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Backtesting ESG Factor Investing Strategies

Backtesting ESG Factor Investing Strategies

Posted May 7, 2020 at 10:49 am


Socially Responsible Investing (also called ESG Factor Investing) grows in popularity. More and more investors enter the stock market not just to invest their savings, but they are also want to support companies that bring positive social or environmental change. ESG factor investing can bring satisfaction to those investors. But does it also brings a real outperformance in a financial sense? Is there some ESG factor alpha? How big is it? These are some of the questions we have decided to investigate – we obtained data, identified ESG factor strategies and tested them. Feel free to explore them with us…

Introduction to ESG

A reader who knows what the ESG investing is can skip this part and go directly to backtesting, as we will shortly explore few ESG basics in the next few paragraphs.

How can we measure if any company is responsible to the environment and overall society? Simply, by grading it – the ESG score measures the firm’s quality in three categories: environmental, social and governance. Each category is affected by different things. The environmental score takes into account carbon emissions, low resource consumption, pollution, innovations aiming to improve environmental protection and much more. Social score focuses on human rights, business ethics, safety standards for workers, cash donations, protection of public health and so on. Factors that affect the governance score can be rights of shareholders or the company’s financial and non-financial goals.

The question of why would someone use ESG score as an indicator has multiple answers. Firstly, there might be a non-financial motivation, for example, a religious belief. Certain religions may want to exclude sectors like the gambling, alcohol or armament industry from their portfolio. Secondly, the philanthropic point of view that seeks green, socially responsible investments is gaining popularity in recent years. High ESG score is seen as appealing for those investors. And lastly, high ESG score can signal forward-thinking and efficient company, and those attributes can signal better financial performance.

Of course, there are a lot of research papers which are focused on the applicable ESG investing strategies; they examine if using ESG score enhances, harms or has no effect on the performance. If researchers confirmed the performance-enhancement, it would mean that ESG scores could be utilized as factors, and used in practice. Also, if there was no correlation between ESG score and performance, an investor, who seeks sustainable investing could be interested in it (as his performance would not be penalized). On the other hand, the focus on the ESG scores could lead to investing in unprofitable companies or reduce an investment universe in a way that wouldn’t be diversified.

The separate problem with ESG score is that it’s hard to measure it objectively. There are multiple data providers with huge databases, but each provider can value different elements of a company on an individual scale. Also, data providers can weight the three scores differently. The result is that the score of one company can widely vary between data providers.

Backtesting setup

Does ESG scoring add any incremental information? Can we build equity factor strategies based on it? We decided to test it for ourselves.

We went to look for the ESG data provider and obtained an unfiltered ESG data from OWL Analytics.

The data set we worked with contained monthly data from March 2009 to October 2019; for each month, we had from 5000 (at the beginning of the sample) up to almost 30 000 companies to work with. For each company, we got its ISIN, shareClassFIGI, region, and over 200 detailed parameters for the E, S, and G score and of course, the total ESG score.

Because we wanted to focus on the U.S. market, we filtered companies from that region, and through their ISIN, we paired them with their ticker and price for each month. Then we deleted small firms and those to which we did not get the ticker or price from our data set. In the end, we were left with around 700 companies.

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