How Online Stock Trading is Being Impacted by Big Data

big data analytics for trading data
Shutterstock Licensed Photo - By Robert Kneschke

The widely adopted use of big data has redefined the landscape of many competitive industries, one of which is online stock market trading. Today, roughly 89% of all businesses believe in using analytics strategies to gain a competitive advantage in the market. This is especially true where larger corporations and organizations are using analytics and data to gain valuable insights and make better, well-informed business decisions. We are now seeing the use of big data in 4 major industries – technology, marketing, healthcare, and financial services.

Big Data Analytics and the Financial Services Industry

Big data analytics have been widely adopted in the financial services industry and is helping online traders make better investment decisions and seeing consistent returns in the process. Consequently, with the rapid changes occurring in stock market data, it has allowed investors quick and easy access to big data. Algorithmic trading now uses a great deal of historical data in conjunction with big data and complex mathematical formulas to help investors maximize the returns in their investment portfolios.

In the past, we crunched numbers and made decisions based on the implications that were drawn from market trends and calculated risks. Today, we use computers to do this on a massive scale and rely on a multitude of resources to reach more accurate conclusions. As a result, inputted data plays a significant role in influencing online trading decisions. A perfect example of one of these resources is Investors Hangout, a prime source of large amounts of data that has been input by investors.

The Changing Online Trading Landscape

In the online trading world, we are seeing an increased use of algorithms and machine learning to compute big data to make decisions and speculations about certain stocks that the average human mind does not have the capacity to handle. There are three different ways in which online trading is being influenced by big data:

To improve online trading by leveling the playing field – the current buzzword in the financial world is “algorithmic trading.”  Machine learning enables computers to make decisions that human beings would otherwise make while executing trades, but at much more rapid frequencies and speeds. With real-time analytics, you have the potential to improve the investment power of individuals as well as high-frequency trading firms.

To estimate probable outcomes and return rates on investments – financial analytics involves integrating principles that impact political, commodity pricing, and social trends. It is no longer about the examination of price behavior and pricing. Furthermore, increased big data access effectively mitigates the inherent risks of online trading and making more precise predictions.

To enhance machine learning and deliver accurate perceptions – we have yet to realize the full potential of machine learning technology. Consequently, the prospects for such applications cannot be measured. However, machine learning can help computers learn and make better decisions based on newer, updated information by employing logic and learning from our past mistakes. In so doing, more accurate perceptions can be delivered by utilizing these techniques. Although this technology is still in its developmental stages, the possibilities look very promising.

In conclusion, everyone from massive financial management firms to the weekend warrior can leverage big data to improve their investment performance. The more information you have at your fingerprints, the better you’ll be able to time the market and better execute trades. And smarter trades equal more profit for traders.

Ryan Kh is an experienced blogger, digital content & social marketer. Founder of Catalyst For Business and contributor to search giants like Yahoo Finance, MSN. He is passionate about covering topics like big data, business intelligence, startups & entrepreneurship. Email: