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FUZZY JENSEN'S ALPHA ANALYSIS IN ROBO-ADVISORS ESG STRATEGIES

Rodrigo Caballero Fernández. Accounting and Finance Academic Department, Business School, Tecnológico de Monterrey. México. E-mail: rodrigocaballero@tec.mx

*Alicia Fernanda Galindo Manrique. Accounting and Finance Academic Department, Business School, Tecnológico de Monterrey. México. E-mail: alicia.galindo@tec.mx

Nuria Patricia Rojas Vargas. Accounting and Finance Academic Department, Business School, Tecnológico de Monterrey. México. E-mail: uriarojas@tec.mx

Abstract

The Sustainable Development Goals set by the United Nations are increasing the importance of more environmental, social, and governance practices. Therefore, investors include companies in their portfolios that care about these aspects as they demonstrate steady financial benefits. This research aims to analyze and compare ESG investment strategies against Core strategies, both in aggressive risk profiles, employing a robo-advisor from the US company Ally. Conducting backtesting, we compared the performance of the strategy.

We employed the Fuzzy Logic Theory and its regression models given the perspective of instability and the need to incorporate all risk elements in decision-making. The findings suggest that incorporating ESG factors into the investment strategy may offer some advantages in terms of abnormal returns, particularly for ETFs focused on larger companies and developed/emerging markets.

 

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