AI in Stock Markets

In the ‘AI in Stock Markets’ Workshop, Expert Quant Trader Dr Anand Jayaraman demonstrated how traders optimize an ML-driven trading strategy in a live setting.

According to MarketsandMarkets, by 2026, the artificial intelligence (AI) market size will grow to USD 309.6 billion worldwide. Companies across industries are leveraging AI’s power in many ways. 

‘Artificial intelligence (AI)’ is the new buzzword in the world of stock and financial markets. Investing and trading in the market is based on a series of reasoning and insights from the data. As AI systems work on crunched data, it tries to find patterns or correlations and helps predict the stocks’ future direction by formulating algorithms. 

There is hype around AI, particularly in the area of the stock market. We need to understand that AI is a set of hyper-intelligent techniques that understand the market and help us make data-driven investment decisions. Artificial intelligence (AI) and machine learning (ML) focus on algorithms that help in data-driven decision-making, especially when things are much larger and we don’t have much time in hand.

In the world of advanced AI capabilities and humongous data, organizations without an effective AI strategy will find themselves trailing their peers in performance. In a recently held ‘AI in Stock Markets’ Workshop, Expert Quant Trader Dr Anand Jayaraman showed how traders optimize an ML-driven trading strategy in a live setting.

He also answered several intriguing questions by further addressing how AI is a game-changer for the stock trading community.  

  • What is AI, and why is there excitement surrounding it?
  • Why do trading firms/traders need an AI strategy to survive?
  • How to predict stock market prices using machine learning techniques?
  • Using AI, how can traders efficiently mitigate risk to provide for higher returns?
  • What mathematics is needed to recognize existing patterns in the markets?
  • What is the current set of techniques used in the market?
  • Which techniques go by the AI terminology in the stock and financial market? And much more.

The workshop also deliberated on how professionals can thrive in a complex world of the Stock Market with Advanced Program in AI for Financial Markets.

Here are the excerpts from the webinar.

Difference between traditional programming and machine learning

In traditional programming, a crucial variable is needed for predictions. For instance, whether a stock will outperform or not, and to find this, one needs to go to an expert. In traditional programming, you get the data of all the stocks and have a rule or criteria that need simple code or a simple excel macro or simple excel filter. Then, the outcome of this code is used to understand whether to buy the stock or not. It is a traditional way of filtering stocks that goes by ‘robotic process automation (RPA).’  

 In the ‘AI in Stock Markets’ Workshop, Expert Quant Trader, Dr. Anand Jayaraman demonstrated how traders optimize an ML-driven trading strategy in a live setting.

Multiple contradictory signals create chaos and make decision-making difficult in traditional programming. However, today, there is a better approach that is ML-based. Instead of using manual rules, the ML-based approach uses relevant market data and computes the data. This method helps to find a pattern from the data. This machine-learning (ML) algorithm automatically finds a significant predictor or rule and gives information about the past quarter or reliably predicts the future. 

Prediction of volatility in the market with machine learning (ML)

Are you wondering if ML can predict a volatile period in the future? The answer is yes. With collected data, the machine learning algorithms can automatically learn the rules and then predict the market’s volatility. The machine learning tools can easily identify safe or volatile regions. These algorithms are used to improve performance, make profits, and help to improve the portfolio. 

ML paradigms and other applications

Supervised learning is an ML method in which the algorithm learns the rules of the past predictors and past targets. In this paradigm, the algorithm allows finding rules so that when a predictor is given, it offers the desired prediction.  

Another commonly used machine learning paradigm is unsupervised learning. In this, available data is entered into the machine with no specific target. The machine then finds out exciting patterns in the data and cluster algorithms. It finds out stocks or sectors that behave similarly to each other or are different. Thus, ML paradigms help to add some amount of diversity to the portfolio. 

Rapid AI adoption in the financial markets is creating exciting career opportunities for professionals who are passionate about AI and the stock market. To help current and aspiring professionals build such AI expertise in the stock market, TalentSprint has designed a unique program leveraging the expertise of NSE, India’s largest stock exchange. 


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