Systematic Alpha Through Independent Agreement
Alpha Convergence is an actively managed, rules-based equity strategy designed to identify and exploit periods of temporary mispricing through the convergence of multiple independent signals. The strategy seeks to generate excess returns while minimizing unintended factor concentration.
The investment process combines two investment models, each developed to capture distinct informational and behavioral inefficiencies in public equity markets. Portfolio construction emphasizes independence across positions, equal weighting, and disciplined rebalancing in order to reduce reliance on any single signal, factor, or market environment.
The strategy currently maintains a concentrated portfolio of exactly 22 equities and is benchmarked against the S&P 500 Equal Weight Index (RSP), reflecting its structural design, without sector tilts. Risk is managed through position sizing discipline, diversification across independent signals, and continuous monitoring of correlation dynamics, volatility, and drawdown behavior.
No hypothetical back tests or simulated performance are presented, as such analyses are unlikely to capture the qualitative judgment, model evolution, and macro-economic decision-making embedded in the process.
Early live results across a varied market backdrop has included sharp factor rotations, elevated volatility, and episodic risk-off conditions. While these observations provide useful insight into how the strategy behaves under stress relative to its design objectives, longer operating history across full market cycles is required before drawing durable conclusions.
Alpha Convergence is best viewed as an emerging strategy with a clearly articulated process, explicit risk awareness, and a commitment to transparency. Ongoing evaluation will focus on the stability of signal behavior, correlation dynamics, drawdown control, and consistency relative to benchmark over time.
The following metrics reflect 275 trading days (June 27, 2025 through July 31, 2026). The strategy completed the 252-trading-day institutional validation window on June 29, 2026. Figures are shown at two horizons with equal prominence, 1 Year (the trailing 252 trading days) and Since Inception (the full record). Returns are presented both gross and net of a 2% annual model advisory fee, the top of the firm’s 1-2% fee schedule, applied monthly; transaction costs are deducted from both. Net figures reflect the model fee only; actual advisory fees vary by mandate and will affect realized returns. See Form ADV Part 2A for the applicable fee schedule.
| Return | 1 Year | Since Inception | ||
|---|---|---|---|---|
| Gross | Net | Gross | Net | |
| Total Returnnet of 2% annual model fee | +50.19% | +46.97% | +54.94% | +51.36% |
| Excess Return vs. RSPEqual Weight S&P 500 | +33.20% | +29.98% | +34.65% | +31.07% |
| Risk & Ratio Characteristics computed on gross returns; total returns above are shown gross and net with equal prominence |
1 Year | Since Inception |
|---|---|---|
| Sharpe RatioNewey-West HAC · 95% interval shown first, point estimate second | [+0.68, +4.23]point2.46 | [+0.76, +4.19]point2.47 |
| Information Ratiovs. RSP | 2.37 | 2.26 |
| Win RateAC beats RSP daily · 95% CI [49.8%, 61.5%] | 56.0% | 55.6% |
| Betavs. RSP, OLS | 0.89 | 0.89 |
| Maximum DrawdownMar 30, 2026 from Feb 11 peak | -8.46% | -8.46% |
| Capture Ratioupside / downside vs. RSP | 1.46:1 111.3 / 76.4 | 1.41:1 108.9 / 77.1 |
Confidence intervals use standard asymptotic formulas; methodology details and interpretive caveats are provided in the Statistical Appendix. Confidence intervals remain sample-size sensitive at 275 trading days. Point estimates represent best available data; true population parameters may fall anywhere within stated ranges. Ratio and risk metrics (Sharpe, Information Ratio, Beta, Capture) are computed on gross returns; net-of-fee ratios are marginally lower.
Across the 275-day record the mean daily active return versus RSP is statistically significant at the 95% confidence level net of the 2% model advisory fee (Newey-West HAC q=4, two-sided p = 0.0218), and the result holds across parametric, stationary block bootstrap, and distribution-free tests, including a sign test in which all five closed periods produced positive net excess. The 95% confidence interval on the net annualized active return runs from +3.1% to +39.8%. The lower bound is the operative figure: at the conservative end of the interval the strategy still adds meaningful net alpha over the equal-weight benchmark, and the width of the interval reflects the short sample and daily return volatility rather than uncertainty about the sign of the edge. These figures come from a single live prespecified strategy with no back test, so no multiple-testing discount applies. The interval describes the estimate of the historical mean active return; it is not a forecast and not a floor on future results. The separate and more demanding risk-adjusted test, the Sharpe ratio against the 1.10 institutional threshold, is not yet met.
