Abhishek's Journal: Being Less Stupid

abhishek_sinha · March 28, 2025, 3:50am

With 15+ good quality businesses bought at reasonable prices, odds are in your favor. The evidence is in crores of Mutual fund investors who do better than 90% of day traders over 5-10 year periods.

I can try to explain Bayesian reasoning with an example →

I screened a company that is a quasi-FMCG/commodity in early 2023. I studied it briefly and rejected it initially, thinking that the company was selling a commodity and had no evidence of competitive advantage.

Last year, during a bike trip to Kaza (a remote Himalayan location), I spotted this company’s niche product prominently displayed at a busy grocery store. This observation made me wonder why the company would place its product in such a remote location. I noticed the product had decent appeal among foreign tourists and bikers who frequent the Himalayan regions.

While this single data point wasn’t revolutionary, it sparked curiosity about whether the company had developed a wide distribution network - possibly indicating some moat. This new insight led me to gather more information and dig deeper into various aspects of the business and develop a more comprehensive view.

Further digging revealed that the company has actually become stronger over the years, has developed a formidable brand, but had been struck by “lightning” twice - rare but probable in business. Following these setbacks, investors had harshly punished the stock, driving down its valuation.

By updating my belief based on this new information, I could recognize that most of the pessimism is already priced in, resulting in an attractive valuation opportunity.

abhishek_sinha · March 28, 2025, 5:39am

Note 4 - My Learnings as a Portfolio Manager
EA Sundaram, YouTube

Summary

Follow Ben Graham/Seth Klarman principles; avoid emotional investing when comparing returns with others
Position limits: 10% per stock, 30% per sector, minimum 4 non-correlated sectors
Investment criteria: 15+ year track record, 500cr+ revenue, 20%+ ROC, strong FCF generation
Focus on what you control (time horizon, competence, position sizing) vs. external factors
Wait minimum 3 years to judge performance; exit when fundamentals don’t improve
Use reverse DCF and historical valuation comparisons; trim positions at +2 standard deviations
Quality companies at 20x can be cheap if historically traded at 25x
Cut through information noise to find meaningful insights

abhishek_sinha · March 28, 2025, 6:42am

Note 5 - The Art of Contrarian Investing
Anthony Bolton, YouTube

IMG_20250315_0005_page-00011754×1240 245 KB

IMG_20250315_0005_page-00021754×1240 196 KB

Summary →

Contrarianism is profitable because few practice it; unpopularity creates opportunity
Requires comfort with being different, strong conviction, emotional detachment, and patience
Best investment ideas are often uncomfortable; by the time they feel comfortable, it’s too late
Start small and increase positions as conviction grows
Consider both fundamentals (“weighing machine”) and sentiment (“voting machine”)
“Cheap can become cheaper” as momentum works bothways
Listen to counterviews and short sellers; encourage dissent
No investment strategy works consistently every year
Market excesses (like crypto) often signal market tops

Cshar · March 29, 2025, 7:04am

Fantastic thought process, being a contrarian is not easy however you will generate a large alpha if your thesis is right and it plays out well. I have created huge gains in bets where i had contrarian outlook and market neglected due to short term under performances. Best part is you can allocate a large capital as safety of margin is high due to low participation. Once it comes to momentum, not easy to make large alpha.

thakurvi · March 29, 2025, 4:02pm

Great thoughts to learn.

abhishek_sinha · October 9, 2025, 8:24am

Thinking in Bets: Why Life (and Investing) is More Poker, Less Chess

Book reading streak has improved drastically in the recent past - putting out summaries / sharing has not :)

I finished reading Annie Duke’s “Thinking in Bets” and it’s one of those rare books that changes how you think about decisions, especially in investing.

The core concept: We’re terrible at separating decision quality from outcomes. e.g. Stock went up 5x? We were geniuses. Stock tanked 40%? Bad luck or unforeseen events.

Annie Duke (professional poker player) challenges this line of thought with a comprehensive approach. Life is poker, not chess. You can make all the right moves and still lose the hand. You can make terrible decisions and get lucky.

The “wanna bet?” framework is powerful. When you force yourself to think “what’s the probability I’m right here?”, you move from certainty to humility. From “I’m right” to “I’m 70% confident.”

The book also talks about building a truth-seeking pod: people who challenge your thinking, not confirm it. Echo chambers kill returns.

Not a long book, very readable. If you invest seriously, worth your time.

Main takeaway: Focus on improving your decision-making process, not judging yourself by outcomes. Good process + probabilistic thinking = better odds over time.

The Book reduced to equations
This is purely for easy retention of key concepts - please don’t take these as rules, do read the book :)

Outcome ≠ Decision Quality
Good decisions can have bad outcomes; bad decisions can have good outcomes.
Decision Quality = (Expected Value × Probability) - (Ego × Certainty)
Your best thinking happens when you maximize EV while minimizing ego-driven overconfidence.
Life = Poker ≠ Chess
Hidden information + luck makes life probabilistic, not deterministic.
Truth-Seeking Group > Echo Chamber
Surround yourself with people who challenge your thinking, not confirm it.
Regret = |Actual Outcome - Imagined Outcome| × Hindsight Bias
We torture ourselves by comparing reality to what didn’t happen but could have happened.
Hindsight Bias × Motivated Reasoning = Self Deception
We remember our past predictions as more accurate than they were.
Bet Size = Edge × Confidence × (1 - Ruin Risk)
Even with an edge, sizing matters: over-bet and you risk catastrophic loss.

Notes taken in Obsidian below
Thinking in Bets.pdf (1.6 MB)

abhishek_sinha · November 13, 2025, 7:37am

Being Intellectually Lazy: Why One Lens Isn’t Enough

On the radiation problem, narrow thinking, and why studying across domains matters more than I thought.

Imagine you’re a doctor treating a patient with an infectious tumor. The tumor cannot be operated on. It must be destroyed, or the patient will die. You have a special ray that can destroy the tumor. At low intensity, the ray is harmless but ineffective. At high intensity, it destroys the tumor but also damages healthy tissue on the way.

How do you save the patient? Think about it.

Thanks for reading Margin of Stupidity! Subscribe for free to receive new posts and support my work.

Don’t feel bad if you can’t solve it. I couldn’t solve it either.

Let’s look at another story while you think about the tumor problem.

