propose a classification system based on corporate disclosure data that aims to enable investors to extend the environmental assessment of companies not rated by providers. This system has been calibrated to implement two common investment strategies, that is, best‐in‐class and exclusion and allows to build portfolios with both environmental and financial profiles similar to portfolios based on providers' scores. The work aims to contribute to the intersection between the analysis of methodologies of E‐scores and their practical use for investment purposes. Rather than asking for a mirage of full comparability of E‐scores, the paper substantiates that is of utmost importance to improve the disclosure of corporate data to enhance the environmental assessment as well as the transparency on providers' methodologies to enable investors to select E‐scores consistent with their risk‐impact preferences. Such transparency will foster the development of sustainable finance. [ABSTRACT FROM AUTHOR]

Concepts are introduced and applied for analyzing and selecting arbitrage portfolios in the face of uncertainty about initial positions and risk preferences. A stochastic arbitrage opportunity is defined as a zero-cost investment portfolio that enhances every feasible host portfolio for all admissible utility functions. The alternative to the existence of such investment opportunities is the existence of a solution to a dual system of asset pricing restrictions based on a class of stochastic discount factors. Feasible approaches to numerical optimization and statistical inference are discussed. Empirical results suggest that equity factor investing is appealing for all risk-averse stock investors with a wide range of initial position and sufficiently low transactions costs by mixing multiple factor portfolios with high after-cost appraisal ratios, low mutual correlation, and negative exposures to the relevant host portfolios. These findings weaken the case for risk-based explanations for the profitability of factor investing. This paper was accepted by Kay Giesecke, finance. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.4892. [ABSTRACT FROM AUTHOR]

With the rapid growth of web applications, web application security (WAS) has become an important cybersecurity issue. For effective WAS protection, it is necessary to cultivate and train personnel, especially beginners, to develop correct concepts and practical hands-on abilities through cybersecurity education. At present, many methods offer vulnerable web environments to support practical hands-on training, including large-scale "Capture the Flag" mode (e.g., Cyber Range), pre-configured virtual machine images (e.g., Mutillidae), pre-built stand-alone applications (e.g., WebGoat), and web-based system (e.g., Damn Vulnerable Web Application). However, beginners need not only hands-on training tools and systems but also assistance to support effective learning. Moreover, pre-built training content and exercises are usually not easy to modify and thus lack the flexibility to meet specific teaching needs. Therefore, this study proposed and developed the Web-based Hands-On Learning Environment (WebHOLE) to efficiently assist beginners in learning WAS. To improve the flexibility of the training content, a web-based authoring tool was developed in WebHOLE to create customized hands-on learning exercises. Accordingly, learners can learn and practice the WAS training content online with learning assistance provided by the hands-on learning system. The hands-on abilities of the learners can be efficiently assessed by the hands-on testing system using online exams with progressive hints and automatic grading. Furthermore, to improve the effectiveness of teaching and testing, a portfolio analysis scheme using a data mining technique was developed to identify learning barriers and problematic test items. WebHOLE was applied to an actual beginner-level WAS course for undergraduate students. The experimental results showed the benefits of WebHOLE on WAS learning, with a significant improvement in learning outcomes. Students expressed high satisfaction with WebHOLE's learning assistance, rating it with average satisfaction scores above 4.0 out of 5.0. The portfolio analysis scheme also showed the effectiveness of WebHOLE in identifying learning problems and refining test items. [ABSTRACT FROM AUTHOR]

This paper demonstrates the benefits, from an expected utility perspective, of including a derivative into the universe of tradeable assets under the affine GARCH model proposed by Heston and Nandi (Rev Financ Stud 13(3):585–625, 2000. https://doi.org/10.1093/rfs/13.3.585). For this purpose, we first introduce a Power Option into the market, derive its value and moment generating function thanks to the affine GARCH structure. We then expand on the results presented by Escobar-Anel et al. (Oper Res Perspect 9:100216, 2022) by solving for the optimal investment allocations into the stock, a cash account and the option. We show that investors who are able to include a derivative indeed outperform those who only invest into the stock and the bank account. In this spirit, investors who fail to include, even a low level of exposure to the derivative, could see up to 7% annual wealth-equivalent losses. This confirms findings in continuous-time models dating to Liu and Pan (J Financ Econ 69(3):401–430, 2003). An empirical analysis on the S &P500 confirms the superiority in terms of Sharpe ratio, and maximum drawdown of portfolios with options, in-sample and out-of-sample. [ABSTRACT FROM AUTHOR]

