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Abstract Negative screening is one method to avoid interactions with inappropriate entities. For example, financial institutions keep investment exclusion lists of inappropriate firms that have environmental, social, and governance (ESG) problems. They create their investment exclusion lists by gathering information from various news sources to kee...
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... data consist of the name of the firm, date of the news report, and 17 categories that classify the negative news report. Table 1 shows a sample of the dataset. ...Context 2
... compare our models with the following basic as well as state-of-the-art methods, both using and not entirely using the heterogeneous information network. For the basic model that does not fully use the heterogeneous information network, we add country, industry categories, and node degree to Table 1, transform the former two into one-hot vectors, and use a random forest model for classification. We call this model the "random forest. ...Similar publications
In tandem with global initiatives to ‘go green’, China is undertaking a series of steps to achieve green economic growth. To investigate the dynamic nexus of green growth, green trade, and green energy (3 G) in China, an index is developed in this study to assess the level of provincial green growth by employing five types of indicators – economic...
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... The main tasks of the project implementation process regulation is to ensure observation of the plan of construction and installation works execution and evaluation of the plan-fact analysis of the project, which will allow, if necessary, to make appropriate additions through a set of organizational, managerial, technical-technological, financial and economic solutions, allowing to improve the efficiency of the implemented project as a whole. This procedure is based on the regular application of the same tasks on the basis of a set of available information base, which consists of a variety of different data, such as: observation of the progress of the project, schedule plan, actual data on the scope of work and the feasibility of changes in the project [1][2][3][4]. ...
The relevance of the study is determined by the need to ensure sustainable functioning of construction enterprises by increasing the efficiency of investment projects through the use of controlling mechanisms and application of the ESG concept. As a result of the research, the interrelation of project implementation processes by management phases was analyzed, problems in terms of control of investment and construction projects were identified and directions for improving the control mechanism with the application of the ESG concept were proposed. The purpose and main result of the study is to develop measures to improve the methods of control over the timing of implementation of investment and construction projects, taking into account compliance with the principles of the ESG concept.
... Even though research in AI-based ESG scores and trends is still in its infancy, it has been gaining interest. Several studies have used company disclosures and social media text to extract ESG information (Mehra et al., 2022;Raman et al., 2020;Nematzadeh et al., 2019;Shahi et al., 2014;Hisano et al., 2020). Other studies generated ESG scores or utilised ESG criteria to inform investment decisions and assess their impact on a company performance (Y. ...
We present ESG-FTSE, the first corpus comprised of news articles with Environmental, Social and Governance (ESG) relevance annotations. In recent years, investors and regulators have pushed ESG investing to the mainstream due to the urgency of climate change. This has led to the rise of ESG scores to evaluate an investment's credentials as socially responsible. While demand for ESG scores is high, their quality varies wildly. Quantitative techniques can be applied to improve ESG scores, thus, responsible investing. To contribute to resource building for ESG and financial text mining, we pioneer the ESG-FTSE corpus. We further present the first of its kind ESG annotation schema. It has three levels: a binary classification (relevant versus irrelevant news articles), ESG classification (ESG-related news articles), and target company. Both supervised and unsupervised learning experiments for ESG relevance detection were conducted to demonstrate that the corpus can be used in different settings to derive accurate ESG predictions. Keywords: corpus annotation, ESG labels, annotation schema, news article, natural language processing
... The options for using BIM Uses are different: from using BIM to solve one problem at one stage and ending with the integrated use of BIM Uses throughout the entire life cycle of an investment and construction project. For example, in some cases, an information model is created to calculate the scope of work and obtain estimates, and in other cases, to coordinate participants in an investment and construction project [8]. ...
The paper focuses on investigating methods to improve the integration of Building Information Modeling (BIM) in the construction sector with the aim of enhancing the efficiency of executing investment and construction projects. The relevance is caused by the complexity of calculating the economic effect in the short-term planning period when implementing investment and construction projects using BIM and, accordingly, the lack of methods for economic justification of the need and possibility of using BIM at the design and construction stages. The study aims to compare modern software products and establish a methodology for evaluating the effectiveness of utilizing BIM in investment and construction projects (ICP). This methodology will facilitate conducting an economic analysis to justify the integration of these technologies in project implementation. The study concluded with an examination of global implementation practices, identification of challenges in BIM utilization, a survey of software alternatives, and an assessment of current approaches for economically justifying the adoption of BIM throughout various stages of the ICP life cycle. The research is based on systemic, process and situational approaches, methods of comparative and factor analysis, as well as methods of economic-mathematical and graphical modeling.
