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Top 3 Ways in which Big Data and Data Science Change Financial Trading

Big data and data science have now emerged as the leading influencers of business decisions in almost every major industry, and the world of financial trading is not behind. According to IBM, as much as 2.5 quintillion bytes of data is globally generated every day, thus providing financial traders with more opportunities for processing, analyzing, and leveraging various information segments to create meaningful profits. The information boom brought about by big data has also boosted the need for a variety of data science courses that train professionals in deriving relevant insights.

Financial trading has always been dependent on accurate inputs to generate profitable business decision-making models. Previously, numbers were crunched by human investors, and financial decisions were based on insights drawn from calculated trends and risks. However, computers have now replaced manual investors. Consequently, big data and data science techniques are being increasingly used in financial trading to generate successful predictions and influence investment decisions. Computations are now done on a massive scale, and inferences are drawn from a multitude of sources to accurately form instantaneous conclusions.

Among their multitudes of applications, here are the top three ways in which big data and data science are changing the scenario of financial trading:

1. Technical Analysis:

Technical analysis uses financial charts as the primary tool to study prices and price behavior. It lies at the heart of any financial trading strategy and is used to map the most likely probabilities and rates of return that would result from specific outcomes. However, modern-day technical analysis works on the principles of moving averages, identification of support and resistance levels, prices discounting of all known information, trending nature of prices, and volume mirroring changes in price. This drives the need for a more detailed and structured analysis of data, and this is where technical analysis comes into the picture.

Consequently, innovations in big data have led to a gradual increase in the application of technical analysis. This has resulted in traders finding more consistency in their numbers, which helps them to further mitigate their risks. Technical analysis has also led high-frequency trading (HFT) firms to join the industry of big data and analytics. Since most HFT firms rely on factors like microsecond latency, big data contributes significantly towards increasing processing time of prices even out of acceptable metrics. Further, the technical analysis also aids HFT firms to manipulate data to find any possible investment potential.

2. Algorithmic Trading:

Algorithmic trading, which is an automated process that enables computer programs to execute financial trades at speeds and frequencies that far outperform a human trader, is a new trend that is lately becoming synonymous with big data. With real-time analytics, algorithmic trading can not only use mathematical models to execute trades at best possible prices but also ensure timely trade placement while reducing human errors due to behavioral factors. While on one hand technical analysis is a favorite of HFT firms, algorithmic trading is used by individual investors to gain access to the same powerful insights that are derived from larger organizations using technical analyses.

The main game-changer about algorithmic trading is that it has virtually zero limitations, that is, algorithms can be generated with both structured and unstructured data. Thus, these algorithms can also be incorporated in a variety of applications like the generation of stock data, social media activity, and real-time news. These applications can thus be further used to take situational factors into account to make intuitive decisions.

3. Machine Learning:

When it comes to financial trading, machine learning is by far the most powerful application of big data and data science. It is based on a simple logic that enables computer systems to learn from past mistakes while making decisions based on new information. Thus, machine learning techniques can deliver extremely accurate perceptions. In financial trading, machine learning tools continuously feed algorithms with data and then learn from past mistakes to get smarter over time. These machine learning powered algorithms then logically deduce new conclusions based on past results and use thousands of unique factors to create new trading techniques. For instance, machine learning is used by hedge-fund trading companies to identify suspicious trading activities. Machine learning algorithms can be used to identify such activity in an automated manner by going through years of trading data.

The entire concept of machine learning revolves around removing human emotional response from the financial model to ultimately make decisions inspired solely by the information, without any external bias. While the complete potential of machine learning has still not been realized, the prospects of its future applications are innumerable.

Big data and data science have an inordinate potential of taking over the sector of financial trading in the approaching future. While big data allows massive amounts of information to be fed into a computer system, data science comes into play to derive meaningful insights from these information blocks. The analytical revolution brought about by data science will thus make it possible for financial traders to trade, thus dynamically impacting the execution of financial transactions.

While big data has also reformed the framework of various industries, from automobile to education, the financial sector is also actively dedicated to adopting data science to maintain its competitive advantage in the trading environment.



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