Bibliotheken bieten zunehmend neue Dienstleistungen und Produkte an, um den sich wandelnden Bedürfnissen ihrer Nutzer*innen gerecht zu werden. In privatwirtschaftlichen Unternehmen hat sich zum Management einer Vielzahl von Produkten und Dienstleistungen das strategische Mittel des Produktportfolio-Managements durchgesetzt. In Bibliotheken ist dieser Ansatz der Steuerung weitaus seltener anzutreffen. Dies ist keineswegs darauf zurückzuführen, dass Bibliotheken administrative und keine gewinnorientierten Einrichtungen sind. Ganz im Gegenteil: mit wenigen Anpassungen am Grundmodell eines Produktportfolios kann sich das Tool auch in Bibliotheken als vielversprechendes Mittel zur strategischen Verwaltung erweisen.Dieser Artikel erläutert, wie die Erarbeitung und Einführung eines gemeinsamen Produktportfolios an der Universitätsbibliothek und der Zentralbibliothek Zürich den Pfad für eine produktorientierte, und damit dienstleistungs- und kundenorientierte, strategische Zusammenarbeit geebnet haben. Der Beitrag bietet einen Einblick in den Prozess der Entwicklung und den Einsatz des Produktportfolios: wie es zu Stande kam, wie es Konsequenzen von Entscheidungen kalkulierbar machte und wie damit Produkte entlang der Strategie weiterentwickelt werden konnten.Es soll gezeigt werden, wie durch die Anwendung von Methoden aus der Betriebswirtschaft auf den Bibliotheksbereich neue Wege im Management von Bibliotheken beschritten werden können. Die Implementierung eines Produktportfolios kann der modernen Bibliothek dabei helfen, ihre Position in der Gesellschaft zu stärken und sich als zukunftsorientierte, dynamische Einrichtungen zu positionieren. Libraries are increasingly offering new services and products to meet the changing needs of their users. In private enterprises, the strategic tool of product portfolio management has become established for managing a variety of products and services. In libraries, this approach to management is far less common. This is by no means due to the fact that libraries are administrative and non-profit organizations. On the contrary, with a few adjustments to the basic model of a product portfolio, the tool can also prove to be a promising means of strategic management in libraries.This article explains how the development and introduction of a joint product portfolio at the University Library and the Central Library of Zurich paved the way for a product-oriented, and therefore service- and customer-oriented, strategic collaboration. The article provides an insight into the process of developing and deploying the product portfolio: how it came about, how it made the consequences of decisions calculable, and how products could be further developed along the strategy.It aims to show how the application of methods from business administration to the library sector can open up new avenues in library management. The implementation of a product portfolio can help modern libraries strengthen their position in society and position themselves as forward-looking, dynamic institutions.

This paper assesses the impact of alternative assets on the performance of Brazilian asset portfolios. Although alternative assets are largely studied throughout the world, that is not the case for Brazil as there are just a few studies on the topic. As the Brazilian capital markets are growing at a fast pace, the high correlated aspect of the market calls for measures to ensure balanced returns for the long run, therefore, the insights of the study may support investors during the process of asset allocation. In this study the alternative assets were divided into two categories: Brazilian alternative assets and international alternative assets. Performance measures were obtained from a portfolio comparison and through a Markowitz frontier analysis. The results show that adding alternative assets (Brazilian or international) to a portfolio usually generates higher returns with lower risks, leading to better risk adjusted results and an overall more balanced portfolio. [ABSTRACT FROM AUTHOR]

The article proposes a methodological approach to the selection of current priorities in expanding Russian exports to Africa based on the solution of classification problems. The technologies of assessing the prospects for increasing the export of Russian products, which make it possible to formulate a strategy for working with a portfolio of industries and products in foreign markets are considered. Open data of the state statistics, the Federal Customs Service, Trade Map, and the International Monetary Fund were used as an information base. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Research in low- and middle-income countries (LMICs), where the majority of global tobacco users reside, is critical to addressing the global tobacco epidemic. This analysis describes the global tobacco control research portfolio funded by the National Cancer Institute from fiscal years 2000 to 2019. METHODS: We used the National Institutes of Health Query, View, Report database to identify extramural grants relevant to global tobacco control research. Abstracts were analyzed to describe grant characteristics, including topic areas, tobacco products, countries, and regions of focus. Bibliometric and co-authorship network analyses were performed for publications associated with relevant grants. RESULTS: Of the 93 relevant grants with foreign (non-US) involvement, the majority (83.9%) supported research in upper and lower middle-income countries. The majority of grants (86.0%) focused on cigarettes, with a small subset of grants addressing smokeless tobacco, waterpipe use, or other non-cigarette products. Most grants focused on at least one of the six tobacco control policy measures in the World Health Organization MPOWER package; almost half (48.4%) focused on monitoring tobacco use and around one-third (32.3%) focused on offering tobacco cessation treatment, while other MPOWER measures received less attention in the research portfolio. While most of these grants, and the funding initiatives that supported them, emphasized research in low- and middle-income countries (LMICs), only 3 of 93 grants were awarded directly to LMIC-based institutions. CONCLUSIONS: There is a critical need for research to develop and test strategies to adapt, implement, and scale up evidence-based interventions across diverse LMIC settings. This study identified gaps in research activity that should be addressed to strengthen global tobacco control research capacity. [ABSTRACT FROM AUTHOR]

