Portfolio Theory: Diversification, Risk, and ETF Portfolio Construction
How combining assets with imperfect correlation reduces risk without sacrificing return β and how to apply it with ETFs.
The "only free lunch in investing"
This phrase, attributed to Markowitz, refers to diversification β not rebalancing. Diversification reduces risk without proportionally sacrificing return. Rebalancing is a risk-control discipline, not guaranteed alpha.
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β Read the Chilean version on finclaro.clMarkowitz in Plain Terms
In 1952, Harry Markowitz published "Portfolio Selection" and changed how we think about investing. Before him, the conventional wisdom was to pick the "best" individual assets. Markowitz showed that what matters is not just each asset's return and risk, but how they behave together.
The key insight: two risky assets, combined in the right proportions, can form a portfolio less risky than either one alone β as long as they don't move in perfect sync.
Correlation: Why It Matters
Correlation measures how much two assets move together, on a scale from β1 to +1. The magic of Markowitz is that you don't need negative correlation to benefit β anything less than +1 helps.
Interactive: Correlation Playground
Drag the sliders to see how correlation and weights affect portfolio risk and return.
Even with moderate positive correlation (0.3), diversification still reduces risk. The portfolio volatility (10.9%) is 4.1pp lower what you'd get holding Asset A alone (15%).
The Efficient Frontier
If you plot all possible asset combinations on a risk (X) vs return (Y) plane, the upper-left boundary is the efficient frontier: portfolios offering maximum return for each risk level. Sharpe (1964) extended this with CAPM: combine the "market portfolio" with a risk-free asset, adjusting proportions to your tolerance.
Diversification: The Real Free Lunch
Diversification works because different assets respond differently to the same economic events. Brinson, Hood & Beebower (1986) found that asset allocation decisions explained over 90% of return variability among pension funds. Ibbotson & Kaplan (2000) refined that number, but the core conclusion stands: allocation across asset classes matters more than individual security selection.
Rebalancing: Discipline, Not Magic
As assets return differently, your portfolio drifts from its target allocation. Rebalancing means selling some winners and buying laggards to restore your target. Evidence (Vanguard, 2019) suggests rebalancing does not generate guaranteed additional returns. Its value is maintaining the risk level you consciously chose.
Interactive: Rebalancing Drift
See how market returns cause your allocation to drift β and what rebalancing means in practice.
Drift: 4.5pp (60/40 β 65/35)
Action: Sell 4.5% of stocks, Buy equivalent in bonds.
Asset Allocation
The variables that determine your allocation: investment horizon, risk tolerance (how much drawdown you can endure without selling), risk capacity (income stability, debts, dependents), and goals.
Model Portfolios: Starting Points
Costs, Taxes, Currency & Inflation
Markowitz's theory assumes a frictionless world. In practice, consider: TER/expense ratios (0.50% vs 0.03% can mean $100K+ over 30 years), taxes (vary by residence β capital gains, dividend withholding, estate tax), currency (investing in a different currency adds FX risk/opportunity), and inflation (nominal 8% with 4% CPI is only 4% real).
DCA vs Lump Sum
Vanguard (2012) found lump sum outperforms DCA about β of the time because markets tend to rise. But DCA aligns with monthly income flows, reduces timing risk, and is psychologically easier. For most investors contributing from salary, DCA is the natural approach.
β DCA vs Lump Sum SimulatorInvestor Behavior
The greatest threat to your portfolio isn't the market β it's you. The most destructive behavioral biases: panic selling during drawdowns, chasing recent performance, overtrading, and abandoning your plan. Studies like DALBAR's QAIB suggest average investors tend to earn less than the market, largely due to poor timing decisions. While DALBAR's methodology has been debated, the directional conclusion is widely shared: discipline matters more than sophistication.
Limitations of the Theory
- Assumes normal return distributions β markets have "fat tails" (extreme events more frequent than expected).
- Correlations are not stable: they increase during crises, precisely when you need diversification most.
- Portfolio optimization is sensitive to inputs: small changes in expected returns produce very different allocations.
- Fama & French (1993) showed risk has multiple dimensions (size, value, momentum) the original model doesn't capture.