This article originally appeared in CommerzBank’s Thinking Ahead Magazine’s September Issue.
Today, we live in a globalised and digitised world where information is generated and flows at unprecedented levels. Consider the numbers: within the next hour alone, we will generate 21.6 million tweets, 8.5 billion emails, 34,200 new websites, and 144 million Google searches. Ours is an age where data shapes almost every aspect of our lives. Through rapid advancements in technology, big data has disrupted entire industries, influencing how we work, how we consume, and how we view the world.
The term ‘big data’ refers to collections of data sets that are enormous and complex, and as such defy attempts to process or manage using more traditional methods. Over the past few years, the term has also come to describe the use of specialised analytical techniques and computing tools to analyse these superlarge and complex data.
Supercomputers, first developed in the late 1960s, are now capable of processing countless operations per second. And as machine learning improves, computers increasingly can not only process vast numbers, but can also ‘read’ text (for example, traditionally unstructured data like news sources, images and videos) and make obscure connections in patterns that would elude even the smartest trader. We have the technology at our fingertips to now quickly scan huge data sets, and reveal untapped trends and correlations, from which new insights and predictions around investment news can be extrapolated.
This explosion of machine power and speed means that we are still learning about the potential of data. We have scaled up our own understanding and application of data science to cope with the new size and usefulness of the data being collected around us. And with the gap closing between the size of data and our ability to master it, investors are now able to produce real insight using correlations across big pools of complicated data sets.
According to a 2017 report, ‘The Innovator’s Advantage’ by The Boston Consulting Group, asset managers that are able to take advantage of data, machine learning, and artificial intelligence will be in the best position to thrive in a future increasingly marked by lower fees and weaker fund flows. Indeed, research earlier this year by S&P showed that 80% of asset managers are planning to increase their investments in big data in 2017. The study, ‘Big data in asset management’, found that 19% expected to boost investments in the area by more than a fifth, while a further 63% would increase investments by a smaller amount. 82% considered big data investments somewhat or very important, compared to only 6% of asset managers who argued it is not important.
The emergence of big data in finance has also coincided with another megatrend in investment, which is the rapidly increasing demand for environmental, social, and governance (ESG) products, as more and more sustainability information flows into the market, and as the value of material ESG data is better understood.
In 2015, Arabesque commissioned Oxford University to undertake the most in-depth review of studies of the link between ESG performance and financial performance. From over 200 academic papers, the review found an 80% positive correlation between the company’s ESG position and its stock price performance.
Furthermore, a 2015 report by George Serafeim from Harvard Business School, entitled ‘Corporate Sustainability: First Evidence of Materiality’, found that companies which demonstrate good performance on ESG issues that matter for their industry subsequently outperform their peers.
Studies such as these have firmly established a correlation between material ESG factors and financial performance, further pushing sustainability into mainstream business conversation. The quality of ESG data is at around 10% of where it will be in five years’ time, but it is increasing all the time. The work began with sustainability standards-setters such as the United Nations Global Compact, and its request for regular communication on progress. The Global Reporting Initiative (GRI), the Carbon Disclosure Project (CDP) and other reporting initiatives were also instrumental in bringing ESG data to the market. They incentivise and standardise the disclosure of thousands of companies on human rights, labour, environment and anti-corruption performance.
As the business case for sustainable business practices is getting stronger, and as technology and machine learning now enables smart analysis of fractured data, full ESG integration based on big data is now charting new pathways.
Arabesque uses self-learning quantitative models and big data to assess the performance and sustainability of companies. With a rules-based approach to stock selection that integrates ESG information with financial and momentum analysis, Arabesque’s technology processes over 100 billion data points via 250,000 lines of code to construct its strategies.
Earlier this year, our firm launched S-RayTM, a new tool that allows anyone to monitor the sustainability of thousands of the world’s largest companies. Inspired by the impact that the X-ray had on medicine in the early 20th century, S-Ray is the latest technology of its kind to capture vast amounts of sustainability information that now exists on companies, and make it relevant and understandable to investors.
It enables a holistic view of companies over and beyond the financial bottom line, applying a values-based lens on universal principles of humanity as advocated by the United Nations Global Compact, together with a systematic assessment of ESG factors that are financially material to corporations.
Up until recently, many investors have struggled to make sense of sustainability-based approaches. A major reason is that dataaround ESG issues has remained scattered, incomplete, incoherent, and unstructured. There has been no uniform measurement or easily applied framework that allows investors to assess corporate performance in alignment with personal values and preferences.
This is where technology-driven approaches such as S-Ray can change the market. Its unbiased algorithms harness the power of machine learning, processing big data to produce a daily snapshot of a company’s sustainability. By offering investors a modular way of aggregating relevant sustainability big data, it can help to improve decision-making on responsible investment in the long term.
Technology has long been a fundamental driver of human progress. It can now be used within asset management to harness unprecedented amounts of corporate ESG data, and allow us to more clearly search beneath the surface of companies across the globe and identify those which consider the interests of all stakeholders. That is the mission upon which Arabesque was founded: mainstream sustainable investment through ESG big data and technology. It is an approach which can deliver improved risk-adjusted returns, and ultimately shine a light on environmental stewardship, social impact, and on universal values.