Big Data brings in analytical revolution in financial markets

big data and analytics revolution

Big data is bringing in an era of revolution across sectors and financial markets are no exemption. Here we are not just talking about the FinTech applications used in banks and financial institutions, but the actual and dynamic financial markets across the globe. These markets are the playing field where individuals, government and individual investors buy and sell stocks, bonds, debentures, currencies, derivatives and much more. The motive? Earning Profits. Not so long ago, trading was dominated by human insights, sentiments and information available in the public domain. However, with about 2.5 quintillion bytes of data being generated on a regular basis, there is immense scope for processing, analysing and using this treasure trove in more constructive ways.

In financial markets across the world, Machine Learning and Algorithms are being used to process huge amount of data, make predictions and decisions that are beyond human perception. The quantity and complexity of data in today’s world is the reason behind increasing dependence of Big Data in finance trading.

Trading in the stock market requires accurate data, an estimate of risk and trend analysis for making the best possible business decisions. Usually, a lot of this data inputs, inferences and order punching was done by human traders. However, computers can now do this work on a larger scale with more accurate outputs.

The major ways in which Big data has become an inseparable part of financial markets are:

Executing large sets of order with accuracy and speed

Earlier traders only used to focus on price and its behaviour based on changes in economic and political news or human sentiment. However, with so much data available, algorithms can now be used to build predictive models on the basis of inherent principles which govern the movement and behaviour of prices. Not just this, these algorithms can also predict the Return on Investment (RoI) more accurately. The speed with which human traders used to execute orders was a challenging factor but with High-frequency Trading, a large number of orders are executed in a manner of nano seconds. This automated trading platform is used by large investment banks or institutional investors.

Real-time analytics for risk mitigation

Financial trading is subject to many kinds of operational risks, such as security or technological risks. Increasing amount of fraudulent activities and security breaches demand real-time responses that can limit the potential damage caused by them in a timely manner. These threats if not handled effectively can have tarnished the brand reputation immensely and lead to a lot of financial losses as well. Thankfully, technology has been able to bring out innovative solutions that analyse real-time patterns, identify potential threats or anomalies in trading pattern using behaviour modelling and troubleshoot issues to the management for timely action. Financial trading can yield best results when the dealings are hedged against technology, security and financial risks.

real time analysis

Source: Cognizant

Predicting volatility in the markets

Share trading and volatility go hand in hand. When markets become volatile, it becomes impossible for even the best of investors to take the correct decision. Asset prices fluctuate and trading volumes become thin. Because of the availability of vast amount of data, predictive models can be built which studies historical price patterns using time-series. These models help to determine the next phase of stock market volatility and also the volatility quotient. This is measured with the standard deviation of the asset prices over a period of time. Popular regression models such as ARCH and General ARCH are used in the predictive analysis of market volatility.

Machine Learning

The highest possible application of Big data is in Machine Learning. Algorithms are continuously fed in the system and the machines get smarter with each mistake it makes. Gradually machines are equipping themselves with more logic to decipher better conclusions based on past results and using better techniques to leverage innumerable other factors. Today, every decision made by a trader or investor is based on millions of data points. These data points are processed and analysed with great details which increases chances of accurate results and better RoI.

Conclusion:

Behaviour-based financial model, Real-time Analytics and Machine Learning is just the beginning, Big Data is here to stay in the world of financial trading. In the coming years, Big Data will be the most dependable tool for accurate and informed decision-making in financial trading. The information fed into the system receives proper direction from expert knowledge. Traders can now smoothly transition from manual trading strategies to strategies based on quantitative analysis and large size mathematical computations. Which trader doesn’t want accurate predictions, maximum returns and complete hedge against operational risk? It goes without saying Big Data will become an indispensable part of financial markets across the globe.