“Those who talk should do and only those who do should talk.”

It sounds provocative.

It is actually a profound principle of risk management.

Because there is an enormous difference between understanding something intellectually and experiencing it financially.

A professor can explain derivatives.

A regulator can design derivatives regulations.

An economist can model market behaviour.

A risk manager can calculate expected losses.

But the trader sitting in front of a screen at 3:27 p.m., watching a position move ₹2 lakh against him in seconds, experiences something none of those models fully capture.

And that difference matters enormously when the thing being regulated is a market.

The problem with protecting traders from trading

SEBI has a legitimate problem.

The evidence is difficult to ignore.

SEBI’s own study found that 93% of individual traders incurred losses in equity F&O between FY22 and FY24, with aggregate losses exceeding ₹1.8 lakh crore over three years.

That is not a small problem.

And regulators have every reason to ask:

Why are so many individuals losing money?

But there are two very different ways of answering that question.

One is:

“Retail traders are taking too much risk. Therefore, we should restrict the environment in which they can take that risk.”

The other is:

“Why are retail traders taking this risk, what function does this market serve for them, and how can we make the market safer without destroying the very characteristics that make it useful?”

The first approach is protective regulation.

The second is market design.

And the difference is enormous.

Because a trader isn’t necessarily trying to avoid volatility.

Volatility is often the product being traded.

A professional trader doesn’t necessarily want a market that is calm.

He wants a market that is:

• liquid,

• predictable in its mechanics,

• transparent,

• continuously tradable,

• reasonably efficient,

• and governed by rules that don’t unexpectedly change the distribution of outcomes.

A volatile market can be perfectly tradable.

An unpredictable market structure is something else entirely.

CAS is a perfect example

India’s new Closing Auction Session (CAS) is a fascinating case study.

The system was introduced from August 3, 2026 for applicable cash-market securities, creating a separate 20-minute closing auction from 3:15 p.m. to 3:35 p.m.

The objective is understandable.

The previous closing-price methodology was based on trading activity over the final portion of the session. The auction mechanism is intended to create a more representative closing price and make it easier for passive funds and large institutional investors to execute around the official close. SEBI’s consultation material specifically argued that auction-based price discovery could improve closing-price quality and reduce tracking error for passive funds.

From an institutional-market perspective, this makes considerable sense.

But markets are ecosystems.

Change one part of the ecosystem and you change the behaviour of everything connected to it.

And that’s where the trader’s perspective becomes important.

The trader doesn’t experience “price discovery”

A regulator sees:

price discovery.

A fund manager sees:

better execution at the benchmark close.

An index fund sees:

lower tracking error.

An exchange sees:

a more structured closing mechanism.

But the trader sees something much simpler:

“What happens to my position at 3:30?”

That is the difference between designing a system and living inside it.

The first major CAS episodes demonstrated precisely this tension.

The new system produced unusual late-session price movements, with particularly dramatic moves around derivatives expiry. Reuters reported that the new closing mechanism produced increased volatility and divergences between benchmark indices, while market participants adjusted to the new structure.

On August 27, reports described extraordinary movements during the CAS, including a reported 14,700% surge in a Bankex put option during the expiry-day episode.

And on August 31, the system faced an even larger stress test when MSCI rebalancing generated roughly $4.1 billion of trades during the closing-auction window—nearly 40 times the average turnover since its introduction.

Now, none of this proves that CAS is fundamentally bad.

It proves something more interesting:

market structure is not an abstract concept.

When you alter the mechanism through which prices are formed, you alter behaviour.

The architect problem

This is where Taleb’s architectural analogy becomes incredibly powerful.

Architects can design buildings that look beautiful in architectural magazines.

But the resident doesn’t care about the architect’s conference.

The resident cares about:

• whether the bedroom gets sunlight,

• whether the kitchen works,

• whether water leaks,

• whether the staircase is usable,

• whether the house is comfortable.

The architect may optimize for architectural elegance.

The resident optimizes for living.

And sometimes those objectives overlap.

Sometimes they don’t.

Taleb describes precisely this separation between the maker and the user, arguing that things designed by people without “skin in the game” tend to become unnecessarily complicated because the designer bears little of the consequences.

The same problem can occur in financial regulation.

The regulator may optimize for:

investor protection.

The exchange may optimize for:

market integrity.

The institution may optimize for:

execution efficiency.

The academic may optimize for:

statistical significance.

The broker may optimize for:

compliance.

But the trader optimizes for something completely different:

survival.

And survival creates a different kind of intelligence

This is perhaps the most important point.

Trading teaches things that are difficult to learn without actually trading.

Not because traders are inherently smarter.

Because feedback is immediate and brutal.

If you are wrong, the market doesn’t give you a grade.

