Research Note / Operational Due Diligence
Quant Hedge Fund Due Diligence Checklist by Strategy Type
A quant hedge fund due diligence checklist by strategy: the main failure mode and five questions each for trend, stat arb, macro, short-horizon and ML.
- Focus
- Quant hedge fund due diligence checklist
- Audience
- Professional investors, allocators, family offices
A quant hedge fund due diligence checklist works only when it is matched to the strategy, because “quant” describes a method, not a single risk profile. The reliable approach is to name the main way each strategy type fails, then put five targeted questions to the manager about that failure.
Why does one checklist not fit every quant manager?
A trend following program, a market neutral equity book and a machine-learning model can all be called systematic, yet they lose money for different reasons. A generic checklist asks about custody, administration and fees, which matter everywhere, and then runs out of useful questions. A strategy-specific checklist adds the questions that probe the actual source of risk.
Start with the questions that apply to every manager, set out in the section below. The questions for each strategy come after that baseline.
| Strategy type | Main failure mode | What to examine first |
|---|---|---|
| Trend following (CTA) | Long stretches without a trend, and trend reversals | Risk sizing, signal diversity, cost of rolling and trading |
| Statistical arbitrage or market neutral equity | Crowded positions unwinding together; hidden factor exposure | Factor exposure, borrow, crowding, correlation in stress |
| Systematic macro | Few independent bets; regime change | Number of independent decisions, data history, regime dependence |
| Short-horizon, execution-sensitive | Simulated fills that never occur live | Fill assumptions, latency, market impact, capacity |
| Machine-learning driven | Overfitting and leakage; behavior outside the training regime | Validation design, trial record, drift monitoring |
Which questions apply to every strategy?
Strategy-specific questions sit on top of a common frame. Reconcile, doubt and confirm, in that order.
Reconcile returns
Tie the return series to sources the manager does not control, such as administrator and broker statements.
Treat backtests as hypotheses
Ask for the trial count and for data that was never examined during research.
Confirm custody and authority
Know who holds the assets and what the manager is permitted to do with them.
Qlumina's 38-point forensic due diligence framework organizes this frame into six pillars: market data provenance, overfitting and backtest forensics, execution architecture, capacity, autonomous risk systems, and governance and custody. Use it as a cross-check after the strategy questions. For the validation side in depth, see the notes on causal factor placebo testing and the blind out-of-sample air-gap.
Trend following (CTA): what are the five questions?
Trend following strategies take positions in the direction of recent price moves across futures markets. The main failure mode is a market that moves sideways or reverses sharply, so signals keep entering and exiting at a loss. The return pattern is lumpy by nature, which makes a short record hard to judge.
- 01How many independent signals and markets drive the result? A program that looks diversified may rest on a few correlated bets. Ask how signals are defined, how they overlap, and how the portfolio behaves when many markets move together.
- 02How is risk sized, and what changes when volatility changes? Ask for the rule that sets position size, the rule that reduces it in stress, and the maximum exposure permitted.
- 03What did the strategy do in prolonged sideways markets? Ask for the manager's own account of the worst stretch, how long it lasted, and what the manager did during it.
- 04What do transaction costs, rolls and slippage contribute? Compare assumed costs with those in live trading. Ask how contracts are rolled and how execution quality is measured.
- 05How was the return measured? For futures, return measured on capital that margin would have consumed overstates results. Ask what denominator is used, and ask to see it reconciled to statements.
The note on CTA and managed futures trend following gives wider context, and the note on CTA replication through an index, a fund or an account compares the ways to hold the exposure.
Statistical arbitrage or market neutral equity: what are the five questions?
These strategies hold long and short stock positions designed to cancel broad market exposure. The main failure mode is a crowded trade that unwinds, where many managers hold similar positions and exit together, and the apparent neutrality hides residual exposure to common factors.
- 01What factor exposures remain after the hedges? Ask for exposure to market, size, value, momentum and sector, how they are measured, and how often they are checked.
- 02How does the book behave when correlations rise? Ask for stress results and what the risk system does on its own when losses accumulate.
- 03How crowded is the portfolio? Ask how the manager estimates overlap with other managers, and what the plan is if a common exit begins.
- 04What does shorting cost and require? Ask about borrow availability, borrow fees, recall risk and whether returns are reported net of borrowing.
- 05How sensitive is the result to turnover and costs? Ask for a net-of-cost breakdown and the effect of small changes in assumed costs.
Systematic macro: what are the five questions?
Systematic macro strategies trade rates, currencies, commodities and equity indices using rules driven by economic and market data. The main failure mode is concentration in a small number of truly independent bets. A long record can contain only a handful of macro regimes, so a strategy fit to one regime can fail in the next.
- 01How many independent decisions does the strategy make in a year? A small number means the track record has little statistical weight. Ask the manager to count them honestly.