We provide a formal statistical theory of consistent estimation of the set of all arbitrage portfolios that meet the description of being a stochastic arbitrage opportunity. Two empirical likelihood ratio tests are developed: one for the null that a given arbitrage portfolio is qualified, and another for the alternative that the portfolio is not qualified. Apart from considering generalized concepts and hypotheses based on multiple host portfolios, the statistical assumption framework is also more general than in earlier studies that focused on special cases with a single benchmark portfolio. Various extensions and generalizations of the theory are discussed. A Monte Carlo simulation study shows promising statistical size and power properties for testing the null, for representative data dimensions. The results of an empirical application illustrate the importance of selecting a proper blocking structure and moment estimation method. [ABSTRACT FROM AUTHOR]

Copyright of Orbis Scholae is the property of Charles University Prague, Faculty of Education and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Starting from twelve distinct factors from the recent literature, plus twelve principal components (PCs) of anomalies unexplained by the initial factors, a Bayesian comparison of approximately seventeen million models in terms of marginal likelihoods and posterior model probabilities shows that {Mkt, MOM, IA, ROE, MGMT, PERF, PEAD, FIN}, plus the nonconsecutive principal components, { PC 1 , PC 5 , PC 7 } are the best supported risk factors. Pricing tests and annualized out-of-sample Sharpe ratios for tangency portfolios suggest that this asset pricing model should be used for computing expected returns, assessing investment strategies and building portfolios. This paper was accepted by Lukas Schmid, finance. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.4668. [ABSTRACT FROM AUTHOR]

We present a study on portfolio investments in financial applications. We describe a general modeling and simulation framework and study the impact on the use of different metrics to measure the correlation among assets. In particular, besides the traditional Pearson's correlation, we employ the Detrended Cross-Correlation Analysis (DCCA) and Detrended Partial Cross-Correlation Analysis (DPCCA). Moreover, a novel portfolio allocation scheme is introduced that treats assets as a complex network and uses modularity to detect communities of correlated assets. Weights of the allocation are then distributed among different communities for the sake of diversification. Simulations compare this novel scheme against Critical Line Algorithm (CLA), Inverse Variance Portfolio (IVP), the Hierarchical Risk Parity (HRP). Synthetic times series are generated using the Gaussian model, Geometric Brownian motion, GARCH, ARFIMA and modified ARFIMA models. Results show that the proposed scheme outperforms state of the art approaches in many scenarios. We also validate simulation results via backtesting, whose results confirm the viability of the proposal. [ABSTRACT FROM AUTHOR]

Oriented fuzzy numbers are useful in portfolio management since they convey information regarding uncertainty and imprecision when considering financial markets. One may apply a fuzzy discount factor and an imprecise present value in the form of a trapezoidal oriented fuzzy number. An investor can obtain recommendations on individual stocks (buy, sell, accumulate, reduce). Analogous recommendations are also issued by experts. In such cases, recommendations are mostly based on available data and expert's knowledge and experience. The purpose of the paper is to present a procedure for comparing the accuracy of both types of recommendations. Also, the real impact the recommendations have on potential changes in portfolio composition in trading-related industries is considered. The research uses quotations from companies from the trading sector of the Warsaw Stock Exchange (WSE). Theoretical considerations are presented in the form of an empirical case study. [ABSTRACT FROM AUTHOR]

This study explores the financial performance of cryptocurrencies during the COVID-19 pandemic. In particular, the research objective is to compare the market price movements of the leading cryptocurrencies Bitcoin, Ethereum, BNB and XRP before and during the COVID-19 pandemic based on a longitudinal, exploratory, and quantitative research design which is centered on the analysis of the statistical moments of logarithmic return distributions, tests for structural changes combined with stationarity tests and portfolio optimization strategies. Results of the analysis show a clear change of the medium- to long-term return behavior of the analyzed cryptocurrencies during the pandemic, although not immediately after the pandemic announcement of the WHO in March 2020. Especially Bitcoin, BNB and Ethereum show comparable and even more favorable return characteristics in most samples compared to traditional investment alternatives. Furthermore, the diversification potential of cryptocurrency portfolios appears to be quite limited. [ABSTRACT FROM AUTHOR]

Analysts' decision-making process, through which they use earnings and non-earnings forecasts to provide their target price revisions, remains opaque. Based on the decision tree analysis, we develop a new multivariate information metric, Internal Relevance (IR), to measure the extent to which the revisions of analysts' consensus earnings and sales forecasts are incorporated into the revisions of consensus target prices. We show that stocks with higher IR have stronger market reactions to target price revisions in terms of abnormal return, abnormal trading volume, and abnormal return volatility. These results remain consistent across a series of robustness checks. Finally, we perform portfolio analysis to show that IR can be used to screen stocks to improve trading profitability. [ABSTRACT FROM AUTHOR]

