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How is Big Data revolutionizing Trading?

Samiksha Paria
Mar 12, 2022

There has been quite a splash when it comes to the influence of Big Data in FinTech. Technology is advancing at an exponential rate, with far-reaching repercussions. Increasing complexity and data production are changing the way companies work, and the financial industry is no exception.

Automatic trading, which heavily depends on AI and bots, and trading based on machine learning remove the human emotion aspect from the equation. At the present, inexperienced traders can also employ tactics designed to help them make trades without bias or illogical swings.

Big Data in Banking and Finance

Innumerable ways, big data is altering the banking business. The banking industry's data analytics market alone is anticipated to be worth $5.4 billion by 2026. The influence of big data on the stock market, on the other hand, is likely to be considerably stronger.

Currently, the world generates 2.5 quintillion bytes of data every day, representing a once-in-a-lifetime potential for processing, analyzing, and using the information in productive ways.

Machine learning and algorithms are increasingly being utilized in financial trading to process massive amounts of data and make predictions and judgments that people just cannot. Financial institutions are looking for innovative methods to harness technology to enhance efficiency in the face of rising competition, regulatory limits, and client demands.

Also Read | Big Data Analytics

What is Algorithm Trading?

Earlier in the 1980s, the New York Stock Exchange employed programme trading, with arbitrage traders pre-programming orders to automatically trade when the S&P 500's future and index prices were far off.

As markets became totally computerized, human presence on the trading floor became obsolete, and the development of high frequency traders occurred. A subset of algo traders evolved with a speed and latency advantage in their trading software, allowing them to respond to order flows more quickly.

Algorithm trading has grown in popularity as a result of the use of computer and communication technology. Algorithm trading involves the use of computer programmes to enter trading orders, with the computer programmes deciding on practically every element of the transaction, such as the time, price, and amount of the order, and so on.

How can you leverage Big Data in Trading?

Accurate inputs into company decision-making models are critical in finance and trade. Traditionally, people analyzed the statistics and made judgments based on conclusions taken from assessed risks and trends.

Computers have taken over this function today. They can calculate on a vast scale and gather data from a wide range of sources to arrive at more precise results practically instantly.

There are several ways in which you can do so.

The Use of Big Data Analytics in Financial Models

Financial analytics is more of the study of pricing and price behavior; it now includes the principles that trade costs, socio political trends, and the identification of degrees of support and resistance.

Big data analytics may be utilized in prediction models to anticipate rates of return and likely investment outcomes. Increased access to big data leads to more exact predictions and, like a consequence, the capacity to more efficiently offset the inherent dangers of stock markets.

Analytics in Real Time

At the moment, the term "algorithmic trading" is all the rage in finance. Machine learning allows computers to make human-like judgements and execute transactions at speeds and frequencies that humans cannot. The business archetype integrates the greatest potential prices that are exchanged at certain periods and avoids manual mistakes caused by behavioral factors.

Real-time analytics has the ability to boost the investing power of both HFT corporations and individuals, since the insights gained through algorithmic analysis have leveled the playing field and provided everyone with access to strong information.

Application of Machine Learning

The entire concept of internet of things has yet to be realised, and the possibilities for application of these advancements are limitless. Machine learning allows computers to learn and make judgments based on new information by learning from previous mistakes and applying logic.

As a result, these strategies can provide extremely precise impressions. Although the technology is still in its early stages, the potential is exciting. This line of study eliminates the model's human emotional reaction and makes conclusions based on data without prejudice.

Watch this video to learn about the role of Big Data Analytics in Trading

However, the mentality is shifting as traders see the importance and benefits of correct extrapolations enabled by big data analytics. The strength of algorithmic trading is found in its nearly infinite capacities.

Social media, financial market information, and news analysis may all be leveraged to make intuitive decisions using organized and unstructured data. Computers have a lot of potential to take over this industry in the near future. Big data enables more information to be fed into a system that lives on knowing all potential influences.

Also Read | Insider Trading

What are the other impacts Big Data is having on Trading?

Big data is propelling the financial industry and has an influence on investment. Huge volumes of data are created every day since internet trading has made the work simpler, and it is now possible to observe the market from your mobile device by utilizing an online trading platform or numerous stock trading programmes.

Big data, for example, provides logical insights into how a company's environmental and social effect drives investment decisions. This is critical, particularly for millennial investors, who tend to be more concerned with the social and environmental consequences of their investments than with the financial aspect.

Effect investing, which is based on the environmental consequences of a person's assets, is being promoted as a win scenario.

Also Read | Effective Trading Principles

How is Big Data Changing the Financial Landscape?

Because gathering and evaluating data is a crucial activity for each organization, data analysis has become valuable in many sectors.

The stock markets are transitioning to data-driven investing techniques. Such models assess public corporations from an objective standpoint. They may create a global picture using the facts they have and then make judgments based on economically motivated themes.

Computers used to be solely focused on interpreting structured data ten years ago. Such information might be readily sorted, measured, or set forth in a certain manner.

With modern tools, it is feasible to evaluate tough or unstructured data. This allows markets to observe and analyze information from a variety of sources, including pictures, audio, and dialects.

Data is crucial for the majority of financial institutions' operations as well as investing habits. Although the majority of data analysis operations are automated, human judgment is still required.

Because Big Data has a significant impact on the financial system, data storage infrastructures and technologies have been developed to enable data capture and analysis in order to make real-time decisions.

Distributed databases are one example. This entails storing data across several platforms, as opposed to keeping data in a single location on a single platform. Vast volumes of data may be handled in parallel and on a large scale using distributed databases.

Also Read | Stock Market Analysis

Conclusion

“Big Data will spell the death of customer segmentation and force the marketer to understand each customer as an individual within 18 months or risk being left in the dust.”- Ginni Rometty, CEO, IBM

Big data analytics are currently making a greater contribution to investing than ever before. However, this does not imply that businesses have machines doing all trades without human intervention. Certainly, technology will execute some activities better than humans, yet some areas of finance will require human intervention.

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