Measuring the risk-adjusted return of socially responsible investment portfolios built with stochastic dominance criteria

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Volume Title

School of Business | Master's thesis

Date

2020

Major/Subject

Mcode

Degree programme

Information and Service Management (ISM)

Language

en

Pages

52+8

Series

Abstract

Socially responsible investing (SRI) has gained growing interest both in practice and academia during recent years. Many investors who implement responsibility themes in their investment strategy gain utility from following their values but also from financial returns. Hence, their goal is not to completely sacrifice the financial performance of the portfolio in order to follow responsibility themes. The risk-return performance of SRI has been studied in many contexts, but no consensus has been reached whether it offers suboptimal returns or possibly some long-lasting advantages. This thesis studies how portfolios with responsibility constraints have performed compared to an unconstrained benchmark portfolio in Helsinki stock market during 2009-2018. Responsibility constraints are applied by using company-specific ESG (environmental, social and governmental) scores. ESG scores have become a widely accepted method for applying responsibility into portfolio selection. In practice, investor risk preferences are often unknown when a portfolio is selected. This thesis applies the theoretically appealing method of stochastic dominance to optimize portfolio weights. Stochastic dominance requires only simple assumptions on investor risk preferences and assets’ underlying return distributions. Recent development in methods has enabled using stochastic dominance to efficiently diversify a portfolio across a wide set of possible assets. Methodology by Kuosmanen (2004) is applied to select portfolios that are efficient in the sense of second-degree stochastic dominance over OMXH25 index benchmark. The results imply that an investor is able to exclude up to 40% of companies based on ESG scores and still form a portfolio that dominates the OMXH25 index. If the ESG limit is increased more, the optimization method does not find portfolios that dominate the index by stochastic dominance criteria, implying that increasing the ESG limit higher prevents forming efficient portfolios in the sense of SSD.

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Thesis advisor

Liesiö, Juuso
Xu, Peng

Keywords

socially responsible investing, portfolio selection, stochastic dominance, unknown risk preferences

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