€150 applies as credit toward the Strategic Session · €750
Concentration
71
Correlation
58
Drawdown
84
Risk-Adj. Return
44
⚠Score anomaly detected in one component
No payment upfront · Anton confirms every request
Score Breakdown
Expected Return
—
Annualised
Volatility
—
Annualised
Sharpe Ratio
—
Rf = 4.0%
Median Terminal Wealth
—
over — years
Prob → Target
—
target: —
Sortino Ratio
—
downside-adj.
Calmar Ratio
—
return / drawdown
Strategy
Portfolio · Live
Analysis prepared by Anton Ladnyi · [email protected]Risk DNA · Institutional Portfolio ProfilerConfidential
Overview
Holdings
Monte Carlo
Risk
Frontier
Comparison
Attribution
⚔ War Room
AI Summary
IPS
A.L. Capital Advisory
Private Wealth Management · Warsaw
Anton Ladnyi · Goldman Sachs Equity Research
J.P. Morgan Wealth Management · 10+ Yrs IB
CFA L1 + L2 Verified · L3 Candidate
MSc International Business Management
Investment Policy Statement
Portfolio Analysis & Advisory Report
Institutional-grade portfolio construction · Black-Litterman · MVO · Monte Carlo
Prepared for
—
Report Date
—
Risk Profile
—
Horizon
—
Confidential · For Addressee Only · Not Investment Advice
REF: ALC-IPS-—
§ 0
Investment Mandate & Policy Declaration
A.L. Capital Advisory
This Investment Policy Statement establishes the investment objectives, risk parameters,
and operational guidelines governing the management of the above-named client portfolio.
It serves as the foundational governance document for all portfolio construction decisions.
All methodology follows CFA Institute standards.
01 · Investment Objective
—
02 · Risk Mandate
—
03 · Return Target
—
04 · Liquidity & Horizon
—
05 · Investment Constraints
—
06 · Governing Framework
Black-Litterman posterior returns · Ledoit-Wolf shrinkage covariance ·
Utility-optimal MVO (SLSQP) · 2,000-path Monte Carlo · VaR/CVaR per Basel III ·
CFA Institute methodology · Annual rebalancing.
Adviser Attestation: This analysis has been prepared
by Anton Ladnyi (A.L. Capital Advisory) using institutional quantitative methods consistent
with CFA Institute standards. All projections are probabilistic estimates based on historical
data and forward-looking assumptions. Past performance is not indicative of future results.
This document does not constitute regulated financial advice. The client is encouraged to seek
independent legal and tax counsel before implementing any investment strategy described herein.
Confidentiality Notice: This Investment Policy Statement is prepared exclusively for the named client and contains proprietary quantitative analysis. Reproduction, distribution, or disclosure to third parties is strictly prohibited without prior written consent from A.L. Capital Advisory.
Appendix A
Quantitative Methodology
A.L. Capital Advisory Model Descriptions
A.1 — Black-Litterman Posterior Return Estimation
The Black-Litterman model (Goldman Sachs, 1990) begins from market equilibrium implied returns (reverse-optimised from market capitalisation weights) and blends them with the adviser's absolute and relative views using Bayesian updating. The posterior mean vector μ_BL and covariance Σ_BL replace historical returns as inputs to MVO, resolving the instability and corner-solution pathologies of classical mean-variance analysis. Ledoit-Wolf shrinkage is applied to the sample covariance matrix prior to BL computation to further reduce estimation error. Confidence parameter τ = 0.05.
A.2 — Utility-Optimal Mean-Variance Optimisation
The recommended portfolio maximises the expected utility function U = E(r) − ½·A·σ², where A is the client's risk aversion coefficient derived from the ML profiling model. Optimisation is performed via Sequential Quadratic Programming (SLSQP) subject to long-only constraints (wᵢ ≥ 0, Σwᵢ = 1). The minimum-variance portfolio is computed separately as a reference benchmark. The efficient frontier is traced by parametric return-targeting across 30 equally spaced target returns.
A.3 — Monte Carlo Wealth Simulation (2,000 Paths)
Portfolio wealth is simulated using correlated Geometric Brownian Motion with Cholesky-decomposed covariance. Daily returns are drawn from a multivariate normal distribution parameterised by BL posterior estimates. Monthly contributions are credited at end-of-month; annual rebalancing is enforced. Terminal wealth distribution statistics (P5, P50, P95) and probability of reaching the wealth target are derived from the full 2,000-path ensemble. IRR is solved by bisection across all paths.
A.4 — Value at Risk & Expected Shortfall (Basel III)
Monthly VaR at 95% and 99% confidence is computed using parametric (variance-covariance) methodology consistent with Basel III/FRTB standards: VaR = −(μ_m + z_α · σ_m) where z_α is the standard normal quantile. Expected Shortfall (CVaR) is computed as ES = −(μ_m − σ_m · φ(z_α)/α), capturing the conditional expected loss in the tail. These metrics are derived from annualised BL posterior parameters scaled to monthly frequency.
A.5 — Scenario Stress Testing Methodology
Each historical scenario applies documented market drawdowns as equity and fixed-income factor shocks. Per-ticker OLS beta versus SPY is estimated from the historical returns series. The portfolio's beta-weighted exposure to each asset class determines the blended shock applied: portfolio_loss = Σ wᵢ · [βᵢ · equity_shock + (1−βᵢ) · bond_shock]. Recovery duration is estimated proportionally to the historical market recovery adjusted for portfolio beta. Scenarios are calibrated to documented CRSP/S&P 500 peak-to-trough figures from academic literature.
