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COREFama–French 3-Factor

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

● 4 holdings· $ 10,000· 69 mo window· 2026.07.08 · 18:00· R² 0.86
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.
●Fama–French 3F · Portfolio personality

The Raging Whale성난 고래

Already a heavyweight roaming the ocean, you keep growing ever bigger.

Regression coefficients · annualized
α (Alpha)미유의
+0.29%
t = 0.10 · p = 0.918
Almost fully explained by factors (alpha ≈ 0).
Market beta (βM)✓ 유의
1.25
t = 24.59 · p = 0.000
Moves about 25% more than the market.
Size beta (βS)✓ 유의
-0.17
t = -2.30 · p = 0.025
Weakly co-moves with large-caps when they lead.
Value beta (βH)✓ 유의
-0.24
t = -4.47 · p = 0.000
Weakly co-moves with growth stocks when they lead.
Regression equation
회귀식
Rp − Rf = α + βM·MKT + βS·SMB + βH·HML + ε
표본: 최근 69개월 월간 데이터 · OLS 회귀 · 표시값은 연환산
연간 기대수익률
+14.8%
⚠ The expected return reflects exposure to risk. Expected losses rise proportionally with expected return.
* Statistical estimate · not a guarantee of realized return.
Visualization

Fama–French 3-factor exposure

MKTSMBHML
My portfolioMarket (MKT only)
ReadThe closer to a corner, the stronger the exposure to that factor. Negative ≠ bad — negative SMB/HML simply means "tilted toward large/growth" — a style diagnosis, not a quality verdict.
Interpretation4 takeaways
01 · Factor
Market dominant + 2 more

Your strongest exposure is moves 1.25× with market (β = +1.25). 2 more factors also break threshold, but this single axis defines the book's main color. The Style Tilt card shows the combination; this card focuses on the leading axis.

02 · Alpha
α +0.29% — effectively zero

Excess return beyond factor exposure is essentially nil (α +0.29%), and it's statistically indistinguishable from zero (p=0.918). Nearly all your performance is being explained by factor exposures — market, size, value, and the rest. That's neither good nor bad; it's a clean factor-driven book.

03 · Stability
R² 85.8% — strong fit

The factor model explains 85.8% of the variation in your returns — a strong fit, typical of reasonably diversified equity books. The familiar axes (market, size, value) are doing a solid job of describing your big picture.

04 · Style Tilt
3-factor composite style

Market +1.25 · SMB -0.17 · HML -0.24. Multiple colors mix to give the portfolio a multi-layered identity. No single axis dominates — if this is by design, fine; if it's accidental, it's a good moment to decide which tilt you actually want to keep.

Portfolio Equalizer

Adjust weights yourself

보유액 슬라이더를 움직이면 시장·규모·가치 베타와 알파, 기대수익률이 종목별 회귀 결과의 가중평균으로 즉시 재계산됩니다.

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
Market β (βM)
0.91-0.34
SMB β (βS)
-0.17
HML β (βH)
-0.24
Expected
+14.75%
Factor Target

Set factor targets → optimized weights

Move the sliders to define a goal like "I want a market β of 0.7" — we propose a weight mix that satisfies the target.

Market beta target
βM
+1.25→+1.25
+0.4+2.0
Size beta target
βS
-0.17→-0.17
-1.0+0.6
Value beta target
βH
-0.24→-0.24
-1.0+0.6
Total target deltas — Market beta: +0.00 · Size beta: +0.00 · Value beta: +0.00Set freely with sliders
Rebalance intensityBalanced (moderate)
Next step

우량성과 투자성향 효과까지 분리하려면?

Fama–French 5-Factor는 RMW(수익성)·CMA(투자성향)을 추가해 우량주 효과를 분해합니다.

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.
Alpha is positive but reported as not statistically significant. What does that mean?
Positive alpha appears, but the regression cannot tell whether it reflects real skill or chance. Significance is usually claimed at |t-stat| > 2 or p-value < 0.05 — this estimate falls short of that bar. Short windows or high variance can leave alpha positive while making the t-stat low. Read it as "there is a positive alpha but more than a 5% chance it is noise", and consider extending the window or re-checking under the 5-factor model.
Does a positive β_S mean my portfolio is full of small-caps?
No. β_S measures whether your portfolio's returns co-moved with small-cap strength during the window — not what is inside the basket. Even a pure SPY (large-cap ETF) position can show a positive β_S if its returns happened to move with the small-cap factor over the sample. The same logic applies to β_H, β_R and β_C — these are all "co-movement patterns of returns", not labels of holdings. The label of what you hold and the factor exposure of its returns are separate concepts.
If R² is low, should I distrust the analysis?
More precisely, "read the result more conservatively" rather than "distrust it entirely". R² shows how much of the portfolio's return variance is explained by the three factors. Above 60% is the typical range for a well-diversified portfolio. Below 40% means idiosyncratic variance dominates, and the confidence intervals on beta and alpha widen — the same beta could move by ±0.3 or more. Take signs and rough magnitudes of beta / alpha as the takeaways, not exact numbers.
What do Tracking Error and Information Ratio tell me?
Both measure "how much active management beyond the factor model" this portfolio carries. Tracking Error is the volatility of the regression residual — how big the swings are in the part the model cannot explain. Information Ratio is alpha / Tracking Error — how efficient the off-model return is per unit of off-model risk. High IR suggests consistent stock selection on top of factor exposure; IR near zero suggests the off-model return is noisy or irregular.
Where does the Fama–French factor data come from? Is it free?
US-stock factors come from the official data library maintained by Kenneth French at the Tuck School of Business (Dartmouth) — free, updated monthly. Korean-stock factors we compute in-house from public Korean market data (Korea Exchange and other public sources), also refreshed monthly. Mixed KR / US portfolios run separate regressions against the two factor sets and combine them by weight. The "Further reading" section in the Theory tab links directly to the French source.