The strategy has operating history of 275 trading days; the 252-trading-day minimum required for full institutional validation was completed on June 29, 2026, and the record remains short by long-horizon standards. Past performance is not indicative of future results. All investments involve risk, including possible loss of principal.
The strategy exhibits favorable asymmetry: on a since-inception basis capturing 108.9% of benchmark gains while experiencing 77.1% of benchmark losses. This 1.41:1 capture ratio reflects the quality bias inherent in our dual-screen approach. Both capture legs moved in the strategy favor this period: downside capture improved from 79.3% to 77.1% while upside capture eased from 110.4% to 108.9%. The upside easing reflects two sessions in which the equal-weight benchmark declined while the strategy advanced, which depresses measured up-capture even though the outcome was favorable.
The current capture ratio (1.41:1) should not be extrapolated as a permanent characteristic. It will compress during adverse regimes. We present it as early evidence of defensive characteristics, not as a guaranteed feature.
The observed maximum drawdown of -8.46% occurred on March 30, 2026 from the February 11, 2026 peak, during the late-March 2026 stress event (VIX peaked at 31.1). This figure is informative but not necessarily representative of our forecast under more severe adverse conditions.
We expect downside capture rising to 40-60% during acute stress (a short-term downward move) as correlations spike and quality factors provide incomplete protection. During sustained bear markets, we expect downside capture in the 50-70% range. During recovery phases, we expect upside capture to compress during early-cycle “junk rallies” when low-quality securities outperform.
This paper excludes multi-year simulated back tests. Portfolio construction incorporates qualitative assessments that cannot be reliably replicated using historical data. Any long-term simulation would create a misleading impression of precision we cannot honestly represent.
Consider how a bank evaluates a loan application: two independent systems examine the same borrower from fundamentally different angles.
These systems have low correlation by design. When both systems independently approve the same borrower, the loan gets funded.
Alpha Convergence applies the same logic to securities selection: two independent analytical systems measuring fundamentally different phenomena.
We monitor correlation between our models weekly as an indicator of how momentum and fundamental signals are aligning. We interpret these movements alongside external indicators like VIX, SPY, and IWM to identify potential regime (market conditions) changes.
Our dual screen requires agreement.
Di Mascio, Lines, and Naik (2017) studied transaction-level data from 752 institutional portfolios and documented a robust finding: newly purchased stocks earn positive risk-adjusted returns that decay predictably over time. Alpha averages 36 basis points in the first month, follows a power-law decay with a half-life of approximately four months, and reaches zero by month twelve with no subsequent reversal. The shape of this decay has direct implications for portfolio design.
The critical finding is the horizon mismatch: the average institutional manager holds positions for 2.2 years (approximately 554 trading days), which is roughly double the 12-month window in which alpha exists. Once alpha is exhausted, continued holding adds zero marginal return while tying up capital that could be redeployed into fresh signals. Most active managers destroy value not through poor stock selection but through holding positions long past the point where their informational advantage has expired.
Figure 7: Di Mascio Alpha Decay Lifecycle. Curve calibrated to reported parameters: 36 bps first-month alpha, 4-month half-life, 12-month exhaustion. Gray zone marks AC rebalance window.
Alpha Convergence is designed to harvest alpha during the steep portion of the Di Mascio curve and rebalance before marginal alpha declines to the flat zone. The information cycle operates as follows:
The rebalance cycles provide independent tests of whether AC captures alpha consistent with the Di Mascio lifecycle. Each period represents a fresh portfolio constructed from new dual-model agreement signals, measured against the theoretical alpha an average institutional manager would generate over the same holding window. P5 is now closed at 62 trading days and P6 is open at 28 trading days with its first month closed.
Figure 8: AC Alpha Harvesting vs. Di Mascio Benchmark (vs. RSP), gross of fees; net excess by period appears in the table below. P2 +4.45% (47d), P3 +5.53% (59d), P4 +6.04% (43d), P5 +7.80% (62d, closed), and P6 +2.31% (28d, open), each closed cycle several multiples of the Di Mascio theoretical. P1 excluded as calibration period.