A.6 — Risk Decomposition & Marginal Contribution
Risk contribution of position i is defined as RC_i = wᵢ · (Σw)_i / w'Σw, representing the fractional share of total portfolio variance attributable to that position. Marginal contribution to risk (MCR) is the first derivative of portfolio volatility with respect to weight: MCR_i = (Σw)_i / σ_p. These decompositions enable identification of risk concentration beyond weight concentration.
References: Black, F. & Litterman, R. (1992). "Global Portfolio Optimization." Financial Analysts Journal. — Markowitz, H. (1952). "Portfolio Selection." Journal of Finance. — Ledoit, O. & Wolf, M. (2004). "Honey, I Shrunk the Sample Covariance Matrix." Journal of Portfolio Management. — CFA Institute (2020). Fixed Income Analysis, 3rd ed. — Basel Committee on Banking Supervision (2019). Minimum Capital Requirements for Market Risk (FRTB).
WP
Full Derivation
The complete mathematical derivation of the Ledoit-Wolf shrinkage estimator, Black-Litterman Bayesian updating, Cholesky simulation framework, and four-layer model governance hierarchy is documented in:
Ladnyi, A. (2026). "Portfolio Construction Under Parameter Uncertainty: A Bayesian Framework for Strategic Asset Allocation."A.L. Capital Advisory Working Paper. Download PDF →
A.L. Capital Advisory
Anton Ladnyi · Goldman Sachs · J.P. Morgan · CFA L1+L2 · 10+ Yrs IB
Portfolio Analysis Report
Date:— Strategy:— Horizon:—
§ 1 — Portfolio Overview & Optimised Weights
Portfolio Weights
Optimal allocation by ticker
Portfolio Health Check · Deliverable 02€150
02
Risk Contribution
Euler attribution — which positions consume the most risk budget relative to their allocation weight
Marginal volatility per holding — the single trim that reduces total portfolio risk most efficiently
Written diagnostic by Anton Ladnyi · PDF delivered within 48 hours
€150 applies as credit toward the Strategic Session · €750
No payment upfront · Anton confirms every request personally
Risk Contribution
% of total portfolio variance
Simulation Assumptions
Monte Carlo simulation parameters
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Broker selection · tax efficiency · rebalancing cadence · position sizing. Advised by Anton Ladnyi — Goldman Sachs Equity Research, J.P. Morgan Wealth Management, 10+ years IB, CFA L3 Candidate.
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Correlation Matrix
5-year daily return correlations
§ 3 — Monte Carlo Simulation & Probability Analysis
Live Simulation Preview · 60 Path Sample
Wealth Projection
Sample paths with percentile bands
Portfolio Health Check · €150
You have the simulation. Now get the interpretation.
Your Monte Carlo results show a range of outcomes — but a number without context is just noise. The Portfolio Health Check is a personalised written analysis prepared by Anton Ladnyi, delivered within 48 hours, that turns this simulation into an actionable framework.
Written scenario commentary on your P5 downside — what it means and what triggers it
Interpretation of your goal probability in the context of your allocation and time horizon
Drawdown stress test: when to hold, when to rebalance, when to act
Concentration risk assessment and the single highest-leverage rebalancing recommendation
Full correlation matrix with factor attribution — delivered as a signed PDF
€150
one-time
Credit applies toward the €750 Strategic Session.
Prepared personally by Anton · PDF within 48h
Terminal Wealth Distribution
Percentile breakdown of final portfolio value
Strategic Session · Full Return Analysis€750
IRR
IRR Distribution
Full annualised return distribution across all 10,000 simulations — including left-tail scenarios
Covered in the Strategic Session alongside your BL-optimised allocation and IPS document
Included with the Strategic Session · €750 · No retainer
Six crises · OLS beta decomposition · Historical methodology
§ 9 — Scenario Stress Test · War Room
Crisis Simulation · Historical Macro Scenarios
Scenario Stress Test — War Room
Six of the most destructive macro crises of the last 50 years, applied to your portfolio using per-ticker OLS beta decomposition. Understand what you would actually survive — not in theory, but in numbers.
Worst Crisis Loss
—
—
Estimated Floor Value
—
Worst-case portfolio trough
Longest Recovery
—
Months to recover (worst case)
Best Outperformance
—
vs S&P 500 in best scenario
Survival Score
—
Avg outperformance · all crises
Scenario Summary — Portfolio vs Market
How this portfolio compares to a 100% S&P 500 position in each crisis
Scenario
Period
Market Loss
Portfolio Loss
vs Market
Trough Value
Recovery Est.
Severity
Scenario projections are illustrative estimates based on documented historical market drawdowns and portfolio beta decomposition. They are not predictions of future performance. Actual portfolio behaviour would depend on asset availability, liquidity, and rebalancing decisions.
§ 8 — AI Executive Summary
✦ Powered by Claude AI · Private Banking Quality
Executive Summary
Portfolio narrative generated by Anton Ladnyi's AI assistant — institutional-quality analysis in plain language ·
Strategic Session · AI Executive Summary€750
AI
AI Executive Summary
Institutional-quality portfolio narrative generated by Claude AI — full risk breakdown, allocation rationale, and priority actions in plain language
Covers Black-Litterman views, Monte Carlo probability interpretation, Sharpe/Sortino commentary, and concentration risk in one coherent document
Available immediately upon booking — regeneratable at any time directly from your dashboard