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PROFama–French 5-Factor

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

● 4 holdings· $ 10,000· 70 mo window· 2026.07.08 · 18:00· R² 0.90
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 5F · Portfolio personality

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Regression coefficients · annualized
α (Alpha)미유의
-0.62%
t = -0.26 · p = 0.794
Almost fully explained by factors (alpha ≈ 0).
Market beta (βM)✓ 유의
0.91
t = 20.86 · p = 0.000
Moves at roughly the same amplitude as the market.
Size beta (βS)미유의
-0.15
t = -1.64 · p = 0.105
Neutral to the small-cap / large-cap cycle.
Value beta (βH)✓ 유의
-0.39
t = -4.64 · p = 0.000
Co-moves with growth stocks when they lead.
Profitability beta (βR)미유의
+0.19
t = 1.93 · p = 0.058
Weakly co-moves with quality (high-profit) stocks when they lead.
Investment beta (βC)미유의
+0.22
t = 1.98 · p = 0.052
Weakly co-moves with conservative-investment firms when they outperform.
Regression equation
회귀식 (5팩터)
Rp − Rf = α + βM·MKT + βS·SMB + βH·HML + βR·RMW + βC·CMA + ε
표본: 최근 70개월 월간 데이터 · OLS 회귀 · 3F 대비 RMW/CMA 추가 · 표시값은 연환산
연간 기대수익률
+14.6%
⚠ The expected return reflects exposure to risk. Expected losses rise proportionally with expected return.
* Statistical estimate · not a guarantee of realized return.
R² Δ vs 3F
+4.2pp
3F 0.86 → 5F 0.90
Visualization

Fama–French 5-factor exposure

MKTSMBHMLRMWCMA
My portfolioMarket (MKT only)
ReadCloser to a corner = stronger exposure to that factor. RMW · CMA are profitability and investment style — they further decompose the 3F α. Positive RMW = quality tilt; positive CMA = conservative-investment tilt.
Interpretation4 takeaways
01 · Factor
HML dominant + 2 more

Your strongest exposure is growth tilt (β = -0.39). 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 -0.62% — effectively zero

Excess return beyond factor exposure is essentially nil (α -0.62%), and it's statistically indistinguishable from zero (p=0.794). 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 90.0% — textbook fit

The factor model explains 90.0% of the variation in your portfolio's returns — the kind of fit you see when broad ETFs dominate the book. With this much explanation, the alpha estimate next door is unusually trustworthy; you can take that result at close to face value.

04 · Style Tilt
3-factor composite style

HML -0.39 · RMW +0.19 · CMA +0.22. 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 5개 팩터 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
SMB β (βS)
-0.15
HML β (βH)
-0.39
RMW β (βR)
+0.19
CMA β (βC)
+0.22
Expected
+14.63%
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
+0.91→+0.91
+0.1+1.7
Size beta target
βS
-0.15→-0.15
-0.9+0.7
Value beta target
βH
-0.39→-0.39
-1.2+0.4
Profitability beta target
βR
+0.19→+0.19
-0.6+1.0
Investment beta target
βC
+0.22→+0.22
-0.6+1.0
Total target deltas — Market beta: +0.00 · Size beta: +0.00 · Value beta: +0.00 · Profitability beta: +0.00 · Investment beta: +0.00Set freely with sliders
Rebalance intensityBalanced (moderate)
Next step

네 모델을 한 번에 비교하려면?

대시보드에서 같은 포트폴리오에 대한 모든 모델 결과를 한 화면에서 비교할 수 있습니다.

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.
Why does a significant 3-factor alpha lose significance under the 5-factor model?
Very common — and arguably a healthy sign. Part of the 3-factor alpha was actually exposure to RMW (quality) or CMA (conservative investment). When you add those two factors, that part is attributed to factor contribution, the residual "alpha" shrinks, and the standard error shifts so significance can disappear. If R² rose from 3F to 5F (it usually does), the 5-factor model has sharper explanatory power, and the earlier alpha was likely unmeasured factor exposure rather than genuine skill.
What VIF (multicollinearity) score is considered safe?
The usual convention: VIF < 5 is safe, 5–10 is a caution zone, 10+ flags high multicollinearity risk. The FF5 model has some intrinsic correlation across SMB / HML / RMW / CMA, so VIFs run a bit higher than under 3F — that is structural, not invalidating. When VIF is very high (10+), individual coefficient standard errors balloon and beta t-stats drop, so trust the sign and rough magnitude of each beta only and avoid fine numerical comparisons.
What changes most in interpretation once RMW and CMA are added?
You get to separate "quality" and "investment style" effects. A Buffett-style basket of high-quality, conservatively-investing companies looks like "just positive alpha" under 3F, but under 5F that alpha shrinks and an explicit β_R > 0 + β_C > 0 pattern emerges. A tech-growth basket flips to β_R < 0 + β_C < 0, revealing "high-growth + aggressive investment" exposure. In short, FF5 is a sharper tool for diagnosing why a portfolio returned what it did.
Is the 5-factor model always better than the 3-factor model?
Not "always", but in most cases 5F has higher R². There is a trade-off, though — adding factors raises multicollinearity and inflates standard errors, and the sample size you need to estimate cleanly grows. In markets where RMW / CMA data is shorter (e.g. Korea vs. US), 3F can be more stable statistically. Run both on the same input here and compare how the sign and significance of alpha move between 3F and 5F — that comparison usually pins down the real character of the portfolio more precisely.