In this study, we investigated some extensions of the classical portfolio theory and try to evaluate them in a situation of crisis. We studied some additional criteria for portfolio selection, based on market multiples representing the overall situation of companies. Additionally, we investigated semi-variance as an alternative measure of risk. We developed a range of portfolios that were built using different criteria for risk and the fundamental values of companies from the Polish stock market. Then, we compared their returns during the crisis that occurred after the outbreak of the COVID-19 pandemic. The results of empirical research on the major companies traded on the Warsaw Stock Exchange reveal that investors can achieve better investment results by augmenting the standard Markowitz model with an additional criterion connected with the fundamental standing of companies, such as book-to-market or earnings-to-market ratios. The second result is that using nonclassical risk measures such as semi-variance instead of variance provides better results, and this method of measuring risk is especially essential in periods characterized by the collapse of the capital market. [ABSTRACT FROM AUTHOR]

We provide a formal statistical theory of consistent estimation of the set of all arbitrage portfolios that meet the description of being a stochastic arbitrage opportunity. Two empirical likelihood ratio tests are developed: one for the null that a given arbitrage portfolio is qualified, and another for the alternative that the portfolio is not qualified. Apart from considering generalized concepts and hypotheses based on multiple host portfolios, the statistical assumption framework is also more general than in earlier studies that focused on special cases with a single benchmark portfolio. Various extensions and generalizations of the theory are discussed. A Monte Carlo simulation study shows promising statistical size and power properties for testing the null, for representative data dimensions. The results of an empirical application illustrate the importance of selecting a proper blocking structure and moment estimation method.

The topic of the paper focuses on the movement of four stock market indices in the period from 2007 to 2022, with the aim of assessing which crisis had a greater impact on individual indices as well as on the portfolio created through mean variance analysis. Also, the risk itself was measured using the VaR (Value at Risk) method. Each index represents one asset class, and computer calculation using mean-variance analysis suggested certain weights, which were used in further calculations. The main conclusion of this paper is that the crisis in 2020 had a stronger impact on all indices, and led to greater volatility in income than the crisis in 2008. Further, a portfolio created through mean analysis for risk-averse investors confirmed these results, showing the highest volatility in the returns of the entire portfolio in 2020.

Boston Consulting Group Matrix is the most popular methodological tool for analysing corporate portfolios. However, the method of using the Matrix has been frequently criticised, and the peak of popularity of this tool has passed. The purpose of this study is to find new areas of application for the Boston Consulting Group Matrix in the economic analysis of entrepreneurial activity. The following research methods were used: monographic, dialectical, abstract-logical, graphic, and tabular. It is shown that the use of the conventional BCG Matrix method is limited due to the ambiguity of conceptual foundations, methodological problems, and lack of complete information about the activities of competitors. The study proved that inaccuracies in the analysis of the market growth rate and the relative share of individual entities in it allow accurately assessing business prospects in this area. Methodological and applied issues arising when a company evaluates its market share of certain products are considered. A modification of the BCG Matrix is proposed, which distributes the assortment units of the analysed business entity according to certain profit growth reserves. Depending on the objects of a particular analysis, grouping in the modified matrix can be carried out according to individual types of products (goods, works, services) or by groups of buyers (consumers). The main problems with application of the BCG Matrix were clarified and systematised. Solutions for certain shortcomings of the conventional BCG Matrix were found, namely regarding the method for calculating the indicators it holds. A modification of the BCG Matrix is proposed, which classifies types of products (goods, works, services) by prospective profit reserves and changes in their share in the company’s profit structure. A similar matrix is designed to classify buyers of goods or consumers of a company’s services. The study figured out the place of the proposed BCG Matrix modification in economic analysis. The proposed variations of BCG Matrix have an advantage over the classical version, since they are based on internal analysis of only one subject of activity, and not on evaluating the potential of competitors. The developed modification of the analysis method can be integrated into the system of comprehensive economic analysis of any company. The results of the analysis using modified BCG Matrix can be used in financial strategic planning and marketing strategy development, specifically when planning advertising campaigns

