Characteristics of Big Data In Finance

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Big data in finance is a popular new slogan in the field of information technology and quantitative methods that refers to the collection and analysis of large amounts of information. Advances in computing power along with falling prices make big data projects technically and economically possible.

In particular, the rise of big data in finance for cloud computing puts the cost of big data analysis within the reach of many smaller companies, who now do not need to make significant capital investments in computing infrastructure.

What is big data in finance?

The development of data in the digital age has experienced a very large development. The International Data Corporation estimates that more data is generated every two days in the world than it was generated than it was at the beginning of time until 2003.

The financial sector is one of the sectors that is experiencing development in the era of Big Data. The conventional definition of Big Data includes 3V (Volume, Velocity, and Variety), but it does not fully describe the opportunities and challenges that the Big Data revolution has generated in academic research and financial practice.

Definition of “Big Data”

The definition of big data in the field of technology is different from the definition in the field of finance. In the field of technology and statistics, researchers focus on data mining techniques, while in finance it uses various approaches to identify problems in economics.

Data in big data in finance

Big data technology is driving financial services companies to improve in a variety of areas – customer data, risk measurement, market expectations, and operating efficiency. There are also several benefits of big data in the financial sector, including:

·         Big data keeps financial institutions on track with customer satisfaction,

By adding unstructured data sources such as customer service calls and social media activity, financial institutions can measure customer satisfaction and address issues before they lose business.

·         Facilitating Risk Management and Regulation,

Increased regulatory oversight has changed the way in which financial institutions can approach risk. Big data analytics makes it possible to assess real-time data streams, such as news, research, social media, audio, and video, to track market events and manage risk more closely.

Seeing the benefits of big data can advance finance in Indonesia, Paques can be one of the solutions, Paques is a Parallel Query System, this Paques is the first Big Data Analytic Tool from Indonesia, and even Asia, which is able to analyze big data very quickly and efficiently using parallelization and MapReduce methods.

Characteristics of Big Data In Finance

Large data sizes: Very large data sets can be absolute or relative. In other words, the size and quantity of big data, such market transaction data, is really huge.

  • High-dimensional:  The term ‘Big Data’ implies more than just a large amount of data. This feature means that big data has many variables relative to the sample size. Machine learning is a common solution to dimensionality-related challenges, and is increasingly being used in the field of finance research.
  • Complex structure: This feature means that data does not always have a traditional row and column format. The structure of big data is complex and even unstructured data including text, images, video, audio, and voice. This unstructured data creates value if it can measure economic activity that cannot be obtained using structured data.

Big data research direction

Machine Learning

The use of computers to make financial decisions is one of the uses of machine learning. Large-scale machine learning studies relating to asset price estimation typically use quarterly CSRP data obtained via form 13F. The gap between high-frequency learning about traders and more in-depth long-term training can be narrowed through the development of new research.

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Feedback effect of the ‘Big Data’ revolution

In the realm of big data, one of the revolutionary shifts occurred when computers took on the role of decision makers. The widespread adoption of machine learning in the investment community and the resulting feedback effects on the securities market and corporate decision-making suggest that businesses must adapt to the big data revolution.

Next, how businesses can take advantage of the revolution brought about by big data while making real decisions. For example, the rise of big data has reduced the need for managers to study market prices, as businesses now have access to more diverse data sources (increasingly leveraging machines).

The heterogeneous impact of the big data revolution

While it doesn’t always have a positive impact, big data provides a wealth of additional knowledge to investors, institutions, and businesses. Research shows that social media can spread market euphoria. Since arbitrators often trade in the opposite direction to ordinary traders, changes in the direction of price pressure by ordinary traders can sometimes reverse quickly.

More complex data

Analysis of big data sets, such as trading and price quotes, is the first step in applying big data techniques to the financial sector. One recent study allowed researchers to use Natural Language Processing (NLP) to extract data from unstructured sources such as text.

Furthermore, researchers can analyze data from more complex structures such as audio, video, and graphs in the hope that this information can generate new insights. Experts in finance can gain insights from more complex data sets if they are able to measure economic activity that was previously impossible to capture with simpler data.


As computers become a necessity in many areas, finance is one of them, it becomes important to evaluate whether rules previously design in a human context need to be adapt to the context of machines. How regulations were draft decades ago need to be update to reflect today’s reality.


In the era of machine learning and artificial intelligence, theory is becoming more important for a simple reason: human judgment is inconsistent, but machines tend to make consistent model-base decisions.

Develop model theory can generate quantitative predictions (numbers) for market liquidity, such as the number of stock price splits. This can happen because liquid asset service providers use algorithms to make decisions, and these algorithms can have designs identical to the theoretical models use in research. Well, that was a little talk about big data in finance. Hope it is useful.


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