Is Cognitive Automation The Future Of Stock Market Investing?
The U.S. cash equities trading desk at Goldman Sachs’s New York headquarters employs two traders. Their job is to buy and sell stock on the orders of the investment bank’s biggest clients.
If that seems a small amount of employees for such a large responsibility, it is; at the turn of the millennium, it would have taken 600 people to do the job they do.
Automated trading systems (operating 24/7 and without bias) have since taken on most of the workload, and their phenomenal success has opened the door for automation across the entire finance sector as institutions realize the competitive edge and agility artificial intelligence affords can be applied further afield, to automate workflows in fields as diverse as investment management, customer service, regulatory compliance and back-office operations.
The premise of cognitive automation is straightforward: By automating simple but time-consuming tasks, financial institutions can free employees to focus on more complex and value-added work.
Whilst being one of the earliest aspects of finance to integrate automation capability, stock market investing – specifically, the research that informs investments – still contains significant unrealized potential.
Day after day, hedge fund workers and other investing professionals spend a crazy percentage (34%) of work hours trekking an endless paper trail, going through mountains of analyst’s reports, regulatory filings, news clips and social media. It’s not time wasted but it is time that should be better spent; in decision-making, creativity, or judgment; tasks with higher ROI than researching.
Investors – or the creativity and judgment their acumen enables – are an investment firm’s most valuable resource. So why are they stuck doing tedious jobs that don’t utilize the part of their brain that makes them valuable?
The answer to that question is a mess: most firms are incredibly fragmented and unorganized. They require humans to sift through all the detritus they and the external world produce, and put it together in a productive way. This means that, for many investing firms, keeping up with – and exploiting – the symbiotic relationship between news and stock prices is hindered by the hugely prohibitive cost of manually sourcing and synthesizing the information relevant to each trade.
Other firms, however, are looking to Cognitive Automation, which enables the intelligent summary and organization of unstructured information from an unprecedented breadth of sources, to plug the manual research-driven resource drain.
Millions of articles, reports and social media containing financial news and opinion can now be transfigured into the few critical bullet points that ultimately dictate the outcome of a buy or sell decision. This information can then be collated along portfolios, with messages prioritized as per their impact on investing decisions, increasing the efficiency and intelligence of investors and advisors.
Cognitive automation is much more powerful than the rules-based algorithms used in high frequency trading, which respond to pre-defined and predictable news like the release of statistics by a central bank. And, rather than replacing humans, cognitive automation works alongside them, allowing investors to make more informed decisions at a faster rate.
This same technology is already being leveraged to disrupt multiple facets of financial enterprise: cognitive chatbots are interacting with customers, giving them better, quicker answers; in the back office, qualitative information, especially that mined from social media, is being folded in with quantitative information to achieve far superior risk analytics; and legal professionals are becoming disruptively productive by outsourcing their research to cognitive assistants.
In a survey of Coseer’s customers engaged with equity investments, research showed cognitive automation of trading alerts saves between two hours and two hours fifty minutes every day. To reiterate: the real value is that investors can occupy this freed time more strategically, making better investment decisions built on greater wells of data.
In the nascent market of cognitive automation, two distinct models are emerging. IBM Watson takes an investment of time and money worthy of a supercomputer before combining text, speech, visual signals and others to solve foundational challenges. Coseer takes a more tactical approach, honing in on text-based workflows, going after specific problems like investment research and achieving 98% accuracy within weeks. To learn more about which approach is right for your organization, see for yourself how cognitive automation can unlock the human capital in your investment firm, request a meeting with our team.