If we compare the relative performance of Silicon Valley’s stock to that of JP Morgan and Bank of America since 1993, its market value rose 250-fold until the market’s peak on Nov 3, 2021, relative to 11-fold for JP Morgan and three-fold for Bank of America. Which raises the obvious question: Since banks borrow and lend at roughly the same rates, how could a bank possibly outperform an industry leader by a factor of 20? The post mortem should offer some important lessons for regulators on the creative ways in which businesses can disguise themselves and deflect scrutiny.

But there’s a larger lesson here for investors: that it pays to take risks when the herd is panicking. On the day of SVB’s failure, a student from my systematic investing class at NYU Stern messaged me that our recent session on “overreactions” actually described the current state of financial markets very well.

My course, which is based on my experience operating a machine-learning-based hedge fund on Wall Street, describes how to exploit mispricing opportunities systematically using algorithms. A challenge, for example, is how to recognize overreactions. An algorithm can do that. In our class assignment, we used a simple volatility-based formula to categorize overreactions and to implement a simple counter-trend system.

To make things interesting, my student sent me results from applying the strategy to JP Morgan since 1993. Her implementation of the assignment showed impressive risk-adjusted performance for JP Morgan and Bank of America, relative to just holding them. The lesson here is that market turmoil often causes mispricing in areas of the market that may be quite far removed from the source of the turmoil, which presents opportunities for savvy investors.

The larger takeaway is that even a simple reversion algorithm like the one above that I used for my class assignment can work across the market because investors often over-react. But it suffers in situations like the Great Financial Crisis when extreme price moves keep going in the same direction until the market finds a new equilibrium. Indeed, that’s the question on everyone’s mind: Will there be significant contagion to the larger economy? Or is the damage limited to financial institutions which had similar views about the future of interest rates or mismanaged their risk exposure to SVB’s?

It is worth noting that during COVID and the Great Financial Crisis, the VIX market volatility index spiked into the high 80s. Today, it’s in the mid-to-high 20s. So, even as the media fans the flames to white heat, the data thus far is telling us not to overreact. Yet, despite the data, we have already overreacted.

If the reality is that the larger fears of contagion are exaggerated, the current turmoil presents a great opportunity for investors. It would imply that the number of mispriced assets has increased. For an algorithm, a simple strategy that goes counter to the extreme moves should continue to be successful.

For a human investor, the challenge is to identify high-quality assets that investors have fled indiscriminately–and those with risks similar to SVB’s that the market has not yet repriced.

As my Stern colleague Aswath Damodaran revealed in our conversation on longer-term investing, an essential condition for successful investing is that the story matches the numbers. This is the guiding principle for discretionary human investors at the moment: Identify where the story is better than the numbers and where it is worse. That will reveal the opportunities. Then have the courage to act and wait for the returns to materialize.

Vasant Dhar is a professor at the Stern School of Business and the Center for Data Science at NYU. An artificial intelligence researcher and data scientist, he hosts the podcast, Brave New World, which explores how technology and virtualization in the post-COVID era is transforming humanity. He brought machine learning to Wall Street in the 90s, and subsequently founded the machine-learning-based hedge fund, SCT Capital Management.

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