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Talking AI: Is Data Really the ‘New Oil’?

Talking AI: Is Data Really the ‘New Oil’?From enhanced stock picking to sharpening our picture ofESG, Big Data is already being used by investors to addvalue, and given the ever-expanding pool of information, itsimportance to asset managers can only grow.Our columnist Elisabetta Basilico investigates

by Elisabetta Basilico 

From enhanced stock picking to sharpening our picture ofESG, Big Data is already being used by investors to addvalue, and given the ever-expanding pool of information, itsimportance to asset managers can only grow. Our columnist Elisabetta Basilico investigates.

Clive Humby, a UK mathematician, famously stated in 2006 that ‘Data is the new oil. It’s valuable, but if unrefined it cannot really be used. It has to be changed into gas, plastic, chemicals, etc to create a valuable entity that drives profitable activity; so must data be broken down, analyzed for it to have value.’ While a powerful analogy, it is not a perfect one. For instance, data is not a dwindling resource.

More than 100 million websites are added to the internet each year and there are now more than 10 trillion individual web pages, of which Google has indexed less than 0.01%. Data will continue to grow and it is estimated that by 2020 the amount of digital information available will grow from around five zettabytes today to 50 zettabytes – one zettabyte is one trillion gigabytes.

In this column, we focus on a particular resource in this area: Big Data.

According to Gartner Research, ‘Big Data is identified by the now popular 3 Vs: high-volume, high-velocity and high-variety informational assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation.’

In other words, while company balance sheet information and stock prices fall into the ‘structured’ type of data, geospatial imagery tracking vehicles in a car park, social media conversations and even text in accounting reports, can be classified as ‘unstructured’ or Big Data.

Eagle Alpha, a data aggregator and service provider to the asset management industry, says there are at least 1,500 unique alternative data sets and it estimates the number will be 5,000 by 2020.

In 2016 almost half of asset managers surveyed by Ernst & Young said they did not use alternative data, but by 2017 78% said they were using or expected to use nontraditional data.

Why this major change of trend?

Gaining an edge

Simply put, managers are starting to recognise that alternative data is a value-adding proposition.

For some concrete examples, iSentium, RavenPack, sentimenTarder, Dataminr and Social Alpha are just a few of the companies offering sentiment analysis for the investment sector by looking at sources such as social media, news media and web searches.

JP Morgan constructed an index based on a sentiment indicator provided by iSentium and preliminary research shows that since 2013 a long/short strategy based on this index would have outperformed the S&P 500 by approximately 1% annually and with lower drawdowns.

Implications for ESG

ESG is also considered a category of alternative data and according to Eagle Alpha, this is an area of increasing interest. This is not surprising given the popularity that ESG is gaining among the investment community. Alternative data sources in this category can provide insights into the ESG standards at a company beyond traditional ESG data sourced directly from firms.

Environmental issues of interest, such as climate change and carbon emissions, air and water pollution, and biodiversity, can be tracked via satellite data sets, the Internet of Things and event detection. In addition, social issues such as customer satisfaction, gender and diversity, and human rights can be tracked via employment data, sentiment and social media. Finally, on the governance side, data from web crawling and expert views can provide information on board composition, bribery and corruption, and executive compensation.

For example, alternative data solutions firm TruValue Labs, mines unstructured data from more than 75,000 sources and applies AI techniques to analyse, track and score ESG company behaviour for a current sample of 9,000 stocks.

According to research available on the company website, it produces three different types of scores – information flow, ESG scoring and ESG momentum – based on topics defined by the Sustainability Accounting Standards Board. Backtested results over the 2007-2017 period show that companies with strong information flow, good ESG scores and positive ESG momentum outperformed the S&P 500 and Russell 1000 benchmarks by adding annual alpha of between 3% and 5%.

However, a final word of caution is important. While Big Data gives us unprecedented insights and opportunities, data privacy, data security and data discrimination are challenges that need to be taken into account when using these data sets.

*Elisabetta Basilico is a quant investment expert and consultant who specialises in ‘turning academic insights into investment strategies’. Follow her at: academicinsightsoninvesting.com and on Twitter: @ebasilico

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