“A General wanted to capture his enemy’s fortress. He gathered a large army to launch a full-scale direct attack, but then learned, that all the roads leading directly towards the fortress were blocked by mines. These roadblocks were designed in such a way, that it was possible for small groups of the fortress-owner’s men to pass them safely, but every large group of men would initially set them off. Now the General figured out the following plan: He divided his troops into several smaller groups and made each of them march down a different road, timed in such a way, that the entire army would reunite exactly when reaching the fortress and could hit with full strength.”

Do you have an answer to the tumor problem now? It’s a bit embarrassing to admit that I couldn’t get it even after reading the general’s story.

When psychologists Gick and Holyoak presented this problem to participants, only 10% could solve it immediately. After reading the general’s story, 30% figured it out.

But what’s fascinating is that when they were explicitly told to use the story as a hint, 75-92% solved i t.

Fortress is analogous to the tumor. The large army corresponds to the high-intensity ray. Small groups of soldiers represent low-intensity rays. Once you see the pattern, the mapping is obvious – but only in hindsight.

Image: Duncker’s Radiation Problem by Blacktc

So, the solution is to split the ray up and direct multiple rays from different angles so they all reach the tumor together. Each individual ray is low intensity - harmless to healthy tissue. But when they converge at the tumor, they collectively deliver high intensity and destroy it.

The breakthrough wasn’t intelligence. It was analogical reasoning - recognizing that a solution from one domain (military strategy) could apply to another (medical treatment).

It’s naturally difficult to think beyond step one. We often do not look for connections outside our immediate domain.

I read this story in a remarkable book called Range , written by David Epstein about active open-mindedness and analogical reasoning.

Watching Someone Who Actually Does the Work

Earlier this year, I attended Prof. Sanjay Bakshi’s “Cases in Business and Investment Analysis” program at Flame University.

Prof. Bakshi (pen name Fundoo Professor) doesn’t need an introduction. I’d read his blogs before, so I knew what to expect – or thought I did. But what left me awestruck wasn’t his track record. It was watching someone who’s already proven everything still work with that level of curiosity and intensity. Crossing boundaries, connecting domains most investors wouldn’t explore. Finding robust business models in companies the market misunderstands and systematically avoids.

What increased my respect even more: his willingness to accept mistakes openly. Here’s someone with numerous multibaggers, yet constantly focused on improving the process. Not chasing returns, just relentlessly getting better.

Watching him dissect business cases, I felt stupid. Not because my returns haven’t been great. But because I realized I haven’t been rigorous and curious enough.

I’d been hiding behind “Buy quality at reasonable prices and hold them.” It sounds sophisticated. But little did I realize that it had become an excuse for intellectual laziness.

And just like the radiation problem, I’d been studying opportunities with only one lens – not looking for connections outside my immediate domain.

What I Was Missing

My approach wasn’t fundamentally wrong. But I’d turned good principles into rigid rules that filtered out entire categories of opportunity.

I’d spent too much time studying behavioral finance, imposing that lens on everything. Every opportunity got filtered: “Is this a quality business at a reasonable price?” If not, dismissed.

Worse, I’d become obsessed with avoiding risk. I’d fold my hands and wait. But the work of studying businesses shouldn’t stop just because valuations are elevated. You’re not building knowledge for next quarter – you’re building it for the next decade.

Youngme Moon, a prof. of Business at Harvard Business School writes in her book Different :

“Once we over learn something, we cease to know it anymore at all.”

She continues, “I have come to believe that a poem perfectly memorized is a poem too easily recited….And a poem performed without effort is a poem that has lost all meaning.”

That hit hard. I’d over-learned the quality investing playbook so thoroughly that I’d stopped actually thinking. Among others, I failed to study -

Companies in transformation: Businesses moving from mediocre to good. Management fixing capital allocation or reducing debt. Operational improvements that don’t show up in recent financials. These don’t fit neat quality checklists. I was studying balance sheets when I should have been studying inflection points.

Value migration: Sometimes the opportunity isn’t picking the best horse – it’s recognizing that the whole stable just got faster. Regulatory changes, government push, technology adoption, demographic shifts that expand profit pools dramatically. When such tailwinds hit, traditional valuation metrics make less sense. Even average companies with decent management get a massive shot in the arm.

I was like the doctor staring at the tumor with one type of ray, unable to imagine that solutions might require perspectives from completely different angles.

Why Learning Isn’t Linear

There’s a phrase that gets thrown around a lot in investing: “let it compound.” The implication is that if you just keep doing what you’re doing, stay consistent, don’t deviate, you’ll get there eventually.

But great compounding stories aren’t linear. They include step changes.

Amazon wasn’t just a bookstore that grew steadily. It made leaps. Books to everything. Everything to AWS. Each was a step change.

Why should learning be any different?

If I keep studying businesses through the same lens, I’m just getting incrementally better at a narrow skill. But if I study across domains - I’m building a library of patterns that can converge when I encounter something new.

That’s the radiation problem applied to investing.

You need multiple perspectives from different angles to see solutions that aren’t visible from a single vantage point.

My process hasn’t been good. I’ve been too static, too cautious, too narrow.

Annie Duke has this line in Thinking in Bets that keeps coming back to me:

“What makes a decision great is not that it has a great outcome. A great decision is the result of a good process, and that process must include an attempt to accurately represent our own state of knowledge.”

So what’s changing? I’m studying more ideas – across geographies, across business models, across industries I’ve ignored.

Will most lead to investments? No. But i hope each one adds to the pattern library.

I’m also changing where I spend my time. Less Twitter commentary, fewer videos of investment managers. More conversations (through podcasts, interviews, books) with operators, business owners, scientists – people actually building things. They understand what makes businesses work in ways analysts seldom do.

And critically: the humility to study businesses deeply without needing to act immediately. Not every analysis needs to end in a trade. But every analysis should end with learning something new.

Will this work? I don’t know. The outcome isn’t guaranteed – it never is in investing. But the process of improvement is what I can control. And right now, that process demands more ambition, more curiosity, and more hard work.

The Only Edge That Matters

I started this post with the radiation problem because it captures what I’d been missing. Most of us can’t solve it not because we’re unintelligent, but because we’re trapped in a narrow view.

I’m more motivated now than I’ve been in years. Not because I’ve had some great insight that’s going to 10x my portfolio. But because I’ve finally admitted I was coasting. That I’d mistaken “not making mistakes” for “doing good work.”

