In this week's newsletter, we talk about weight loss drugs, SEBI's regulated algo trading setup, the CPU rule and more.

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Quote of the day 📜:

"A budget is telling your money where to go instead of wondering where it went." - Dave Ramsey

Can weight loss drugs reduce healthcare costs?

What if you could lose a few kilos just with some simple injections under your skin?

It sure sounds like wishful thinking, but it’s actually happening!

Denmark’s Novo Nordisk, the pharmaceutical company behind the magical weight loss drugs, Ozempic and Wegovy, and US-based Eli Lilly’s Mounjaro have made this a reality.

Add to that, the US Food and Drug Administration (FDA) giving them the go-ahead signal, these drugs are being prescribed left, right and centre, across the US. To put that into perspective, about 7 million Americans are taking these weight-loss drugs, and that number might reach 24 million by 2035.

Here’s the thing, though. These drugs were initially made to treat type 2 diabetes. They help regulate blood sugar by reducing appetite and triggering insulin release. But patients taking them started to notice something unexpected. They were shedding kilos in a big way. And that’s how these drugs emerged as a solution for weight loss.

If you’re wondering how that happened, let’s break that down a bit. GLP-1 (glucagon-like peptide-1) is a hormone your gut releases after you eat. It tells your brain, “Hey, you’re full now”. It also helps release insulin, but only when your blood sugar is high, helping to stabilise glucose levels.

The drugs in question — Ozempic, Wegovy and Mounjaro, are GLP-1 agonists or simply medications that mimic the hormone. In simple terms, these drugs amplify the natural signals that tell your body to stop eating. And the results are impressive. Clinical trials show that people can lose up to 15-20% of their body weight with consistent use of these drugs.

But here’s where it gets even more interesting. These drugs are promising for tackling a range of other chronic conditions. Some early studies suggest that GLP-1 agonists could help reduce addiction to alcohol or nicotine, opening up new possibilities for addiction treatment. There’s also evidence that these drugs could help with sleep apnoea, chronic kidney disease, and even cardiovascular problems. Researchers are even exploring their potential in treating Alzheimer’s and Parkinson’s diseases.

That basically means one drug could tackle multiple issues, potentially cutting down healthcare costs for people. And guess what? That’s exactly what Novo Nordisk and Eli Lilly are claiming too.

So just imagine how much it could help bring down long-term healthcare costs! After all, over 1 billion people are classified as obese globally. Obesity rates have doubled for adults and quadrupled for children since 1990. With the growth of these drugs, analysts predict that the market for these drugs could be worth $130 billion by 2030. That’s a lot of potential.

In fact, just last week, Elon Musk took to social media to argue that these drugs should be made more affordable because these drugs don’t come cheap. A month’s supply can cost over $1,000, which is beyond reach for many people. To put that in perspective, for the average American, that’s about 17% of their annual household income.

And that’s exactly why he’s pushing for this through his Department of Government Efficiency (DOGE), suggesting that reducing obesity-related healthcare costs could save billions in the long run. Musk’s point is simple. Obesity is already a massive public health issue. About 70% of American adults are obese or overweight. So if we spend smartly on prevention today, we could reduce healthcare costs down the road.

But does it really work that way?

Well, not quite. Because here’s where things get tricky. Even though these GLP-1 drugs are helping people lose weight, they don’t seem to lower medical costs overall. A recent study found that while people on the drugs may shed pounds, their medical expenses actually go up. For example, the average annual medical costs for obese patients were about $12,695 before starting the medication. Two years later, that number had shot up to $18,507 — a 46% increase.

In comparison, those not on the drugs saw their costs rise by only 14%. What’s most concerning perhaps is that there wasn’t any noticeable drop in obesity-related health issues like heart attacks, strokes or type 2 diabetes. People still needed medications for high blood pressure and cholesterol, just like before.

So, while these drugs are helping people get rid of their excess weight, they’re not necessarily making the overall healthcare situation any better.

