Quantitative intelligence, built to improve decision quality.
We conduct quantitative research and build machine-learning models and research tooling for hedge funds, family offices and professional investors. Data. Research. Discipline.
Checked before it's called an edge.
Each candidate goes through structured checks on robustness, execution and capacity — so you get a verdict, not a backtest to admire.
Six disciplines, one research culture.
August Quants is built around the conviction that institutional-grade investing demands an engineering mindset, a research culture and a refusal to confuse activity with insight. Our work spans the full quantitative stack — from hypothesis to implementation.
Quantitative Research
Rigorous, data-led investigation of return drivers across equities, fixed income, FX and commodities.
Machine Learning Models
Production-grade ML pipelines, feature engineering and validation discipline calibrated for financial data.
Portfolio Construction Research
Decision-support for risk-budgeted, regime-aware portfolios — designed around horizon, drawdown tolerance and mandate. Clients build and decide.
Systematic Strategy Research
Trend, carry, mean-reversion and cross-sectional strategy research — frameworks that clients implement themselves.
Market Intelligence
Institutional-grade synthesis of macro, micro and flow data. Signal over noise, always.
Bespoke Quant Solutions
Custom research engagements, model development and decision-support for sophisticated allocators.
Institutional-grade artifacts.
Every engagement produces documents built for investment committees.
Research report
Strategy factsheet
Market intelligence brief
From mandate to model to actionable research.
Understand
We start with mandate, constraints and intent — not with models. Every engagement begins with a structured discovery conversation.
Research
Hypotheses are framed, data engineered, signals tested with cross-validated discipline and capacity in mind.
Construct
Robust portfolios are built using risk parity, factor budgeting and regime-aware sizing — not point-estimate optimisation.
Deliver
Research output, documentation and an ongoing research dialogue. Clients implement their own investment decisions.
What usually happens instead.
The backtest is taken at face value
Walk-forward, survivorship-corrected, costs-in testing
A bull-market run is mistaken for edge
Validated across full cycles — including 2020 and 2022
Strategies die on real execution
Slippage and capacity modelled before anything is called an edge
Conviction with no falsifiable process
Every claim published with the conditions that would disprove it
How we differ from the typical desk.
What a firm is paid for shapes what it puts in front of you. We hold no product inventory — so nothing here is pushed to clear a shelf.
| August Quants | Typical research / advisory | |
|---|---|---|
| Starts with your mandate, not an existing shelf | ||
| Publishes falsifiable claims — with what would disprove them | ||
| Costs, slippage and capacity modelled before an edge is claimed | ||
| Every backtest survivorship-corrected and walk-forward tested | ||
| No product inventory of our own; research is fee-based, not commissioned | ||
| Discloses the capacity in which each document was prepared | ||
| Monitoring and review continue after delivery |
"Typical research / advisory" describes common practice across distribution- and pitch-led providers.
Rigour is the only durable edge.
We believe markets reward intellectual honesty more than complexity. Most great strategies are simple ideas implemented with discipline.
Our research is shaped by four principles: every hypothesis must have a coherent economic explanation, every test must be honest about multiple-comparison risk, every model must respect capacity, and every portfolio must be understood before it is owned.
A repeatable process for an unrepeatable market.
Discretionary judgement is necessary but insufficient. Systematic process delivers consistency, removes behavioural drift, scales across markets, and makes risk explicit and visible.
Every decision is reproducible. Every position has a documented rationale.
Risk is sized, not guessed. Drawdowns are budgeted before they occur.
A research framework that works across markets, instruments and time horizons.
Start here if you are a…
Hedge Fund
A research mandate or collaboration — signal research, validation and capacity work delivered to your desk.
Family Office
Independent evidence on managers and structures, plus an implementation channel where suitable.
Wealth Manager or Advisor
Sharp, falsifiable market intelligence you can bring into client and IC conversations.
Professional Investor
Original research essays and a monthly letter written for readers who value rigour over noise.
Where our research looks.
We look past the story to the operating and statistical evidence.
Systematic equities
Cross-sectional and time-series signals across the NSE universe.
ExploreOptions & market microstructure
Order flow, auction theory and the mechanics of how prices form.
ExploreFactor research
Value, momentum, quality and low-volatility, tested honestly.
ExploreMachine learning in finance
Where ML genuinely helps — and where it quietly overfits.
ExploreExecution & capacity
Slippage, turnover and the real limits of a strategy at size.
ExploreRegime & risk
Drawdown behaviour and stability across full market cycles.
ExploreSelected papers from the desk.
The Empirical Record of Systematic Strategies (1990–2024)
A consolidated review of out-of-sample evidence for the canonical systematic premia — trend, carry, value, momentum, quality, low-volatility — across asset classes and decades.
Regime-Aware Volatility Targeting: An Adaptive Framework
A study of dynamic volatility-targeting estimators that adjust to macro regimes and market microstructure conditions.
Liquidity Fragility in Indian Mid-Caps
Empirical patterns in queue dynamics, order-flow toxicity and impact functions in NSE mid-cap names.
Notes from the research desk.
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.
Every backtest survivorship-corrected. We publish our discipline, not our forecasts.
Try the cost-drag calculatorTalk to our research team.
For institutional mandates, custom research engagements and quant collaboration enquiries. We respond within one business day.