Plain-English reads on money.
Start with the basics of SIPs, costs and risk. The research collection goes deeper for readers who want it.
How to choose your first mutual fund
Read →Regular and direct plans: what the difference costs you, and what it buys
Read →What happens when you miss a SIP
Read →Understanding a fund's Riskometer
Read →
How to choose your first mutual fund
There are over a thousand schemes. You need one. Here is how to narrow it down in three questions — without reading a single factsheet.
Regular and direct plans: what the difference costs you, and what it buys
Every scheme comes in two versions with identical holdings and different prices. Knowing which one you are buying — and why — is the most important cost decision you will make.
What happens when you miss a SIP
Short answer: nothing dramatic. Here is exactly what the fund house does, what your bank might charge, and when a SIP actually gets cancelled.
Understanding a fund's Riskometer
The little speedometer on every scheme document is the single most useful thing on the page. Here is what each level means and how to use it.
Trend Following: A Multi-Century Edge Hidden in Plain Sight
Why a simple, century-old idea — buy what is going up, sell what is going down — remains one of the most academically validated and behaviourally durable sources of return in modern markets.
Risk Parity Reconsidered: Beyond the 60/40
A measured look at risk parity twenty years after Bridgewater first popularised it: where the intuition still holds, where the criticism is fair, and how institutional investors are using it today.
Factor Investing in 2025: Value, Momentum, Quality, Low-Volatility
A practitioner’s view of where the canonical equity factors stand after a decade of crowding, drawdown and renewal — and how to construct factor exposure that survives.
Machine Learning in Asset Management: What Actually Works
The honest answer to where machine learning has produced durable edge in investing — and where it has mostly produced expensive overfitting.
Market Microstructure for Systematic Traders
Edge in modern markets is increasingly determined by execution. A primer on the structural features every systematic trader should understand.
Volatility Targeting: The Most Underrated Risk Tool
A simple sizing rule — trade smaller when markets are choppy, larger when they are calm — has produced one of the largest improvements in risk-adjusted return in the systematic literature.
Cross-Sectional vs Time-Series Momentum
Two cousins of the same family, often confused, with materially different risk characteristics. Understanding the distinction is fundamental to portfolio design.
Bayesian Methods in Portfolio Construction
Why principled handling of uncertainty — not point-estimate optimisation — is the foundation of robust institutional portfolios.
Why Diversification Across Strategies Beats Diversification Across Assets
In a world where asset-class correlations rise in crises, the next layer of true diversification is across return-generating processes, not across instruments.
Macro Regimes and Systematic Allocation
Inflation, growth and monetary policy combine into regimes that explain a large share of cross-asset return. A disciplined regime framework is more useful than any single forecast.
Statistical Arbitrage in Indian Equities: A Practitioner’s View
India’s equity market has matured into one of the more interesting venues for stat-arb research. Where the inefficiencies still live, and how to think about them.
Alternative Data and the Future of Research Edge
Alt data is no longer alternative. The frontier has moved from raw datasets to the engineering and modelling discipline that turns them into actionable research signals.
Why Backtests Lie: A Practitioner’s Guide to Honest Validation
Nine out of ten beautiful backtests will not survive contact with live capital. The reason is rarely technical — it is statistical and behavioural. A field guide to building research you can stake real money on.
Tail Risk Hedging: When the 50-Year Storm Arrives
The strategies that protect a portfolio in 2008, 2020 or the next crisis look uncomfortably expensive in calm markets. The honest framing is not insurance versus cost — it is convexity, mandate and time horizon.
Building a Quant Team: Skills, Culture, Process
The hardest problem in quantitative finance is rarely the maths. It is the institutional question of how to combine specialised talent, sustainable culture and reproducible process into something that compounds.
The Quant Researcher’s Reading List
A curated reading list for serious quant researchers and institutional investors. Books that improve thinking, not just technique — spanning markets, statistics, history and the philosophy of model use.