It takes your money.

If your position sizing is wrong, your account tells you.

If your thesis is wrong, the market tells you.

If your execution is poor, slippage tells you.

If your risk model doesn’t work during a volatility spike, the P&L tells you.

There is no committee meeting.

There is no theoretical defense.

There is no footnote explaining why the model was correct.

You lose.

And that creates a form of knowledge that cannot be completely replicated through observation.

Taleb makes essentially this argument when discussing “skin in the game”: when there is actual risk on the line, people’s attention, understanding and behaviour change dramatically.

The teacher who teaches derivatives but doesn’t trade them

Imagine two people teaching options.

The first has spent ten years studying:

• Black-Scholes,

• Greeks,

• implied volatility,

• probability distributions,

• option pricing,

• market microstructure.

The second knows all of those things too.

But every morning the second person also puts capital at risk.

He has experienced:

• an option going from ₹40 to ₹7 in minutes,

• an IV crush after an event,

• a hedge that worked beautifully in backtests but failed in reality,

• liquidity disappearing precisely when it was needed,

• spreads widening during stress,

• an apparently harmless overnight position becoming a catastrophic morning gap,

• and the psychological difference between losing ₹5,000 and losing ₹5 lakh.

Who understands derivatives better?

The answer isn’t necessarily the second person.

But the second person understands something the first person doesn’t.

The first understands the theory of the game.

The second understands the consequences of the game.

And regulation needs both.

But here is where the argument becomes uncomfortable

It would be intellectually lazy to say:

“SEBI officials don’t trade, therefore SEBI shouldn’t regulate.”

That doesn’t follow.

A pilot doesn’t need to be an airline passenger every day to regulate aviation.

A doctor doesn’t need to suffer every disease to practice medicine.

A judge doesn’t need to commit a crime to understand criminal law.

Expertise does not require personal participation in every activity.

The stronger argument is different:

Those who design rules for people taking risk should have mechanisms that systematically incorporate the knowledge of people who actually take that risk.

That is the missing piece.

Regulation needs the trader’s feedback loop

Imagine if before introducing a major market-structure change, regulators had to stress-test it with:

• intraday traders,

• options market makers,

• arbitrageurs,

• prop traders,

• institutional execution desks,

• retail traders,

• algorithmic traders,

• and brokers.

Not simply ask:

“Does this theoretically improve market integrity?”

But also ask:

“What new behaviour will this incentivize?”

Because every rule changes incentives.

And traders respond to incentives.

The unintended consequence problem

Suppose you want to reduce retail speculation.

You increase the cost of trading.

Some traders stop.

Great.

But others don’t.

They adapt.

They may:

• trade fewer but larger positions,

• move to different products,

• increase leverage,

• shift to another exchange,

• trade farther-out strikes,

• trade overnight,

• trade offshore instruments,

• or concentrate risk into shorter windows.

The regulator measures the intended outcome.

The trader searches for the new equilibrium.

This is one of the oldest problems in economics:

people respond to rules.

A regulation is not the end of the game.

It is a new rule inside the game.

And intelligent participants adapt to it.

SEBI’s derivatives reforms illustrate the dilemma

SEBI’s October 2024 framework introduced several measures intended to strengthen the equity-index derivatives framework and improve investor protection and market stability.

The motivation was understandable, particularly given the enormous losses experienced by individual F&O traders.

And the subsequent data suggests the market did respond.

SEBI’s July 2025 comparative study reported that approximately 91% of individual traders incurred net losses in equity derivatives in FY2025.

That statistic should make us pause.

Because if the fundamental problem is that most individuals lose money, we have to ask a deeper question:

Is the objective to prevent people from losing money, or to create a market in which informed risk-taking remains possible?

Those aren’t the same objective.

A professional trader is not a confused retail investor

This distinction is often lost.

There are at least three different participants:

1. The uninformed gambler

He sees an option doubling.

He buys it.

He doesn’t understand probability, volatility or position sizing.

Protecting him may be justified.

2. The informed but inexperienced trader

He understands markets but hasn’t yet developed risk discipline.

Education and better risk controls may help.

3. The professional trader

Trading is his profession.

He understands his edge.

He understands his risk.

He knows his expected value.

He knows his drawdowns.

He deliberately chooses instruments and timeframes.

And he may need the volatility that regulators are trying to reduce.

Treating all three as the same person is a regulatory mistake.

The paradox of investor protection

There is an uncomfortable paradox here.

The safest market for an inexperienced investor may be a worse market for a professional trader.

And the safest market for a professional trader may be too dangerous for an inexperienced investor.

So what should regulation do?

Not eliminate risk.

Segment risk.

That is a much more sophisticated solution.

