It's rare to experience a time like we are experiencing now.
Both risk appetite and uncertainty are arguably at their highest levels in recent history.
These two phenomena rarely exist simultaneously and typically emerge during periods of transformative innovation.
Normally, investors do not seek risk exposure into uncertainty.
But, this time is different. Investors know AI is the "next big thing," but no one knows exactly where it all leads.
The result will be broader swings in the market amid historic innovation.
Capitalize on the volatility.
Embedded Files
On India Valuations: An Exact Answer to the Wrong Question
John Tukey — the statistician who wrote Exploratory Data Analysis and taught the world how to actually look at data — would be horrified at how Wall Street uses averages to justify valuations.
His most quoted line:
"Far better an approximate answer to the right question, which is often vague, than an exact answer to the wrong question, which can always be made precise."
The current consensus on India is an exact answer to the wrong question
The Market Is Solving the Easy Problem India's relative P/E to the US and EM has fallen below its long-term averages. The logic: a historical multiple is fair value; we trade below it; therefore, we are overweight.
The arithmetic is right. The question is wrong. Valuations are regime-dependent.
Three charts on why the current regime is materially different than past .
#1. The Earnings Regime Has Materially ChangedThe valuation premium was built on relative earnings strength. The chart tracks 12-month trailing EPS versus the US and EM, rebased to 100 in March 2012. India was ahead of EM until September 2025, then gave it all back. Both lines now point the same way: down — India's relative earnings continue to deteriorate
Article contentThe second chart shows how quickly the picture has changed. A few years ago, India led both on growth. Today, it trails both — and the gap is still widening.
Article content#2 America Has Changed Its Mind. Russia Has Run Out of Options.For two decades India had a free pass. Washington was focused on China — in 2004, the US goods deficit with China was $162B; India's was $9B. By 2024, India's deficit had grown to ~$46B. Meanwhile IT exports, the H-1B pipeline, and student visa flows (80,000 in 2004/05 to 360,000 in 2024/25) accumulated quietly. India is no longer under the radar.
Trump saying India has "taken advantage" of the US is one sign. The clearer signal came from US Deputy Secretary of State Christopher Landau at the Raisina Dialogue:
Recommended by LinkedIn"Why Nifty 50 Underperformed in the Last Year: A Comprehensive Data-Driven Analysis" "Why Nifty 50 Underperformed in the Last Year: A…Vaidyanathan Ravichandran 8 months agoIs India Heading Toward a Market Crash? A 3-Year Outlook Based on Historical Patterns & Valuation SignalsIs India Heading Toward a Market Crash? A 3-Year…Prasad Ballari 6 months agoBeyond the Ledger: Re-evaluating India's Macroeconomic Trajectory Amid Base-Year Revisions - Dr. Nirmal GangulyBeyond the Ledger: Re-evaluating India's Macroeconomic…Nirmal Ganguly 1 week ago"We are not going to make the same mistakes with India that we made with China 20 years ago — saying 'you will be able to develop all these markets,' and then the next thing we know, you are beating us in many commercial things."
Alexander Gabuev's recent podcast argues that Russia is becoming a vassal state of China, acting as a monopsony buyer for its commodities while relying on China as a monopoly supplier of technology and industrial inputs. Moscow's options have collapsed.
India's traditional strategic hedge against China is being absorbed by China itself. India enters this new US posture with fewer counterweights, not more.
#3. Indian Misery Is Higher Than the Official Numbers SuggestThe Misery Index — inflation plus unemployment — has been a staple for macro allocators in US since the 1970s. When misery is high, starting valuations are low, and forward returns tend to be good.
Article contentIndian official statistics are not always reliable, so we built our own measure of economic stress, which we call the Grassroots Distress Index. The index combines three indicators: real entry-level IT fresher salaries, microfinance loan delinquencies, and household net financial savings as a share of GDP.
As the chart shows, the Grassroots Distress Index is now at a multiyear high.
Stop Averaging Across Regimes You Can't Average AcrossThe 20-year average is a starting point, not the answer. Tukey would have examined the components first. All three are above — none support the lazy conclusion that India is cheap because the relative P/E is below average.
