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예시 포트폴리오 — 미국 분산 (AAPL · MSFT · SPY · TLT)
●Fama–French 5F · Portfolio personality
The Small-Town Wanderer뚜벅이 소도시 여행자
Sticking to safe public transit, you still seek out hidden, hip little spots one by one.
Regression coefficients · annualized
α (Alpha)Not sig.
-1.59%
t = -0.54p = 0.592
-1.6%/yr excess loss beyond factor explanations.
Market beta (βM)✓ Sig.
0.83
t = 15.17p = 0.000
Moves about 17% less than the market.
Size beta (βS)Not sig.
-0.12
t = -1.09p = 0.281
Neutral to the small-cap / large-cap cycle.
Value beta (βH)✓ Sig.
-0.38
t = -3.64p = 0.001
Co-moves with growth stocks when they lead.
Profitability beta (βR)✓ Sig.
+0.30
t = 2.90p = 0.005
Co-moves with quality (high-profit) stocks when they lead.
Investment beta (βC)Not sig.
+0.17
t = 1.33p = 0.189
Weakly co-moves with conservative-investment firms when they outperform.
Regression equation
Regression (5-factor)
Rp − Rf = α + βM·MKT + βS·SMB + βH·HML + βR·RMW + βC·CMA + ε
Sample: last 70 months of monthly data · OLS regression · RMW/CMA added to 3F · values annualized
Annual expected return
+14.2%
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
+3.7pp
3F 0.80 → 5F 0.84
FF5 · Quick guide
Check which risks your portfolio is exposed to. Adjust the portfolio strategically to take the risk exposures you want.
Visualization
Fama–French 5-factor exposure
My portfolioMarket (MKT only)
Closer 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.
Interpretation
4 takeaways
FactorValue beta dominant + 2 more
Your strongest exposure is growth tilt (β = -0.38). 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.
Alphaα -1.59% — effectively zero
Excess return beyond factor exposure is essentially nil (α -1.59%), and it's statistically indistinguishable from zero (p=0.592). 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.
StabilityR² 83.5% — strong fit
The factor model explains 83.5% 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.
Style Tilt3-factor composite style
Value beta -0.38 · Profitability beta +0.30 · Investment beta +0.17. 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.
Factor Target
Set factor targets → optimized weights
Move the sliders to define a goal like "I want a market beta of 0.7" — we propose a weight mix that satisfies the target.
Market beta target
βM
+0.83→+0.83
+0.0+1.6
Size beta target
βS
-0.12→-0.12
-0.9+0.7
Value beta target
βH
-0.38→-0.38
-1.2+0.4
Profitability beta target
βR
+0.30→+0.30
-0.5+1.1
Investment beta target
βC
+0.17→+0.17
-0.6+1.0
Rebalance intensityBalanced (moderate)
Next step
Want to compare all models at once?
The dashboard shows every model result for the same portfolio on one screen.
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 run fetches the latest prices from yfinance and recalculates. When new trading days arrive or the factor data is updated between runs, the sample changes and the means, variances and regression coefficients shift slightly. The risk-free rate used for the Sharpe ratio (the US 13-week T-bill yield) is also refreshed daily. If the change is large (e.g. alpha flips sign, or a beta moves more than 0.3), the sample may be too short for stable results — check the analysis reliability score at the top of the result page.
How are Korean and US stocks handled together?
Holding amounts are converted to KRW at the FX rate at analysis time to set the weights. Returns, however, are calculated from each holding's local-currency price, so the returns and volatility of US holdings do not include KRW/USD exchange-rate moves. If you invest in won, your actual account can swing more or less than shown, by the amount the exchange rate moves. Markowitz and HRP analyze holdings from both markets as one portfolio. Fama–French (3- / 5-factor) regresses Korean holdings on DIVA's own Korea factors and US holdings on factors from the Kenneth French Data Library, then combines the two by portfolio weight into a single result.
How is the "analysis reliability" score calculated?
A 0–100 measure of how much the numbers could shift because of limited data or chance (not whether the portfolio is good). For Markowitz and HRP it averages ① the risk estimate (data window, and window length relative to the number of holdings) and ② the return estimate (the 95% error range of the past average return), so the same portfolio over the same window scores the same in both. Fama–French averages the beta estimate (window and beta error range) and explanatory power (R²). 75+ reliable, 55+ worth referencing, 35+ read conservatively, below that interpret with care. A heuristic for reference, 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 (profitability) or CMA (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.
My value and size betas differ between the 3-factor and 5-factor results for the same portfolio. Which is right?
Both are correct within their own model. The 5-factor model adds the profitability (RMW) and investment (CMA) factors, which tend to move with the existing ones — value and investment especially. So part of what the 3-factor model attributed to value beta moves to investment beta under five factors, and the overlap widens the error on individual betas. If a beta that was significant under three factors loses significance in the 5-factor coefficients, that signals overlap with the new factors; read that beta for its sign and rough size only.
What changes most in interpretation once RMW and CMA are added?
You get to separate "profitability" 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 positive profitability beta + positive investment beta pattern emerges. A tech-growth basket flips to negative profitability and investment betas, 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". Adding factors almost always raises R², so a higher R² alone does not make it the better model. There is a cost, too — more coefficients estimated from the same number of months widen the error on each beta, and new factors that overlap with existing ones make interpretation harder. DIVA runs both models on the same window (up to 72 months) and the same factor sources, so run both on the same input and compare how the sign and significance of alpha move from 3F to 5F — that comparison pins down the portfolio's real character more precisely.
Where does the Fama–French factor data come from? Is it free?
US-stock factors come free from the official data library run by Kenneth French at Dartmouth's Tuck School of Business (US factors for the 3-factor model, North American factors for the 5-factor model). They are usually published a month or two late, so the latest month may be missing from the regression. Korean stocks use DIVA's own KRW factors, computed from every KRX stock including delisted ones (from July 2016). DIVA calculates and adds new months separately, so recent months not yet added are left out of the Korean regression. Mixed KR / US portfolios are regressed on the two factor sets separately and combined by weight. Source links are in References at the bottom of the Fama–French model explanation page.