Each period is an independent portfolio built from new dual-model agreement signals, so consistency across them cannot be the product of a single lucky position or a one-time rally. The closed P2-P5 cycles delivered between +4.45% and +7.80% of excess versus RSP, several multiples of the Di Mascio theoretical. The most informative cycle is P4: AC returned +1.74% while RSP fell -4.31%, generating +6.04% of excess in the most hostile macro window of the validation period rather than in a rally. P5 closed at the top of that distribution after a Month 1 softened by Phase 1 defensive positioning, a complete absorb-and-recover lifecycle. P6 opened soft, recovered within its first month to close Month 1 at +2.41%, and stands at +2.31% over 28 trading days. Its second month is partial at seven sessions and its movement to date is concentrated in a single session, so the P6 reading remains directional until the period closes. Future periods may land anywhere in this range depending on conditions, but the repeatability across independent cohorts is consistent with the design.
| Period | Dates | Days | AC Gross | AC Net | RSP | Excess Gross | Excess Net |
|---|---|---|---|---|---|---|---|
| P1 (calibration) | Jun 27 – Aug 15, 2025 | 35 | +4.62% | +4.27% | +3.12% | +1.50% | +1.15% |
| P2 | Aug 18 – Oct 22, 2025 | 47 | +7.44% | +7.09% | +2.99% | +4.45% | +4.09% |
| P3 | Oct 23, 2025 – Jan 16, 2026 | 59 | +10.38% | +9.83% | +4.85% | +5.53% | +4.98% |
| P4 | Jan 20 – Mar 20, 2026 | 43 | +1.74% | +1.40% | -4.31% | +6.04% | +5.71% |
| P5 (closed) | Mar 24 – Jun 22, 2026 | 62 | +17.15% | +16.57% | +9.35% | +7.80% | +7.22% |
| P6 (open) | Jun 23 – Jul 31, 2026 | 28 | +4.89% | +4.54% | +2.58% | +2.31% | +1.97% |
| Total | Jun 27, 2025 – Jul 31, 2026 | 275 | +54.94% | +51.36% | +20.29% | +34.65% | +31.07% |
Per-period excess versus RSP. Each period is an independent portfolio of newly selected names; P4 produced +6.04% excess while RSP fell -4.31%. Period boundaries are the authoritative firm attribution boundaries; the March 23, 2026 transition day is excluded from period attribution, so the rows sum to 274 attributed days against the 275-day total. Net figures deduct the 2% annual model advisory fee applied monthly; because the fee accrues by calendar month, per-period net figures are approximate to within a fraction of a basis point of an exact daily accrual.
The Di Mascio framework describes average institutional behavior across a large sample. Individual manager results vary substantially. AC outperformance relative to the theoretical benchmark reflects both the dual-model signal quality and the rebalance discipline, neither of which guarantees continued outperformance. Past alpha capture does not ensure future results.
We build portfolios with exactly 22 concentrated positions through rigorous correlation management. Each portfolio represents high-conviction positions designed to approach the diversification benefits of 50+ randomly-selected securities based on the academic research presented below.
Antón, Cohen, and Polk (2021) demonstrated managers’ highest-conviction positions typically outperform the market by 2.8% to 4.5% annually, while positions beyond a manager’s best ideas contribute little or no alpha. The authors concluded, “investors would benefit if managers held more concentrated portfolios.”
Foundational research on equal-weighted portfolio diversification demonstrates risk reduction benefits diminish rapidly beyond approximately 20 securities. Raju and Agarwalla (2021) confirmed that a 20-stock equal-weighted portfolio diversifies away approximately 90% of idiosyncratic risk on average, while the CFA Institute’s “Peak Diversification” study (2021) found that for large-cap equal-weighted portfolios, peak diversification occurs between 15-26 stocks, with minimal incremental benefit beyond this range.
We target:
Inter-Model Correlation between -5% and +5% for genuinely independent signals.
Academic research on ensemble methods demonstrates that near-zero correlation between predictors—whether slightly positive or negative—maximizes variance reduction while ensuring models address related investment problems (Dietterich (2000); Brown et al. (2005)). Even small negative correlation remains orthogonal and provides additional information about market conditions through model disagreement patterns.