Instead of constantly changing the market itself to discourage bad behaviour, regulators could increasingly focus on:

• suitability,

• risk disclosure,

• leverage,

• position limits,

• capital requirements,

• margining,

• broker risk controls,

• product complexity,

• and transparency.

In other words:

Protect people from risks they don’t understand without preventing people from voluntarily taking risks they do understand.

The greatest danger isn’t volatility

This is where I think many discussions about trading regulation go wrong.

Traders don’t necessarily fear volatility.

They fear unknown volatility.

They fear:

“The rules changed and I didn’t know how the new mechanism behaves.”

They fear:

“The official close suddenly means something different from what I thought it meant.”

They fear:

“My hedge behaved differently because the settlement mechanism changed.”

They fear:

“The market I understood yesterday isn’t the market I’m trading today.”

A professional trader can build a strategy around a volatile market.

What is much harder to build a strategy around is structural uncertainty.

The difference between risk and uncertainty

This is an important distinction.

Risk is something you can estimate.

If I know that a stock can move 3% intraday, I can construct a position around that.

Uncertainty is when I don’t know how the system itself will behave.

A trader can manage:

“There is a 5% chance of a 4% move.”

But it is much harder to manage:

“The closing mechanism may create a new price-discovery dynamic that hasn’t existed before.”

The second is not simply market risk.

It is model risk.

And regulatory changes can create model risk for everyone trading the market.

The irony of protecting retail

There is another uncomfortable possibility.

If regulation makes the market increasingly difficult for sophisticated domestic traders, it doesn’t necessarily make the risk disappear.

It may simply change who captures it.

Sophisticated institutions have:

• better technology,

• better connectivity,

• better execution,

• quantitative models,

• larger balance sheets,

• dedicated risk teams,

• and the ability to adapt quickly.

A retail trader doesn’t.

So a regulation designed to protect retail can, under some circumstances, unintentionally make the market more favourable to the participants best equipped to adapt to complexity.

That is precisely why unintended consequences deserve as much attention as intended consequences.

The Jane Street problem

The recent regulatory environment around India’s derivatives market makes this question even more relevant.

In August 2026, SEBI’s action against entities including Copthall Mauritius, associated with JPMorgan, and broker Mansi Share & Stock Broking showed that the regulator is increasingly focused not merely on retail losses but on sophisticated trading behaviour and potential manipulation.

That is important.

Because it reminds us that the Indian derivatives market is not simply:

Retail vs. the casino.

It is a complex ecosystem containing:

• retail traders,

• professional traders,

• prop desks,

• market makers,

• foreign institutions,

• arbitrageurs,

• algorithms,

• exchanges,

• brokers,

• and regulators.

Each participant responds differently to the same rule.

What would “skin in the game” actually look like?

This is where the idea can move beyond criticism.

We don’t need SEBI officials to become full-time traders.

We need institutional skin in the game.

Before major market-structure reforms, imagine requiring something like:

1. Trader impact assessments

Not just:

“Will this improve investor protection?”

But:

“How will this affect execution, hedging, liquidity, arbitrage, spreads and volatility?”

2. Independent trader panels

A permanent group consisting of people who actually trade:

• equities,

• futures,

• options,

• arbitrage,

• market making,

• and intraday strategies.

Not lobbyists.

Not influencers.

Actual practitioners.

3. Simulation before implementation

Run the proposed mechanism through historical expiry days.

Run it through:

• March 2020,

• COVID volatility,

• election results,

• budget days,

• RBI decisions,

• index rebalances,

• global crashes,

• flash events.

Ask:

What would have happened under the new rules?

4. Sandbox periods

Introduce structural changes in controlled environments before making them permanent.

Let traders adapt.

Measure the consequences.

Then modify.

5. Sunset clauses

Every major structural intervention should have a predetermined review date.

If the consequences are worse than expected:

change it.

Regulation should be capable of admitting:

“The model was reasonable, but reality behaved differently.”

That isn’t weakness.

That’s science.

Markets are evolutionary systems

This is perhaps the deepest lesson.

A market isn’t a machine.

You cannot simply change input A and assume output B.

Because the participants are intelligent.

They adapt.

They arbitrage.

They discover loopholes.

They change strategies.

They move liquidity.

They change behaviour.

And eventually the market evolves around the regulation.

This is why regulating markets is fundamentally different from regulating static systems.

You are regulating something that is simultaneously responding to your regulation.

The regulator needs humility

The regulator has something the trader doesn’t:

authority.

The trader has something the regulator may not:

continuous exposure to the consequences of the rule.

Both forms of knowledge are valuable.

But authority can create a dangerous illusion:

“Because we control the rules, we understand the system.”

The opposite may actually be true.

The more complex the system becomes, the more dangerous it is to assume that the designer understands all downstream consequences.

