Premium inputs: perimeter-changed trailing ROE/margin and distorted EPS growth add no differential

engine/predict_yearly.py

@@ -3917,11 +3917,23 @@ def derive_fair_value(current_price, pred_fund, data, state=None):
     _dmi = data.get("damodaran_inputs") or {}
     _ind_st = (_ddp.industry_stats(_dmi.get("industry"), _dmi.get("region") or "US")
                or _ddp.industry_stats(_dmi.get("industry"), "US")) if _dmi.get("industry") else None
-    _cg, _cg_src = None, None
+    _cg, _cg_src, _nc = None, None, []
+    _g_ok, _trl_ok = True, True
     if state is not None:
         _f1, _f2 = state.value("consensus.eps_fy1"), state.value("consensus.eps_fy2")
         if _f1 and _f2 and _f1 > 0:
             _cg, _cg_src = _f2 / _f1 - 1.0, "sell-side consensus EPS, next fiscal year over this one (FY2 ÷ FY1)"
+            _r1, _r2 = state.value("consensus.revenue_fy1"), state.value("consensus.revenue_fy2")
+            _rg = (_r2 / _r1 - 1.0) if (_r1 and _r2 and _r1 > 0) else None
+            # earnings expected to shrink while revenue grows: this year's EPS carries items not expected to recur,
+            # so the ratio is not a growth rate
+            if _rg is not None and _cg < 0 < _rg:
+                _g_ok = False
+                _nc.append(f"growth: consensus EPS {_cg:+.1%} vs revenue {_rg:+.1%} (non-recurring items in this year's EPS)")
+        # a business separated inside the trailing window: trailing ROE and margin mix two perimeters
+        if (state.historical.get("perimeter") or {}).get("event"):
+            _trl_ok = False
+            _nc.append("ROE and net margin: trailing figures include a separated business")
     if _cg is None and pred_fund.get("eps_growth") is not None:
         _cg, _cg_src = pred_fund.get("eps_growth"), "reconciled engine forecast EPS growth (next twelve months)"
     _rf_p = canonical_risk_free(data)
@@ -3934,7 +3946,8 @@ def derive_fair_value(current_price, pred_fund, data, state=None):
     else:
         _prem = _ppm.industry_premiums({"growth": _cg, "growth_source": _cg_src,
                                         "roe": safe_float(overview.get("ReturnOnEquityTTM")),
-                                        "beta": safe_float(_dmi.get("beta")), "net_margin": safe_float(overview.get("ProfitMargin"))},
+                                        "beta": safe_float(_dmi.get("beta")), "net_margin": safe_float(overview.get("ProfitMargin")),
+                                        "growth_comparable": _g_ok, "trailing_comparable": _trl_ok, "not_comparable": _nc},
                                        _ind_st, _rf_p, _erp_p, _rf_p)
     _regime = _ppm.historical_regime_adjustment()
     # Damodaran lifecycle stage → dynamic method weighting in the blend below.

engine/pricing_premium.py

@@ -65,9 +65,16 @@ def justified_pe(g, roe, r, g_s, r_s, roe_s, n=N_HIGH_GROWTH):
 def industry_premiums(company, industry, rf, erp, g_stable):
     """{'pe': {...}, 'ps': {...}} — the industry-relative premiums with every input and difference recorded.
 
-    ``company``: {growth, growth_source, roe, beta, net_margin}; ``industry``: Damodaran industry stats row."""
+    ``company``: {growth, growth_source, roe, beta, net_margin, growth_comparable, trailing_comparable, not_comparable};
+    ``industry``: Damodaran industry stats row. An input marked not comparable takes the industry's value, so it adds
+    no differential (a company is never rewarded or penalized for a figure that does not describe it)."""
     out = {}
     ind = industry or {}
+    company = dict(company)
+    if company.get("growth_comparable") is False and ind.get("expected_growth_5y") is not None:
+        company["growth"], company["growth_source"] = ind["expected_growth_5y"], "industry value (company growth not comparable)"
+    if company.get("trailing_comparable") is False:
+        company["roe"], company["net_margin"] = None, ind.get("net_margin")
     need = {"industry growth": ind.get("expected_growth_5y"), "industry ROE": ind.get("roe"),
             "industry beta": ind.get("levered_beta"), "risk-free rate": rf, "ERP": erp}
     missing = [k for k, v in need.items() if v is None]
@@ -89,7 +96,8 @@ def industry_premiums(company, industry, rf, erp, g_stable):
     pe_i = justified_pe(g_i, roe_i, r_i, g_s, r_s, roe_i)
     rel = {"relative_growth": g_c - g_i, "relative_roe": roe_c - roe_i, "relative_cost_of_equity": r_c - r_i}
     inputs = {"company": {"growth": g_c, "growth_source": company.get("growth_source"), "roe": roe_c,
-                          "roe_source": "historical.roe_ttm" if (croe is not None and croe > 0) else "industry ROE (company ROE unusable)",
+                          "roe_source": "historical.roe_ttm" if (croe is not None and croe > 0) else "industry ROE (company ROE unusable or not comparable)",
+                          "not_comparable": company.get("not_comparable") or [],
                           "cost_of_equity": r_c, "beta": b_c},
               "industry": {"growth": g_i, "growth_source": "Damodaran pedata: expected growth, next 5 years", "roe": roe_i,
                            "roe_source": "Damodaran roe: ROE (unadjusted)", "cost_of_equity": r_i, "beta": float(ind["levered_beta"]),

engine/tests/test_pricing_premium.py

@@ -140,3 +140,13 @@ def test_own_history_moves_the_target_only_through_its_own_weight():
     w = la["sector_weight"]
     assert lb["own_history"] == pytest.approx(2 * la["own_history"], rel=1e-6)
     assert lb["target_multiple"] - la["target_multiple"] == pytest.approx((lb["own_history"] - la["own_history"]) * (1 - w), rel=1e-6)
+
+
+def test_inputs_that_do_not_describe_the_company_add_no_differential():
+    base = PP.industry_premiums(_like_industry(), IND, 0.042, 0.0409, 0.042)["pe"]["value"]
+    distorted = PP.industry_premiums(_like_industry(growth=-0.05, growth_comparable=False), IND, 0.042, 0.0409, 0.042)
+    assert distorted["pe"]["value"] == pytest.approx(base)
+    spun = PP.industry_premiums(_like_industry(roe=0.45, net_margin=0.40, trailing_comparable=False,
+                                               not_comparable=["ROE and net margin: separated business"]), IND, 0.042, 0.0409, 0.042)
+    assert spun["pe"]["value"] == pytest.approx(base) and spun["ps"]["value"] == pytest.approx(base)
+    assert spun["pe"]["inputs"]["company"]["not_comparable"]