Inter-Stock Correlation <0.30 for genuine risk reduction over factor similarity.
Principal Component Analysis (PCA) Target at 9–11 Independent Factors. Creates natural hedges where single-factor risks offset one another.
Signal-Driven Reconstitution. Portfolio rebalancing is signal-driven, aligned to the Di Mascio alpha decay lifecycle and bounded by a maximum holding period of 90 calendar days. Di Mascio et al. (2017) documented that alpha on newly purchased securities decays with a 4-month half-life and reaches zero by month 12. Our rebalance cadence harvests alpha during the steep-slope portion of the decay curve and redeploys into fresh signals before marginal alpha diminishes.
Although Alpha Convergence launched in June 2025, Life UnLocked Partners has navigated significant market dislocations for existing clients using protocols that inform our current approach. These responses demonstrate our risk management philosophy in practice.
During the 2022 market decline, we executed a defensive reallocation for client portfolios:
Defensive Shift: Reallocated equity exposure to volatility-impaired, bond-like instruments (primarily fixed-to-floating rate preferred securities) to preserve capital while maintaining income generation.
Damage Path Assessment: Waited for clear damage paths to establish before seeking re-entry opportunities while earning significant alpha.
Adjacent Opportunity Focus: Sought recovery positions “three degrees from the damage path” — companies that benefit from dislocation without direct exposure to damaged sectors.
The April 2025 market volatility provided a more recent test of our risk management approach:
Immediate Action: Smaller reallocation to preferred securities during the initial volatility spike.
Model Enhancement: Extended defensive positioning to complete reengineering of our fundamental model to its current Alpha Convergence architecture.
Controlled Re-entry: Client rotation to the enhanced Alpha Convergence model on August 18, 2025, following strong early results from testing.
The March-April 2026 stress event provided the most recent and most severe live test of our crisis management framework. We executed a staged defensive response in the face of an unbounded oil shock and structural risk to private credit and tariff-exposed equities. Our framework distinguishes three phases of escalating defensive action; only the first two have been triggered to date.
Phase 1 (March 20-23, 2026, Capital Preservation): Following the Storm Center structural-event flag on March 19, we initiated defensive action on March 20 and completed it by March 23. The action trimmed both the equity sleeve and the VCIT (intermediate-term corporate bond) holding to fund a 15% SCHO (short-term Treasury) position, repositioning the portfolio for capital preservation ahead of the scheduled P5 rebalance on March 24. The P5 rebalance itself executed on March 24 in the context of this Phase 1 posture: equity selections diversified away from private credit exposure and tariff-vulnerable names. We deliberately retained selected oil and energy positions whose operations sit largely outside the Strait of Hormuz transit risk, providing diversification to the dollar without taking on direct geopolitical exposure to the choke point.
Phase 2 (April 20, 2026, Credit De-Risking): As the Hormuz risk failed to bound and the duration of the stagflation regime extended, we executed a second defensive shift on April 20, selling the remaining VCIT position and rotating the proceeds into additional SCHO. This trade reduced credit duration and credit spread risk inside the fixed income sleeve while preserving income generation. The objective was to insulate the bond allocation from any further widening in corporate credit spreads driven by the oil shock filtering through to investment-grade issuers.
Current Allocation State: As of late April 2026, client portfolios sit at approximately 55% equity / 45% fixed income, with roughly 30% of overall portfolios allocated to SCHO. This is materially more defensive than the standard positioning of approximately 65% equity, and reflects our judgment that the Hormuz event has structural rather than transitory characteristics. With the cease fire MOU signed in June, we have backed off our oil sector avoidance as of the June 22nd rebalance while maintaining the same fixed income posture and allocation against continued uncertainty. The two-phase design preserves optionality: the SCHO allocation can be unwound rapidly when the Storm Center signal de-escalates, and the equity sleeve remains positioned in dual-model agreement names that should benefit from any normalization.
Phase 3 (Conditional, Not Yet Triggered): The framework defines a third escalation step that has not been required during this event: an additional equity-to-fixed-income trim that would bring portfolios to approximately 45% equity / 55% fixed income. This would be triggered if the Storm Center signal escalates further or if the four-week material-change rule confirms a deeper regime shift. The framework also reserves a fourth contingency, mostly cash, for a true tail-event scenario; that contingency has never been triggered and is not described in detail here.