... We mentioned earlier how, when it comes to AI and text mining, there is a notable concern regarding the substantial risk posed by fake news and echo chambers distorting outcomes (Hughes et al., 2021). In the above regard, the work of Hisano et al. (2020) provides an initial prompt for further research, as the development of more sophisticated models might help in the fight against fake news. However, there exists a certain degree of division within the field regarding the difficulties faced by companies in analysing and disclosing their ESG performance. ...
... However, financial institutions also use ESG rating as a lending decision tool. Their emphasis is not only to discourage companies with financial problems to keep their portfolios profitable, but there is also a rising interest in putting the burden on publicly traded companies to increase their ESG practices (Hisano et al., 2020). ...
The study’s objective is to predict the environmental, social, and governance (ESG) ratings of firms. Applying six machine learning algorithms, we collect a global data sample of 6166 firms in 73 countries from 2005 to 2019. We use firm-specific and macroeconomic predictors in the model and find that Random Forest Classifier provides the highest accuracy (78.50%) among the six machine learning algorithms by considering Kappa, area under the curve, receiver operating characteristic, and logLoss. The variable importance factor reveals that the lagged ESG score has the highest contribution to the model. Firm size has the second highest, and debt to equity ratio has the third-highest contribution, which indicates that a firm’s total assets and a firm’s financial leverage impact the ESG rating. In addition to a contribution to the growing body of ESG literature, the study’s findings can help practitioners, firm regulators, and policymakers in social and environment-friendly decision-making and investors in investment decisions.
... An increasing number of investors, especially in the West, when making decisions about investing in a particular company, take into account its impact on the environment and society. The United States is expected to introduce mandatory requirements for issuers to disclose financial information on climate risks and greenhouse gas emissions, and is also expected to introduce, following the example of some European countries, mandatory standardized disclosure of ESG information [1][2][3][4][5][6][7][8]. ...
ESG standards are based on the philosophy of sustainable development of economic activity, which follows the principles of a responsible attitude to the environment, high social responsibility, and high quality corporate governance. In Russia, the principles of ESG are less common than abroad, but they are already gradually being introduced into business. At the same time, the three categories of ESG are increasingly being integrated into investment analysis, processes and decision making. Many Russian companies are firmly integrated into the agenda by implementing numerous projects, as developed countries are pushing domestic companies to implement such standards to improve the image and investment attractiveness of companies. And here we should not forget about the importance of both climate change issues and corporate governance practices, as well as the efforts and achievements of companies in the field of ecology and social responsibility, which, on the one hand, is the matter of being responsible for the well-being of employees, and, on the other hand, is contribution into the development of the territories where such companies are located.
... Label propagation is another essential target to be achieved in SEO and it becomes quite challenging in presence of heterogenous information. Study in such problem is addressed by Hisano et al. [40] by storing a voluminous information in the form of a network followed by applying Jacobian iteration for learning weights. This technique also contributes towards performing better analysis. ...
Among the potential tools in digital marketing, Search Engine Optimization (SEO) facilitates the use of appropriate data by providing appropriate results according to the search priority of the user. Various research-based approaches have been developed to improve the optimization performance of search engines over the past decade; however, it is still unclear what the strengths and weaknesses of these methods are. As a result of the increased proliferation of Machine Learning (ML) and Natural Language Processing (NLP) in complex content management, there is potential to achieve successful SEO results. Therefore, the purpose of this paper is to contribute towards performing an exhaustive study on the respective NLP and ML methodologies to explore their strengths and weaknesses. Additionally, the paper highlights distinct learning outcomes and a specific research gap intended to assist future research work with a guideline necessary for optimizing search engine performance.
