What’s happening?
In today’s world, we are often overwhelmed with information – and it seems like there is more of it every year. In 2011, the volume of information created by humankind all over the world totaled 1.8 zettabytes. If we were to attempt to transfer all of this information on to CD’s, the stack would be longer than the Moon’s orbit. And there is no end to it: analysts insist that the total amount of data will increase tenfold by 2023. New information provides a plethora of new opportunities, but also creates challenges. Processing large volumes of data becomes even more complicated when one considers the fact that human beings are simply incapable of comprehending certain problems – ones that we will only encounter once we start dealing with these large volumes of data.
What’s the problem?
Take a simple example – the evolution of cinema. Initially, when cinema had just been invented, each movie was unique and worth watching simply because it was new. But as the industry and the number of films grew, viewers became more picky about which films to watch. Films were categorized by genre and age group – fictional films, documentaries, arthouse films, blockbusters. Soon enough, categories such as “arthouse drama” were suddenly no longer enough for viewers looking for specific types of films. Then there were also films ratings and votes that help viewers choose. But then there was another problem: what if a person’s tastes did not fall in line with the majority?
At this point, there was another technological breakthrough. The traditional subsections were replaced with personalized recommendations for individual users – each viewer was now seeing recommendations that were specific to his or her taste. Personalization is no longer a novelty and the film industry is not the only sphere to use it – similar technology is popular across music, advertising and e-commerce sectors. Popular platforms that use various collaborative filtering technologies (that is, technology that predicts what certain users would like based on what similar users enjoy) include Reddit, YouTube, Amazon, Last.fm, Foursquare and others.
Using personalization in the financial sphere
Much like the film industry (and any other industry), the stock market developed gradually. At first, all stocks were government-issued securities – the state had to attract private funds to help cover the gaps in the state budget. In the beginning of the 17th century, private companies (mostly companies like East India Company, which was involved in trade and the exploration of new territories) started to issue securities as well. However, so few companies issued debt that it was not very difficult to figure out how the securities market worked. Soon enough, hundreds of companies followed suit. The stock market started to get exposure in the media. At the time, simply reading The Wall Street Journal was enough to keep abreast of stock market trends. Years went by and the market was flooded with derivatives, hybrid instruments, ETFs, structured notes and dozens of different kinds of bonds. Information on existing stocks turned into a giant database, which was stored, processed and analyzed by companies like Bloomberg and Reuters. Their online directories significantly reduced the workload of investors: all one had to do is click a ticker and get quotes, news on related topics, analyst opinions and much more.
However, the next step – personalization – has yet to be taken. Traders can look at information on any security, but only if he or she knows that it exists and may be worth looking into. This is how screeners work – they can help investors find the stocks of companies from a particular industry, companies that pay the highest dividends and so on. However, such searches are similar to non-specific Google searches: if you need information about a fire that happened in a specific area of the city in a specific month and year, you likely won’t find what you need by typing in “fire in New York.” If you have no further details of the event, you probably won’t find what you’re looking for.
Is there a solution?
It is obvious that the investment sector needs personalization. Along with the increase in information volumes, there are also more opportunities to make money – opportunities that investors can’t take advantage of simply because the right tools are not widely available yet.
But there are already algorithms on the market that can be called “personal financial advisors” – advisors that process colossal amounts of information that would be inaccessible to the human brain. Our main task today is to give these algorithms a convenient, user-friendly interface – to package them into the right format and bring them to market.
One example of a product that helps resolve these challenges is Portfolioand.Me (http://portfolioand.me/) – a website for working with stocks that uses an algorithm based on Artificial Intelligence. Unlike screeners, which are limited to searching for stock based on a set of criteria, this site also suggests stocks that the user may be interested in as well as gives recommendations on how to improve the user’s existing portfolio. The widespread use of products such as this one will indicate that the financial market has entered a new stage of evolution – data personalization.