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.
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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