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COREMarkowitz · Mean-Variance

예시 포트폴리오 — 미국 분산 (AAPL · MSFT · SPY · TLT)

● 4 holdings· $ 10,000· 72 mo window· 2026.07.08 · 18:00
This is a "sample analysis". It is not based on your own portfolio — we are showing a pre-prepared analysis of 예시 포트폴리오 — 미국 분산 (AAPL · MSFT · SPY · TLT) for learning purposes. To analyze your own holdings, click the button on the right to go to the input page.All numbers were computed against real market data at the analysis time. The snapshot is fixed, so values may differ from the most recent market conditions.
●Markowitz · Portfolio personality

The Easygoing Stroller평화주의 산책꾼

Spreading assets wide brings you peace of mind as you head, slowly, toward your destination.

Key metrics · annualized
Annual σ
16.26%
Prices swing an average of 16.3% up and down per year.
Expected μ
+14.67%
Past average implies +14.7% return per year.
Efficiency (Sharpe)
0.68
Earns 0.68 per unit of risk — moderate.
95% VaR
-12.07%
A 95% chance annual loss stays within 12.1%.
Visualization

Max return at the same risk level

1.2%6.5%11.8%17.1%22.4%27.8%σ 12.1%σ 17.2%σ 22.3%σ 27.4%μ ↑ 연간 기대수익σ → 연간 변동성범위 밖: TLTAAPLAAPLMSFTMSFTSPYSPY내 포트폴리오
Read.The efficient frontier traces the maximum expected return achievable at each level of volatility. If your portfolio sits below the curve, a different weight combination could yield more return at the same risk; on the curve means already efficient.
Current diversification · 17.0%Sharpe · 0.68
Interpretation4 takeaways
01 · Risk
Moderate volatility · in line with market — σ 16.26%

An annual σ of 16.26% is a normal absolute range — from broad equity indices (S&P 500, MSCI ACWI) to single-country equity funds. It sits essentially in line with the market benchmark (σ 15.7%, ~1.0×). (Annual 95% VaR -12.1%. σ assumes a normal distribution, so real losses can exceed it.)

02 · Efficiency
Sharpe 0.68 — reasonable

Sharpe 0.68 runs a notch above the long-run market average (roughly 0.25–0.40) — a reasonable efficiency. Excess return of +11.07 pp is being paid out fairly across σ 16.3%. If you want to push higher, trimming volatility at the same return usually moves the needle more reliably than chasing more return at the same risk.

03 · Diversification
Diversification 17.0% — moderate

Diversification of 17.0% is the normal range for a portfolio that mixes asset classes — stocks with bonds, or domestic with overseas. There's enough low-correlation material in the mix that one leg can cushion another when conditions turn.

04 · Concentration
Heavy concentration (AAPL 55.0%)

AAPL carries 55.0% — more than half the book sits in one position. The HHI of 0.375 is in the "high concentration" range by academic standards. Other metrics may look good, but it's worth knowing that risk and return rest heavily on this single name.

Portfolio Equalizer

Adjust weights yourself

Move the amount sliders to see how volatility, Sharpe and expected return change instantly.

AAPL
AAPL
25.0% · $ 2,500
MSFT
MSFT
20.0% · $ 2,000
SPY
SPY
35.0% · $ 3,500
TLT
TLT
20.0% · $ 2,000
Total $ 10,000$ 0 (+0.0%)
Live metrics
Vol (annual)
16.26%
Sharpe
0.68
Expected return
+14.67%
Diversification
17.0%
Next step

Where does this portfolio's alpha come from?

Fama–French 3-Factor decomposes market, size and value exposure, and tests whether any excess return is statistically significant.

FAQ

Frequently asked questions

Questions newcomers to this model commonly ask. Click any question to expand the answer.

