diff --git a/openbb_terminal/portfolio/portfolio_optimization/optimizer_model.py b/openbb_terminal/portfolio/portfolio_optimization/optimizer_model.py
index 594c70356ffd..31333b1da61b 100644
--- a/openbb_terminal/portfolio/portfolio_optimization/optimizer_model.py
+++ b/openbb_terminal/portfolio/portfolio_optimization/optimizer_model.py
@@ -225,7 +225,10 @@ def get_equal_weights(
         method=method,
     )
 
-    weights = {stock: value * round(1 / len(symbols), 5) for stock in symbols}
+    weights = {
+        stock: value * round(1 / len(stock_returns.columns), 5)
+        for stock in stock_returns.columns
+    }
 
     return weights, stock_returns
 
diff --git a/openbb_terminal/portfolio/portfolio_optimization/yahoo_finance_model.py b/openbb_terminal/portfolio/portfolio_optimization/yahoo_finance_model.py
index baa20f422940..a0d0ae5697e7 100644
--- a/openbb_terminal/portfolio/portfolio_optimization/yahoo_finance_model.py
+++ b/openbb_terminal/portfolio/portfolio_optimization/yahoo_finance_model.py
@@ -334,6 +334,16 @@ def process_returns(
     # Select stocks with low number of nans
     selected_stocks = np.isnan(stock_returns).sum(axis=0)
     selected_stocks = np.where(selected_stocks <= maxnan * stock_returns.shape[0])[0]
+    filtered_out = [
+        s
+        for s in stock_returns.columns
+        if s not in stock_returns.iloc[:, selected_stocks]
+    ]
+    if filtered_out:
+        print(
+            "The following stocks were filtered out, due to too many NaNs: "
+            + ", ".join(filtered_out)
+        )
     stock_returns = stock_returns.iloc[:, selected_stocks]
 
     # Replace values above and below threshold