Позитивные изменения. Том 3, № 4 (2023). Positive changes. Volume 3, Issue 4(2023)

Impact investing funds (IIFs) are a type of investment fund that aims to achieve both financial and social goals, and they have become a valuable resource for social purpose organizations (SPOs) seeking capital. Research evaluates the investment process by comparing approaches in two countries, Italy and Australia. Authors conducted semi-structured interviews with 9 IIFs and 12 SPOs and used the Gioia Methodology to conduct the data analysis. They found both similar features and divergences. The investing process by IIFs in Australia is conservative, more risk averse and more profit-oriented with a predominant “finance-first” strategy. By contrast, in Italy there is a clear preference for “impact-led” investment. Researchers argue this illustrates a fundamental difference in investment philosophy and that IIFs in the two countries navigate, interpret, and understand the field of impact investment in contrasting ways. Finally, study contributes to the growing literature on impact investing by putting forward an “ideal” impact investment process that respects the integrity and peculiarities of the Social Impact Investment approach.

https://clck.ru/37Hquw

9. HARNESSING THE WEB AND KNOWLEDGE GRAPHS FOR AUTOMATED IMPACT INVESTING SCORING

Q. Hu, D. Daza, L. Swinkels, K. Ūsaitė, R. Hoen, P. Groth

Paper, August 2023

Social Science Research Network

The Sustainable Development Goals (SDGs) were introduced by the United Nations in order to encourage policies and activities that help guarantee human prosperity and sustainability. SDG frameworks produced in the finance industry are designed to provide scores that indicate how well a company aligns with each of the 17 SDGs. This scoring enables a consistent assessment of investments that have the potential of building an inclusive and sustainable economy. As a result of the high quality and reliability required by such frameworks, the process of creating and maintaining them is time-consuming and requires extensive domain expertise. In this work, authors describe a data-driven system that seeks to automate the process of creating an SDG framework. First, they propose a novel method for collecting and filtering a dataset of texts from different web sources and a knowledge graph relevant to a set of companies. Then they implement and deploy classifiers trained with this data for predicting scores of alignment with SDGs for a given company. Results indicate that best performing model can accurately predict SDG scores with a micro average F1 score of 0.89, demonstrating the effectiveness of the proposed solution. Researchers further describe how the integration of the models for its use by humans can be facilitated by providing explanations in the form of data relevant to a predicted score. They find that their proposed solution enables access to a large amount of information that analysts would normally not be able to process, resulting in an accurate prediction of SDG scores at a fraction of the cost.

https://clck.ru/37Hr2m

10. PAST, PRESENT AND FUTURE OF IMPACT INVESTING AND CLOSELY RELATED FINANCIAL VEHICLES: A LITERATURE REVIEW

H. Chiappini, N. Marinelli, R. Nabeel-Ud-Din Jalal, G. Birindelli

Paper, August 2023

Sustainability Accounting, Management and Policy Journal