Framework Implication: The staged response demonstrates a key feature of our crisis management approach. We do not treat defensive repositioning as a single binary decision. Phase 1 addressed the initial structural-event signal with the most easily reversible defensive shift. Phase 2 escalated only when the underlying risk drivers failed to resolve within their initial estimated bounds. Phase 3 remains in reserve. This staged approach reduces the cost of false positives while preserving the ability to escalate when warranted by the evolving evidence.
Our recovery positioning philosophy avoids both the risks of bottom-fishing in damaged securities and the opportunity cost of complete sector avoidance. Instead, we seek adjacent beneficiaries:
This framework provides asymmetric exposure: participation in sector recovery without direct exposure to damaged balance sheets or uncertain restructuring outcomes once our model correlations return to more reliable levels.
Hormuz Application (March 2026): First-degree positions (tanker operators and producers with direct Strait of Hormuz throughput exposure) were excluded from the P5 portfolio. Second-degree positions (refiners and petrochemical companies dependent on crude flows through the Strait) were treated with caution. Third-degree targets (companies with mild energy input sensitivity whose prices declined from broad risk-off sentiment rather than operational disruption) formed the core of the rebalanced portfolio. The P5 portfolio accepted mild oil exposure as structurally unavoidable while maintaining deliberate distance from the primary damage path.
Our risk management framework includes quantified triggers for defensive repositioning. These thresholds inform, but do not mechanically determine, allocation decisions:
These triggers inform discretionary decisions rather than mandate automatic actions. Market conditions require human judgment to distinguish temporary volatility from genuine regime change. Our “4-week material change rule” requiring four consecutive weeks of material economic changes in one direction avoids overreaction to transient market noise or fear spikes while capturing genuine regime shifts with an estimated 80% confidence.
Alpha Convergence launched June 27, 2025 after our model completion achieved operational readiness. What followed was an unexpected and welcome stress test where we’ve encountered multiple conditions adverse to quality-focused mean reversion strategies:
Trash Rally conditions (small cap speculation outperforming quality);
Risk-Off Selloff conditions (broad market decline); and
Momentum-led recovery (sentiment-driven broad rally)
Markets have cycled through several distinct regimes, each presenting a different challenge for a quality-focused strategy:
| Period | Dates | Regime | VIX Range | Stress Level | Days |
|---|---|---|---|---|---|
| Period 1 | Jun 27 – Aug 15 | Trash Rally | 14 – 20 | Hostile | 35 |
| Period 2 | Aug 18 – Oct 22 | Severe Trash Rally | 14 – 25 | Severe | 47 |
| Period 3 | Oct 23 – Jan 16 | Mixed/Choppy | 13 – 26 | Stress | 59 |
| Period 4 | Jan 20 – Mar 20 | Mixed/Recovery | 16 – 29 | Mixed | 43 |
| Period 5 | Mar 24 – Jun 22 | Phase 1 / Relief Rally | 15 – 31 | Hostile | 62 |
| Period 6 | Jun 23 – Jul 31 | Transitional | 15 – 21 | Open | 28 |
| Total | Jun 27 – Jul 31 | Multiple Regimes | 13 – 31 | 76% Hostile | 275 |
The following table shows Alpha Convergence performance against benchmarks across each regime:
| Regime | Days | AC | RSP | Alpha vs RSP | Condition |
|---|---|---|---|---|---|
| Severe Trash Rally | 34 | +24.69% | +16.91% | +7.78% | Hostile |
| Momentum/Trash Rally | 53 | +32.54% | +21.05% | +11.49% | Hostile |
| Broad Market Rally | 52 | +45.07% | +29.28% | +15.79% | Favorable |
| Quality Rally | 13 | +4.68% | +5.77% | -1.09% | Favorable |
| Choppy/Range-Bound | 51 | -14.35% | -16.69% | +2.35% | Hostile |
| Risk-Off/Quality Flight | 68 | -30.27% | -27.02% | -3.25% | Hostile |
AC = Alpha Convergence. RSP = S&P 500 Equal Weight ETF (primary benchmark). SPY = S&P 500 ETF. IWM-RSP Spread measures small cap speculation vs. quality; positive spread indicates “trash rally” conditions hostile to quality strategies. Regime figures are calculated gross of fees; net-of-fee total returns are presented with equal prominence in the Performance Summary, and net excess by rebalance period appears in the period table. Regime classification uses sigma bands recomputed from the full classified sample at each update; as the sample grows, band movement can reclassify a small number of historical sessions, so regime day counts and per-regime statistics may shift between editions without any change in the underlying return data. Methodology and current band parameters are documented in the firm Regime Performance Report.