- 02Which regime does the strategy depend on? Ask what economic conditions the edge requires, and what happens when they change.
- 03Which data does it use, and how was it timed? Ask about release dates, revisions and vintage. Data that was revised after publication can leak the future into a backtest.
- 04What happens in a crisis? Ask how the strategy performed in past stress episodes, how the manager tested them, and whether the tests were run on data the research never touched.
- 05How is discretion involved? Ask who may override the system, how often it happens, and whether overrides are logged and reviewed.
Short-horizon, execution-sensitive strategies: what are the five questions?
These strategies trade quickly and earn small amounts per trade, so cost, latency and queue position decide whether the edge survives. The main failure mode is that the simulated fills were better than anything the market would have given.
- 01What fill assumptions does the backtest use? A simulation that fills every order at the midpoint overstates earnings. Ask how fills, queue position and partial fills are modeled.
- 02How do live fills compare with simulated fills? Ask for the comparison on the same trades, with the data to check it.
- 03What is the latency budget, and where does it come from? Ask about network paths, hosting, order handling and the effect of delays on results.
- 04How much capital can the strategy absorb? Ask for the manager's capacity estimate, the method behind it, and the point where market impact removes the edge.
- 05What happens when connectivity or data fails? Ask for the fail-safe design, who can halt trading, and whether the halt is automatic.
For the underlying problem, see the note on microstructure feasibility.
Machine-learning driven strategies: what are the five questions?
Machine-learning strategies use models that learn patterns from data. The main failure mode is overfitting and information leakage, where the model learns noise or sees information it would not have had at the time. A second risk is behavior outside the conditions it was trained on.
- 01How was the model validated? Ask whether the validation purged overlapping labels and embargoed the data after each test set, and whether a single historical path was the only evidence.
- 02How many models, features and settings were tried? Ask for the full trial record. A good result among many tries is expected by chance, and the Deflated Sharpe Ratio exists to adjust for that.
- 03Was any data kept untouched? Ask for a period the research never saw, and ask who guarded it.
- 04How are inputs and labels built? Ask whether preprocessing was fitted on the full sample, and how timestamps are handled, since either can leak the future into training.
- 05How is drift detected and what happens next? Ask what the monitoring watches, who decides to retrain or stop, and how changes are recorded.
The second risk is covered in the note on regime collapse.
What does a good answer contain?
A good answer is specific and checkable. It names a document, a dataset or a log, not a reassurance. The table lists the evidence to ask for with each strategy type.
| Strategy type | Evidence worth requesting |
|---|---|
| Trend following | A reconciled return series with a stated denominator, the roll log, and the manager's own write-up of its worst flat stretch. |
| Equity market neutral | A factor exposure report over time, borrow cost and recall history, and stress results that include a correlation spike. |
| Systematic macro | A count of independent decisions by year, the data vintage used, and tests on data the research never touched. |
| Short horizon | Live fills lined up against simulated fills on the same trades, plus the latency and capacity assumptions behind them. |
| Machine learning | The trial log, the purge and embargo settings, an untouched holdout used once, and a drift monitoring record. |
How does the delivery structure change what you can check?
The delivery structure also shapes what an allocator can check. In a separately managed account, the account sits in the allocator's own name at its own broker, so the statements come from a source the manager does not control. Programs on the Apex Marketplace are in this format, and the program catalog lists them. Qlumina describes admission as a quantitative review, an operational review and a live pilot allocation. Further research is in the insights library.
Frequently asked questions
What should a quant hedge fund due diligence checklist cover?
It should cover the common frame first: reconciled returns, backtest forensics, capacity, risk controls and custody. It should then add questions for the strategy type, since each type fails differently. The five questions per type above are a starting point.
Why is trend following hard to diligence on a short record?
Because its returns arrive in clusters, with long flat or losing stretches between favorable periods. A short record can sit entirely inside one of those phases. Look at risk sizing, signal diversity and the manager's account of past drawdowns rather than at the recent return alone.
How do I check whether a backtest is overfitted?
Ask for the number of variations tried, the validation method and a period the research never touched. Validation that purges and embargoes data, run across many paths, gives a distribution rather than one outcome. Adjusting the Sharpe ratio for the number of trials is a further check.
What is the biggest risk with short-horizon strategies?
The gap between simulated and live fills. If the backtest assumed better prices than the market offered, the edge may not exist. Ask for a comparison of live and simulated fills on the same trades.
Does a platform listing replace my own due diligence?
No. A listing, or the admission process behind it, can supply organized evidence, but the allocator remains responsible for its own review. Ask what was verified, by whom, and whether the sources are independent of the manager.
Risk and eligibility
This note is educational only and is not an offer or solicitation. It is intended for professional investors only. Investing involves substantial risk of loss, and past performance is no guarantee of future results. Qlumina is a BVI FSC approved investment manager. Eligibility rules apply, and retail clients are not eligible.
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