Research Question: Can the F-Score predict the stock market returns in the cross section of international stock markets? Motivation: The majority of the literature, in the area of the F-Score metric, has examined whether it can be used to predict future financial profitability, the relationship of F-Score with book-to-value metrics and the momentum premium and whether it can be used as a successful investment strategy tool. There only three studies that examine the relationship between the F-Score and future stock returns, without the use of complementary variables, and in other countries except Europe. This paper seeks to fill this gap. Data: The dataset of the present research consists of listed European companies from 21 countries (in random order: Finland, United Kingdom, Switzerland, Turkey, Hungary, Portugal, Spain, Poland, Norway, Luxembourg, Italy, Netherlands, Ireland, Greece, Belgium, Germany, Denmark, France Czech Republic, Sweden, Austria), from 1989 to 2016. We collect firm-level accounting information as provided by Worldscope, as well as the monthly total returns for common stocks from Datastream. Tools: With the use of a dataset consisting of European companies from 21 countries, portfolio analysis and time series regressions are performed using abnormal monthly returns (monthly returns minus risk-free interest rates). Findings: We find that the F-Score is a statistically significant predictor as well as an economically meaningful index. Its performance forecasting ability is visible in developed Europe, both in small and large companies, and remains stable after controlling for established cross-sectional determinants (such as book market, investment, and company size). Contribution: This study seeks to fill the gap in the stock return and F-Score relationship in a European setting controlling for the other financial variables. Our empirical models are tested across a number of different economic and stock market backgrounds and the implications of our results are of particular interest for academics, for investors (retail and institutional) and for policy makers.

This paper investigates the low risk anomaly, which suggests that less risky stocks outperform riskier ones. Focusing on the European stock market, the present study examines the influence of coskewness, a measure of asymmetry in stock returns with respect to the market return. Stocks are sorted into 2x5 quintile portfolios based on coskewness and beta volatility. Regression analysis using Fama-French three and five factor models reveals a significant low risk anomaly in the low coskewness category, where less risky portfolios consistently outperform riskier ones. However, as coskewness increases, the low risk anomaly weakens and loses significance. In the high coskewness category, less risky portfolios no longer consistently outperform riskier ones. In other words, accounting for coskewness significantly lowers the profitability of low risk and betting-against-beta strategies in Europe. These findings enhance the understanding of the relationship between risk and returns in the European market. [ABSTRACT FROM AUTHOR]

Almost universally, crop insurance uptake has been very low in the absence of significant governmental subsidies. We proposed an explanation for the low uptake by analyzing farmers' optimal behavior in the presence of subsidized crop insurance and incomplete financial markets. Under specific replication conditions, farmers' valuation of crop insurance is lower than insurers' valuation if farmers have access to fewer financial assets than insurers. A potential rationalization is that farmers and insurers have dissimilar price formation processes. Our model implies that subsidized crop insurance changes farmers' production choices by altering both the relative returns across states of Nature and their risk choices. However, because of the separation between consumption and production decisions, farmers will use crop insurance in a manner consistent with profit maximization, independent of risk preferences. [ABSTRACT FROM AUTHOR]

Portfolio analysis is widely used in empirical asset pricing to explore the cross-sectional relation between two or more variables. In this article, we introduce the methodology of portfolio analysis and describe a new command, portfolio, that provides a one-step solution for portfolio analysis. portfolio calculates the equal- or value-weighted returns with a t statistic for the portfolio and tests the significance of a long-short strategy in portfolios. portfolio also provides the Newey–West standard-error adjustment option for alleviating the impact of potential autocorrelation and heteroskedasticity in financial time series. [ABSTRACT FROM AUTHOR]

Subject and purpose of work: This study investigates the effectiveness of equity investment in renewable energy companies. The aim of the research is to compare the effectiveness of investing in the stocks of companies from the conventional and renewable energy sector on the Warsaw Stock Exchange.

This study investigates the return and volatility interconnectedness between emerging digital assets, specifically agricultural tokens, and U.S. equity sectors using the TVP-VAR model. Analyzing data from August 7, 2020, to January 2, 2024, the findings indicate modest interconnections between agricultural tokens and U.S. sectors, with time-varying behavior. Notably, return interconnectedness is generally stronger than volatility, except at the sample period's outset, where volatility dominates. Return spillovers predominantly drive the connectedness system, though agricultural tokens uniquely act as net recipients of both return and volatility spillovers, while U.S. equity sectors mainly transmit spillovers. Optimal portfolio analysis, utilizing portfolio weights and hedge ratios, reveals that incorporating agricultural tokens offers portfolio diversification benefits and enhances hedging performance. Investors are advised to frequently adjust portfolios to maximize diversification and hedging gains. These findings provide significant portfolio implications for policymakers, market participants, and investors. [Display omitted] • Agricultural tokens can serve as effective risk management assets. • U.S. sectors dominate portfolio allocations, with Asyagro showing the highest token weight, enhancing portfolio diversification. • Frequent portfolio adjustments are necessary as optimal weights and hedge ratios vary over time. [ABSTRACT FROM AUTHOR]

The capital market in China has progressed rapidly within the realm of ESG and sustainability. This study investigates whether and how ESG investing works in China. The portfolio-level analysis shows that both high- and low-level ESG portfolios can earn higher abnormal returns, which implies a non-linear relationship between ESG and portfolio excess returns. In stock-level analysis, the effect of ESG on future stock returns varies by pillar and sector. Governance and social pillars work in opposite directions to predict returns. In the secondary (tertiary) sector, higher ESG scores predict lower (higher) returns. Furthermore, we find that higher ESG performance is associated with worse future profitability, which impairs firm value, and lower cost of equity capital, which increases firm value. [ABSTRACT FROM AUTHOR]

