From Backtest to Allocation: How Meridian Quantitative Secured $40M with a Validation Passport
This case study is illustrative. "Meridian Quantitative" is a composite based on conversations with systematic fund managers during product development. The scenario, challenges, and outcomes represent realistic patterns in the institutional allocation process. No real fund or allocator is depicted.
The Firm
Meridian Quantitative is a systematic equity fund based in Chicago. The firm runs four strategies across US and European equities: two momentum variants, a statistical arbitrage model, and a mean-reversion strategy on sector ETFs. Combined AUM sits at $85 million, mostly from the founding partners' capital and a handful of high-net-worth investors.
The team is five people. Two quants who build and maintain the strategies, a head of operations, an analyst, and the managing partner (who is also the original strategy developer). Everyone has a quantitative background. Three of five hold graduate degrees in mathematics, physics, or financial engineering.
The strategies have been running live for three years. Composite performance: net Sharpe of 1.12, maximum drawdown of 11.3%, annualized return of 14.8% after fees. By the numbers, they're in the top quartile of systematic equity managers tracked by institutional databases.
The numbers should speak for themselves.
They don't.
The Problem
Meridian wanted to raise $40 million from institutional allocators (endowments, family offices, fund-of-funds) to scale from $85M to $125M. The managing partner had spent 14 months in conversations with seven allocators. The result: zero commitments.
The feedback was consistent. Not identical, but thematically similar enough to constitute a pattern.
Allocator A (endowment, $2B AUM): "Your track record is short. Three years isn't enough for us to distinguish skill from luck. We need more evidence that your edge is structural."
Allocator B (fund-of-funds, $800M AUM): "We've seen dozens of systematic funds with similar Sharpe ratios. Most don't hold up when we stress-test the methodology. How do you know your backtest results aren't overfit?"
Allocator C (family office, $500M AUM): "We like the returns. We can't get comfortable with the risk. Your drawdown stats look good, but they're all from one market regime. What happens in a 2008-style event?"
Allocator D (pension consultant): "Your internal risk models aren't independently verified. We need third-party validation before we can recommend allocation to our clients."
Four allocators, four variations on the same theme: we can't tell if your edge is real.
This is the credibility gap that every sub-$500M systematic fund faces. The strategies might be genuinely good. But institutional allocators evaluate hundreds of managers, and distinguishing a 1.12 Sharpe from luck requires more statistical evidence than three years of returns can provide on their own.
The industry term is "operational due diligence." In practice, it means the allocator's quant team tries to break your strategy by asking questions you may not have answers to. How many parameter combinations did you test? What's the probability of backtest overfitting? How does performance decompose by market regime? What happens to your execution costs if you scale 3x?
Meridian's team could answer some of these questions informally. They couldn't answer them with independent, structured, quantitative evidence.
The Approach
Meridian ran all four strategies through seven-layer validation over a single week. The process for each strategy was the same: upload the strategy code, wait for the engine to process it (under five minutes per strategy), and receive a Validation Passport with layer scores, diagnostic detail, and a composite verdict.
What They Submitted
| Strategy | Type | Backtest Sharpe | Parameters |
|---|---|---|---|
| Alpha-M1 | Equity momentum (cross-sectional) | 1.31 | 6 |
| Alpha-M2 | Equity momentum (time-series) | 0.94 | 4 |
| Alpha-SA | Statistical arbitrage (pairs) | 1.48 | 11 |
| Alpha-MR | Mean-reversion (sector ETFs) | 0.87 | 5 |
Total development: roughly 18 months of iterative research across all four strategies. The team estimated they'd tested approximately 200 parameter combinations across the suite during development.
What They Expected
The managing partner expected all four strategies to pass. The live track record was strong. The Sharpe ratios were high. The strategies were diversified across style factors.
That's not what happened.
