# Portfolio Review > Use when reviewing a portfolio's construction and exposure. Covers concentration, correlation, factor and sector exposure, hidden bets, and whether the portfolio expresses the intended view. - Skill: `nimadorostkar/portfolio-review` (Agent Skill) - Install (CLI): `npx skillmds@latest add nimadorostkar/portfolio-review` - Raw SKILL.md: https://api.skillmd.com/api/skills/nimadorostkar/portfolio-review/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: nimadorostkar (https://skillmd.com/u/nimadorostkar) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/nimadorostkar/portfolio-review --- # Portfolio Review ## Purpose Determine what a portfolio is actually betting on, which is frequently not what its owner thinks. A portfolio of twenty names can be a single concentrated bet, and the position list will not tell you that. ## When to Use - Periodic review of holdings and exposure. - After a drawdown, to understand what actually drove it. - Before adding a position, to see what it does to the whole. - Assessing whether diversification is real or nominal. ## Capabilities - Concentration analysis: position, sector, factor. - Correlation and effective number of positions. - Factor exposure: size, value, momentum, quality, volatility. - Hidden-bet identification. - Attribution: what actually drove the return. ## Inputs - Holdings, weights, and cost basis. - Return history for correlation and factor analysis. - The stated investment thesis, so the portfolio can be checked against it. ## Outputs - What the portfolio is actually exposed to. - Divergences between the intended and the actual bet. - Concentration risks that are not visible in the position list. ## Workflow 1. **Compute the effective number of positions** — Twenty names with a 0.85 average correlation is not twenty bets. The effective number tells you how many independent bets you actually hold, and it is usually far lower than the count. 2. **Aggregate by sector and by factor** — Sector exposure is visible. Factor exposure — a portfolio that is entirely long-duration growth, whatever the sector labels say — usually is not. 3. **Find the hidden bet** — Ten names in different sectors that all depend on the same input (interest rates, the price of oil, one customer) is one bet. This is what a correlation matrix reveals and a position list conceals. 4. **Attribute the return** — What actually drove performance? If the portfolio is up 12% and 11 points came from one position, the strategy has not been validated; one position has. 5. **Compare with the thesis** — Does the portfolio express the intended view? A portfolio built on a "value" thesis whose factor exposure is momentum has drifted. 6. **Stress it** — What does this portfolio do in a 20% market decline, a 200bp rate move, or a sector rotation? ## Best Practices - Correlation rises in a crisis. A portfolio that appears diversified in normal conditions can become a single position at the worst possible moment, and that is when it matters. - The effective number of bets is the honest diversification measure. Position count is not. - Attribution matters more than the total return. A profitable quarter driven entirely by one position tells you nothing about whether the process works. - Factor exposure is the most common hidden bet. Every high-growth name is a bet on rates, regardless of what sector it is classified in. - A position you have not reviewed in a year is a position you have forgotten why you own. Re-derive the thesis or exit. - Compare against a relevant benchmark. Beating the market by taking three times its risk is not skill. ## Examples **What a portfolio is actually betting on:** ```python def review(portfolio: Portfolio, returns: pd.DataFrame) -> PortfolioReview: weights = portfolio.weights corr = returns[portfolio.symbols].corr() # The effective number of independent bets. A position count is a fiction # when correlations are high. w = weights.values portfolio_variance = w @ returns[portfolio.symbols].cov().values @ w weighted_avg_variance = (w**2 @ returns[portfolio.symbols].var().values) effective_n = float(weighted_avg_variance / portfolio_variance) if portfolio_variance else 0 # Groups of positions that move together: these are ONE bet, not several. clusters = cluster_by_correlation(corr, threshold=0.70) # Factor exposure via regression against factor returns. factor_betas = regress(portfolio.returns, FACTORS[["mkt", "size", "value", "momentum", "quality"]]) return PortfolioReview( position_count=len(weights), effective_positions=effective_n, clusters=clusters, factor_betas=factor_betas, top_5_weight=float(weights.nlargest(5).sum()), sector_exposure=portfolio.by_sector(), ) ``` **A review that finds the bet nobody placed deliberately:** ```text Portfolio: 22 positions, "diversified across sectors". Position count : 22 Effective positions : 3.4 <- this is the real number Correlation clusters (rho > 0.70): Cluster 1 (58% of book): NVDA, AMD, AVGO, TSM, ASML, MU, ARM, MRVL Labelled: Technology, Semiconductors Actual bet: AI capital expenditure. One bet, eight ways. Cluster 2 (21%): PLTR, SNOW, DDOG, NET, CRWD Labelled: Software, Technology Actual bet: also AI capital expenditure, plus long-duration growth. Correlation with cluster 1: 0.74. It is not a separate bet. Cluster 3 (14%): JPM, BAC Cluster 4 (7%): XOM, CVX Factor exposure: Market beta : 1.42 <- 42% more market risk than the index Momentum : +0.71 <- a large, unintended momentum bet Value : -0.58 <- short value Quality : +0.12 Attribution, trailing 12 months (+31%): NVDA alone : +19 points Everything else: +12 points across 21 positions Findings: 1. This is a leveraged bet on AI capex, wearing the costume of a diversified 22-position portfolio. 79% of the book is in two clusters that correlate at 0.74 with each other. 2. Beta of 1.42 means a 20% market decline implies roughly -28% before any idiosyncratic damage. 3. The return is one position. The process has not been validated. 4. The stated thesis is "quality growth at a reasonable price". The actual factor exposure is momentum, short value. The portfolio has drifted from its thesis, or the thesis was never what was being executed. ``` ## Notes - The gap between 22 positions and 3.4 effective positions is the single most useful thing a portfolio review produces. It is invisible in a position list and obvious in a correlation matrix. - A market beta of 1.42 is a leverage decision, whether or not it was made deliberately. Most portfolios have never had it computed. - This is educational material about portfolio-analysis methodology, not financial advice or a recommendation regarding any holding.