Munger was right. The only edge that truly compounds is learning. Everything else – the returns, the track record, the reputation – those are just outputs of how good a learning machine you are.

One ray wasn’t enough to solve the tumor problem. One lens won’t be enough for investing either.

The work starts now.

abhishek_sinha · November 21, 2025, 2:53am

I’ve avoided discussing individual stocks until now. But having surrendered my advisory license (due to regulatory overburden), I’m free to share my learning journey and decision-making process. I hope to learn more from fellow forum members :)

I’ve spent the last few months studying the energy transition. The transition is among the defining megatrends of our time, with profound implications for climate, human progress, and capital allocation. The study began with a hypothesis: the transition will happen, but there’s considerable hyperbole around it, particularly from investors who overlook the physics .

Influenced by Vaclav Smil’s work How the World Really Works and Power Density , I’ve come to appreciate that energy transitions are decadal processes, not overnight revolutions. The shift from coal to oil, for instance, took roughly 60 years to reach 25% of global primary energy supply. Oil’s share peaked at 45% in 1970 and has slowly declined to ~30% today. That decline will continue.

But the relevant question for a country like India isn’t whether renewables will grow - undoubtedly, they will. The question is whether they can serve the energy needs of a large, industrializing nation alone. Despite decades of growth, solar and wind still contribute less than 7% of global primary energy. As Smil writes, “Energy transitions have been, and will continue to be, protracted affairs because of the enormous scale of the shift. ”

Understanding these constraints is essential for evaluating which companies genuinely benefit from the transition versus those riding narrative momentum.

Many companies are riding the energy transition wave - manufacturers of solar panels, wind turbines, inverters, and the like. But their valuations reflect the investors’ enthusiasm. I reckon that I’m late to this party. I missed the opportunity of participating early in this massive capex upcycle. Entering now means exposure to asset-heavy companies facing commoditization risk and potential oversupply.

The better approach is to find a business that benefits indirectly from this tailwind while enjoying some durable competitive advantage - a tollbooth rather than a truck on the highway.

A Fallen Darling

The company I have in mind is IEX, the Indian Energy Exchange.

Until 2022, it was a darling of both institutional and retail investors - a rare Indian platform business with exceptional 80%+ EBITDA margins and 80%+ share in total volumes traded among exchanges. Then came the clouds of regulatory headwinds, the narrative broke, and the stock price halved in a few months.

Source: Screener

The bears aren’t wrong about the facts. The bear case is fairly easy to articulate and sounds absolutely logical -

“IEX is a monopoly exchange about to lose its monopoly. Market coupling (loss of price discovery) will let participants trade on any exchange with the same price. IEX’s 95% market share will collapse. The competitive advantage is gone. Game over. Sell.”

So why write a deep dive on a company that seems destined for mediocrity? Because I believe the bears are missing the nuance of how the grid is evolving.

Before we look at the stock, we have to look at the grid.

Disclaimer: I may hold positions in the companies/sectors discussed. This analysis is purely educational; I’ve been wrong before, and outcomes are uncertain. Please don’t construe this as investment advice.

Inside the Grid

India’s installed power capacity stands at 501 GW as of September 2025.

Capacity additions over the past seven years have been dominated by renewables . Approximately 120 GW was added between FY19 and FY25, with solar leading at 84 GW, followed by wind at 16 GW.

India’s installed capacity is expected to reach 700-710 GW by FY30, requiring net additions of 230 GW over five years. As per CRISIL’s estimates, over 80% of this, i.e, ~190 GW will come from renewable sources (excluding large hydro).

But capacity is not generation. A gigawatt of solar capacity is not equivalent to a gigawatt of coal capacity in terms of actual electrons delivered to the grid. Consider Solar power - while it contributes about a quarter of the installed capacity, it accounts for only 8.5% of actual generation.

This is why it’s crucial to consider plant load factor (PLF), which is the ratio of actual electricity generated to the maximum possible if the plant ran at full capacity around the clock.

Nuclear runs at 70-80% PLF, coal at 55-75%, hydro at 30-40%, and solar at 15-25%. Solar’s plant load factor is inherently low because the sun shines only during daytime.

This distinction matters because variable renewables’ share in the grid has risen from 5.6% in FY16 to nearly 14% today . Solar and wind are clean, abundant, and increasingly cost-competitive. But they share a fundamental problem: the sun doesn’t shine at night, and the wind doesn’t blow on command. Hence the term intermittent.

Extreme weather events compound this uncertainty as experienced this year amid extended monsoon around the country.

This intermittency makes the lives of grid operators difficult. Balancing supply and demand in real-time, managing transmission infrastructure, and ensuring power availability when consumers need it most become challenging. Unreliability has been the fundamental barrier to renewables fully replacing conventional thermal power (also called baseload).

To replace one reliable gigawatt of coal, you do not build one gigawatt of solar. You must build three or four gigawatts of solar, plus wind, plus storage, to hope to match the reliability of the fossil fuel you are replacing.

These challenges can be somewhat addressed through Firm and Dispatchable Renewable Energy (FDRE). In simple terms, you add a huge battery (energy storage systems) in the mix to store and draw power. This reduces the problem of unreliability. Let’s see how:

Consider a typical day.

In the morning, solar is ramping up slowly while demand surges as people wake up and head to work. The battery discharges to fill the gap.
During afternoon, sun’s blazing at full power but demand is moderate, so the battery charges with excess generation.
In the evening, solar drops to near zero when demand is peaking, so the battery discharges again.
At night, wind continues generating while demand is lowest. The battery may charge if wind exceeds requirements.

FDRE/Hybrid power is important for power distribution companies (Discoms) because they want stable power. They expect that reliability of RE should come close to a conventional, reliable power plant

Firm Power: Power that is always available (Baseload reliability).
Dispatchable Power: Power that can be ramped up or down on demand.

The share of hybrid and complex RE tenders (like FDRE) has jumped from 32% in FY21 to 59% in FY25.

How FDRE Contracts Work? On a typical day, Discoms present their energy requirements at intervals of 15 minutes (96 times a day). Developers must meet a 90% Demand Fulfillment Ratio (DFR) on a monthly basis. The penalty for missing this is severe: 1.5x the PPA tariff of the undelivered energy.

