Artificial intelligence (AI), and machine learning (ML), which is a subset of AI technology, have now become the buzzwords within virtually all market verticals. And why wouldn’t they? AI turns out to be the solution for a majority of industry problems, after all.
The technology is also enabling people to achieve financial fitness. AI is today finding good use in the broking industry and is relieving modern investors from the stresses associated with trading.
It goes without saying that trading is all about data. However, with so much data flowing in from all corners, it often becomes difficult for even seasoned traders to keep a tab. To help you understand better, let us quickly analyse how trading essentially works and how market action is determined by professional traders. Here is a broad example of a possible scenario.
There are largely four markets that operate in conjunction with each other, ie., commodity, bond, stock, and currency markets. Usually, all four of them move in a cyclic order and give us hints as to when to enter or exit a trade. Now, as the price of commodity increases, it simultaneously raises the cost of goods and makes the price action inflationary. As a result, interest rates are moved upwards to adjust inflation. This, however, makes the bond prices go down because of the inverse relationship between it and bond prices.
Bond prices and stocks typically tread in the same direction. So, the downward trajectory of the bond market is followed by the stocks as well. The reason for this trend is attributed to expensive borrowing as well as an increased inflation, which increases the cost of doing business. Here, it also needs to be noted that there is generally a lag between the bond market and the stock market. Further, the currency market affects all markets by primarily driving commodity prices. Commodity further impacts bonds and stocks, as explained before.
There are too many variables to keep a tab on for any professional investor. This is when we have still not delved into any particular market, any sector, its subsector, their intricacies, and other factors that affect the trade. In trading, if you miss even a single dot, you will just end up in the red.
This is where AI proves to be a game-changer. Irrespective of the volume and variety of the incoming data, the tech-driven approach can easily correlate and categorise the factors that impact the daily trade, thereby enabling us to take informed decisions based on our investment appetite. This is while taking negligible time as compared to a human professional.
Machine learning can further augment this and assist humans to fine-tune algorithms. The deductions made by the machine learning algorithm can give us new angles to enrich the algorithm based on the incoming data and historic trends.
Cutting-edge brokerage firms are today extending the AI advantage to modern investors via smartphone apps. This approach, while giving the investors the touch-of-a-button trading experience, also ensures round-the-clock accessibility so that smart trades can be made whenever there is an opportunity. They also serve as a perfect point of communication for industry insights, advices, and expert opinions. Ultimately, an AI-driven approach makes trading less of a craft and more of a science.
Today, retail investors are rapidly increasing in India. NSE has more than 2.78 crore investors, as they continue to grow with a CAGR of 11 percent since the past decade. This figure is about 4.58 crore for BSE, which has registered a 26 percent year-on-year growth over the last year itself.
So, as the investor sentiment continues to improve in India, the rise of AI-driven full-service broking will further pave the way for unbridled efficiencies. Perhaps, it will serve as the true enabler of a financial fit nation, starting with 2020.
(Edited by Evelyn Ratnakumar)
(Disclaimer: The views and opinions expressed in this article are those of the author and do not necessarily reflect the views of YourStory.)