The information age (the period starting in the year 2000) could not have gotten off to a better start.
Digital platforms have become pivotal in gathering market information including consumer behavior and new shopping trends. Alphabet Inc. (GOOG, Financial)(GOOGL, Financial)'s Google has become smarter, Facebook Inc. (FB, Financial) and Twitter Inc. (TWTR, Financial) have disrupted social media and Amazon.com Inc. (AMZN, Financial)'s shopping search engine has grown to top Google search.
To capitalize on the data obtained from users of the internet including from the platforms mentioned above, businesses have employed the use of big data tools and artificial intelligence to convert the data into useful information. For some years, this practice appeared to be limited to business operations with companies using big data analytics to gain insights on consumer trends and how their products are doing in the market
However, over the last few years, the use of big data in the market appears to have grown and is now cutting across all disciplines including investment management.
Investment managers are using AI and big data to gain market and client insights.
According to a report published by PwC Canada, “Investors are increasingly bolstering their analysis by analyzing market variables such as trade volumes, price ratios, and momentum against other unstructured variables, like social media presence and political volatility.” This is revolutionizing the traditional concepts of investing.
For several years, investors relied on financial statements to analyze the market. But over the last decade or so, many have embraced big data and business analytics tools to analyze the market. Today, more investors are taking advantage of the vast amounts of publicly available data that is helping in targeting investment opportunities based on risk, industry type and holding period.
In the investment management space, fund managers are also using AI and machine learning to better assess the risk tolerance and investment suitability of their clients. The firms have invested in intuitive systems that help them to gain insights about their clients, as well as improve equity research and decision making.
Research also indicates that about 20% of investment managers have deployed a Robotic Process Automation system, which improves trade execution and efficiencies across client servicing, data management and operational support.
Another benefit that fund managers have yielded after deploying AI and big data analytics tools in their processes is an improvement in compliance with various operational requirements like speed and security.
Some of the notable asset managers that have already embraced AI and big data include BlackRock Inc. (BLK, Financial), U.K.-based Schroders PLC (LSE:SDRC), JPMorgan Chase (JPM, Financial) and Bank of New York Mellon (BK, Financial), among others.
The world’s largest asset manager Blackrock, with a market cap of $67 billion, announced early this year that it was setting up a new center for research into AI. The company wants to leverage AI, big data and other technologies that it has invested in over the last few years to improve fund performance while at the same time helping to cut costs.
Schroders and BNY Mellon are also among a host of fund managers looking to take advantage of this developing trend in investment management. According to a report published by Standard and Poor’s last year, 80% of asset managers planned to increase their investment in big data in 2018 while only 6% of asset managers surveyed argued that big data wasn’t central to their operational plans.
Financial regulatory bodies in various countries have also adopted big data to their systems as they seek to identify strategic trends in the market, as well as, trying to monitor market developments and emerging risks at an early stage. For instance, according to a recent report on the impact of big data on investment management Germany’s Federal Financial Supervisory Authority, BaFin partnered with the Boston Consulting Group (BCG) to conduct a study that helps to highlights the implications of technology-driven market developments from various regulatory and supervisory perspectives. Used together with technologies like machine learning, financial regulators and asset managers can use big data analytics to detect and prevent fraud in the market.
Reports show that dynamic AI “could be used to track and monitor investors’ or clients’ transactions, and their patterns of subscriptions and redemptions.” Gaining such insights can provide the asset manager or a financial regulator to detect outliers to the set standards, which in turn can help to trigger red flags automatically notifying the relevant parties.
Recent reports indicate that fund managers are increasingly usng insights from social media feeds, satellite data and credit card data. Others have deployed analytics tools that are helping them to perform analysis of traffic through corporate websites, text analysis of earnings calls transcripts, as well as looking at smartphone geolocation data to see where people are shopping in a bid to improving investment performance. Clearly, the applications of big data in investment management appear to be vast and there could be more in the coming years.
Disclosure: I have no positions in the stocks mentioned in this article.