Now, let’s assume that you’re a developer who is building a FDRE project to deliver 250MW (contracted capacity). Since you already know that renewable energy is intermittent, and you don’t want to pay the penalties, you decide to install more capacity.

Solar: 508 MW
Wind: 90 MW
Battery: 700 MWh

Generation

There are 8,760 hours in a year (365 days × 24 hours). Here’s what each component produces:

Solar Generation: 508 MW × 25% × 8,760 hours = 1,113 MU per year (On average, producing 127 MW every hour, all year long)
Wind Generation: 90 MW × 35% × 8,760 hours = 276 MU per year (On average, producing 31.5 MW every hour, all year long)

Battery: 700 MWh

This is the project’s reservoir: think of it as a giant tank that stores excess electricity when generation exceeds demand, then releases it when the grid needs power but the sun isn’t shining.

Total Generated = 1,389 MU per year

Generated using Claude.ai

Distribution

Not all generated energy goes directly to the grid. Around a quarter is routed for battery charging.

Why does wind contribute proportionally more to storage (37% of its output) compared to solar (21%)? Wind generation often peaks during nighttime hours when demand is low, requiring more storage. Solar generation aligns better with daytime demand, allowing more direct supply.

Generated using Claude.ai

Deliverable Energy

The project sells roughly 92% through long-term PPAs and 8% on the power exchange at spot prices. That 8% sold on spot markets isn’t incidental, it’s structural.

Generated using Claude.ai

I hope you’re still with me.

The Flywheel Spins

Approximately 87% of electricity in India is transacted through power purchase agreements (PPAs). PPAs are long-term contracts (15-25 years) between generators and distribution companies that provide revenue certainty for project financing but offer little flexibility. The remaining 13% trades in short-term markets, which comprise three channels:

Bilateral contracts negotiated directly between buyers and sellers (typically for weeks or months),
Deviation settlement mechanism (DSM) charges for real-time imbalances between scheduled and actual generation, and
Power exchanges

The share of transactions through power exchanges has grown at 18% CAGR - from 54 BU in FY19 to 122 BU in FY24.

IEX is the leading power exchange. When a power plant in Gujarat has excess electricity at 2 PM because it’s sunny and demand is low, and a factory in Tamil Nadu needs power at that moment, IEX connects them. The plant sells, the factory buys, and IEX takes 4-5 paise clip per unit traded. That’s the crux of IEX’s business model. IEX operates across multiple market segments, each serving different trading needs.

The Day-Ahead Market (DAM) is the foundational product—participants bid a day in advance for delivery in 15-minute time blocks the following day.
The Real-Time Market (RTM), launched in 2020, allows trading just one hour before delivery, enabling generators and discoms to balance deviations from their day-ahead positions.
The Term-Ahead Market (TAM) facilitates contracts ranging from intraday to 11 days ahead for participants seeking slightly longer horizons.
Beyond electricity, IEX operates the Green Day-Ahead Market for renewable energy with explicit green attributes, and runs auctions for Renewable Energy Certificates (RECs) and Energy Saving Certificates (ESCerts) that help obligated entities meet regulatory compliance. The company also holds a majority stake in IGX, the Indian Gas Exchange.

Source: IEX Investor Presentation, Q2FY26

What makes electricity trading extraordinary is a physical constraint: electricity must be consumed the instant it’s produced . You can’t store it economically at scale . You can’t ship it in a truck. Supply and demand must balance every second, or the system collapses. This constraint creates continuous trading opportunities.

IEX is a classic two-sided platform exhibiting strong network effects. Each additional trader increases value for all other participants, creating a self-reinforcing feedback loop :

More buyers attract more sellers (better prices, faster execution)
Deeper liquidity attracts more traders (tighter spreads, lower transaction costs)
Higher volumes enable better price discovery and more reliable clearing
Better execution attracts even more participants

Pulak Prasad, the founder of Nalanda Capital, describes this phenomenon in What I Learned About Investing from Darwin through Naukri’s dominance in job platforms: “Given Naukri’s dominance, if you are a job seeker in India, you would almost certainly post your résumé there. If you are a company trying to hire, you are almost compelled to subscribe to Naukri because it has the most extensive and diverse pool of candidates.”

He continues: “Naukri is number one because it is number one .” (emphasis mine). This isn’t circular logic: it’s how platforms work. The leader accumulates advantages that become self-reinforcing. Market concentration in platform businesses is a feature, not a bug.

IEX’s dominance follows the same logic. If you’re a power seller, you list on IEX because that’s where the buyers are. If you’re a buyer, you trade on IEX because that’s where the deepest liquidity exists. IEX has near-monopoly in its key segments.

Regulators don’t like that.

Elephant in the room: Market coupling

What is Coupling? Currently, IEX discovers one price, and PXIL (the competitor) discovers another. Coupling means an external agency (led by Grid India) aggregates bids from all exchanges to discover a single national price. This effectively kills the “liquidity moat” IEX has in DAM segment.

The concept of coupling has been under discussion for some time. Expert groups recommended it in 2016, pilots ran in RTM and DAM in 2022-23, and the Central Electricity Regulatory Commission (CERC), the regulator, issued its order in 2025. Until July 2025, the matter was still being debated. Now the writing is on the wall—it’s no longer a matter of if, but when.

Some experts have questioned the motive of enforcing market coupling. A former IAS officer Raj Pratap Singh bluntly observes in his comment to CERC: “Regulatory intervention to change market design to increase volume of non-performing power exchanges is highly unjustified.” CERC itself accepts the same in Section 1.6 of the discussion paper: “the power exchanges with lower liquidity have been advocating for market coupling. ”

CERC, the regulator, looked at three exchanges, saw unequal market shares, and assumed regulatory intervention was required. They possibly overlooked the economic reality of winner-takes-most dynamics in platform businesses. Other Indian exchanges too, demonstrate the power law: NSE has held 90% market share for over 15 years, MCX sustains 95% share despite NCDEX competition. Liquidity begets liquidity.

In a recent seminar on Market Coupling and Electricity Derivatives, organised by MDI Gurgaon, Mr. S R Narsimhan (former CMD of Grid India) said “coupling had been under discussion since 2008 when congestion management emerged as a problem. Expert groups recommended it in 2016, pilots were run in RTM and DAM in 2022–23, and finally CERC issued its order in 2025. The pilots showed that DAM coupling offered only marginal welfare gains (around 0.3 percent) , while RTM+SCED coupling could deliver savings of up to ₹500 crore per month. Despite this, the regulator decided to start with DAM coupling .”