In recent years, the relationship between agricultural commodities and crude oil has become increasingly close with the promotion of biofuel policies. This study examines the dynamic correlation between global crude oil futures and seven agricultural commodity futures by applying the consistent dynamic conditional correlation and dynamic equicorrelation models. The empirical results show that the dynamic correlation between the global crude oil futures market and China's agricultural futures market is weak compared to the global agricultural futures market. In particular, soybean oil has the strongest correlation with crude oil, while Dalian Commodity Exchange (DCE) corn and Zhengzhou Commodity Exchange wheat have the weakest correlation with crude oil. There is an indirect linkage between crude oil futures and DCE soybean meal and DCE soybean oil. Moreover, the dynamic correlation between crude oil and agricultural commodities increased during the financial crisis, the novel coronavirus (COVID‐19) epidemic, and the crude oil crash crisis. Brent crude oil has a stronger co‐movement with China's agricultural commodities than West Texas Intermediate crude oil and can better hedge the risk of agricultural commodities. The findings of this study provide some insights into the contagion risk management of crude oil futures and agricultural futures markets. [ABSTRACT FROM AUTHOR]

The subject of the research is the assessment of Investment decision-making efficiency considering the sustainable development requirements. The article aims to identify the relationship between environmental, social and governance (ESG) performance and market returns for investors and the reasons for it. The relevance of the paper is determined by the need to develop research in the field of ESG integration and evaluation of the portfolio investment effectiveness in the context of responsible investment practices popularity. Scientific novelty: the study develops the theory of ESG integration and allows the authors to conclude that ESG commitment is a driver of market profitability for investors. The authors apply methods such as theoretical analysis of scientific publications (analysis, synthesis, generalisation) and quantitative methods, including statistical data analysis, regression analysis, financial modelling. The research base is scientific works of domestic and foreign authors, analytical reports of rating agencies, ESG funds, historical stock market data on companies analysed in the course of this study. All the information used in this study is publicly available or provided by the Bloomberg database. In the course of the study, authors form model portfolios of ESG-oriented and ESG-neutral companies shares and perform a comparative analysis of their fundamental indicators and financial returns. The authors conclude that the portfolio of ESG-oriented companies demonstrates profitability no lower than the portfolio of ESG-neutral companies, considering the risks. At the same time, the values of the fundamental indicators of ESG-oriented companies are inferior to the values of ESG-neutral companies. The relationship between the degree of a company’s ESG compliance and its investment attractiveness is due, among other things, to non-financial value drivers. The authors recommend integrating ESG into the analysis of investment portfolios, significant for the development of investment strategies.

Background: The US government created an initiative to end the HIV epidemic in the United States by the year 2030 (EHE). This multiagency initiative was structured around four pillars: Prevent, Diagnose, Treat, and Respond to improve HIV programs, resources, and service delivery infrastructure. In support of its research mission, the National Institutes of Health (NIH) has funded implementation research (IR) projects by addressing the four pillars and encouraging investigators to collaborate with local partners and Health and Human Services (HHS) grantees in 57 priority jurisdictions. Methods: This paper analyzed data from the NIH funded CFAR/ARC supplement projects from 2019 to 2021. The Exploration, Preparation, Implementation, Sustainment (EPIS) framework was used to characterize projects by stage of implementation. Results: The Prevent pillar was most frequently studied, with Pre-Exposure Prophylaxis (PrEP) being the most studied intervention. The most common partners were health departments, community-based organizations (CBOs), and Federally Qualified Health Centers (FQHCs). The Consolidated Framework for Implementation Research (CFIR) framework was the most utilized to investigate implementation determinants, followed by the REAIM framework and Proctor model to assess implementation outcomes. Conclusion: Monitoring the projects resulting from NIH investments is fundamental to understanding the response to EHE, and achieving these results requires systematic and continuous effort that can support the generalizable implementation knowledge emerging from individual studies. There are some remaining gaps in the project portfolio, including geographical coverage, range of implementation outcomes being measured, and interventions still requiring further research to ensure equitable scale-up of evidence based interventions and achieve EHE goals. [ABSTRACT FROM AUTHOR]

This paper offers a novel approach for optimizing banks’ accounts and portfolios by using both static and dynamic simulation analysis to perform stress tests using several strategies and scenarios driven by exogenous shocks, such as the Covid-19 pandemic. The results of this analysis are explored and discussed using real cases in which dynamic analysis in stress scenarios has been applied to specific banking portfolios that may be impacted by Covid-19.

