Big Data Analytics Has Potential to Massively Disrupt the Stock Market

Big data is having a big effect on the direction of the stock market and helping traders in major ways.

big data in stock market
Shutterstock Photo License - By Phongphan

Big data is changing the nature of the financial industry in countless ways. The market for data analytics in the banking industry alone is expected to be worth $5.4 billion by 2026. However, the impact of big data on the stock market is likely to be even greater.

Automated trading software is fast changing the approach a lot of individuals take to investing. A good example of this, an investment strategy like Fibonacci trading uses the Fibonacci sequence. The strategy is a reflection of nature since it orders the structures in line with the Fibonacci sequence.

Traders have been using this strategy for quite some time. The issue is that traders who would manually work with Fibonacci ratios also had to fight their personal emotions. A strategy based on Fibonacci is an effective one, but then emotions creep in, making investors believe they’ve got a hot hand. They’ll make an alteration to their strategies as a result of errors resulting from emotions. Big data algorithms that understand these principles can use them to forecast the direction of the stock market.

Automatic trading, which hugely relies on artificial intelligence and bots, and trading that operates on machine learning are eliminating the human emotion factor from all this. At the moment, new traders can as well use strategies tailored to assist them in making trades without any bias or irrational moves.


What Impact Is Big Data Having Towards Investing?

Big data is pushing the financial industry, and also have an impact on investing. Huge amounts of data are generated each day since online trading has simplified the job and it’s easier to view the market from your mobile by using an online trading platform or various stock trading applications. New innovations in artificial intelligence, analytics, and machine learning are revolutionizing how well people dealing in the financial industry can determine the impact that data has on the stock market.

For instance, big data is offering logical insights into how a business’s environmental and social impact influences investments. This is vital, mostly for the millennial investors who have appeared to care a lot about the social and environmental effects of their investments than they do about the financial factor. The best thing is that big data is allowing these young investors to make decisions based on non-financial factors without reducing the returns they acquired from their investment.

Investing that relies on social and environmental impact, caused by an individual’s investments – the impact investing – is being steered as a win-win situation. It’s enabling experienced investors as well as millennials to obtain information relating to the social and environmental impact of their investment and invest in a direction that is likely to give lower returns when it’s off period but go above expectations and show some resilience, mostly when there’s a slump in the economy. Many ask the question are smaller companies delivering the best returns?

As time goes by, the benefits of big data will be largely impactful as business activities continue to pose a huge environmental risk and many people begin investing dependent on the impact of these businesses. Companies that fail to consider the environmental and social factors that determine the investing decisions people make will likely face risks they’re not currently thinking about.


How Big Data Is Changing the Type Of Information Under Analysis of the Financial Markets

Data analysis became useful in many industries because acquiring and analyzing data is an essential procedure for any industry.

Financial markets are shifting to data-driven investment strategies. Such models evaluate public companies from an objective vantage point of view. The data they have allows them to have a global picture and then come up with decisions based on economically motivated motifs.

Big data is enabling firms to view huge sets of specific data, like market data prices, returns, volumes, publicly available financial statements, and much more. Non-traditional sources of data like satellite imagery, internet web traffic, and patent filings can be used to compile this. The financial industry can acquire useful information that offers them an upper hand when making investment decisions, by using nuanced and unconventional data.

The target is to get businesses that produce attractive sentiment and have positive valuations. This is not all about numbers alone. The relationship between a firm and a positive theme in the market can be analyzed using big data.


Progress made in computing and analytics has enabled financial experts to analyze data that was impossible to analyze a decade ago. Ten years ago, computers used to focus on analyzing structured data alone. Such data could be easily organized, quantified, or laid out in a certain way.

With the new technologies, it’s possible to analyze data that are difficult to quantify or unstructured data. This enables the markets to view and interpret information from various sources, for example, images, speech as well as languages. Being able to access such kinds of data, together with being able to put together and analyze data fast, has revolutionized the way markets evaluate investment motifs, such as profitability, momentum, and value.

What Technology Infrastructures Are Required to Effectively Analyze Big Data?

Since big data influences the financial system a lot, data storage infrastructures and technologies have been formed to enable the capturing and analyzing of data and come up with real-time decisions. An example is distributed databases. This involves storing data in many platforms unlike where data is stored in one place on a single platform. Distributed databases enable large amounts of data to be processed parallelly and on large scale.

The processing time for many applications is reduced in parallel processing. Being able to store unstructured data has boosted flexibility with onboarding and retrieving data. This is crucial when looking for data from non-traditional sources and while managing large amounts of textual information. This is arguably one of the biggest ways that the stock market is responding to changes in big data.


Data is critical for most financial institution’s business as well as investment patterns. Although most of the data analysis processes are automated, human judgment is still necessary. Profile managers are required to make wise judgments while picking analytics and data put together while investing.

Creating sensible profile positions is the goal here. They must be well scaled and economically intuitive to match the current market conditions.


Big data and analytics are contributing hugely towards investing now than ever before. But, this doesn’t, mean companies have computers making all the trades without human involvement. Indeed, computers will perform some functions better, whereas some aspects of finance need human involvement.


Sean is a freelance writer and big data expert. He loves to write on big data, analytics and predictive analytics.