The problem lies in what happens after you stop taking them. Their appetite-regulating magic fades, and for most folks, the hard-lost weight slowly creeps back. Experts believe that this happens because the drugs don’t fix the underlying issues with appetite control; they just mask them for a while. So technically, you’re not really losing weight and that can’t help you control other obesity related health issues.

To be clear, weight loss happens when you create a calorie deficit. Then, your body taps into the energy stored in fat cells. But here’s the thing. When you lose weight, your body burns muscle tissue as well and not just burns fat. So, whether you’re losing weight through exercise or with the help of GLP-1 drugs, you’re losing muscle mass and fat.

The twist here is that studies show that 20-40% of the weight lost because of these medications is actually muscle. And if you lose too much muscle, it can slow down your metabolism and leave you feeling drained.

So yeah, these drugs may be a breakthrough, but much more research is required before we can say for sure if they’re a sustainable solution to the obesity crisis.

SEBI has an algo trading plan for retail investors

India has nearly 10 crore retail investors. That’s one in every five households across the country!

Many of these investors are everyday folks like us, juggling full-time jobs. So while the stock market might lure us with the prospect of making some cool profits, keeping track of price tickers all day is tough and sometimes even impractical. That could often mean higher chances of losses or stepping away from markets altogether.

Enter algorithmic trading, or algo trading, which is a game-changer for situations like these. Think of it like having a computer trade for you. You set rules for price, timing and volume, and it executes trades automatically. For instance, if you’re interested in textile stocks, you could program an algorithm to buy stocks in this sector when prices rise 5% over a week, assuming that demand is strong, or sell when they drop by 5%. The best part? You don’t have to stay glued to the screen. It’s all automated.

But here’s the thing. Despite all the buzz around algo trading, Indian retail investors have largely been left out of it. That’s because institutional investors have enjoyed greater freedom since algo trading launched in 2008. But retail investors only got a set of these regulations in 2021. These rules placed the responsibility for algo trading on brokers, meaning retail investors had to rely on pre-built algorithms provided by brokers, which were executed exclusively on their servers.

And this setup wasn’t ideal. Just think about it. A glitch in the broker’s system, a loophole in their strategy or even manipulation from their end could lead to massive losses for retail investors. Unregulated algo providers also offered risky services, leaving investors with no way to address grievances. SEBI seemed to catch on to these problems and naturally it cracked down.

But it didn’t stop there. It also realised that simply clamping down on algo trading wasn’t the solution. It needed to go a step further, regulate the space and take charge of overseeing it.

And there’s enough reason that nudged it to think on these lines. A recent study revealed that individual traders lost over ₹61,000 crores in the equity F&O segment in FY24, while large entities using trading algorithms, like proprietary traders and foreign investors, walked away with gross profits of ₹33,000 crores and ₹28,000 crores, respectively.

Clearly, retail investors were fighting a losing battle against sophisticated algorithms. And that’s exactly why SEBI is now working to make algo trading more accessible to retail investors, in a safer and well-regulated environment.

And SEBI’s suggestions are simple.

It proposes tagging orders exceeding certain speed or volume as algo orders. Then, it requires brokers to get stock exchange approvals for any algorithm they offer. Third-party algo providers will act as agents of brokers and must also meet eligibility criteria and register with stock exchanges.

Then, SEBI splits these algorithms into two types. First up, there are White Box Algos that are fully transparent. Here, investors can see and understand the logic, like say, an algo that buys shares when a stock hits ₹100. And second, we have Black Box Algos, where the investors don’t really know how the algorithms work internally. Such algos are opaque and therefore providers must register as “Research Analysts” and submit detailed reports for these to stock exchanges.

Exchanges will also supervise algo trading and ensure everything runs smoothly.

So stock exchanges will now have to have a rule book to test algorithms. Basically, rules so that stock brokers can verify how these algos would have performed using past data. This helps traders figure out which parts of their system work and which don’t. It also allows exchanges to ensure that brokers submitting algos for approval are ironing out any flaws beforehand.