The Results
Strategy-Level Validation Passports
| Strategy | L1 | L2 | L3 | L4 | L5 | Composite | Verdict |
|---|---|---|---|---|---|---|---|
| Alpha-M1 | 71 | 65 | 54 | 62 | 73 | 66 | Weak Pass |
| Alpha-M2 | 68 | 62 | 58 | 59 | 70 | 64 | Weak Pass |
| Alpha-SA | 42 | 38 | 45 | 51 | 54 | 44 | Fail |
| Alpha-MR | 74 | 70 | 62 | 68 | 76 | 71 | Weak Pass |
Three weak passes and one failure. The failure was the strategy with the highest backtest Sharpe.
Alpha-SA: The Surprise Failure
Alpha-SA, the statistical arbitrage pairs strategy, had the best backtest numbers in the suite. Sharpe of 1.48 over the full backtest period. Maximum drawdown of 7.2%. It looked like the strongest strategy in the portfolio.
Validation told a different story.
L1 (Statistical Core): With 11 free parameters and approximately 80 parameter combinations tested during development, the PBO came back at 47%. Nearly a coin flip whether the best configuration was overfit. The OOS Sharpe degraded to 0.71 (a 52% decline from the 1.48 in-sample). The CPCV folds showed high variance: OOS Sharpes ranged from -0.12 to 1.03. That spread indicates instability.
L2 (Causal Inference): The pair relationships the strategy traded had weakened significantly over the test period. Several pairs that co-integrated during 2005-2015 showed declining co-integration scores from 2016 onward. The causal mechanism (mean-reversion of spread) was deteriorating. Two pairs failed the Engle-Granger test entirely when tested on the most recent five years alone.
L4 (Regime Intelligence): In bear-market regimes, the strategy's Sharpe dropped to -0.41. Pairs that converged in calm markets diverged in stress. The strategy was implicitly long correlation: it bet that spread relationships would hold, which is exactly what breaks during crises. Regime-conditional performance revealed a hidden tail risk that the composite numbers concealed.
The managing partner's reaction, in his words: "We've been running this strategy live for two years. It's made money. How can it fail validation?"
The answer: it's made money in a favorable regime. The validation doesn't say the strategy is unprofitable. It says the strategy's edge is statistically fragile, the causal relationships it depends on are weakening, and its performance is regime-dependent in ways the composite numbers don't show. All three of those can be true while the strategy is still making money. They predict what happens next, not what happened already.
Alpha-MR: The Surprise Winner
The sector ETF mean-reversion strategy had the lowest backtest Sharpe at 0.87. It was the strategy the team considered removing from the portfolio.
It scored highest in validation.
L1 (Score: 74): Only 5 parameters, OOS degradation of just 19%. PBO of 11%. The strategy wasn't heavily optimized. Its modest backtest numbers were close to its genuine edge.
L4 (Score: 68): Per-regime performance was the most stable in the suite. Bear-market Sharpe of 0.12 (barely positive, but not negative). The strategy's mean-reversion logic worked across regimes because sector ETFs maintain relative value relationships even in downturns. The regime-dependence was minimal.
L5 (Score: 76): Sector ETFs are highly liquid with tight spreads. Transaction costs consumed only 8% of gross returns, the lowest in the portfolio.
The strategy with the lowest headline Sharpe was the most structurally sound.
The Fundraising Impact
Before Validation
Meridian's pitch deck contained:
- 3-year track record
- Composite Sharpe ratio
- Maximum drawdown statistics
- Monthly return tables
- Strategy descriptions
Standard materials. Identical in structure to what every other sub-$500M systematic fund presents. Allocators had no way to distinguish Meridian's 1.12 Sharpe from the dozens of other managers claiming similar numbers.
After Validation
Meridian added to their pitch materials:
- Validation Passports for all four strategies
- OOS Sharpe ratios alongside backtest Sharpes
- PBO analysis showing probability of overfitting per strategy
- Regime-conditional performance tables
- Execution realism analysis with capacity estimates
- An honest assessment: Alpha-SA needs work, and they know exactly what the weaknesses are
The last point mattered more than the scores.
The Allocator Response
Meridian went back to three of the original allocators with the updated materials. Two agreed to continue the conversation. Here's what changed.
Allocator B (fund-of-funds) had asked "How do you know your backtest results aren't overfit?" The Validation Passport answered this directly: PBO of 11% to 47% across strategies, with the highest-risk strategy identified and the specific mechanism (parameter count, declining co-integration) documented. The allocator's quant team spent three hours reviewing the passports instead of three weeks trying to reverse-engineer Meridian's methodology.