Performance in Hostile Regimes: Across 206 trading days classified hostile to quality strategies, Alpha Convergence returned -1.29% while RSP fell -13.96%, generating +12.67% cumulative alpha vs RSP. The two trash-rally regimes were the largest contributors (Severe Trash Rally +7.78% over 34 days, Momentum/Trash Rally +11.49% over 53 days). A quality-focused strategy should suffer when small cap speculation dominates; instead, we drafted the rally while maintaining quality discipline.
Performance in Favorable Regimes: Across 65 trading days classified favorable, Alpha Convergence returned +51.86% while RSP returned +36.74%, generating +15.12% cumulative alpha vs RSP. The Broad Market Rally bucket produced +15.79% alpha over 52 days as the strategy participated in upside. The Quality Rally bucket (13 days) sits at -1.09% on a thin sample and is discussed below.
VIX Regime Consistency: Across VIX regimes, the strategy generated positive alpha in three of four buckets: Low Vol +1.44% (24 days), Normal Vol +34.96% (200 days), High Vol +1.90% (15 days). Elevated Vol (36 days) produced -1.72% cumulative alpha. Maintaining consistent alpha generation across volatility environments demonstrates likely mean reversion capture rather than regime-dependent momentum.
Cumulative Alpha: +34.65% alpha vs. RSP across 275 days of varied market conditions. This represents an annualized information ratio of 2.26.
Expected Soft Spots: The two regimes with negative alpha, Quality Rally (-1.09%) and Risk-Off/Quality Flight (-3.25%), share a common structure: both compress the cross-sectional dispersion the strategy is built to harvest. The selection edge operates against a quality baseline, owning quality names the market has temporarily mispriced relative to that baseline. In a Quality Rally the market is already rewarding the baseline itself, so the gap between quality and the crowd narrows and there is less dispersion left to capture. In Risk-Off conditions correlations spike toward one and cross-sectional dispersion collapses outright, taking the strategy’s raw material with it. We therefore expect thin or negative alpha in these regimes rather than treating it as anomalous. The same mechanics explain where alpha concentrates: trash-rally conditions, where the gap between quality and speculation is widest, supplied the largest contributions. This is a structural trade-off of the design, not a calibration error, and it is disclosed as such.
We seek continued stress testing although current evidence suggests resilient characteristics by generating alpha precisely when conditions are most hostile. We also caution against extrapolating this pattern to untested regimes until we have live data.
Alpha Convergence is a deliberately transparent, live-data-driven strategy. Its advantage emerges from structurally independent models and disciplined rebalancing. The core insight: two analytically independent screens measuring genuinely different phenomena provide more reliable signal when they agree than either screen alone.
Life UnLocked Partners
Mark Tennenbaum, Chief Investment Officer
[email protected]
The following tests evaluate whether observed metrics are statistically distinguishable from zero. All tests use 275 trading days (June 27, 2025 through July 31, 2026). n = 275.
| Metric | Hypothesis | Test | Result |
|---|---|---|---|
| Excess Return | H₀: α=0 | t-test | p < 0.05 |
| Information Ratio | H₀: IR=0 | Lo (2002) | p < 0.05 |
| Win Rate | H₀: rate=50% | Binomial | p = 0.062 (n.s.) |
| Sharpe Ratio | H₀: SR=0 | Newey-West HAC | p < 0.01 |
The Information Ratio standard error follows Lo (2002), "The Statistics of Sharpe Ratios," Financial Analysts Journal. The approximation SE = sqrt((1 + ratio^2/2)/n) is standard but assumes iid returns. Daily financial returns violate this assumption through autocorrelation, volatility clustering, and non-normal tails, so the reported t-statistics and p-values should be understood as indicative rather than exact. The Sharpe Ratio is assessed under a Newey-West HAC standard error (q=4) per Lo (2002) to account for that autocorrelation.
