In this paper, we assess sentiment and emotion regarding green energy through employing a social listening analysis on Twitter. Knowing the sentiment and attitude of the population is important because it will help to promote policies and actions that favor the development of green or renewable energies. We chose to study a crucial period that coincides with the onset of the 2022 Ukrainian–Russo conflict, which has undoubtedly affected global energy policies worldwide. We searched for messages containing the term “green energy” during the days before and after the conflict started. We then performed a semantic analysis of the most frequent words, a comparative analysis of sentiments and emotions in both periods, a dimensionality reduction analysis, and an analysis of the variance of tweets versus retweets. The results of the analysis show that the conflict has changed society’s sentiments about an energy transition to green energy. In addition, we found that negative feelings and emotions emerged in green energy tweeters once the conflict started. However, the emotion of confidence also increased as the conflict, intimately linked to energy, has driven all countries to promote a rapid transition to greener energy sources. Finally, we observed that of the two latent variables identified for social opinion, one of them, pessimism, was maintained while the other, optimism, was subdivided into optimism and expectation.

Robo advising is an emerging business model that aims to popularize investment advisory services by automating all the activities involved. The robo-advisor (RA) stream of research is relatively new, yet it has started to attract the attention of numerous scholars. This manuscript aims to analyze the academic literature on RA based on its impact on the different phases that make up the investment advice process. Our aim is to investigate which phases of the investment advice service prove of most interest to scholars. Since selecting relevant and precise keywords increases the visibility, accessibility, and indexing of a research paper, we explore the RA research landscape using keyword relational analysis. We analyze metadata from 195 identified papers combining the Scopus and Web of Science databases until May 2022. The analysis unfolds in two parts. First, we examine the relationships and associations of keywords in titles, abstracts, and author keywords to extract knowledge patterns from said associations. As a novelty, a specific dictionary of RA-synonyms formed by 19 disjoint families of concepts is obtained. The second part presents the RA's conceptual and relational structure. For this, five clusters are determined to identify and organize those studies dealing with similar topics. The results of our study suggest that research is more interested in aspects related to the human factor, namely, those with phases requiring more direct contact with the client or investor. The most relevant research interests include the automation of client profiling functions and client response to the latter automation.

Nowadays, global uncertainty has consequences for financial investors. Some of them consider sustainable investment as a refuge from instability. Environmental, social, and governance (ESG) scores are fundamental tools for assessing a company's sustainability and other ethical and social aspects. Due to this challenge, several rating agencies provide ESG grades and scores for companies, investors, and stakeholders. However, the uncertainty associated with the linguistic descriptions of the rating scales, along with partial information regarding their methodology, can complicate the decision‐making process for investors. For this reason, this paper proposes a new approach to constructing ESG scores by integrating two methodologies: ordinal proximity measures and the extended best–worst method. The former considers the different perceptions that investors may have concerning the ratings on the scale. At the same time, the extended best–worst method seeks to define criteria weight coefficients using fuzzy set theory to manage ambiguity and imprecision. These procedures are applied to obtain linguistic assessments for 115 companies worldwide in the energy utilities sector, considering a specific investor's preferences. The dataset was obtained from Refinitiv Datastream and encompasses the period from 2018 to 2021. The findings demonstrate that the proposed procedures complement and enrich the rating methodology used by Refinitiv and can enhance the investment decision‐making processes under uncertain situations.

In this paper a multi-criteria decision-making (MCDM) method is developed to rank a set of insurance companies. The proposed method is based on combining two MCDM methods: Extended Best–Worst (EBW) and Multiple Reference Point (MRP) methods. We formulate the problem of finding a priority vector from a set of interval pairwise comparisons applying an EBW method which allows the decision maker (DM) to use interval values in order to describe the relative importance of one criterion over another. The EBW method, using fuzzy set theory, can successfully handle the vagueness and ambiguity present in the judgments. Lastly, the MRP method is employed to obtain an overall score for each company using the weights established at the first stage. A case study is presented to rank Spanish non-life insurance companies based on the constructed model. Since the evaluation of insurance companies involves a great number of indicators, it is a complex MCDM issue. The results show the effectiveness of the proposed method and offer an insightful reference for an evaluation of the insurance industry.

The aim of this paper is to provide a tool for finding investments in the stocks of energy firms that achieve both good financial and reasonable environmental, social, and governance (ESG) performance. Our methodology entails two steps and is based on diversification-consistent DEA models. The first step involves constructing a financially efficient frontier of investment portfolios by applying the model originally proposed by Branda (Omega 52:65–76. 10.1016/j.ejor.2007.04.014, 2015). In the second step, a new DEA model is proposed in order to find the ESG-efficient portfolios among the ones already identified in the first step and to rank them with respect to their ESG performance. This model is parameterised by a weighting system that allows us to assign different importance to the various ESG outputs. Additionally, the proposal allows an evaluation of both ESG and financial efficiency related to the financial energy market over two periods (the pre-COVID-19 and COVID-19 periods), considering renewable energy and non-renewable energy firms both jointly and separately. The results support the better financial performance of the renewable energy stock market compared with that of the non-renewable energy market.