Through big data analytics, financial trading becomes speedier compared to traditional methods.

The world is data-hungry. As more data is getting generated, more opportunities are opening up for the industry. To accumulate, analyze and process the data is known as big data analytics, a process which is heavily leveraged by different sectors of the industry. Big data analytics processes fundamentally large and unorganized datasets, which is not possible for traditional software. The global market size of big data size is expected to grow from US$138.9 billion in 2020 to US$229.4 billion at CAGR of 10.6%.

Moreover, since it generates powerful insights, it is proving lucrative for the sector which relies on altering customer behavior. As every sector is gaining enlightened with big data analytics, the financial services sector is not exempt. A study by IBM finds that the world is generating close to 2.5 quintillion bytes of data every day. This exponential data is sufficient for financial traders who analyze the datasets and extracts information. The global Trade Finance Market is expected to grow from US$63.540 billion in 2019 to US$79.410 billion by 2026, at a CAGR of 3.2%.

As financial trading is governed by the computational power of algorithms, the big data analytics will ensure extracting accurate insights so that analysts and traders can take the smart decision. This article focuses on how big data analytics can benefit financial trading services.

Algorithmic Trading

Most individual traders leverage algorithmic trading to buy and sell assets. Algorithmic trading involves the computational process through algorithms. This generates profits at a speed and frequency which exceeds human capacity. By integrating big data analytics into algorithmic trading, individual traders can extract powerful insights. Moreover, since algorithmic trading doesn't have any limitations, it can utilize both the structured and unstructured data to carry out tasks such as tracking social media activity, news analysis and generating stock market data.

Technical Analysis using Big Data

The existing analysis of financial trade is not confined to only examining the asset price and price behavior. It also includes analyzing the social and political trends, identifying the support and resistance level, and monitoring clients or stock market behavior amongst others. Since many elements are compiled for excellent data analytics, the data that is generated is huge.

Integration of big data analytics into the technical analysis will promote accurate predictions. This aids the traders and investors to plan out a strategy, that can mitigate the risk associated with financial trading.  Utilized by only the high-frequency firm, technical analysis functions into a microsecond latency period. Big data analytics will contribute these firms in having faster insights, to avoid the risk of failure.

Machine Learning

The true capabilities of machine learning are yet to be realized in the financial trading world. Leveraged by only a few firms, it involves computers or systems learning to perform tasks without human intervention. Machine learning improvises existing trading methods. Through this technology, the computer would be able to learn from past mistakes, analyze the situation of investment, and make decisions for investments. Since the technology is still evolving, the integration of big data analytics in machine learning has a positive outlook.