... Using news as data source mitigates issues like under-or overreporting of ESG / CSR topics according to them. Hisano et al. [11] use news as information source too, arguing similar to Azhar et al. [2] that these are less susceptible to manipulation. They utilize a network-based approach with the goal to predict the appearance of a company on an investment exclusion list due to ESG issues. ...
Environmental, social and governance (ESG) engagement of companies moved into the focus of public attention over recent years. With the requirements of compulsory reporting being implemented and investors incorporating sustainability in their investment decisions, the demand for transparent and reliable ESG ratings is increasing. However, automatic approaches for forecasting ESG ratings have been quite scarce despite the increasing importance of the topic. In this paper, we build a model to predict ESG ratings from news articles using the combination of multivariate timeseries construction and deep learning techniques. A news dataset for about 3,000 US companies together with their ratings is also created and released for training. Through the experimental evaluation we find out that our approach provides accurate results outperforming the state-of-the-art, and can be used in practice to support a manual determination or analysis of ESG ratings.
... For example, authors tried to increase interpretability using a fuzzy expert system (FES) [60] or a fuzzy analytic network process (FANP) [62]. Arguing that the reports analysed by ESG rating agencies were too unreliable, other researchers analysed Twitter data [40] and other online sources using natural language processing (NLP) [5] or used a heterogeneous information network [23] to achieve more accurate ESG ratings. García et al. [20] theorized that certain companies should receive better ESG ratings because of their financial situation. ...
... Some partly relied on human experts [60] which exposes them to the same issues traditional ESG ratings face. Others looked at the problem from an internal perspective [62] or created a binary "bad or not" rating [23] not comparable to standard sustainability ratings. Some authors relied exclusively on textual data [5,40] whose quality is not guaranteed. ...
Over the past years, topics ranging from climate change to human rights have seen increasing importance for investment decisions. Hence, investors (asset managers and asset owners) who wanted to incorporate these issues started to assess companies based on how they handle such topics. For this assessment, investors rely on specialized rating agencies that issue ratings along the environmental, social and governance (ESG) dimensions. Such ratings allow them to make investment decisions in favor of sustainability. However, rating agencies base their analysis on subjective assessment of sustainability reports, not provided by every company. Furthermore, due to human labor involved, rating agencies are currently facing the challenge to scale up the coverage in a timely manner. In order to alleviate these challenges and contribute to the overall goal of supporting sustainability, we propose a heterogeneous ensemble model to predict ESG ratings using fundamental data. This model is based on feedforward neural network, CatBoost and XGBoost ensemble members. Given the public availability of fundamental data, the proposed method would allow cost-efficient and scalable creation of initial ESG ratings (also for companies without sustainability reporting). Using our approach we are able to explain 54% of the variation in ratings R2 using fundamental data and outperform prior work in this area.
... Data sources, such as news, social media and stock exchange filings have also been used in the ESG context. The authors of [12] use adverse media coverage to predict companies that are likely to be blacklisted in sustainable investment practices. In [13], real-time event detection techniques is paired with sentiment classification in order to identify Twitter controversies around a given company and associate the controversies with the stock performance of the company. ...
The integration of Environmental, Social and Governance (ESG) considerations into business decisions and investment strategies have accelerated over the past few years. It is important to quantify the extent to which ESG-related conversations are carried out by companies so that their impact on business operations can be objectively assessed. However, profiling ESG language is challenging due to its multi-faceted nature and the lack of supervised datasets. This research study aims to detect historical trends in ESG discussions by analyzing the transcripts of corporate earning calls. The proposed solution exploits recent advances in neural language modeling to understand the linguistic structure in ESG discourse. In detail, firstly we develop a classification model that categorizes the relevance of a text sentence to ESG. A pre-trained language model is fine-tuned on a small corporate sustainability reports dataset for this purpose. The semantic knowledge encoded in this classification model is then leveraged by applying it to the sentences in the conference transcripts using a novel distant-supervision approach. Extensive empirical evaluations against various pretraining techniques demonstrate the efficacy of the proposed transfer learning framework. Our analysis indicates that in the last 5 years, nearly 15% of the discussions during earnings calls pertained to ESG, implying that ESG factors are integral to business strategy.
