Big data is transforming many aspects of the way businesses are run across every industry nowadays - and financial trading is definitely not an exception. In fact, there are a number of ways that it can be used to gain efficiency, cut potential for risks, and to make more accurate predictions before committing to a trade.
Here are six of the most notable ways you can use big data for more successful trading.
The Rise of Big Data
While big data has been around since the 90s, it's been gaining much more traction in the last few years thanks to an increasing amount of data becoming freely available and advancements in the technology surrounding it. The term big data itself basically refers to datasets that are so large in size that they can't be processed using more traditional programs.
The data contained can be either structured (i.e. numerical), or unstructured (i.e. images or social media posts), and encompasses four factors:
Volume - How much information is stored in the data set.
Velocity - How quickly the processing of the data is achieved.
Veracity - The quality of the data itself; incredibly important for achieving more accurate insights.
Variety - The type of information it contains, allowing the analysts to decide how it can be used.
Using Big Data in Financial Trading
Whether you're using your own internal data or big data from third-party resources like business information companies, there are multiple ways you can put it to good use in the financial trading sphere. These include...
This involves monitoring prices and their behaviour in order to work out how much of a return you'll get and also make predictions about possible outcomes. Graphs are usually used to map everything out, and factors such as price trends, moving averages, and support and resistance levels are all taken into account.
The rise of big data has meant that the technology needed for technical analysis has become more advanced, and this, alongside the fact there is more data to work with, means that it is able to produce very accurate results, allowing traders to significantly reduce their risks of investing in a bad trade.
Nowadays it's fair to say that big data and algorithmic trading go hand in hand. The process allows for the use of computers to carry out trades at rapid speeds that are much much quicker than humans would be able to do. Not only is this more efficient, but the use of computers also means there's no room for human error or human emotion to make an impact on the trade.
These algorithms can not only be used for structured data, but also things like social media posts and up-to-the-second news, allowing trades to be made based on the most current trends and changes to the market or wider economy.
Big data is also changing financial trading through its role in the advancement of machine learning. This type of technology involves computers being able to learn for themselves based on the data they process and the outcomes achieved, along with any mistakes that are made.
While it's still impossible for a computer to make predictions that are 100% correct, the use of these advanced programs in financial trading means that traders can base their choices on more data than ever before, in a more efficient way.
Backtesting involves testing a strategy for a trade using historical data. It can be used in the case of both manual or automated trades, and provided enough data is used alongside a relevant time period, it can add to the possibility of a potential trade meeting an individual's goals.
The process uses complex analytics to run through numerous strategies until one with the wanted result is found. Due to the complexity of the process and the advanced technology required, firms might need to outsource the activity to a business partner who specialises in the field.
Big data is also revolutionising the way that organisations are marketing to potential customers. It offers opportunities for targeting prospects that more traditional methods do not, and allows for much greater personalisation thanks to the wide range of insights available. This could include things like financials, company structure and ownership, technologies used and web traffic, allowing you to tailor your approach completely to each segment or individual account.
Much of the legwork behind these insights is now taken care of for you, too; if a firm is expanding into the Spanish market and wants to look up prospects or carry out background checks before a trade, they now only have to use a business directory; Spain company information and contacts are just a few clicks away.
Financial trading firms can tap into big data to help them identify the types of products and services that their target audience are likely to need, or to redevelop their existing offerings to make their portfolio more appealing and gain more revenue.
They can also use data to provide a much more personalised experience for each customer. When you have a clear picture of your client's situation, you'll be much more likely to accurately predict their future needs, as well as any issues that are likely to spring up. Unstructured data such as that from social media can come in very useful here and ultimately help you build a picture of how you can meet the person's needs.
This personalised customer service can even extend to things like automated phone systems. For example, by addressing the client by name or providing them with numbered options that are specific to the products they've purchased, or any potential issues that have been flagged for them.
As we've explored in this article, big data has the potential to be a very powerful asset for financial trading. With the technologies surrounding big data being improved and advanced every day, staying up to date with the processes involved will be vital in order to have the greatest chance at success.