Markets have millions of participants making decisions every second.

No committee can model all of them.

The teacher should occasionally take the exam

Imagine a school where the teachers design an examination.

But they never sit for it.

They decide:

• the syllabus,

• the questions,

• the time limit,

• the marking system,

• the difficulty,

• and the rules.

Then students complain:

“This exam doesn’t work.”

And the teachers respond:

“But statistically, the exam is fair.”

Perhaps it is.

But there is an obvious missing experiment:

Take the exam yourself.

Trading is similar.

If you want to understand what a market structure does to traders, there is tremendous value in experiencing it firsthand.

Not because personal experience makes someone infallible.

But because experience exposes blind spots that models often miss.

The real meaning of “skin in the game”

Skin in the game doesn’t mean:

“Only traders should regulate traders.”

It means:

Those who make consequential decisions should remain connected to the consequences of those decisions.

That principle should apply everywhere.

To CEOs.

To politicians.

To academics.

To regulators.

To fund managers.

To journalists.

And yes—

to traders.

A trader who gives reckless advice should have something to lose.

A regulator who designs a market mechanism should have mechanisms that expose the design to real-world feedback.

A teacher who teaches derivatives should understand the difference between knowing an option’s Greeks and actually managing an option book.

Perhaps the real question isn’t “Are regulators wrong?”

That is too simplistic.

The better question is:

Are the people designing the rules sufficiently exposed to the reality of the people who must live under them?

Because a regulator can be intelligent.

Well-intentioned.

Highly educated.

Extremely competent.

And still miss something.

Not because they aren’t smart.

But because they aren’t standing in the same place as the person bearing the consequences.

That is the essence of skin in the game.

Regulation should protect the participant—not redesign the participant

The ultimate purpose of a financial market should not be to make everyone behave safely.

Markets are supposed to allow people to make different judgments about risk.

One person wants to hedge.

Another wants leverage.

Another wants liquidity.

Another wants to speculate.

Another wants to arbitrage.

Another wants to make markets.

The regulator’s job is to make sure the game is:

fair, transparent, resilient and difficult to manipulate.

It should not necessarily make the game comfortable.

Because uncomfortable markets are often where price discovery happens.

The trader doesn’t need a safe market

He needs a fair market.

That distinction is everything.

A safe market might mean:

fewer opportunities, less leverage, less volatility, fewer participants taking risks.

A fair market means:

everyone knows the rules, the rules don’t arbitrarily favour one participant, prices are formed transparently, manipulation is punished, and participants can manage the risks they knowingly choose to take.

The second is far more important.

Because trading was never supposed to be safe.

It was supposed to be voluntary.

And perhaps this is the lesson regulators should borrow from traders

A trader learns one brutal lesson very quickly:

Being right isn’t enough.

You can have the correct thesis and still lose money.

You can have a brilliant model and still blow up.

You can predict the direction and lose because of timing.

You can understand the market and lose because of execution.

And therefore a good trader constantly asks:

“What am I missing?”

Regulation should do the same.

Not:

“We have solved the problem.”

But:

“What new problem might our solution create?”

That is the mindset of someone with skin in the game.

The final irony

Sextus Empiricus was writing about intellectual authority more than two thousand years ago.

Taleb brought the idea back into modern discussions of uncertainty, expertise and asymmetric consequences.

And Indian financial markets are now providing a perfect real-time laboratory for the principle.

The issue isn’t whether SEBI cares about investors.

It clearly does.

The issue isn’t whether retail traders lose money.

They clearly do.

The issue isn’t whether regulation is necessary.

It clearly is.

The deeper issue is this:

Can you fully understand a system whose consequences you don’t personally experience?

Perhaps.

But you should be humble about what you might be missing.

Because the person sitting on the other side of the regulation isn’t looking at a circular, a consultation paper or a statistical study.

He is looking at his position.

His capital.

His risk.

His livelihood.

And when the market moves unexpectedly, there is no theoretical model between him and the loss.

There is only the loss.

That is why the ancient principle remains relevant:

Those who talk should do, and only those who do should talk.

Not because only traders can be right.

But because skin in the game changes what you notice.

And sometimes the thing you notice only after losing money is precisely the thing the spreadsheet never showed you

8 July
25 June
2 June

22 May

13 May

10 April

Plot those on the charts and see what happened in Nifty

The future dates are as follows.

20 August 2026, 9:30 onwards till 2:45

02 September 2026, 1:15 onwards

11 September 2026

30 September 2026



Will post more details with trades in the Outlook Premium group
https://cosmofeed.com/vig/6a59c8a0bf6f0100137d7f76

Might give a hint as to how I calculate it. If you have done Raphael and watch enough charts, you might have already found it.

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