Asset managers applying advanced analytics to investment decision-making

Mar 27, 2019
Superior analytics will be key driver of success in future, highlights McKinsey

Recent reports have been ubiquitous with the idea of robots rendering humans obsolete – including those working in finance and asset management.

A new intellectual ecosystem of think tanks and consultants has emerged out of an obsession with the impact of artificial intelligence (AI) on the future of work and commerce, with some presenting a near Terminator-style future.

This trend is evident in the language being used within asset management: in 2017 there were almost 14,000 research publications in the asset-management industry that used the terms ‘Big Data’ or ‘analytics’ as keywords – four times the number in 2012.

This is one of the more obscure pieces of fascinating insight from a new study by consultancy McKinsey, entitled ‘Advanced analytics in asset management: Beyond the buzz’, which reveals asset managers are increasingly applying data science and advanced analytics to investment decision-making.

Over the last couple of years, the application of advanced analytics to specific business problems has started to deliver value for traditional asset managers, notes the report – not by replacing humans but by enabling them to make better decisions quickly and consistently.

‘A broad set of firms are embracing new analytics methods at multiple points across the asset-management value chain – and beyond the alpha-generating use cases favored by quant firms – from increased sophistication in distribution and better investment decision-making to step changes in middle and back-office productivity,’ notes the report, written by McKinsey associate partner Sudeep Doshi and partners Ju-Hon Kwek and Joseph Lai.

Not man versus machine
It is on the investment side that some traditional asset managers are now engaging more fully in advanced analytics.

Kwek tells IR Magazine: ‘Advanced analytics in investments is too often framed as a case of man versus machine. In reality, the combination of analytic techniques, the commoditization of computing power and the explosion of new data sources create significant opportunities to enhance the human insight that sits at the core of fundamentals-based investment processes in place at many traditional asset managers.’      

Breaking down the asset management advanced analytics, these efforts focus on three areas, notes the report:

Debiasing investment decisions – Eliminating systematic biases from the investment decision-making process has long been a topic of interest to investors. The ability to stitch together a broad set of data sources about an individual or team’s trading history, communication patterns, psychometric attributes and time-management practices allows firms to identify drivers of performance and behavioral root causes at a more granular and individualized level than previously

Using alternative sources of data to generate alpha – The availability of greater quantities of data is putting a premium on having both data-acquisition capabilities and the data-science skills to stitch these sources together into predictive models that improve decision-making

Enhancing research processes – The application of techniques such as natural language processing (NLP) is also helping asset managers process vast amounts of information more quickly than before. For example, it can automate the ingestion and analysis of public filings and flag changes in sentiment that a research analyst can focus on. This is an example of machines complementing the human process instead of replacing it: the technology helps narrow down what is relevant in much the same way that a recommendation engine on Netflix or Amazon would, and allows investors to spend more of their time on high-value decisions.

Advanced analytics is also being used to improve productivity in the asset management middle and back offices. As firms contend with the growing complexity of products, legal entities, vehicles and markets, economies of scale are coming under pressure. In response, asset managers are looking for ways to increase the productivity of their middle and back-office functions through advanced analytics-driven solutions. Two particular areas of focus are:

Process automation of time-consuming tasks – Asset management firms are using NLP and other techniques to analyze text and voice communications and to recommend optimal actions for certain processes, such as suggestions for how to deal with policy breaches picked up in conversations and deploying machine-assisted conversations to answer common operational questions

Improving quality of risk management – New US trading regulations, such as those preventing traders from benefiting from old proprietary trades, are leading to the need for heightened compliance in asset management. Some firms are deploying forensic analytics to monitor traders and cross-check transactions with personal data to uncover instances of misconduct, scanning communications for anomalies or breaches of ethical divides, and building datasets across trading data, external data and personal employee data to increase the flexibility to expand the number of checks or run different scenarios. Asset managers that have implemented these techniques have seen a 55 percent to 85 percent reduction in time spent on trade surveillance activities and, more importantly, improved risk identification.

Kwek adds: ‘In our experience, asset managers that are most successful in harnessing the potential of advanced analytics are the ones that take a very practical approach by focusing on very specific analytics and use-cases that create tangible value for their investment processes. This is not about relying on some sort of unwieldy centralized tech or analytics platform that tries to be all things to all people.

‘Those that are successful in this space also treat analytics as a team sport, ensuring they integrate new data-science talent closely into their investment teams rather than treating it as a separate island of capability.’    

Highlighting this further, the report observes: ‘While some firms are using analytics to enhance productivity of existing practices, others are taking advantage of these new capabilities to ask more fundamental questions about their operating models.

‘While there is still some uncertainty around the extent and pace with which analytics will impact asset management, it is our view that superior analytics capabilities will be a key driver of success in the industry going forward.’

Summing up, Kwek says: ‘The adoption of advanced analytics in investing is still in its early days. To be sure, there are several highly sophisticated quants that have raced ahead to build their entire investment processes around superior analytics and data.

‘A large part of the traditional asset management industry is still in experimentation mode at best. But we expect the adoption of analytics to accelerate among the winners in the industry over the next few years, as a way of generating alpha, ensuring consistency of outcomes and increasing the speed and efficiency of traditional investment processes.’


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