Copyright of Informe Gepec is the property of Informe Gepec and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Our objective in this report is to examine the evidence surrounding technical analysis' profitability. In writing this report, we aim to provide an in-depth assessment of technical trading strategies, both theoretically and empirically. In this article, we examine surveys that have directly explored the experiences and opinions of market participants regarding technical analysis. Approximately 30 to 40 percent of market participants in futures markets and foreign exchange markets believe technical analysis is an important factor in predicting price movement within the next six months, according to a survey. This next section is designed to give an introduction to theoretical models, which includes implications about how technical analysis can be profitable. Technical trading strategies may be profitable because of noise in the market or investors' irrational behaviour, yet conventional efficient market theories, such as the martingale model and random walk models, exclude technical trading profits in speculative markets. The last section surveys empirical studies. By analyzing the testing procedures used in the studies, we categorize "early" and "modern" studies. Of the 58 studies that produced positive results, 24 produced negative results. The remainder produced mixed findings. Despite the fact that several empirical studies have shown that technical trading strategies can be profitable over a long period of time, the majority of test procedures have significant limitations, such as data snooping, the ex-post choice of trading rules or search engines, and the inability to estimate transaction costs and risks. To prove that technical trading strategies are profitable, researchers will need to correct these shortcomings in testing. [ABSTRACT FROM AUTHOR]

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Introduction: Pilot projects ("pilots") are important for testing hypotheses in advance of investing more funds for full research studies. For some programs, such as Clinical and Translational Science Awards (CTSAs) supported by the National Center for Translational Sciences, pilots also make up a significant proportion of the research projects conducted with direct CTSA support. Unfortunately, administrative data on pilots are not typically captured in accessible databases. Though data on pilots are included in Research Performance Progress Reports, it is often difficult to extract, especially for large programs like the CTSAs where more than 600 pilots may be reported across all awardees annually. Data extraction challenges preclude analyses that could provide valuable information about pilots to researchers and administrators. Methods: To address those challenges, we describe a script that partially automates extraction of pilot data from CTSA research progress reports. After extraction of the pilot data, we use an established machine learning (ML) model to determine the scientific content of pilots for subsequent analysis. Analysis of ML-assigned scientific categories reveals the scientific diversity of the CTSA pilot portfolio and relationships among individual pilots and institutions. Results: The CTSA pilots are widely distributed across a number of scientific areas. Content analysis identifies similar projects and the degree of overlap for scientific interests among hubs. Conclusion: Our results demonstrate that pilot data remain challenging to extract but can provide useful information for communicating with stakeholders, administering pilot portfolios, and facilitating collaboration among researchers and hubs. [ABSTRACT FROM AUTHOR]

Purpose: To analyze communication-focused grants funded by the National Cancer Institute (NCI) between fiscal years 2013 and 2019 to provide insight into the characteristics of funded projects and identify promising areas for future research.Methods: iSearch, a portfolio analysis tool, was queried to identify communication-related grants funded by NCI. Abstracts and specific aims were coded for key study characteristics. 344 unique competing grants with a substantial communication component were included in the final analysis. SAS version 9.4 was used to calculate code frequencies.Results: Most communication grants focused on cancer prevention (n = 197), with fewer targeting diagnosis, treatment, survivorship, or end-of-life. Tobacco product use was the most frequently addressed topic (n = 128). Most grants targeted or measured outcomes at the individual (n = 332) or interpersonal level (n = 127). Cancer patients/survivors (n = 101) and healthcare providers (n = 63) were often the population of focus, while caregivers or those at increased risk for cancer received less attention. Studies were often based in healthcare settings (n = 125); few studies were based in schools or worksites. Many grants employed randomized controlled trials (n = 168), but more novel methods, like optimization trials, were uncommon.Conclusion: NCI's support of health communication research covers a diverse array of topics, populations, and methods. However, the current analysis also points to several promising opportunities for future research, including efforts focused on communication at later stages of the cancer control continuum and at multiple levels of influence, as well as studies that take advantage of a greater diversity of settings and leverage novel methodological approaches. [ABSTRACT FROM AUTHOR]

A stochastic bound is a portfolio that stochastically dominates all alternatives in a reference portfolio set instead of a single alternative portfolio. An approximate bound is a portfolio that comes as close as possible to this ideal. To identify and analyze exact or approximate bounds, feasible approaches to numerical optimization and statistical inference are developed based on linear programming and subsampling. The use of reference sets and stochastic bounds is shown to improve investment performance in representative applications to enhanced benchmarking using equity industry rotation and equity index options combinations. This paper was accepted by Kay Giesecke, finance. [ABSTRACT FROM AUTHOR]

The present paper presents a practical example of methods and tests which are used by companies in the Romanian insurance market with regards to choosing the most feasible model in order to validate their partial internal model and to use it for internal calculation of solvency capital requirement. The focus of the analysis carried on by the author was on earthquake model and content of the paper presents the validation of individual components in the model starting with the exposure and its processing. The back test considers how the rare past catastrophes are described in the models. The validation tests show that the used model – Impact Forecasting – is the best to be chosen of the available models.