The Information Ratio of 2.26 strengthened over the added month. Recent strength notwithstanding, IRs of this magnitude remain characteristic of early-stage strategies with favorable sequencing, and almost always compress toward the 1.0 to 1.5 range over full market cycles as the strategy encounters a broader distribution of market conditions. We present the current figure as a point-in-time observation, not a long-run expectation.
The Win Rate 95% confidence interval of [49.8%, 61.5%] includes 50.0% at its lower bound, and the binomial test is not significant at the 5% level (p = 0.062, two-sided). As the sample grew, the proportion of winning days moved toward a coin flip even as the average size of winning days stayed favorable. Win rate measures how often the strategy beats the benchmark, not by how much.
All hypothesis tests assume returns are drawn from a stationary process. To the extent that the strategy's signal quality, market conditions, or portfolio composition have changed materially during the observation period, the assumption of stationarity is approximate. The 252-day validation threshold has been reached; formal tests for structural breaks are deferred until additional post-validation sample accrues.
Three of the four metrics are statistically significant at the 5% level (Excess Return, Information Ratio, and Sharpe versus zero); the Win Rate is non-significant (p = 0.062) as the proportion of winning days sits near a coin flip even though the average size of winning days remains favorable. Sharpe inference is assessed under the Newey-West HAC standard error, whose 95% interval [0.76, 4.19] excludes zero; the separate test against the 1.10 institutional threshold reported in the Statistical Note is not yet met. These results are directionally supportive but should be interpreted with appropriate caution given the limited sample size, non-iid return dynamics, and the expected compression of early-stage performance ratios. The 252-day minimum observation period was completed on June 29, 2026.
This white paper is published for institutional investors, investment consultants, financial professionals, and qualified counterparties conducting due diligence on the Alpha Convergence strategy. It is educational and informational in nature and is not an advertisement directed at retail investors. Sharing the address of this page is permitted; reproducing or excerpting its contents out of context, or repackaging them for retail distribution, is not authorized. The definitive record of the strategy is the firm-distributed white paper document and Form ADV.
This white paper describes a general equity investment strategy and is for informational and educational purposes only. It is not individualized investment advice and should not be relied upon as a recommendation to buy or sell any security or to adopt any investment approach. The information presented does not consider the financial circumstances, risk tolerance, or objectives of any specific investor.
The material herein does not constitute an offer to sell or a solicitation of an offer to purchase any security, investment product, or advisory service. Any advisory services are offered only through the adviser’s Form ADV, advisory contract, and required disclosure documents.
The Alpha Convergence Equity Strategy commenced operations on June 27, 2025 and has an operating history of 275 trading days as of the reporting date; the 252 trading days generally required for institutional validation were completed on June 29, 2026, and the record remains short by long-horizon standards. Strategies with limited track records may behave differently as markets evolve, and short-term performance should not be assumed indicative of future outcomes.
The strategy discussed involves investments in equity securities, which are subject to significant risks. Equity values may decline due to company-specific events, market volatility, changes in interest rates, geopolitical developments, earnings revisions, and sector or style rotations. Equity securities generally experience greater price volatility than fixed income instruments. Growth, value, small-cap, mid-cap, or thematic exposures may add additional layers of risk.
If the strategy emphasizes specific sectors, factors, or themes, performance may be significantly affected by events that impact those areas. Concentrated exposures can increase volatility and may result in higher risk of loss.
Security selection within an equity strategy depends on research, valuation assumptions, financial data, and subjective judgment. There is no guarantee that selected securities will perform as expected. Company fundamentals, competitive conditions, and management quality can change rapidly and materially impact performance.
Historical performance results, whether actual or hypothetical, do not guarantee future results. Equity markets may experience periods of extreme volatility and loss. Investors may lose all or a substantial portion of their investment.
Any forward-looking statements, forecasts, or expectations represent the adviser’s judgment at the time of publication. These statements involve known and unknown risks and uncertainties. Actual results may differ materially from any projections or hypothetical outcomes included in this document.
The strategy uses quantitative screens, factor models, ensemble techniques, ranking systems, and proprietary analytical tools. These tools rely on historical relationships that may not persist. Data errors, structural changes in markets, changes in factor premia, or methodological limitations may adversely impact model outputs. Models may perform inconsistently across different market cycles, volatility regimes, or liquidity environments.
