Due to digitalization, the size of the electronic form of data has increased exponentially during the last couple of decades. It is natural that the Government as well as large corporations want to study these data in order to make better decisions. Recent advances in the field of artificial intelligence have led to the creation of new mathematical tools like deep learning and reinforcement learning. These tools are not only effective in discovering the patterns in the data but also very helpful in automated decision making. Because of this, in almost every profession, there are employment opportunities for the people who are experts in data. These days almost all large organizations require data scientists, data engineers, and data managers. The role of these professionals is to study, to develop, and to apply these models to solve real-world problems.
Financial data is a large component of all the electronic data. For example, an average stock exchange produces trillions of Gigabytes (GB) of trade and order book data in a month. So naturally, there are massive applications of machine learning and data science tools in the field of finance. For example, in Indian stock markets, a large part of trading decisions is made via computer programs, also known as algorithmic trading. Many of these trading computer programs are very sophisticated and make decisions using data. Here are some of the engagements of data science professionals in the securities markets.
1) Algorithmic Trading:
Algorithms are not only making buy/sell choices but also recommending the product. At the back end, there are computer programs that are based on some mathematical models. These programs scan through a large amount of data to correctly predict the expected profit. To understand and model these algorithms, one requires a strong background in the field of mathematics, statistics, programming, and finance. Furthermore, knowledge of deep learning, deep reinforcement learning and other machine learning techniques is needed to create these algorithms which make correct decisions.
2) Financial computations:
One of the most important aspects of data science is to create a product from the data which are useful for further applications. The pricing of the complex financial product requires various statistical computations and simulations based on the data. These computations are ready-to-use data resources for further applications. For example, to price the American option, we need a large sample of simulated data. A readymade tool that simulates these data based on the historical parameters can be helpful to smoothly do this.
3) Data Resource management:
Often the primary or the secondary data is not used directly for any meaningful application. Sometimes data could be missing, corrupted, or raw. So, normalizing, missing data handling, cleaning the raw data, etc. are some of the aspects of data resource management. A data science professional equipped with these learning is always an asset for a large financial organization.
Apart from the above, data visualization tool development, database management, etc are extra skills a data science person is expected to know. Currently, National Institute of Securities Markets (NISM) is the only institute in the country which runs an academic program for working professionals and fresher’s to train them in all the above aspects of data science in finance. NISM is also the first institute in the country to set up a centralized database for all the financial data in India.
Mr. Suneel Sarswat,
Visiting Faculty, NISM
