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Harnessing the data science revolution

TopicsHarnessing the data science revolution

A mass of new information types, combined with new methods of analysis is giving investors the tools to enhance long-term performance, argue Ben Wicks, Head of Data Insights and Research Innovation, and Mark Ainsworth, Head of Data Insights and Analytics, both Schroders.

The process of collecting and analysing data has undergone a revolution. A mass of information, from geospatial data to customs records and demographic data, is now available and can be manipulated in ways unheard of 30 years ago. Investors who can marry industrial-scale data processing with tried and tested investment expertise will emerge as winners.

The dramatic increases in computer processing power, storage capacity and information mean that the amount of “alternative data” that can potentially be interpreted by an analyst or fund manager is growing at an exponential rate. These developments pose disruptive challenges to the investment industry. But they also provide a major opportunity.

A positive role to play

Alternative data is an umbrella term for information that is not already part of the core currency of investment research, being, broadly, everything that is not company accounts, security prices or economic information. Because alternative data is often unstructured, it may need considerable processing before it can yield meaningful conclusions. A cadre of data science professionals has emerged to meet this challenge and fashion the necessary techniques to handle large data sets.

Good data scientists have several distinct qualities. A sound knowledge of maths, statistics, programming and algorithms is essential. But a firm understanding of the security under consideration, where the data is from and the way in which it is being applied are equally valuable for understanding what really matters. Combining an investor’s deep knowledge of the market and securities with skilled data scientists, whose specialised work becomes part of the investment process, is optimal.

Perhaps the biggest impact of data science on the active manager is capacity, by the freeing up of time and intellectual capital. A comparison can be made with the impact of avionics on pilots: the information provided by advanced instrumentation provides for better decision-making and liberates the pilot’s mind to do other things that improve overall performance, such as contingency planning. Similarly, an investor who can rely on other specialists to help extend their vision in all the above ways will be that much freer to think and explore fresh ideas.

Scale and resources

The proliferation of information available for investment research is a profoundly disruptive force. Data science poses technical and organisational challenges and involves substantial research and development work. But data science also offers a huge opportunity for active fund managers.

The injection of new, and potentially unique, methods of data analysis into existing investment processes should enhance long-term performance.

Organisations that successfully adapt to this data-heavy world will have a mind-set of innovation and collaboration. They will also be large enough and have sufficient technological prowess to compete. The scale and variety of the data available today requires considerable engineering and data management resources if it is to be used optimally. Larger teams of data scientists will be able to exploit large volumes of data from diverse sources in ways that would be more difficult for a single data scientist acting alone.

Far from creating a level playing field, where more readily available information simply leads to greater market efficiency, the impact of the information revolution is the opposite. It is creating hard-to-access territories and opportunities for those few players with the scale and resources to take advantage of it.

To read the full report as a PDF, click here.

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