Can I trade exactly the way the analysis suggests?
No, that is not recommended. Every analysis here is a statistical estimate based on historical market data, and it does not guarantee future returns or losses. Trading costs, taxes, FX costs, market impact, and your own investment goals and horizon are also not reflected. Use the results as a diagnostic of "what kind of risk and return profile this portfolio has", and before any real investment decision review your own situation and consider consulting a qualified professional.
I re-ran the analysis on the same tickers but the result is slightly different — why?
That is expected. Each analysis fetches the latest data from yfinance and the Kenneth French library at the moment you run it. When new trading days or factor updates land between runs, the sample changes and the means, variances and regression coefficients shift slightly. If the change is large (e.g. alpha flips sign, or a beta moves more than 0.3), that is a signal that the sample is too short for statistical stability — check the reliability score at the top of the result page.
How are Korean and US stocks handled together?
It depends on the model. Markowitz and HRP convert all amounts to a single currency (KRW) and treat the two markets as one portfolio — USD holdings are converted at the FX rate at analysis time. Fama–French (3- / 5-factor) needs region-specific factors, so Korean tickers are regressed against our own Korea factors (computed in-house from public Korean market data) and US tickers against Kenneth French's North America factors, with the two regressions combined by weight average. Mixed KR / US portfolios appear as a "split analysis" on the result page, with per-market regression results included.
How is the "reliability score" calculated?
A 0–100 score combining the sample window (months) with model-specific quality signals. The window component reaches 70+ once it crosses 60 months (5 years, the academic recommendation). Per model: Markowitz adds diversification benefit and a single-name concentration penalty; Fama–French adds R² and the share of betas that are statistically significant; HRP adds diversification benefit and how much HRP reduces current volatility. 75+ is "excellent", 55–75 "good", 35–55 "fair", below 35 "low". This is a heuristic for "how seriously should I take this result" — not an academic standard.
My portfolio dot sits far to the left (lower volatility) of my holdings' average — is that a bug?
It is not a bug — it is diversification working exactly as the theory predicts. Portfolio volatility is not the average of each holding's volatility; it also reflects how much the holdings move together (correlation). When assets from different sectors and markets swing at different times, their swings partially cancel out, trimming the portfolio's overall volatility. With low enough correlations, the portfolio can end up less volatile than the average — and even less volatile than the single calmest holding. That is the core result of Markowitz's theory. The further left the dot sits, the better diversified you are, and the gap versus the average is the "diversification benefit" figure in your result.
Why isn't a single "optimal weight set" shown in the result?
The efficient frontier is a curve, not a point, and there are infinitely many "optimal" points along it. Which one to recommend — max Sharpe (tangency), min variance, target-return — depends on the goal, and mean-variance optimization is famously sensitive to the input estimates of means and covariance. We deliberately do not push a single "optimal weight" recommendation. Instead, we plot your portfolio on the efficient-frontier curve so you can see "how much room there is to improve return at the same risk", and we leave the rebalancing call to you.
Is a Sharpe ratio above 1 always good?
Industry convention is that an annualized Sharpe above 1.0 is "healthy" and above 2.0 is "excellent", but if the window leans bull-market it will overstate the true ratio. Sharpe is (return − risk-free) / volatility, so any window where volatility happened to be unusually low gets a flattering numerator-to-denominator ratio. The normal-distribution assumption behind volatility also underweights tail risk (black swans), so realized risk-adjusted rewards can be lower than Sharpe suggests. Read it together with the reliability score and ideally re-validate across a 5+ year sample.
Does "VaR 95% = −10%" mean a 5% chance of losing 10% in a year?
More precisely: "under normal market conditions, the probability of losing more than 10% within one year is 5% or less". We use parametric VaR with a normal-distribution assumption, which fits typical markets but underestimates realized losses during shocks (financial crises, wars, pandemics — i.e. tail risk). Read VaR as a "normal-market lower bound", not a worst-case ceiling, and pair it with a more conservative scenario for real money management.
Is diversification near 0% always bad?
It depends on the intent. Low diversification means your holdings move almost in lockstep — usually a sign of sector / theme / country concentration. If "diversify" is the goal, add assets or rebalance to lift the effect. If you intentionally bet on a single sector or theme, low diversification is a natural consequence and not a problem in itself — just be aware that single-name blow-up risk rises with concentration.
How can I tell whether my portfolio sits exactly on the efficient frontier?
On the "Efficient frontier" chart you can visually see whether your portfolio sits on the curve (green marker) or below it (red marker). Below the curve means there is a weight mix that achieves higher return at the same volatility, and a dashed line illustrates the gap. Important: "on the curve" does not mean "perfect" — the curve itself depends on the sample's mean / covariance estimates, and out-of-sample the curve can take a different shape.