Over the last couple of decades, data science and big data have majorly influenced the process of decision-making across all industries. The study of methods for analyzing, extracting information from, or dealing with data volumes that are too large and complex to be handled by standard data-processing application software is referred to as big data.
The world of financial trading has also seen a significant rise in the use of big data and is nowadays entrenched in its daily operations. The market for big data is projected to reach over $14.8 million by 2023 in the banking industry alone.
Since there’s a big amount of data generated each day, this is somewhat of a gold mine for financial traders as they’re provided with excellent opportunities to process, analyze and leverage other vital information and increase profits.
In this article, we look at 3 ways big data is transforming financial trading.
Real-Time Analytics
Something that is currently creating quite the buzz in the financial industry is algorithm trading. Machine learning has made great progress as it has enabled computers to make human-like decisions and managed to execute trades at fast speeds and frequencies that no human could ever be able to achieve. The business archetype can incorporate the best possible prices, traded at specific times and it can reduce the number of manual errors that could end up being caused by behavioral influences.
Real-time analytics can boost the investing power of HFT firms and individuals alike, as they’ll be able to provide better and more complete analysis which has created a leveled playing field with access to the right information.
In addition, real-time analytics has become crucial for bank development as it has the potential to provide major value and direct savings to banks through the reduction of fraud loss and the early detection of suspicious transactions. Financial institutions also apply big data to better understand the needs of their customers and when trying to make more accurate decisions, leading them to be more efficient and prompt in responding to market demands.
Banks are increasingly spending money on data management technologies and many bank trading desks have come to use historical option prices to gain accurate insights and make data-driven decisions when determining an asset’s price movement. Without access to high-quality market and reference data for the market you’re trading, it’s impossible to trade.
Machine Learning
Machine learning is by far one of the most powerful technologies and trusted applications of big data in the financial trading industry. It enables computer systems to learn from past mistakes when making decisions using fresh information. For this reason, machine learning techniques give insights at a greater depth and allow traders to make new conclusions based on past results.
Many hedge fund trading firms leverage machine learning techniques to recognize dubious trading activities by analyzing massive amounts of data. It eliminates human-like emotional responses to certain financial situations and makes decisions only based on data and information without being influenced by external factors.
By using predictive models, big data complements financial trading. Big data and data science play a major role in making informed decisions by analyzing a substantial amount of data to draw a meaningful conclusion. The revolution of big data is radically influencing the execution of financial transactions and assisting traders to maintain a competitive advantage in the trading world.
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Leveraging Big Data Analytics in Financial Models
Financial analytics no longer involves exclusively the examination of prices and price behavior. It also integrates the principles that affect such prices, current social and political trends, as well as the clarification of support and opposition levels.
Big data analytics can be utilized in analytical models to assess the rates of return and potential outcomes on investments made. As there is greater access to big data, more precise predictions can be made which ultimately increases the ability to effectively mitigate the inherent risks associated with financial trading.
Up until now, high-frequency trading has been used rather successfully with machines trading independently. However, the computing timeframe sets this method aside since seconds are literally of the essence with this kind of trade and big data typically means increasing time of processing. However, this is changing as traders recognize the value and importance of precise extrapolations enabled by big data analytics.
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Final Thoughts
There are many ways in which big data impacts how financial trading transactions are carried out. It helps to make swifter and more accurate trades and reduces risks while maximizing the success of trading strategies. However, it’s important to note that big data analytics has its imperfections too but it presents far more benefits than disadvantages to financial trading and has therefore become an inevitable necessity for financial institutions.