Allocator C (family office) had asked "What happens in a 2008-style event?" The regime intelligence layer provided per-regime Sharpe ratios for each strategy. Instead of arguing that past drawdowns were manageable, Meridian could show exactly how each strategy performed in bear regimes, including the one that didn't perform well and what they were doing about it.
Allocator D (pension consultant) had required "third-party validation." The cryptographically signed Validation Passport, with its tamper-evident audit trail, satisfied this requirement directly.
The Outcome
Meridian received a $25 million allocation from Allocator C and a $15 million allocation from Allocator B, both conditional on restructuring the Alpha-SA allocation (reducing its portfolio weight from 30% to 15% until the co-integration issues were addressed).
Total new capital: $40 million. Due diligence cycle from initial meeting to term sheet: 8 weeks. The previous 14 months of conversations had produced zero commitments.
What Actually Changed
The strategies didn't change. The track record didn't change. The team didn't change.
What changed was the nature of the evidence.
Before validation, Meridian presented performance data and asked allocators to trust that the data reflected genuine skill. Every systematic fund makes this request. Allocators have been burned enough times to be skeptical of all of them.
After validation, Meridian presented structured analysis of why the performance was real (OOS Sharpe, low PBO, cross-asset causal evidence) and where it was fragile (Alpha-SA co-integration decay, regime dependence of the momentum strategies). The allocators didn't have to trust Meridian's claims. They could examine the evidence independently.
The counterintuitive finding: admitting weakness strengthened the pitch. When Meridian showed that Alpha-SA failed validation and explained the specific mechanism, it made the passing scores on the other three strategies more credible. A validation that passes everything is suspicious. A validation that catches real problems and explains them demonstrates that the process has teeth.
The Structural Advantage
The institutional allocation process has a fundamental information asymmetry. Managers know their strategies. Allocators don't. The entire due diligence apparatus exists to close that gap: questionnaires, on-site visits, reference checks, quant team analysis.
This apparatus is slow and expensive. A typical institutional due diligence cycle runs 3 to 6 months and costs the allocator $50,000 to $100,000 in staff time per manager evaluated. For emerging managers with sub-$500M AUM, the cost of due diligence often exceeds the expected fee revenue from the allocation, creating a structural bias against smaller funds.
A Validation Passport doesn't replace due diligence. It accelerates it. The allocator's quant team gets pre-computed answers to their core questions (overfitting probability, regime sensitivity, execution realism, causal evidence) in a standardized format. They can verify the results independently. They can compare across managers using consistent methodology.
For the manager, the passport changes the conversation from "trust us" to "examine the evidence." That's a different kind of meeting entirely.
Lessons from the Pilot
The best backtest Sharpe isn't the best strategy. Alpha-SA scored 1.48 in backtesting and 44 in validation. Alpha-MR scored 0.87 in backtesting and 71 in validation. The backtest number measures in-sample performance. The validation score measures structural soundness. They answer different questions.
Regime analysis is the single highest-value diagnostic. All four allocator objections traced back to regime risk in some form. "Short track record" means they can't see across regimes. "Can't distinguish skill from luck" means they can't assess regime-conditional performance. "What happens in 2008" is a regime question. Per-regime Sharpe ratios answered all of them directly.
Honesty about weakness builds credibility. Showing that one strategy failed and explaining why made the entire presentation more believable. Allocators expect problems. Managers who can identify and quantify their own problems demonstrate a level of self-awareness that allocators value, because it predicts better risk management going forward.
Speed matters. The validation took one week. The fundraising closed in eight weeks. Fourteen months of prior conversations had produced nothing, not because Meridian's strategies were worse, but because the evidence was insufficient. The strategies were the same before and after validation. The evidence wasn't.
The scenario described in this case study reflects patterns observed in conversations with systematic fund managers during Sigmentic's product development. Specific allocator responses and timelines are composited from multiple sources. No real firm, fund, or allocator is represented. If you're a systematic fund manager navigating similar challenges, we'd like to hear about them.