By Dr Jan-Carl Plagge, STOXX – The investment industry has always relied on information patterns and trends to boost returns, with items such as regulatory filings and analyst reports long considered to be the traditional sources of data.
However, in the last few years, the volume of data available, and the channels producing it, have grown exponentially. It was estimated in 2013 that 90 per cent of data in the world then had been created in the previous two years alone.1 Much of it is derived from the Internet of Things (IoT) and has led to the rise in use of what is termed alternative data.
A myriad of unofficial and non-conventional sources of information have been used for years to determine whether an investment is a buy or sell. But investors who use the latest data streams are raising the stakes by looking in hitherto unexplored places.
A recent report by JP Morgan2 classifies alternative data into three segments depending on how it is generated:
- Individuals – includes social media posts and search trends.
- Businesses processes – varying from company exhaust statistics to credit card transactions.
- Sensors – information such as ship locations and satellite images.
Some of these are already very popular. According to Greenwich Associates and Arcadia Data,3 over 60 per cent of asset managers are currently using social media and social-driven news feeds as part of their investment process.
While the abundance of high-quality data can be a pool of valuable investment information, sophisticated analytical processes are needed to organise and analyse it, and determine its applicability and utility. Here is where artificial intelligence (AI) techniques and so-called machine learning play a key role. Both are the key to construct algorithms that identify data patterns and make predictions.
In brief, AI is technology that behaves and thinks like humans. Machine learning is a subset of AI, and it denotes an automated process of cognitive detection of patterns and past experiences to `learn’ outcomes.
Faster computer speeds and large-scale cloud storage mean that big data applications that use AI software have the ability to quickly identify and measure risks from staggering volumes and velocity of information.
As such, products where the underlying processes are driven by technology and big data can be customised into `intelligent’ investments that generate smart financial performance.
The use of alternative data has been of particular significance in the realm of passive investing, where traditional market-weighted indices are evolving into new, data-driven and targeted products based on quantitative and qualitative analysis. These are designed with methodical precision and are generating countless new possibilities for investors. In particular, they are proving a more economic alternative when considering the cost of analytical infrastructure and personnel.
STOXX, in its role as an Intelligent Investments Factory, is collaborating with the best data providers in each field to provide the right tools. For example, the iSTOXX FactSet Thematic Indices are based on FactSet Revere data, the iSTOXX Europe Factor Indices were developed with Alpha Centauri and the STOXX Climate Impact and Climate Awareness Indices use CDP data.
By defining systematic and smart benchmarks, data-driven decisions can be incorporated into portfolio construction via indexing approaches. These simple yet powerful products enable investors to tackle the threats and opportunities presented by today’s interconnected global economy, while optimising their investment objectives.
1. `Big Data, for better or worse: 90 per cent of world’s data generated over last two years,’ SINTEF, May 22, 2013.
2. `Big Data and AI Strategies,’ J.P. Morgan, May 2017.
3. `Putting Alternative Data to Use in Financial Services,’ Arcadia Data and Greenwich Associates, September 2017.
