In today’s Finshots, we unpack some real lessons from SBI’s alternate universe.

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The Story

About a month ago, the RBI Governor, Sanjay Malhotra, surprised everyone by cutting interest rates by 0.5%, bringing the repo rate (the rate at which the RBI lends money to commercial banks) down to 5.5%. Analysts didn’t really see such a huge cut coming, especially given the uncertain global backdrop with wars, tariffs and economic slowdowns looming large.

But what if you could guess what the Governor was about to do just by looking at the colour of his tie before he even spoke?

Yup, we know that sounds bizarre, but SBI’s (State Bank of India) Research team was bold enough not to think so.

In a recent report, they imagined an alternate universe for monetary policy — one where interest rates aren’t guided by data and complex models, but by subtler, stranger signals. Like the colour of the central bank governor’s neck tie. Or even their personality.

They call it the Monetary Multiverse. And one of the things they looked at was whether tie colours could quietly hint at policy direction. To test it, they built a statistical index called the Tie Volatility & Tilt Index (TVTI).

And here’s what they found. 

• Warm ties like red or orange likely signal a rate hike. 
• Cool tones like sky blue or aqua suggest the RBI might hold rates steady. 
• A dark tie — black, silver or navy, says less about which way rates will go and more about certainty. It’s a clear sign that a decision is coming, whether it’s a cut or a hike, but definitely not keeping rates the same. 
• And if you spot a mixed or flashy tie like purple or yellow, well, it could mean anything.

Funny enough, during the latest rate cut, the Governor showed up in a black and silver tie. Sure, analysts expected maybe a 0.25% cut, but they knew that a cut was on the table. And that’s exactly what happened.

But that was just one part of this quirky report. The other bit takes it up a notch.

The Research team asked: what if Donald Trump ran the US Federal Reserve and set rates not based on data, but on tweets, moods or market tantrums?

Sounds crazy, right? But they actually built a simulation for it. 

They took the Taylor Rule (a standard formula central banks use to set interest rates) and gave it a Trump makeover. Since Trump often pushed Powell to slash rates, they adjusted the formula to reflect his low rate bias and called it the Trump-adjusted Taylor Rule.

And surprise, surprise! When they ran the numbers, they found that for many quarters between 2018 and 2025, actual Fed rates were 40–70% higher than what Trump’s model would’ve prescribed. In one quarter, the Fed’s actual rate was a whopping 94% tighter than Trump’s version! On average, if Trump had called the shots, US interest rates would’ve been about 1% lower.

And they didn’t stop there. They also tried to find out whether people would’ve paid more attention to the Fed if someone like Trump was in charge. 

So they looked at Google Trends to see how many people searched for stuff like “Fed rate cuts” each week and added a “Trump factor” to see if his headline-grabbing style would boost public interest, and found that people might’ve paid 30% more attention, not because the policy changed, but because the personality did.

But here’s the thing. This report isn’t meant to be taken too seriously. It’s just thought-provoking research, done on a lighter note, to see how creative, imaginary scenarios could pan out. 

But it really did its job and made us think about what real-life lessons we can actually draw from SBI’s alternate universe.

Well, Lesson #1: how much should we trust statistical models to make sense of finance and the economy?

What answers that better than the 2008 global financial crisis?

One big reason the 2008 meltdown turned into a full-blown crisis was the failure of risk models. Back then, quants and bankers used fancy math to build complex products like mortgage-backed securities (MBS) and collateralised debt obligations (CDOs). In simple terms, they bundled thousands of home loans into big packages and sold slices to investors who could trade them and earn money as long as people paid their mortgages. CDOs took it up a notch. They were mega baskets mixing not just home loans but also car loans, credit card debt or even chunks of other MBS, all sliced up again.

On paper, it all looked brilliant. And the risk models said these investments were safe. But the catch was the assumptions buried inside. The math showed that if US home prices fell by 10–20%, these products could blow up. But the experts shrugged and thought, “Come on, housing prices never drop that much, and that too everywhere at once!” So they gave that ‘worst-case scenario’ almost no chance of happening.

But when the housing market crashed nationwide, those complex products turned toxic almost overnight. Banks, investors, even pension funds were stuck with piles of “safe” securities that suddenly weren’t worth much at all.

So, the big lesson is this. Even the smartest models can get it wrong because the world doesn’t always follow the script. And it’s wiser to balance the numbers with good old human judgment, a healthy respect for worst-case scenarios and solid oversight. When you mix stats with common sense, you’re more likely to catch problems early and keep the financial system steady.

And that brings us to Lesson #2: Just because a clever model says that the Fed might run a looser policy if someone like Trump were in charge, does that mean political interference is a good idea?

Not really.

Because central banks like the Fed or the RBI aren’t built to make profits. Their real job is to keep the economy stable, which means staying independent from politics. But that’s easier said than done.

When central banks act independently, they can make governments look bad. Take interest rates, for example. During crises like the 2008 crash or COVID, central banks slashed rates to zero to get people spending again. But once rates hit rock bottom, they couldn’t cut more. So they turned to Quantitative Easing (QE). That basically means buying up government bonds to pump extra money into the system.

It worked. But when inflation returned, they had to hike rates. And suddenly, those bonds they bought during QE dropped in value because higher rates make old, low-yield bonds look unattractive. So on paper, it seemed like central banks were taking a loss as their assets are now worth less than what they paid.

And this can worry the government because central bank profits often help fund government budgets. They reduce the need to raise taxes or slash spending, which can be politically unpopular. If the central bank takes losses, that extra money vanishes. So governments might pressure central banks to keep rates low just to make borrowing cheap and voters happy, even if that stirs up more debt or runaway inflation later.

So here’s the takeaway. The Trump-Adjusted Taylor Rule might suggest that a looser policy makes markets happy. But that doesn’t mean it’s the right move. Political interference is rarely a good idea. And maybe, just maybe, central banks should be free to take losses when needed so they can deal with the economy’s ups and downs independently. A looser policy might look tempting in the short term, but without independence, it can create bigger problems later.

And finally, Lesson #3: Can clues beyond the usual official data tell us something about economic policy?

Absolutely. 

Another fun nugget we haven’t mentioned yet from SBI’s Monetary Multiverse is how much policy wording can reveal. Like if the word “growth” pops up more than “inflation”, it’s a hint that the focus might be shifting towards boosting the economy, which can mean a rate cut is likely.

Take the last rate cut. The Governor’s statement mentioned “growth” 24 times but “inflation” only 18 times. Maybe that proves what former Fed chairperson Ben Bernanke once said: “I think monetary policy is 98% talk and 2% action — and communication is a big part.”

So yeah, who says what, and how they say it shapes what people expect, and that affects how they spend and invest.

Not bad for lessons pulled from a not-so-serious report, eh?

We’ll wrap up on that note.

Got something you learned from this too? Tell us. We’d love to know. And don’t forget to share this story on with friends and strangers on WhatsApp, LinkedIn and X.

Until next time…

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