R² Ladder
Varianza explicada incremental
{divergenceFlag && (
cargas inestables
)}
{/* Period switch */}
{["in-sample", "1995-2007", "2008-2024", "last-5y"].map(p => (
))}
{/* Bars stacked horizontally */}
{layers.map((l, i) => {
const cumulative = l.value;
const start = i === 0 ? 0 : layers[i - 1].value;
const startPct = (start / max) * 100;
const endPct = (cumulative / max) * 100;
return (
{l.label}
{l.factors}
+{l.delta.toFixed(2)}
R² = {l.value.toFixed(2)}
{/* prior bars (filled) */}
{start > 0 && (
)}
{/* this layer */}
);
})}
{/* Footer reading */}
Click en barra → drill-down regresión sub-período (coef., SE, t-stats).
R² incremental, no aditivo en términos económicos.
);
}
// ────────────────────────────────────────────────────────────
// Glossary tooltip (inline)
// ────────────────────────────────────────────────────────────
function GlossPop({ factor, onClose }) {
const meta = FACTOR_META[factor];
if (!meta) return null;
return (
{meta.name}
{meta.long}
Sensibilidad estimada de R_i − R_f al factor. Estimado por OLS panel multi-empresa.
Inestable en períodos < 60 meses.
GLOSS.AF.{factor.toUpperCase()}_M01 · ver más
);
}
// ────────────────────────────────────────────────────────────
// Main panel
// ────────────────────────────────────────────────────────────
function MultifactorRegressionPanel() {
const [activeModel, setActiveModel] = useState("ff5");
const [period, setPeriod] = useState("in-sample");
const [betas, setBetas] = useState({ ...COMPANIES.C001.betas });
const [interpretations, setInterpretations] = useState({});
const [tilts, setTilts] = useState({});
const [glossOpen, setGlossOpen] = useState(null);
const [simFactor, setSimFactor] = useState("value");
const [showUnderclaim, setShowUnderclaim] = useState(false);
const company = COMPANIES.C001;
const factors = MODELS.find(m => m.id === activeModel).factors;
const ke = computeKe(activeModel, betas);
// Divergence flag for R²Ladder
const r2_min = Math.min(...Object.values(company.r2_periods).map(p => p[activeModel]));
const r2_max = Math.max(...Object.values(company.r2_periods).map(p => p[activeModel]));
const divergenceFlag = (r2_max - r2_min) * 100 > 10;
return (
{/* Slot header */}
S3 · LAB · CALC+CASE
·
Multifactor Regression Panel
cargas pre-computadas
M04 · E02
{/* Formula display */}
Modelo activo · regla del Tutor
Ri − Rf
=
α
+
setGlossOpen("mkt")}>βM
(RM − Rf)
{activeModel !== "capm" && <>
+
setGlossOpen("size")}>βS SMB
+
setGlossOpen("value")}>βH HML
}
{(activeModel === "ff5" || activeModel === "carhart") && <>
+
setGlossOpen("quality")}>βR RMW
}
{activeModel === "carhart" && <>
+
setGlossOpen("momentum")}>βW WML
}
+
ε
{/* Tabs */}
{MODELS.map(m => (
))}
{/* Main grid: 6/6 inside slot */}
{/* LEFT 6: factor table */}
Cargas factoriales — {company.label}
n=60 meses
Factor
β
Interpretación
Tilt
{factors.map((f, i) => {
const meta = FACTOR_META[f];
const beta = betas[f];
const inferred = inferTilt(f, beta);
const tilt = tilts[f] ?? inferred;
return (
{glossOpen === f && setGlossOpen(null)} />}
{beta >= 0 ? "+" : ""}{beta.toFixed(2)}
setInterpretations({ ...interpretations, [f]: e.target.value })}
style={{
width: "100%", padding: "6px 8px",
background: "var(--surface-2)",
border: "1px solid var(--border)",
borderRadius: 6,
color: "var(--fg)",
fontSize: 12,
fontFamily: "var(--font-sans)",
}}
/>
);
})}
{/* Feedback banner */}
{showUnderclaim && (
●
Underclaim
Has visto el dato pero no explicas por qué importa. Conecta carga con riesgo económico o valoración.
)}
{/* RIGHT 6: R² ladder */}
{/* FULL WIDTH: Sim-explorer + Ke result */}
{/* Sim explorer */}
Sim-explorer
¿Qué pasaría si{" "}
{" "}fuera{" "}
{betas[simFactor] >= 0 ? "+" : ""}{betas[simFactor].toFixed(2)}
?
setBetas({ ...betas, [simFactor]: parseFloat(e.target.value) })}
style={{ width: "100%", accentColor: "var(--signal-loading)" }}
/>
−1.000.00+1.00+2.00
Drivers de Ke
Rf
{PREMIA.rf.toFixed(1)}%
{factors.map(f => (
{FACTOR_META[f].name} · E[{f}]
{(betas[f] * PREMIA[FACTOR_META[f].premiaKey]).toFixed(2)}%
))}
{/* Ke result card */}
Ke {MODELS.find(m => m.id === activeModel).label}
{ke.toFixed(1)}%
Coste del equity estimado · cargas pre-computadas
ICC empresa (M02.E01): {company.icc}%
Δ = = 2 ? "var(--signal-hypothesis)" : "var(--fg-muted)" }}>
{(ke - company.icc).toFixed(1)}pp
{/* Send footer */}
S3 → S4 · al enviar, este output se guarda en student_workspace para transclusión en M03/M04