At a recent webinar, Prof. Anand Jayaraman and Prof. Sudhakara Reddy, lead faculty of Advanced Program in AI for Financial Markets shared some interesting views, case studies and insights into leveraging AI’s power in financial markets.
How artificial intelligence and machine learning are transforming the financial markets?
How are the financial market participants able to drive informed decisions with AI’s power?
Why are several financial institutions and start-ups alike hiring talent with relevant AI expertise?
How can Advanced Program in AI for Financial Markets help professionals gain in-depth understanding and build relevant capabilities to tap into opportunities in this space?
These questions were delved into at a recent webinar. Prof. Anand Jayaraman and Prof. Sudhakara Reddy, lead faculty of the Advanced Program in AI for Financial Markets, shared some interesting views, case studies, and insights into leveraging AI’s power in financial markets. Further, they discussed how the program could help professionals build relevant expertise by explaining the various modules in the curriculum.
AI is transforming the financial markets
Artificial intelligence (AI) is helping in predictive analysis, fraud detection, personalized wealth management, credit scoring, consumer behavior forecasts, task automation, corporate performance management, risk assessment, and more. All these are possible because AI can reduce data-crunching time to seconds. As a result, market participants who leverage AI’s power are seeing improved productivity, reduced costs, and enhanced customer experiences.
What can a good AI program do? An interesting use case was discussed by Prof. Anand Jayaraman, an ex-hedge fund trader, a former research analyst, and a builder of algorithms in the financial space. Prof. Jayaraman shared a lot of his hands-on insights here. In India, the mutual fund fees by an average active fund are 2 percent, while the passive one can be 0.75 percent. But the average outperformance has dropped from 6 percent a decade back to 1 percent. They need analytics to continue charging these higher fees and to do better than a passive fund. A retirement management fund recently explored how to put money better in blue-chip companies in the S&P 500 Index. If they diversify their portfolio between equities and bonds, then the portfolio can have a smoother ride – as the fund manager asked. Can we use machine learning to be smarter than before? Can we time the market?
Prof. Anand Jayaraman and Prof. Sudhakara Reddy sharing their views and insights on AI in Financial Markets
As Prof. Anand Jayaraman explained, “new indicators can be looked into. Like the price-to-book ratio. Fundamental indicators can throw up contradictory data. Can AI help here with a coherent decision? Typically, decisions are based on fundamental indicators. Can machine learning do something better? A data-driven approach, which algorithmic trading folks have used for decades, is more of a back-testing that may have some issues. It tests some hypotheses and then leads to decisions. This is looking back in history. But in machine learning, we do not rely on domain expertise or intuition. It helps to solve inherent flaws in back-testing. Now we try the walk-forward testing. This is a machine learning approach for building trading strategies. The machine finds patterns based on data available only to that point and can predict volatility and assigned weights based on predictions.”
Of course, the model missed the pandemic. None of us could have predicted it. But despite that, when this algorithm is applied, performance gets marginally better based on data-driven patterns. He also illustrated the use of sentiment analytics.
Translation for talent
Data Science has touched every sector. But, although many low-hanging fruits have been covered, from now on, domain specialization is crucial to make actual progress, he added. That’s why MBA courses have added a course in Data Science. “People with a solid understanding of financial markets may not have excellent knowledge of data analytics methodology. But people with Data Science expertise may lack domain knowledge. So we are trying to blend both capabilities in this program. Our goal is to teach Data Science together with the requisite financial background.”
“The idea is not to have a textbook approach. Instead, we will cover fundamentals of valuations, machine learning, unsupervised learning, and optimization with modern portfolio theory and applications of portfolio allocation”, added Prof. Sudhakara Reddy, assistant professor in Finance and Control at IIM Calcutta.
Prof. Anand Jayaraman stressed how knowledge of the domain and Data Science helps equip professionals with lucrative growth in this space.
There is no magic algorithm. It is like a carpentry class. Each tool can be taught to students. But everyone comes with one’s approach. Some people can create magic knowing that others learned, he argued.
Why create new algorithms if old algorithms work forever? Instead, be on your toes to discover new opportunities. That’s what Data Science does. That’s what the Capstone project will also do, he explained.
Nikhil Reddy from TalentSprint anchored this interesting webinar where many talent aspects about AI in Financial markets were discussed.
Can we overcome a crisis like the pandemic we just saw? Prof. Anand Jayaraman cited how a good algorithm can miss out on detecting the pandemic, but it recommended a different approach after the pandemic. The machine learning looked at the data, recognized some patterns, and suggested a low-risk period. That’s what we teach here. To work smartly with concepts. Then, perhaps armed with that strength, you can work confidently around the next crisis in the future.”
It’s all about dots and which ones to join or ignore. Pick yours well with the right learning investments.