The aims of this paper are (i) to identify which documents are the most influential when analyzing socially responsible funds, and (ii) to identify the conceptual structure of the field of research through co-word analysis. To achieve the proposed objectives, the VOS Viewer and two databases, Web of Science (WOS) and Scopus, were used for the period 1988–2018, and a total of 209 research papers were analyzed. This analysis provides an insight into the nature and trends of research on socially responsible investment (SRI) funds.

The current concern with sustainability issues pervades all scientific fields. Sustainable investment is not an exception judging by the significant increase in the literature. This work provides an extensive up-to-date overview of studies on sustainable investment, revealing current trends, identifying gaps and future research topics. A total of 1091 documents are reviewed and bibliometric analysis is used with two approaches: data analysis and conceptual structure of the field. Additionally, a classification into 12 categories by topic is proposed. The results show that more than 40% of the publications belong to analysis of classical investment tools with a gradual evolution towards the concept of "green" instruments. Socially Responsible Investment (SRI) strategies have changed over time from "screening" to the current "best-in-class", "Environmental, Social and Governance (ESG) integration" and "impact investing." The results of the study are valuable for academics and practitioners, as they show the current state of knowledge, evolution paths and emerging topics.

Part of a country's emissions are caused by producing goods for export to other countries, while a country's own needs also generate emissions in other parts of the world that are associated with the products they import. Our interest was to evaluate the influence of imports and exports of goods and services on greenhouse gas (GHG) emissions in a data panel composed of 30 countries over 21 years. We included as control variables the gross domestic product per capita, employment, an indicator of the economic crisis and a non-linear trend and inferences were performed using a Bayesian framework. The results showed that it was the exports and imports of goods, rather than services, that were related to CO 2-equivalent levels. Exports and imports of goods were very inelastic, albeit less so in the case of the index. In summary, the more a country imports, the higher their GHG emission levels are. However, it is important to point out that when employment rates are higher more energy is consumed and GHG emissions are greater. In richer countries, GDP per capita is the factor that best explains why their emissions are so high.

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The paper aims to identify which variables related to capital structure theory predict business failure in the Spanish construction sector during the subprime crisis. An artificial neural network (ANN) approach based on Self-Organizing Maps (SOM) is proposed, which allows one to cluster between default and active firms’ groups. The similarities and differences between the main features in each group determine the variables that explain the capacities of failure of the analyzed firms. The network tests whether the factors that explain leverage, such as profitability, growth opportunities, size of the company, risk, asset structure, and age of the firm, can be suitable to predict business failure. The sample is formed by 152 construction firms (76 default and 76 active) in the Spanish market. The results show that the SOM correctly predicts 97.4% of firms in the construction sector and classifies the firms in five groups with clear similarities inside the clusters. The study proves the suitability of the SOM for predicting business bankruptcy situations using variables related to capital structure theory and financial crises.

Recently, the total net assets of mutual funds have increased considerably and turned them into one of the main investment instruments. Despite this increment, every year a considerable number of funds disappear. The main purpose of this paper is to determine if the neural networks can be a valid instrument to detect the survival capacity of a fund, using the traditional variables linked to the literature of disappearance funds: age, size, performance and volatility. This paper also incorporates annualized variation in return and the Sharpe ratio as variables. The data used is a sample of Spanish mutual funds during 2018 and 2019. The results show that the network correctly classifies funds into surviving and non-surviving with a total error of 13%. Moreover, it shows that not all variables are significant to determine the survival capacity of a fund. The results indicate that surviving and non-surviving funds differ in variables related to performance and its variation, volatility and the Sharpe ratio. However, age and size are not significant variables. As a conclusion, the neural network correctly predicts the 87% of survival capacity of mutual funds. Therefore, this methodology can be used to classify this financial instrument according to its survival or disappearance.

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