Big Data In Investing: The Quants Have Taken Over
One of the reasons why many never really get into trading stocks and shares is because it’s complicated. The process and concept itself are relatively easy, each share has a value which goes up or down depending on market conditions, but in a world where there are tens of thousands of companies to invest in, how is it possible to ever know what the market is saying?
People like Warren Buffett seemed to have an innate understanding of where the market is at any one point, but in reality, there is no way for a human being to know if a company is going to announce something big or have a negative incident shared across the internet. For instance, in early April United Airlines saw their share price take a hit thanks to a video appearing on social media showing a passenger being forcibly removed from a flight. For those tracking the markets, a video appearing on Facebook is not within any kind of regular trading algorithm’s remit, but through an increasing use of big data, companies can include data from a huge range of places.
June 2015 marked a big shift in this direction, with Thomson Reuters reporting that it was at this point that they had more customers who were machines than humans. The financial information service was, therefore, providing more information to be fed directly into algorithms than for humans to make their own informed decisions. We are likely to see this trend continue. With financial services and banks regularly amongst the least trusted institutions in the world thanks to headlines surrounding scandals like the rigging of the libor rate and films portraying true stories of recklessness like The Wolf of Wall Street and The Big Short, it seems prudent to make these decisions based on more than just gut feeling alone.
The spread of big data and the speed in which changes can occur to share price based on a huge number of factors is perfectly suited to big data, AI, and machine learning. We have seen how Tweets from specific people can impact share people and net or lose people millions, such as Donald Trump’s tweet criticized Toyota saw the car maker lose $1.2 billion in value. It has led to some journalists from NPR even creating algorithms based purely on the relatively erratic public statements of the president, to see if they can take advantage of this phenomena. However, if somebody else with fewer followers were to tweet that they were unhappy with their Toyota, it is unlikely to have any impact on the company’s share price.
However, as we saw with the United video, which was posted by an innocuous person with a tiny fraction of the influence of Trump, viral posts can come from anywhere and have equally damaging effects. Data technologies allow people to know about a post potentially going viral about a company before a journalist has a chance to spread it to the world and create the kind of damage seen by United in early April.