While the rationale for coupling is unclear, the implementation timeline by January 2026 looks ambitious. Prof. René Aïd, Professor of Economics at Université Paris Dauphine, France, sharing the European experience at the same webinar, emphasized that Europe took 10-12 years to achieve market coupling across 25+ countries, with challenges in harmonizing rules and governance. India, with a centralized structure, could move faster, though six months (by January 2026) is very challenging.”

The operational requirements are substantial. The CEO of PXIL - notably, the proponent and biggest beneficiary of coupling - outlined them himself: “standardized bid formats, settlement systems between exchanges, fungible collateral, and a joint governance council with CERC, Grid India, and exchanges .”

Read that again - essentially, CERC will have to regulate not only Grid India (Market Coupling Operator) and the exchanges, but itself. This is a herculean task, in my view.

There’s another crucial detail. Market coupling only applies to DAM. CERC’s July 2025 order explicitly states that coupling of Real-Time Market will be considered at a later stage, based on experience from DAM coupling. Why? Because RTM coupling is much harder technically. DAM trades 24 hours ahead, providing time to run complex matching algorithms. RTM trades 1 hour ahead, requiring near-real-time matching. BESS operators need sub-15-minute execution while coupling systems take 30-60 minutes.

The direction is clear: DAM coupling will happen . But regulatory timelines and operational timelines rarely align. Europe’s experience suggests implementation will take considerably longer than the announced timeline. The more likely outcome is a phased, delayed rollout - giving IEX more runway than the headline suggests.

The Other Side

While the sword of market coupling hangs over IEX’s DAM segment, some developments are structurally positive for non-DAM volumes.

Mandated Supply: The Government of India has mandated that all thermal generating stations must offer unrequisitioned surplus power on exchanges. Subsequently, Coal India allowed open market sale of surplus power under FSA.

Source: Business Standard

IEX’s management has confirmed the same in its recent quarterly results presentation

BESS deployment. The Ministry of Power has finalized VGF support for battery storage, with 13,200 MWh awarded and 47 GW targeted by FY32. BESS operators will likely use exchanges. Their buy-low-sell-high arbitrage model is more compatible with power exchanges than bilateral contracts.

FDRE pipeline. Storage-linked tenders grew from 32% of renewable auctions in FY21 to 59% in FY25. Each FDRE project structurally generates surplus that must be sold on exchanges.

The Bet

The investment case for IEX rests on a simple observation: renewable intermittency is a physics problem that creates a structural need for real-time power trading .

Every solar farm that overbuilds to meet FDRE commitments generates surplus that must be sold somewhere . Every BESS operator arbitraging day-night price spreads needs a liquid spot market . Every 15-minute deviation from forecast requires settlement.

IEX is the dominant platform serving this need, with over 80% market share built over 17 years of compounding network effects. The Real-Time Market is growing at a fast clip driven by factors mentioned in the sections above.

The risks are real. Market coupling will compress DAM share. Regulatory uncertainty won’t disappear. Another related risk is apathy among the institutions. When regulators strike, institutional investors often flee regardless of fundamental merit. The reputational and compliance risks of holding a stock under regulatory scrutiny outweigh the potential returns.

However, DAM coupling implementation may not be as swift and smooth as the regulators and bears anticipate.

The question isn’t whether IEX faces headwinds - it does. The question is whether those headwinds are priced in while the structural tailwinds are ignored. The share of DAM volumes in the total volume mix has declined rapidly in the last three years as IEX has successfully launched new products and diversified into other segments.

Source: Motlilal Oswal Financial Services

At current price, it’s below its 10-year median EV/EBITDA. It’s not a screaming undervaluation but a more modest proposition: an irreplaceable platform trading at an attractive value. The discount exists because of coupling fears. The question is whether those fears are appropriately sized - or oversized? I believe it’s the latter.

The highest-growth segment (RTM) is exempt from coupling for the foreseeable future. The physical dynamics driving exchange volumes: renewable intermittency, BESS arbitrage, FDRE surplus - are strengthening, not weakening. This creates a favorable asymmetry.

The market is focused on the DAM coupling headline. The RTM growth story is hiding in plain sight.

PavanM · November 21, 2025, 4:10am

Good compilation.

One aspect of electricity is, in India, it is (probably) treated as a critical commodity, much like fuel (petrol/diesel) - the government needs to be in control of the narrative or repurcussions are quickly felt at the voting booth. The OMCs, though having been liberated to float their daily prices in mid 2010s - were then coaxed back into having fixed prices.

Electricity affects ALL and we are not there yet in terms of leaving it to market-forces.

In the above equation - what does 25% and 35% represent ?

[Utilization factors over a day - only 25% of solar power in a day is captured for grid?]

The recent news of insider trading with possibility of collusion is not favorable news. However such matters are not investigated in depth with the possibility of more roaches appearing out of the woodwork. Generally the concerned are transferred or promoted to a place out of limelight.

RTM has the potential to be the unintended winner. As long as government bureaucracy is not sorted out in implementing DAM coupling, a delayed implementation will only weaken the case for RTM integration.

However, eventually (read as years) RTM will be brought into the fold - as ‘one country, one price’ will be an overriding factor.

abhishek_sinha · November 21, 2025, 6:17am

Thank You, Pavan for sharing your insights. Really appreciate them

You’re absolutely right in this assessment. What’s interesting is at least so far, the prices on exchange are trending downwards vs traders, so my guess is that buyers and sellers would continue to favor exchanges.

Yes, it’s average utilization - Solar about 25%, Wind about 35%

Wouldn’t this be a negative for CERC? From what I’ve read so far, I couldn’t find involvement of IEX officials.

Absolutely. There is not an iota of doubt about high regulatory risk in this business - possibly that’s what would keep the valuation under check.

abhishek_sinha · December 5, 2025, 3:57am

Headlines Sell Newspapers. Nuance Saves Portfolios.

Headlines are rarely designed to inform; they are designed to arrest. They are forced into brevity to sell newspapers, capture clicks, or power political speeches.

