Big data analysis is generally designed to take advantage of large global trends. Through big data, financial analysts are able to look for patterns over very long periods of time. But how does big data and financial analysis react to large world view events such as Brexit?
Technical Investing and World News Trading
There are two competing strategies within the finance and investment industry — though many traders will shift between them as necessary. Technical investing is the type of investing that most people are familiar with: it requires an intense study of financial charts to find indicators that a price could go up or down. Technical investing is an area in which big data shines; big data is able to collect large numbers of seemingly disparate information to connect the dots, such as discovering that when oil prices go down a certain stock price goes up (at its simplest). World news trading is another simpler strategy; it involves trading based on the potential consequences of things that are about to occur.
World news trading is generally a riskier way to trade, as the investor needs to decide exactly to what extent the news will affect the market and what areas of the market it will impact. In the case of Brexit, the entire stock market crashed — but only temporarily. And during that time it’s likely that most technical analysis would have been too volatile to be used. But just because there isn’t a technical analysis associated with world events doesn’t mean that big data cannot contribute during these events.
Sentiment Analysis and Investing
Sentiment analysis takes information from the Internet — often from the most reputable news resources and financial guides — and determines the sentiment throughout. Sentiment analysis is fairly new because it requires advanced techniques such as artificial intelligence and machine learning. Through sentiment analysis, big data can be used to determine whether investors are feeling confident, uncertain, scared, or worried, during specific trades or about specific things. Sentiment analysis could determine, for instance, that residents of Scotland were unhappy, or that residents of the United Kingdom were concerned. This opens the door for an entirely new type of technical analysis through big data that could anticipate many global events — or at least react to them very quickly. When paired with a large social media platform such as Twitter, big data could be used to identify global events faster than most news cycles.
Sentiment analysis and artificial intelligence are still on the bleeding edge of financial technology. Nevertheless, they are steadily becoming more advanced and are likely to enter into the arsenal of tools that many leading analysts are using. Sentiment analysis will be able to react to global changes very quickly, thereby helping investors capitalize on the consequences of world events such as Brexit.