A trend that has already started in recent years, the integration of ESG (Environmental, Social, and Governance) criteria has, since the COVID crisis, become an essential part of business analysis. The evaluation of financial characteristics is complemented by an assessment of the sustainability of the activity, its climate and societal impact, and the resulting investment risks.
In quantitative finance, the recent proliferation of ESG data, reported by third-party analysts or by companies alongside their income statements, allows for systematic incorporation into investment algorithms. Most of this data is so-called “structured”, i.e. presented in numerical form and therefore easily analyzed, modeled, and then integrated into a quantitative process.
Read more about the RAM-AI research on ESG company news and find the latest business headlines with the best online news aggregator, our companion app Born2Invest.
The topics addressed focus mainly on the health and well-being of employees
Financial markets are, however, influenced by a multitude of signals, which can sometimes take less standardized forms. In addition to conventional structured data, it therefore seemed essential to explore the predictive power of so-called “unstructured” information, representing all non-numerical data such as text or images. At the forefront of the RAM-AI research is the systematic analysis of media information flows (press, websites, social networks, …) on ESG topics, for two main reasons.
Firstly, many studies have highlighted the links between a company’s media activity and future movements in its action. Secondly, these dynamic flows are a perfect complement to traditional ESG data characterized by a very low discounting (often annual). This approach is therefore essential in such a volatile market context. The ESG themes captured by algorithms, in the pre-COVID (December 2019) and post-COVID (June 2020) periods focus mainly on employee health and well-being, topics that have only anecdotally appeared before.
The integration of unstructured data brings an additional difficulty: due to the symbolic nature of the text, quantitative models cannot directly assimilate the information. Thanks to the incredible progress in artificial intelligence (AI) observed in recent years, and more specifically in natural language processing (NLP), the RAM-AI research team has been able to rely on linguistic algorithms to transform textual data into digital representations, while preserving semantic proximity in a digital space.
Most of these machine learning models (BERT, RoBERTa, …) have been developed by major technological players (Google, Facebook, …) in “open source”. They were then adapted to meet our needs in a precise way. Indeed, the basic algorithms have been trained on general text corpora such as Wikipedia, and therefore cannot capture all the nuances of information related to the ESG information flow. A “fine-tuning” process based on a large ESG text database was therefore introduced.
An example of the use of RAM-AI infrastructure is the prediction of the volatility of the shares of companies mentioned in media feeds. RAM-AI quantitative approach integrates more than ten years of structured and unstructured data to train neural networks to identify interaction patterns, and thus estimate future volatility. In the graph below (simulations based on the European market), stock volatility is indeed much higher/lower when, following an ESG media flow, the predicted volatility is high/low (Qu_4/Qu_0).
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