Lately, data science and big data are heavily influencing business decisions in the majority of the industries. The world of financial trading is no exception. The impact of big data in the financial world is not merely a ripple in the pool but is now entrenched in daily operations. The technology is increasing at an unprecedented pace and is large in the scope of its consequences. A study by IBM states that the world is generating around 2.5 quintillion bytes of data. This is the ultimate gold mine for financial traders which is presenting them with enormous opportunity to process, analyze and leverage other critical information to expand profits.
The financial trading industry, primarily, depends on accurate inputs to assist decision making. Traditionally, human investors have been crunching numbers and the decision was based on the insights generated from risk and calculated trends. Today, manual investors have been replaced with computers. Big data and data science techniques are being used to generate successful predictions to drive investments. Machine learning and algorithms are computing vast amounts of data to draw insights which a human does not have a capacity for.
The International Data Corporation (IDC) had predicted in 2016 that sales of big data analytics solutions would reach $187 Billion by 2019. Henceforth, financial services institutions are the prominent markets for data analytics solutions. Let us now understand how big data is changing the face of the financial trading industry.
In the financial sector, it is imperative to study the price and price behavior which is done by technical analysis. The analysis is the heart of any financial trading strategy to map the possibilities and predict outcomes based on the study. Today, technical analysis works on the identification of support and resistance levels, the principle of moving averages, trending nature of prices, monitoring behavior among others. All these aspects drive the need for a more detailed and structure analysis of data, bringing forth the need for technical analysis. Innovations in big data and big data analytics are leading to an increase in the application of technical analysis. This has allowed traders to find more consistency in the numbers, giving them better insights and helping to mitigate risks. Technical analysis has furthered high-frequency trading firms (HFT) to adopt big data analytics. Given the fact that high-frequency trading firms rely on microsecond latency, the application of big data analytics contributes towards increasing processing time of prices which, more often than not, is beyond acceptable metrics. It also aids in finding possible investment opportunity.
Algorithmic trading is an automated process where computer programs can execute financial trades at faster speed and frequency, outperforming human trader. This is becoming a new trend in parallel to big data analytics adoption. Giving real-time analytics, algorithmic trading which is based on the mathematical model, not only trades at best possible prices, but it also eliminates human error caused by behavioral factors. High-frequency trading firms use technical analysis, whereas individual traders use algorithmic trading to delve deeper into powerful insights, helping them make the right decision when it comes to investments. The bright side to algorithmic trading is that it has no limitations, which means that algorithms can be generated with both kinds of data- structured and unstructured. Hence, these algorithms can be used in a plethora of applications like tracking social media activity, generation of stock data etc.
In the financial trading industry, machine learning is the most powerful machine learning and trusted application of big data and data sciences. Machine learning enables computer systems to learn from past mistakes when making decisions using new information. Therefore, machine learning techniques deliver insights at a greater depth which allows traders to deduce new conclusions based on the past result. Hedge-fund trading companies leverage machine learning techniques to identify suspicious trading activities by going through ounces of data. Machine learning takes away the human element- eliminating emotional response to financial situations- and makes the decision solely based on the data and information without any bias or being influenced by external factors. Leveraging big data using machine learning techniques, the prospects of its future applications are insurmountable.
Big data complements financial trading decisions using predictive models. Both Big data and data science play a paramount role in making informed decisions by analyzing massive amounts of data to come to a meaningful conclusion. This big data revolution is drastically impacting the execution of financial transactions and helping traders to maintain a competitive advantage in the trading environment.