Besides, if an algo goes rogue, it prescribes a kill switch mechanism or a kind of emergency brake tool. If an algorithm malfunctions, the kill switch can instantly halt trading to minimise damage. Think of it as the last line of defence, kicking in automatically based on pre-set conditions.

So yeah, with these measures, SEBI wants to make algo trading safer, hold brokers accountable and protect retail investors like us from unnecessary risks.

And these regulations are actually an upgrade from their 2021 version.

Back then, SEBI suggested that all orders coming through an API i.e. Application Programming Interface, or a bridge that allows different software to communicate, should be treated as algo orders. Because frankly, neither exchanges nor brokers could differentiate between algo trades and non-algo trades if they came through an API link. But here’s the thing. APIs are everywhere! Even the trading apps you use to log in or place simple limit orders (orders at a specific stock price) rely on APIs. So treating every API order as an algo trade would have been quite overboard and could have slowed down tech advancements designed to make trading easier for investors.

SEBI seems to have realised this and tweaked its stance. Its new approach focuses on speed and volume thresholds instead. So now, only orders exceeding a certain speed or volume threshold will be treated as algo trades.

And this thoughtful regulation could transform India’s markets for the better. For context, today, around 50% of traders in India use algorithms, compared to 60–70% in markets like the US and Europe. And making algo trading more accessible could bring more accurate and cost-effective trades.

Moreover, algorithms can analyse and execute trades at lightning speed and eliminate emotional biases like fear or greed that we humans have, reducing the chances of poor decisions. So yeah, this truly feels like a step forward.

It’s not all smooth sailing, though.

One potential hurdle lies in the requirement for stock exchanges to approve every trading algorithm. According to the Indian Association of Investment Professionals, a similar rule in SEBI’s 2021 regulations could make things tricky. Because trading strategies need to adapt quickly to changing market conditions. And by the time an exchange approves a strategy, it might already be outdated and the trading opportunity could slip away.

So this time, SEBI has suggested fast-track registration for certain algos, aiming for quicker approvals. But the turnaround time is still up to the exchanges, so we’ll have to see how well it works in practice.

And even with these regulations, algo trading isn’t immune to Black Swan events — those rare, unpredictable shocks. Take the example of textile stocks we mentioned earlier. If an algo is programmed to buy when prices rise 5% in a week, an unexpected event, like the Bangladesh crisis pushing textile stocks up by 20% overnight, could throw it off. The algorithm might be in a situation where it’s unable to handle such wild swings and end up buying or selling at the wrong time, causing poorly timed trades and huge losses.

That’s where stock exchanges could step in. If they not only back-test algorithms with past data but also stress-test them for extreme scenarios, much like they do with equity derivatives like F&O, it could reduce risks and improve preparedness.

For now, though, these rules are just part of a consultation paper SEBI floated last week. And we’ll only have to wait and see how things shape up after the feedback rolls in.

Infographic 🤔: Then vs Now!

Today’s Discussion 💸: The CPU hack to stop wasting money

Do you often find yourself spending on stuff that you don’t need? Fret not.

Here’s a fab idea to get you out of the money-wasting loop. It’s called the CPU rule. And that just means cost-per-use or understanding how much something actually costs you every time you use it.

Let’s break it down for you.

Imagine you saw a cool pair of sneakers at a store. You go for it without a second thought. But you can’t wear them every day because they just aren’t that kind of footwear. Maybe you could wear it for casual outings just twice a month. Which means you’ll only wear them 24 times a year.

Now if the sneakers cost ₹8,400, that’ll mean that every time you wear them you’ve spent about ₹350. But a more comfortable daily wear kind of footwear would obviously cost you less.

Now just for the sake of comparison, even if it cost the same as that fancy pair of sneakers, you’d still have worn them at least 4 times a week. Meaning, 208 times a year. How much would the cost per use be then?

Just about 40 bucks!

How does that make you feel?

If it blew your mind away, then try using this analysis every time you’re lured into buying things that may not actually be financially beneficial.

And hey, if this sounds familiar then you're not wrong. You might’ve come across a simpler term for this on social media: girl math!

And that's all for today folks!

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