To the extent the strategy incorporates fundamental analysis, such analysis relies on public filings, third-party data, management commentary, and economic indicators that may be outdated, inaccurate, or incomplete. The adviser makes no warranty regarding the accuracy or completeness of information obtained from external sources.
The ensemble architecture relies on maintaining low inter-model correlation in the -0.05–0.05 range, a characteristic that may not persist in future markets. Correlations typically rise during periods of market stress, diminishing diversification benefits. Ensemble agreement filters may not prevent false positives or false negatives during regime transitions.
The strategy holds exactly 22 equal-weighted securities across all 11 GICS sectors. This approach, while diversified across sectors, may still exhibit meaningful position-level and sector-level risk. Concentrated portfolios may experience higher levels of volatility and larger drawdowns than more diversified strategies.
The observed beta of 0.89 versus RSP reflects behavior during a varied period including the late-March 2026 stress event, with VIX ranging from 13.4 to a brief stress-event peak of 31.1. Beta can rise during periods of stress, reducing downside protection. The strategy is not market neutral and will generally participate in broad market movements.
Comparisons to equity benchmarks are provided for informational purposes only. Benchmarks may not reflect the risk profile, sector exposures, factor tilts, or investment constraints of the strategy. Benchmarks cannot be invested in directly. Outperformance relative to a benchmark is not guaranteed.
Benchmark data is provided solely for informational comparison. The Invesco S&P 500 Equal Weight ETF (RSP) is unmanaged, cannot be invested in directly, and does not reflect advisory fees, platform fees, transaction costs, or expenses. The strategy’s holdings, risk characteristics, factor exposures, and concentration differ materially from the benchmark. Outperformance relative to a benchmark is not guaranteed.
Execution quality, bid-ask spreads, market impact, and liquidity constraints can materially affect returns in an equity strategy. Securities with lower liquidity may experience wider spreads or may be difficult to sell during adverse market conditions. Trading activity may increase taxable events for taxable accounts.
Assets are held at an independent qualified custodian. Trading and operational processes are subject to custodian policies, market conditions, and platform limitations. SMA and TAMP operational features, such as rebalancing frequency and drift thresholds, may influence performance outcomes.
Advisory fees, platform fees, custodial fees, and transaction costs reduce net returns. Investors should review the adviser’s Form ADV Part 2A and fee schedule for complete details. The strategy’s performance may differ significantly between gross and net of fees.
Equity strategies can generate taxable capital gains, dividends, wash sale limitations, and other tax consequences. This document is not intended to provide tax advice. Investors should consult a qualified tax professional regarding their individual situation.
If back-tested or hypothetical equity performance is provided, such results are subject to limitations. They are generated with the benefit of hindsight, do not reflect actual trading, may understate the effect of market stress, and cannot account for all economic and market conditions. Hypothetical performance does not reflect actual investor experience.
The described equity strategy may not be suitable for all investors. Prospective clients should perform their own independent review and consider their objectives, time horizon, liquidity needs, and financial circumstances. The adviser will provide individualized advice only after entering into an advisory agreement and obtaining necessary information from the investor.
All performance, risk metrics, statistical tests, and visualizations are based on internal systems or third-party data believed to be reliable. Independent verification has not been performed. Methodologies may differ from other industry-standard calculations. Academic citations are provided for context and do not imply endorsement.
Individual account performance may differ substantially from composite or model results due to timing of contributions and withdrawals; tax considerations; restrictions on securities; execution timing and price variance; custodial or platform constraints; account size; tracking error; and differing fee structures.
Actual investor outcomes may be higher or lower than the performance figures shown.
The adviser is a California-registered investment advisor. Registration does not imply any level of skill or training. Investors should review the adviser’s Form ADV for important information about business practices, disciplinary history, and conflicts of interest.
The equity strategy, methodology, assumptions, security selection process, and portfolio construction techniques are subject to change without notice. Market environments evolve and may require adjustments to the strategy’s implementation.
All information contained herein is provided “as is.” The adviser makes no warranties, express or implied, regarding accuracy, completeness, timeliness, or suitability. All information is subject to change.