The right question is not: "Where does India trade versus history?"
The right question is: "What multiple does India deserve in this regime?"
That answer is less precise. It is also more useful.
John Tukey — the statistician who wrote Exploratory Data Analysis and taught the world how to actually look at data — would be horrified at how Wall Street uses averages to justify valuations.
His most quoted line:
"Far better an approximate answer to the right question, which is often vague, than an exact answer to the wrong question, which can always be made precise."
The current consensus on India is an exact answer to the wrong question
The Market Is Solving the Easy Problem India's relative P/E to the US and EM has fallen below its long-term averages. The logic: a historical multiple is fair value; we trade below it; therefore, we are overweight.
The arithmetic is right. The question is wrong. Valuations are regime-dependent.
Three charts on why the current regime is materially different than past .
#1. The Earnings Regime Has Materially ChangedThe valuation premium was built on relative earnings strength. The chart tracks 12-month trailing EPS versus the US and EM, rebased to 100 in March 2012. India was ahead of EM until September 2025, then gave it all back. Both lines now point the same way: down — India's relative earnings continue to deteriorate
Article contentThe second chart shows how quickly the picture has changed. A few years ago, India led both on growth. Today, it trails both — and the gap is still widening.
Article content#2 America Has Changed Its Mind. Russia Has Run Out of Options.For two decades India had a free pass. Washington was focused on China — in 2004, the US goods deficit with China was $162B; India's was $9B. By 2024, India's deficit had grown to ~$46B. Meanwhile IT exports, the H-1B pipeline, and student visa flows (80,000 in 2004/05 to 360,000 in 2024/25) accumulated quietly. India is no longer under the radar.
Trump saying India has "taken advantage" of the US is one sign. The clearer signal came from US Deputy Secretary of State Christopher Landau at the Raisina Dialogue:
Recommended by LinkedIn"Why Nifty 50 Underperformed in the Last Year: A Comprehensive Data-Driven Analysis" "Why Nifty 50 Underperformed in the Last Year: A…Vaidyanathan Ravichandran 8 months agoIs India Heading Toward a Market Crash? A 3-Year Outlook Based on Historical Patterns & Valuation SignalsIs India Heading Toward a Market Crash? A 3-Year…Prasad Ballari 6 months agoBeyond the Ledger: Re-evaluating India's Macroeconomic Trajectory Amid Base-Year Revisions - Dr. Nirmal GangulyBeyond the Ledger: Re-evaluating India's Macroeconomic…Nirmal Ganguly 1 week ago"We are not going to make the same mistakes with India that we made with China 20 years ago — saying 'you will be able to develop all these markets,' and then the next thing we know, you are beating us in many commercial things."
Alexander Gabuev's recent podcast argues that Russia is becoming a vassal state of China, acting as a monopsony buyer for its commodities while relying on China as a monopoly supplier of technology and industrial inputs. Moscow's options have collapsed.
India's traditional strategic hedge against China is being absorbed by China itself. India enters this new US posture with fewer counterweights, not more.
#3. Indian Misery Is Higher Than the Official Numbers SuggestThe Misery Index — inflation plus unemployment — has been a staple for macro allocators in US since the 1970s. When misery is high, starting valuations are low, and forward returns tend to be good.
Article contentIndian official statistics are not always reliable, so we built our own measure of economic stress, which we call the Grassroots Distress Index. The index combines three indicators: real entry-level IT fresher salaries, microfinance loan delinquencies, and household net financial savings as a share of GDP.
As the chart shows, the Grassroots Distress Index is now at a multiyear high.
Stop Averaging Across Regimes You Can't Average AcrossThe 20-year average is a starting point, not the answer. Tukey would have examined the components first. All three are above — none support the lazy conclusion that India is cheap because the relative P/E is below average.
The right question is not: "Where does India trade versus history?"
The right question is: "What multiple does India deserve in this regime?"
That answer is less precise. It is also more useful.
How Jim Simon's Algorithm Prints Money While You Sleep (The Exact Quant Framework)
How Jim Simon's Algorithm Prints Money While You Sleep (The Exact Quant Framework)
The most successful hedge fund in history never had a losing year. In 34 attempts. Here's the exact system - every formula, every signal, runnable code.
