Markets Wobble When We Misread Uncertainty

Your academic path spans engineering, economics, and finance, culminating in a PhD from IIM Calcutta. What inspired this shift, and how has your multidisciplinary background shaped your approach to research in financial economics?

Honestly, engineering was more of a default choice for me. I grew up in a small town where, after 12th grade, the standard path was either medical or engineering. Since I wasn’t inclined towards biology, I chose mechanical engineering. But during my undergrad, I realised my real interest was not in machines but in understanding how the financial world and the economy function. That’s what motivated me to switch tracks and jump into financial economics.
Now, how this multidisciplinary background has helped me — engineering taught me to think in very structured and practical ways. In engineering, you work with real, tangible systems. In economics and finance, however, we deal with human behaviour, which can’t be observed or measured directly. So we often rely on models that assume people behave in certain predictable ways. My engineering days make me a bit sceptical of overly simplified assumptions. It pushes me to ask: can we make these models more realistic and see what happens to the results then? That’s where my research interests lie; relaxing some of the assumptions in traditional models and trying to bring them closer to real-world behaviour. In a way, engineering gave me the mindset to question assumptions behind economic models, and that continues to influence how I approach research in financial economics.

A central theme of your research is “belief distortions” and their role in financial markets. For a general audience, how would you describe belief distortions, and why are they so critical to understanding financial stability?

Building on the previous question, one of the most important assumptions in economics is that human beings are rational. Since we can’t actually observe how people behave in every real-world situation, models usually assume that people make decisions in a very logical, predictable way. For example, if you face uncertainty, the assumption is that you’ll pull out a statistical model or some formula to calculate the best decision.
But in reality, that’s not how humans usually work. Most of us rely on mental shortcuts, intuition, or rules of thumb when faced with uncertainty. When people’s actual behaviour deviates from what the mathematical model predicts, we call that a belief distortion. Now, in finance, almost every decision involves uncertainty about the future. If you invest in stocks or bonds, you don’t know exactly how they’ll perform. If a bank gives out loans, it can’t be 100 per cent sure whether the borrower will repay. In such situations, assuming that everyone makes perfectly rational predictions often leads to misleading conclusions.
Belief distortions help explain why markets sometimes behave in surprising or unstable ways — like bubbles, crashes, or excessive optimism or pessimism. By incorporating these distortions into our understanding, we get a much richer, more realistic picture of financial stability, one that actually reflects how humans make decisions under uncertainty.

You’ve written about how banks tend to overreact to recent losses — becoming overly cautious in some periods and excessively risk-taking in others. What are the key takeaways from this research for banking institutions and regulators?

My research shows that banks don’t always respond to risks in a balanced way. Instead, they tend to overreact to recent experiences. When they face losses, they often become overly cautious, pulling back credit even more than necessary. On the other hand, when things are going well, they sometimes get carried away and extend too much credit.
This overreaction has big consequences. In downturns, when the economy actually needs credit the most, banks’ excessive pessimism ends up drying out lending. That means productive businesses may struggle to get funding at precisely the wrong time. In good times, the opposite happens — banks’ optimism fuels excessive lending, sometimes financing poor-quality or risky projects. Both patterns create instability and can amplify booms and busts in the financial system.

So what’s the takeaway for regulators and banking institutions?

It’s important to recognise that these belief distortions are not just random mistakes — they’re systematic in nature. Policies and regulations need to be designed with this in mind. For example, countercyclical measures, stress tests, and buffers can help dampen the extremes of optimism and pessimism. By explicitly acknowledging these belief distortions, we can move towards a banking system that is not only more resilient but also better aligned with the needs of the broader economy.

Regulators are often judged by how they handle credibility, especially when publishing stress test results or guiding markets during crises. From your research, what strategies can regulators adopt to strengthen trust while still being transparent?

One of the key lessons from my research is that crises are very different from normal times, and regulators need to adapt their
communication strategy accordingly. In normal times, people generally have trust in the financial system. If regulators become too transparent about every short-term fluctuation, they may unintentionally create panic. Imagine you invest in an asset with a five-year horizon. What ultimately matters is the return at the end of five years. Along the way, the asset might fall sharply in year two but recover in year three, leaving you with a healthy return overall. If regulators highlight every interim dip in detail, investors may overreact and withdraw prematurely — even though the long-term outlook is fine. In such situations, measured transparency is actually better for stability.
But during a crisis — like the 2008 financial crisis — the context changes completely. At that point, trust in the system is already broken. People don’t distinguish between good and bad assets or institutions; they just want to pull their money out. In such moments, regulators need to do the opposite: be fully transparent. By clearly differentiating between strong and weak institutions, transparency helps restore credibility, prevents blanket panic, and ensures that fundamentally sound institutions survive.
So, the strategy is not one-size-fits-all. Regulators should calibrate their transparency — restrained in stable times, but fully open and credible in crises — to strengthen trust and maintain financial stability.

Banking opacity has long been debated — too little can create instability, while too much can stifle efficiency. What balance do you think is most effective in fostering both credit growth and long-term resilience?

This question is closely linked to the earlier one on regulatory credibility, but here the focus is on bank opacity. In normal times, when confidence in the banking system is intact, a degree of opacity can actually be beneficial. If every small fluctuation or short-term risk exposure were disclosed in detail, it could create unnecessary alarm.
Depositors or short-term investors might react too strongly, pulling money out even when the system is fundamentally stable. By shielding against this kind of panic-driven behaviour, some opacity allows banks to continue lending smoothly, supporting credit growth and investment in the economy.
But the situation flips during times of stress. In a crisis, when trust has already eroded, opacity backfires. If all banks, whether they are good or bad, are opaque, depositors and investors lose confidence in everyone, leading to credit freezes and contagion. That’s when transparency becomes essential — so markets can distinguish between strong and weak institutions. So the balance is dynamic: limited opacity in stable times to sustain confidence and growth, but full transparency in crises to preserve resilience and prevent systemic collapse.

At XLRI Delhi-NCR, you teach future business leaders about finance and economics. How do you bring research-driven insights — like macro-finance and regulatory credibility — into the classroom in ways that students can connect with and apply?

In my classes, I try to bridge theory with the real world. Finance and economics can sometimes feel abstract, but once students see how these ideas play out in real events, the concepts come alive. For example, when I teach
macroeconomics, I don’t just show models — I connect them to episodes like the global financial crisis or the Covid
crisis. Students immediately see how business cycles or regulatory choices affect businesses and everyday lives.

The financial world is rapidly evolving with fintech innovations, digital banking, and even emerging risks like climate change and cybersecurity. Looking ahead, where do you see the biggest challenges and opportunities for financial regulation in the coming decade?

Fintech, digital banking — all these innovations create exciting opportunities, like improving financial inclusion, reducing transaction costs, and making credit more accessible. But they also create new challenges for regulators.
The biggest challenge that I see is not the technology itself but the fact that technology moves much faster than regulation. For example, crypto-assets grew very fast, even before regulators had any framework in place. This mismatch can create instability in the system. Of course, another challenge is emerging risks like cybersecurity and climate change. Cyber risks can destabilise entire financial institutions overnight, while climate risks — though slower — pose systemic threats.
The real test will be striking a balance: encouraging innovation so finance serves people better, while implementing regulations that ensure resilience in the face of new and uncertain risks.