Here’s a trick you can try: after reading any financial news piece, pause and ask a simple question- did that headline actually reflect the nuance of the conclusion? My experience says that the answer is often “No”. Economic reality is far too complex, too layered, and too messy to be captured in a single bold typeface number.

Thanks for reading. Join our community of curious minds - subscribe for free to receive exclusive, in-depth analyses.

Which brings me to India’s recent quarterly GDP print. India reported a real GDP growth of 8.2% for the quarter ending September 2025.

Triangulation 101

Seasoned emerging market investors have an unwritten rule: the more precise a developing nation’s GDP number looks, the less you should trust it.

For Instance, take China. Global institutional investors take the country’s official headline GDP print with a ~~pinch~~ bucket of salt. Li Keqiang, former Premier of China made a stunning remark about China’s GDP figures to a visiting US Ambassador in 2007. He termed the GDP figures “man-made”. He argued that the accuracy of economic growth is better measured using three high-frequency indicators - outstanding bank loans (credit growth), electricity consumption, and rail freight volumes. This inspired The Economist Magazine to create The Keqiang Index, which became a go-to indicator for triangulating actual growth.

A headline that seldom fails to spark a discourse among economists and analysts/investors is quarterly GDP figures. This quarter’s GDP print is the highest since the March 2024 quarter.

Like China, India’s GDP estimate also deserves a healthy scrutiny. Not because the numbers are fabricated - but because in an economy as vast and segmented as India’s, that estimate may measure a different reality from the one consumers and corporations actually experience.

One Number, Many Indias

The IMF has given a C-grade for India’s national accounts in its Data Adequacy Assessment Exercise. This implies that the data received by IMF has “some shortcomings that somewhat hamper surveillance.” The agency has flagged that India’s GDP series with 2011-12 as the base year is “outdated” and should be rebased.

To be fair, MoSPI is planning to launch a new GDP series with 2022-23 as the base year in February 2026.

This leaves us with an important question: Which India does the data capture?

Formal India (large corporations, banking, organized manufacturing) is well-measured.
Informal India (which employs the majority of the workforce) is largely estimated using proxies and surveys.

When reforms like GST accelerate formalization , previously invisible economic output is pulled into the statistical net. Measured GDP can then grow faster than the underlying actual physical activity, simply because the measurement is suddenly better.

I’m not saying the numbers are wrong. I’m saying they might measure a different economy than the one most businesses experience.

For investors, a single GDP number is insufficient. You need corroboration.

The Investment Thesis

Let me start with what actually matters to an equity investor in India. The long-term thesis is simple:

India delivers 6-7% real GDP growth. Add 4-5% inflation, and you get 10-12% nominal GDP growth. Corporate revenues and earnings roughly track nominal GDP over time. Therefore, Indian equities should compound at low-double-digit rates, with additional returns from multiple expansion (aka growth premium or re-rating) during good times. Or, lower returns from multiple contraction (aka derating) during bad times. Over last two decades, the relationship holds: nominal GDP growth of ~12% translated to equity returns of ~13%.

But this linkage requires three conditions:

Corporate earnings must participate in GDP growth
Nominal growth must stay in double digits
Growth must be structural, not stimulus-borrowed

Here’s what the headline obscured: the same quarter that printed 8.2% real growth showed nominal GDP growth of 8.7%. The gap between real and nominal (the GDP deflator or inflation) was only 0.5%. Inflation has been collapsing.

Source: The Times of India

This matters because corporate India lives in nominal terms. Revenues are nominal. Costs are nominal. Margins are nominal. When nominal growth compresses toward single digits while cost structures stay sticky, earnings growth stalls.

When Nominal GDP growth is barely higher than Real GDP growth, it means companies have volume growth but no pricing power . Since they still have to deal with legacy cost inflation (wages, interest), their margins get squeezed, and the “Earnings Growth” that investors paid for evaporates.

The macro looks great. The micro looks messy.

The team at HDFC Institutional Securities compiles 60+ indicators to track the economic progress. It’s the kind of unglamorous, methodical work that helps investors see what headlines hide. The report, called Bharat Barometer is a good quick handbook to track the proxies of real growth.

Why do these proxies matter? In a developing economy with a large informal sector, official statistics are compiled with lags, subject to revisions, and dependent on surveys that may not capture ground reality.

High-frequency proxies - especially those tied to physical movement of goods, energy consumption, and tax collections - are harder to manipulate and faster to report. They measure what’s actually happening, not what models estimate should be happening. When trucks move, E-way bills get generated. When tolls are paid, FasTag registers it. When factories run, power gets consumed. These are the footprints of real economic activity.

The picture that September 2025 barometer report presents isn’t grim, but not rosy either. Formal sector services and rural agriculture seem to be doing well. Urban consumption, industrial activity, and trade are struggling.

Corporate earnings trajectory tells its own story. Nifty 500 companies reported 6% sales growth in 2QFY26.

More tellingly, quarterly earnings levels have been stagnant for the last six consecutive quarters.

This stagnation is partly mean reversion. Earnings growth since FY21 until FY24 was exceptional - driven by a depressed post-COVID base, pent-up demand. Small and mid cap companies delivered better returns as they got re-rated from beaten-down valuations. That tailwind seems to be fading. Valuations largely remain elevated, though not as expensive as they were in mid-2024. Expecting 15-20% earnings growth across board amid single-digit nominal GDP growth doesn’t sound rational.

If this resonated with you, please share it with a friend or fellow investor who you think would benefit most.

Humility Over Conviction

The macro case for India remains intact: The structural drivers- demographics, consumption growth, infrastructure investment, digitization etc. haven’t changed. Over the next 15-20 years, the GDP-to-returns linkage should hold.

But the near-term earnings cycle is challenged: Nominal GDP growth of 8-9% cannot support the 15-20% earnings growth the market priced in. Expect estimate downgrades to continue until nominal growth re-accelerates, or valuations de-rate to reflect lower growth.

There is no single India. There’s IT parks India, rural agricultural India, urban informal India, MSME India, large corporate India. A single GDP number cannot capture these divergent realities.

For investors, this uncertainty should breed humility, not conviction. The GDP debate will continue - statisticians will argue methodology, economists will debate deflators, commentators will spin narratives. None of that helps you allocate capital.

What matters is triangulation: cross-check the headline against high-frequency proxies, validate macro claims with micro earnings, and accept that in a country of many Indias, no single number reveals the whole truth.