Spring 1988. A mathematician who spent his career cracking Cold War codes walked into a trading office and did something nobody on Wall Street had ever tried: he fired all the traders and replaced them with signal detectors.
His name was Jim Simons. His fund was called the Medallion. The results: $60 billion in performance fees extracted from markets. $30 billion net worth for Simons alone. 39% annual returns after a 44% performance fee. Zero losing years in 34 attempts.
Every quant on this platform has seen these numbers. Almost nobody knows what is actually running under the hood.
Until now.
LAYER 1: The Signal Detection Engine
LAYER 1: The Signal Detection Engine
Renaissance doesn't trade on news. It doesn't read 10-Ks. Simons' team relies on data-driven methods, using statistical analysis and computational power to identify market inefficiencies - collecting enormous amounts of data and analyzing it to find statistical patterns and non-random events across a wide range of markets.
The first layer is a pattern extractor. Think of it as a seismograph - not reading earthquakes, but reading the invisible tremors in price data that precede large moves.
LAYER 2: The Factor Model (Why Stocks Move Before Anyone Knows Why)
LAYER 2: The Factor Model (Why Stocks Move Before Anyone Knows Why)
Serious quant operations pull options chains, order-book depth, fundamental data (earnings, revenue, cash flow), corporate actions, news sentiment, social media volume, and increasingly alternative datasets such as satellite imagery, anonymised credit card transactions, web traffic, app downloads, and shipping container movements.
Renaissance identifies why price moves happen by decomposing returns into factors. The Fama-French 5-factor model is the public version of this. Renaissance's private version goes 200+ factors deep.
LAYER 3: The Markov Chain Regime Machine
LAYER 3: The Markov Chain Regime Machine
One of the most-cited secrets of top quant funds is regime detection - knowing which market state you're in before you trade. Natural Language Processing scans news articles and social media, extracting insights into public sentiment, but the deeper regime signal comes from price structure itself.
Markov Chains model the probability of transitioning between market states:
If the model says 76% probability of remaining in Bear regime over the next 5 periods - you're not going long.
LAYER 4: The Neural Network Edge Extractor
LAYER 4: The Neural Network Edge Extractor
AI systems use deep learning to generate and execute trading signals in real-time, with reinforcement learning to continually optimize trading strategies and improve timing for entries and exits.
The neural net layer extracts non-linear relationships no formula can capture:
The key insight: you're not predicting price. You're predicting probability of direction. Even 52% accuracy - if your Kelly-sized correctly - prints money.
LAYER 5: The Execution Layer (Where 80% of Quants Bleed Out)
LAYER 5: The Execution Layer (Where 80% of Quants Bleed Out)
Renaissance must have built infrastructure to keep execution costs very low - the reported gross returns are after trading costs, making Medallion's performance even more extraordinary.
Most retail quants blow their edge on execution. The fix:
THE MEDALLION PLAYBOOK: All 5 Layers Every other quant fund employs thousands. Renaissance has 400 people total, maybe 200 touching the models. Every other fund has multiple competing strategies. Renaissance has one model everyone works on together.
That's the secret nobody talks about: the edge isn't any single formula. It's the pipeline - each layer filters out noise until only statistical certainty remains.
THE NUMBERS DON'T LIE
THE NUMBERS DON'T LIE
Most quant strategies die from overfitting. Medallion survives because:
They never show the full system to outsiders - keeping alpha alive
They closed to outside money - preserving capacity
They use half-Kelly - surviving drawdowns that kill over-levered quants
Every signal is out-of-sample tested first - no data leakage
By 2025, more than 75% of US equity trading volume was driven by quantitative or algorithmic systems. The market is quant. Your edge is building the best pipeline.
The code above is your starting point. Every formula is runnable. Every layer is stackable.
KWIL
KWIL
Kwality Wall’s is making an interesting move
They are shifting from frozen desserts to real milk based ice cream.
Even better - some products are actually going to get cheaper
Looks like they are focusing on better quality while trying to reach more customers
Smart play.
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