Abstract Introduction: Pilot projects (“pilots”) are important for testing hypotheses in advance of investing more funds for full research studies. For some programs, such as Clinical and Translational Science Awards (CTSAs) supported by the National Center for Translational Sciences, pilots also make up a significant proportion of the research projects conducted with direct CTSA support. Unfortunately, administrative data on pilots are not typically captured in accessible databases. Though data on pilots are included in Research Performance Progress Reports, it is often difficult to extract, especially for large programs like the CTSAs where more than 600 pilots may be reported across all awardees annually. Data extraction challenges preclude analyses that could provide valuable information about pilots to researchers and administrators. Methods: To address those challenges, we describe a script that partially automates extraction of pilot data from CTSA research progress reports. After extraction of the pilot data, we use an established machine learning (ML) model to determine the scientific content of pilots for subsequent analysis. Analysis of ML-assigned scientific categories reveals the scientific diversity of the CTSA pilot portfolio and relationships among individual pilots and institutions. Results: The CTSA pilots are widely distributed across a number of scientific areas. Content analysis identifies similar projects and the degree of overlap for scientific interests among hubs. Conclusion: Our results demonstrate that pilot data remain challenging to extract but can provide useful information for communicating with stakeholders, administering pilot portfolios, and facilitating collaboration among researchers and hubs.

Resource allocation for land acquisition is a common multiobjective problem that involves complex trade‐offs. The National Wildlife Refuge System (NWRS) of the U.S. Fish and Wildlife Service currently uses the Targeted Resource Acquisition Comparison Tool (TRACT) to allocate funds from the Migratory Bird Conservation Fund (MBCF; established through the Migratory Bird Hunting and Conservation Act of 1934) for land acquisition based on cost–benefit analysis, regional priority rankings of candidate land parcels available for acquisition, and the overall biological contribution to duck population objectives. However, current policy encourages decision makers to consider societal and economic benefits of lands acquired, in addition to their biological benefits to waterfowl. These decisions about portfolio elements (i.e., individual land parcels) require an analysis of the difficult trade‐offs among multiple objectives. In the last decade the application of multicriteria decision analysis (MCDA) methods has been instrumental in aiding decision makers with complex multiobjective decisions. In this study, we present an alternative approach to developing land‐acquisition portfolios using MCDA and modern portfolio theory (MPT). We describe the development of a portfolio decision analysis tool using constrained optimization for land‐acquisition decisions by the NWRS. We outline the decision framework, describe development of the prototype tool in Microsoft Excel, and test the results of the tool using land parcels submitted as candidates for MBCF funding in 2019. Our results indicate that the constrained optimization outperformed the traditional TRACT method and ad hoc portfolios developed using current NWRS criteria. [ABSTRACT FROM AUTHOR]

This article focuses on the advancement of common tools in the field of measurement, visualization, and controlling of intangible assets. In this course, the fundamentals are the Boston Matrix, the McKinsey Portfolio, the life cycle analysis, and the SWOT analysis. The authors apply a hermeneutical and conceptual approach based on a literature review in order to design new instruments for portfolio analyses of intangible assets. The newly adapted tools are examined with respect to their adequacy for the strategic evaluation and development of knowledge bases in organizations. The authors introduce a Knowledge Matrix linked to a life cycle model of knowledge. Furthermore, the SWOT analysis is adjusted for the use in strategic knowledge management. The results of this paper shall be the groundwork for the implementation and evaluation of such tools in practice. [ABSTRACT FROM AUTHOR]

This paper examines whether a set of filters - for size, ROIC, free cash flow, and high valuations can shift the distribution of returns earned by an investor to the right of the return curve. Paper shows that a portfolio created on the basis of these filters (the expensive growth stock portfolio) consistently outperforms the broader market, while exhibiting lower downside volatility and lower maximum drawdown. Every portfolio created is held for a period of two years. In the study, conducted an empirical assessment of returns made from the expensive growth stock portfolio, from 2016 to 2020 - a total of 4 portfolios held for the full 2-year period, and 1 portfolio, created in 2020, held for 1 year - to assess the robustness of the strategy across different time periods and conditions in stock markets. Then paper compared the risk-return metrics - the Calmar, Sharpe and Sortino ratios - of the expensive growth stock portfolio and the benchmark index, and have shown that the expensive growth stock portfolio has better risk adjusted returns. Furthermore, our results pertaining to shift in the distribution of the returns earned by the investor, to the right of the return curve by investing in the expensive growth portfolio and test the statistical significance of the same. The results of the study states that systematic investing strategies in general and the expensive growth stock portfolio in particular, will aid the investor in beating broad market indices over the long run while having lesser downside volatility. [ABSTRACT FROM AUTHOR]