What did I miss? I’m always learning from fellow investors. Share your thoughts below!

newrb · December 5, 2025, 1:15pm

Lovely write up Abhishek!

While macro is important, I think the trick is still to focus on bottoms up. There are businesses growing at ~15% clip, with minimal debt and good return metrics. If we have the patience to wait for them to come to reasonable valuations, we will do okay over long term.

This is my key learning from seeing this bull run followed by last one year’s correction in the broader market. Even though this is not my first cycle, it is always tempting to buy when everything seems to be going up!

The_Seeker · December 6, 2025, 8:11am

Excellent insights. Thanks a ton for sharing your thought process and the reference to bharat barometer reports. Just now downloaded Oct’25 report (for data till Sept’25). It is an excellent source to get a view on macro economic picture.
thank you again! keep writing and sharing the wisdom. Its really helpful!

abhishek_sinha · December 18, 2025, 2:25pm

@The_Seeker @newrb Really appreciate the kind words. It motivates me to share my learning.

abhishek_sinha · December 21, 2025, 5:24am

My post on KRBL
part 1 - KRBL- The King of Basmati rice - #1199 by abhishek_sinha

part 2 - KRBL- The King of Basmati rice - #1201 by abhishek_sinha

abhishek_sinha · January 5, 2026, 8:10am

Pari-Mutuel Notes

On the arrogance of active betting, the “Margin of Stupidity,” and why I’m learning to hunt outside my own backyard.

In 2023, I studied a beaten-down platform business. The company IPOed near the peak of euphoria in 2021 at a bizarre valuation. As global markets began correcting in November 2021, the stock price continued to go south. I studied the business model deeply and its peers in other geographies. I did “scuttlebutt” research when the stock was being dumped by investors and few analysts had any interest in the company. I worked to understand the competitive landscape, the management team, and the unit economics. I took a deep interest in the business because it fell within my circle of competence.

Then I bought exactly 10% of what I should have bought based on my conviction.

The stock went up 3x over two years. I made some money, but I made rounding-error money. Someone else - possibly someone who knew half as much but bet ten times as much made life-changing money.

I learnt an expensive lesson: You can be an expert at identifying a “multibagger”, but still remain an amateur at betting.

At The Betting Window?

If there is one person I have learned the most from on this journey, it is Michael Mauboussin. I spent the final weeks of the year revisiting his work. Mauboussin is a living legend precisely because of his humility; he treats investing as a perpetual learning journey rather than a destination of “expertise.”

I stumbled upon his recommendation of Steven Crist’s essay on horse racing. Crist is a legend in American handicapping who took an unconventional approach by applying math and probability to wagers while avoiding crowd favorites. Reading Chapter 3 of the book Bet with the Best was an epiphany.

He describes horseplayers who know everything about thoroughbreds: bloodlines, track conditions, and jockey statistics - but surrender their edge at the betting window. Crist labels them as:

He is like the chemistry scholar who knows the molecular structure of the coffee bean but has no idea how much water to put into the percolator. Each is unable to convert his knowledge into something useful and pleasurable—a steaming cup of java, or a consistent profit on racetrack bets.

Investors (including me) spend days or months reading annual reports, concalls, studying competition, and building models, only to buy at any price because they “admire” the business. I too am guilty as charged.

Crist calls this “handicapping well and betting poorly.”

The Pari-Mutuel Market: Why “Liking” a Business is a Fatal Sin

The most common error in investing is confusing fundamentals (how good a company is) with expectations (what the price implies).

Crist reduces the entire game to one brutal equation:
Value = Probability × Price

“There is no such thing as ‘liking’ a horse to win a race, only an attractive discrepancy between his chances and his price.”

Replace “horse” with “stock” and you have the entire discipline of sensible investing in one sentence.

But most investors can’t make this shift. We spend too much time analyzing companies and then pay a premium because we “like” the business.

We fall in love with the story and lose sight of the math. We confuse a great business with a great investment. We mistake understanding for edge.

As Crist notes, it is not enough to lose enthusiasm when the horse you like is “odds-on.” You must have a clear sense of what the price should be and be prepared to seize new opportunities depending solely on the “tote board” (the price).

In roulette, you play against the house. In horse racing—and in the stock market—you play against every other bettor.

Horse betting is a pari-mutuel system. Wikipedia defines it as “a betting system in which all bets of a particular type are placed together in a pool; taxes and the house-take are deducted, and payoff odds are calculated by sharing the pool among all winning bets.”

Crist explains:

“One of the great romantic myths of racing is that the players are a merry band of brothers united in their quest to smoke out the winner of each race. This is the case at the blackjack table, where everyone is playing against the house and all the players win when the dealer busts out. At the racetrack, however, every bettor is playing only against the other bettors. The house takes its cut off the top and has no financial interest in how the remaining money is carved up.”

The stock market is also a pari-mutuel system. This means investing is, by definition, a zero-sum game . In fact, after costs and taxes, it’s negative-sum .

For every dollar of alpha I generate, someone else has to underperform. That someone is probably sitting in an office just like mine, reading the same annual reports, believing just as strongly that they’re right.

The current price is just the collective bet of all participants. I’m not playing against “the market.” I’m playing against you. I’m playing against the consensus.

Crist makes this explicit:

Your opportunity for profit at the racetrack consists entirely of mistakes that your competition makes in assessing each horse’s probability of winning.

If you believe a stock is undervalued and start to buy it, you will help raise the price, thus driving down prospective returns.

My edge comes from other investors making systematic, predictable mistakes:

Extrapolating recent performance indefinitely.
Overpaying for narrative-driven growth with poor earnings quality.
Pricing peak cyclical margins as permanent.
Panicking during volatility.

When the crowd overvalues one segment of the market, they’re creating opportunity somewhere else. Possibly in boring, overlooked, out-of-favor stocks. The game isn’t to avoid the crowd’s mistakes . It’s to exploit them systematically.

However, it’s easier said than done. This feat means being willing to look stupid. To own things nobody wants. To sell things everyone loves. To sit on cash when “you’re missing the rally.” To deploy cash when everyone else is paralyzed by fear.

The Margin of Stupidity

We talk a lot about the Margin of Safety , but I prefer to invert the concept and look at it as my Margin of Stupidity .

