| name | portfolio-review |
| description | 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. |
| metadata | {"category":"finance","version":"1.0.0","tags":["portfolio","correlation","exposure","diversification","factors"]} |
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
- 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.
- 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.
- 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.
- 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.
- 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.
- 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:
def review(portfolio: Portfolio, returns: pd.DataFrame) -> PortfolioReview:
weights = portfolio.weights
corr = returns[portfolio.symbols].corr()
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
clusters = cluster_by_correlation(corr, threshold=0.70)
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:
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.
