When you think of industries that are reliant upon statistics, numerical data, and performance reporting, one, in particular, comes to mind; high finance and the stock market. It’s an industry that seems tailor-made for the kinds of big data and predictive analytics tools that have matured so rapidly in recent years. When you add in the rise of artificial intelligence systems that has happened lately as well, you have a match made in heaven.
Putting the Art in State of the Art
While the combination of analytics and stock trading would seem to be an easy one, there’s a catch. The difficulty is that trends in the stock market aren’t based upon simple numerical data, but rather a mixture of financial indicators, public sentiment, and seemingly nonsensical factors. This is the reason that stock trading has resisted automation and remained something of a dark art.
Deep learning and A.I. may be poised to change all of that soon. It will require analyzing disparate data sets including raw performance metrics, social media postings, and historical trends in real time. Since an A.I system can consider far more factors at once than any human could, the end result should be better informed (and thus better performing) investment decisions.
Drawing from Human Sources
For the developers of A.I. systems for investment decisions, acquiring financial data for their algorithms is the easy part. It’s determining which human factors to include, how to weigh them, and where to find them that is the challenge. Fortunately, some of the work is already being done for them. In recognition of the reality that crowdsourced knowledge can boost performance for traditional brokers; a whole range of social trading platforms have attracted the best and brightest brokers and analysts. They share insights and observations daily that a properly-tuned A.I. could turn into data points or actionable intelligence.
There’s also a mountain of unstructured data that could be mined being generated by social media platforms like Twitter and Facebook every second of every day. In fact, many hedge funds are already relying on such data, turning to analytics companies like Dataminr to seek an advantage over their peers. An A.I. system could react to breaking news and global sentiment the moment it occurs, which would beat the curve of market reaction and drive up profits.
The Future is Now
While there hasn’t been a “magic bullet” in artificial intelligence for financial markets yet, there are already examples of some early success stories. Late last year, the A.I. Powered Equity ETF was launched by industry upstart EquBot. Initially, the fund didn’t fare well, but it has since gone on to see significant gains. That’s because the technology behind the fund is analyzing up to one million data points per day, from any and all relevant sources. It is getting smarter, and the results are starting to prove it.
Another early entrant into the race to use A.I. to power investments in an Israeli firm known as I Know First. They’ve built a self-learning predictive analytics engine that is able to discern potential movement in markets with high accuracy. Their technology is interesting because it’s available to the general public, and wrests control of the markets from large institutions. It points to a future market that is centered outside the world of boardrooms and corporate gifting, and inside the living rooms of small-time investors.
If developments continue in the field at the same pace they’ve seen over the last few years, it won’t be long until stock markets worldwide are dominated by automated investing. That could very well signal a whole new era of wealth creation that is accessible to all and will change the lives of millions. For all of the predictions that insist that A.I. will bring about a doomsday scenario, this is one that portends a brighter future for us all.