Most investors view a Margin of Safety as a buffer against market volatility. I view it as a buffer against myself . I am a buggy biological machine. My software (brain) has glitches - recency bias, loss aversion, and a desperate need for consensus.

Because I cannot trust my own knowledge or judgment to be perfect, I need a price that allows me to be wrong.

In my framework:

Allowable Stupidity = Margin Of Safety

Valuation matters because it defines my capacity for error. If I buy a stock at a deep discount to its intrinsic value, I am essentially giving myself a Quota of Stupidity . I can be 50% wrong about the growth rates, 50% wrong about the terminal value, or 50% wrong about the management’s execution, and I still might not lose my principal.

The lower the price I pay, the more “stupidity” I can absorb without blowing up.

Loss aversion in others creates panic-deprived prices.
Panic in the crowd creates a higher Margin of Safety for me.
Therefore: My job is to act when the market’s collective panic (towards a stock, sector or broader markets) maximizes my own Margin of Stupidity .

Frameworks I am Still Building

Being unemotional about the companies I own is a constant battle. As I continue to learn, three areas remain works in progress: position sizing, selling, and home bias.

Most investors eventually develop some intuition about position sizing, even if it’s imperfect. But most of us struggle with selling. And as international investing is now somewhat more accessible. While barriers still exist — like TCS and limitations on LRS — I believe it’s worthwhile to explore.

I don’t want to make this my core portfolio; however I don’t want to miss the opportunity to own great franchises.

I’m working on frameworks for these. They’re not complete. They’re probably not even right. But they’re better than what I had before, which was nothing.

1. Position Sizing & The “Net Worth” Test

If you’re a stock picker, you’ll see tons of ideas floating around Twitter/X and WhatsApp groups. When one becomes a multibagger, the author often takes a victory lap.

But my first question is always: “How much of your net worth did you bet on it?”

As Nassim Taleb says: “Don’t tell me what you ‘think,’ just tell me what’s in your portfolio.”

I’d also add - how much of “that” is in your portfolio.

Or as George Soros put it:

“It’s not whether you’re right or wrong that’s important, but how much money you make when you’re right.”

There is no right answer, but the answer tells you the conviction.

A young investor might bet 40%.
A 40-year-old with a family might bet 4%.

Position sizing separates the men from the boys. I can make 50 predictions, and the likelihood of hitting one or two 10-baggers is high purely by chance. That doesn’t make me a genius.

It’s how much you make when you are right that counts.

2. Knowing When to Sell

My guess is that over 90% of investing literature is on what to buy. Value, quality, catalyst, turnarounds, momentum, sector rotation, and whatnot.

My favorite investors are permanent, long-term buyers of great businesses.

Knowing who I am? I don’t have a fraction of their capital, nor do I have their temperament. I get nervous when the stocks I own trade at peak valuations. I regret not taking money off the table when valuations were absurd more than I regret missing the next leg up.

Maybe one day I’ll build the temperament to buy great and forget. But not today. And pretending I have that temperament when I don’t is just another form of self-deception.

I want to clarify that it’s not about maximizing returns , but following a process. Most investors around me have strong processes about buying. Few talk about selling.

Selling is harder than buying because it requires psychological closure. It’s the moment you admit you were either: a) wrong about your thesis, b) overpaid, or c) the price is way ahead of fundamentals.

Many investors avoid this pain by never selling at all. In the past, I’ve sold impulsively at the worst possible time. I’m trying to build a framework that removes emotion from the decision.

Tactical Trimming

When a stock’s valuation spikes to two standard deviations (+2SD) above its ten-year median - whether measured by price-to-earnings, enterprise value to EBITDA, or any reasonable metric—it’s not just a number. It’s information. If this coincides with broader euphoria in the markets — it’s even more actionable.

It’s telling me the herd has arrived. The consensus has shifted from “undervalued” to “fully valued” to “priced for perfection.”

The pari-mutuel odds have moved against me.

My rule: trim 20%-70% of the position. Take the principal off the table. Let house money run if it wants to.

This isn’t about liking the company less. The business might still be excellent. Management might still be executing. The moat might still be widening. But the bet has changed. I’m in the business of making bets with favorable odds, not holding vigils for businesses I admire.

The crowd isn’t always wrong. But when they crowd into a trade, they move the odds. My job is to notice when the odds have shifted.

Full Exit: Kill Your Darlings

Busted Thesis. The narrative I bought into didn’t happen. The thesis was wrong. The company is losing market share rapidly. Excess supply or competition has eroded business attractiveness. The competitive advantage didn’t materialize. The business model is under threat.

Bend in Management. The moment capital allocation becomes minority-unfriendly - excessive dilution, value-destructive acquisitions, diworsification, or any behavior that prioritizes insiders over shareholders - I exit.

Management integrity isn’t something you can diversify away. Quite often, one red flag is more than enough. Sleep is more important. Life’s too short and the opportunity set is too large to give management the benefit of the doubt when they’re showing you who they are.

I had written recently about one such instance.

3. Fighting Home Bias / Crossing Geographical Boundaries

I’ve realized that asset allocation is far more important than any single stock pick. If I restrict myself to my home market, I’m likely suffering from a comfort bias that handicaps my returns.

Developed markets host IP-heavy, global franchises and tech giants that simply don’t exist elsewhere. I have to look where the best businesses live (for the price I’m paying), regardless of borders. More on this soon.

The Only Guaranteed Return

Markets are more efficient than they were a few years ago.

Yes, information spreads faster. Yes, more capital is managed by sophisticated players.

But the market is still full of mistakes - emotional mistakes, structural mistakes, behavioral mistakes.

The game isn’t easy. It never was. But it’s not impossible either. It’s definitely winnable. Not because I’m smarter than everyone else - I’m not.

But because I’m willing to do what Mauboussin and Crist highlight: Think in probabilities, bet only when I have an edge, and be ruthlessly unsentimental about price instead of falling in love with stories.

I’ll be wrong often. I’ll miss opportunities. I’ll sell things that keep running. I’ll buy things that keep falling. I’ll look stupid in the short run while other people look brilliant.

Good process doesn’t guarantee short-term vindication.

But if I keep refining this process - learning from mistakes, adapting to how the game evolves, staying intellectually honest about what I know and don’t know - I’ll probably end up a little less stupid.

That’s the only return that’s guaranteed.