- ESG at Ossiam
Ossiam was founded in 2009, after the 2007-2008 financial crisis with the mission of providing transparent and non psychologically-biased financial solutions.
The company is a subsidiary of Natixis Investment Managers and develops transparent and systematic investment solutions based on quantitative and fundamental analysis. Ossiam’s strategies are embedded in ETFs (Exchanged Traded Funds), index funds, and mandates.
We were pioneers in Europe in developing Smart Beta ETFs and now we are pioneers in combining ESG data with quant-based approaches including Artificial Intelligence.
The company is a UN PRI (United Nations Principles for Responsible Investment) signatory since April 2016 and a FAIRR signatory since July 2019.
At Ossiam, we strongly support Responsible Investing as it plays a fundamental role in evolving corporate behavior towards a more sustainable society.
We believe that today, given the quality and depth of ESG data, it is possible to apply quant-driven approaches and deliver strategies that respect both ambitious ESG targets and predefined risk-return profiles.
To achieve these levels of performance, it is important to master the different ESG styles and apply a combination of them according to markets’ and clients’ needs.
More precisely, at Ossiam we apply combinations of the following ESG styles:
The Best-In-Class approach excludes companies with low ESG scores in each sector. The exclusion is carried out within Peer Groups (i.e. homogeneous groups of stocks that operate in the same field hence comparable from an ESG point of view) so that we keep almost the same economic mesh exposure as the initial benchmark (indeed, doing the exclusions at the investment universe level would most likely produce a filtered universe with very few stocks in structurally low-rated sectors as Energy, Utilities, Industrial, etc…).
The clear advantage of the Best-In-Class approach is that it is pro-active, i.e. it excludes companies that are not doing enough to improve their global ratings. The main disadvantage is that a filter based on ESG scores reacts slowly to new upcoming information as usually ESG ratings are updated once a year.
The Normative-Exclusion approach excludes stocks that undergo severe controversy (a severe violation of an international norm). It also excludes stocks involved in the business of controversial weapons and stocks in breach with one or more of the 10 Principles of the UN Global Compact. The Normative-Exclusion approach is clearly more dynamic and can exclude a company any time it breaches one of the criteria listed above. While the controversial weapon criteria is by definition quite static (a company is involved or not and this tends not to change suddenly) the other two can change rapidly.
The main disadvantage of this approach is that companies that do not make significant efforts to improve their ESG ratings remain eligible, as long as they are not in breach with one of the Normative-Exclusion filters.
The Sector Exclusion approach excludes sectors that, for some investors, are no more considered as responsible as they create harm to the community and the environment such as tobacco and coal.
We consider different emissions-related metrics such as:
• Carbon emissions across the three Scopes,
• Carbon intensities (carbon emissions / corporate revenues)
• Potential carbon emissions from oil reserves
• Green-to-brown ratio: energy produced from renewable sources / energy produced from non-renewable sources.
Given a target portfolio (example: an existing strategy or a cap-weighted benchmark), we derive the optimal weights that achieve the required carbon reduction along the different dimensions, while minimizing a financial criterium (example: Tracking Error).
At Ossiam, we decided to push ESG Integration a step forward by using Machine Learning to identify the ESG indicators associated to positive financial performance.
Ossiam’s Machine Learning-based algorithm explores hundreds of ESG indicators to identify robust patterns in the data, which in turn are able to make a distinction of promising companies compared to the risky ones in terms of market performance.
These techniques have been applied to one of our most recent ESG strategies, the Ossiam World ESG Machine Learning strategy, launched in November 2018.
In March 2019, Ossiam won the European ETF Innovation Award from L’Agefi for this strategy.
In order to raise companies’ levels of Corporate Social Responsibility, on top of the above described styles, and for all the companies in our investment universes, we do Proxy-Voting and Engagement.
The Proxy-Voting activity is carried on by ISS. For our Proxy-Voting policy, please click here
We follow ISS’ Engagement policy which focuses on companies involved in severe violations of international norms (ESG controversies). Engagement is preceded by in-depth research and factfinding dialogues with companies and stakeholders in order to identify gaps in terms of disclosure and evaluate the levels of responsibility and the possibility to have a constructive dialogue. The Engagement service is pooled and carried on by ISS.
Antonio Celeste - Sales Director and Responsible of ESG Development
Antonio joined Ossiam in May 2018. He has 12 years of professional experience in ESG. He spent 8 years at Sustainalytics as Director of Institutional Relations to advise asset owners and managers across EMEA on the implementation of ESG strategies. Before Sustainalytics, Antonio worked at Vigeo-EIRIS for 3 years as Head of International Business Development. Antonio Celeste holds a degree in Mechanical Engineering from the Polytechnic of Turin and an MBA from HEC, Paris.
Carmine de Franco, PhD – Head of Fundamental Research
Carmine joined Ossiam in May 2012 as a Quantitative Analyst after working 4 years in the Faculty of Mathematics, Université Paris VII, Denis Diderot. Carmine De Franco graduated from Université Paris VII, Denis Diderot. He holds a PhD in Financial Mathematics and a Master degree in financial random modeling.
Discover here our publications on Responsible Investment related topics:
Performance of ESG and Machine Learning investment approaches - January 2019
ESG controversies and their impact on performance - October 2018
Carbon Footprint for dynamically rebalanced portfolios - January 2017