How big data is reshaping investment opportunities
Big data continues to drive market’s competitive landscape because the information is more accurate, based on bigger but also more refined and intuitive data sets. As a result of big data, investment insights have evolved because there is an improved access to better information which means more accurate, favourable decisions that achieve consistent returns.
BlackRock investment giant actually claims that data and information now fuels their business more than money, supporting that data is the most valuable commodity available in the world today. There are a billion data points generated ever single day and this is how it is reshaping the landscape of investments:
Human investors have to compete with algorithmic trading
Algorithmic trading is an action performed by a computer using mathematical models to complete a trade. These computer systems are reported to minimise manual errors, but they are also optimised to maximise investments, making trades at the best possible prices, with timely trade placements. They operate as an antidote to human investment decisions, which are so often based on behavioural and emotional factors that can make the move more risky.
Algorithmic trading systems are able to react quicker to potential investment opportunities, especially when compared to a manual trader. They maintain competitive edge by acting quickly, yet they do require some unstructured data. This means that algorithmic trading, although more accurate than manual decisions, are still a risk and a gamble, and not yet a guarantee.
Unstructured data is a pillar when it comes to algorithmic trading. If this is misinterpreted, it can put a lot of cash at risk, with many individuals suffering from an ill-informed trade. Many experts believe that this advanced process currently appears to deliver better results because there has not been enough time for errors to occur. Big data’s impact on trading means that these systems are often used as a research tool, and then authorised by a human trader.
Financial services and big data
Despite the speed at which algorithms can adapt, there are some challenges that those who utilise big data will have to address to ensure they are always working optimally and legally. These challenges are most present in the finance sector.
The analytical algorithms that interpret big data will need to be adjusted for regulatory updates. For example, changes to the payday loan industry means that there are fewer people who are eligible for the loan. This means that investment opportunities have been drastically impacted and algorithms will need to be updated to include new parameters.
One of the other big issues when it comes to big data, as the data sets grow, so does the appeal to hackers. As already established, data is the most valuable commodity in the world, overtaking oil in 2017. This means that it’s a prime target for hackers. This presents an issue both on the in-house business level and for investors.
Investors should ensure they are financing a company that has proven security and are mitigating the risk to data breaches and continuously updating their security processes.
