diff --git a/strategies/momentum_quality.py b/strategies/momentum_quality.py
index f56581e..293b4cc 100644
--- a/strategies/momentum_quality.py
+++ b/strategies/momentum_quality.py
@@ -46,9 +46,13 @@ class MomentumQualityStrategy(Strategy):
         inv_dd = rolling_max_dd(data, self.quality_window)
 
         # --- Cross-sectional ranking ---
-        mom_rank = momentum.rank(axis=1, pct=True, na_option="bottom")
-        con_rank = consistency.rank(axis=1, pct=True, na_option="bottom")
-        dd_rank = inv_dd.rank(axis=1, pct=True, na_option="bottom")
+        # na_option="keep" so NaN stocks stay NaN in the composite. With
+        # "bottom" + default ascending=True, NaN entries receive pct=1.0 and
+        # the additive composite ends up maximal for NaN rows, silently
+        # selecting pre-IPO / delisted names as "top".
+        mom_rank = momentum.rank(axis=1, pct=True, na_option="keep")
+        con_rank = consistency.rank(axis=1, pct=True, na_option="keep")
+        dd_rank = inv_dd.rank(axis=1, pct=True, na_option="keep")
 
         # Composite: momentum 50%, consistency 25%, drawdown 25%
         scores = 0.50 * mom_rank + 0.25 * con_rank + 0.25 * dd_rank
diff --git a/strategies/multi_factor.py b/strategies/multi_factor.py
index eb26cdf..637a3e9 100644
--- a/strategies/multi_factor.py
+++ b/strategies/multi_factor.py
@@ -49,8 +49,10 @@ class MultiFactorStrategy(Strategy):
         value = stock.rolling(self.value_period).min() / stock
 
         # --- Cross-sectional ranking (each row ranked across assets) ---
-        mom_rank = momentum.rank(axis=1, pct=True, na_option="bottom")
-        val_rank = value.rank(axis=1, pct=True, na_option="bottom")
+        # na_option="keep" so NaN stocks (pre-IPO / delisted / masked) stay
+        # NaN in the composite score instead of being assigned pct=1.0.
+        mom_rank = momentum.rank(axis=1, pct=True, na_option="keep")
+        val_rank = value.rank(axis=1, pct=True, na_option="keep")
         scores = mom_rank + val_rank  # combined score, higher = better
 
         # --- Select top_n assets per row ---
diff --git a/strategies/recovery_momentum.py b/strategies/recovery_momentum.py
index 896f3e0..acae59f 100644
--- a/strategies/recovery_momentum.py
+++ b/strategies/recovery_momentum.py
@@ -36,9 +36,13 @@ class RecoveryMomentumStrategy(Strategy):
         # Factor 2: 12-1 month momentum
         momentum = data.shift(self.mom_skip).pct_change(self.mom_lookback - self.mom_skip)
 
-        # Cross-sectional percentile ranks
-        rec_rank = recovery.rank(axis=1, pct=True, na_option="bottom")
-        mom_rank = momentum.rank(axis=1, pct=True, na_option="bottom")
+        # Cross-sectional percentile ranks. na_option="keep" is critical:
+        # with "bottom" + ascending=True default, NaN stocks get pct=1.0, so
+        # non-member / delisted / pre-IPO names leak into the composite as
+        # "top" candidates. "keep" propagates NaN and the final ascending=False
+        # + "bottom" rank pushes them to the end where they are not selected.
+        rec_rank = recovery.rank(axis=1, pct=True, na_option="keep")
+        mom_rank = momentum.rank(axis=1, pct=True, na_option="keep")
 
         # Composite score (50/50)
         composite = 0.5 * rec_rank + 0.5 * mom_rank