diff --git a/tests/pipeline/test_events.py b/tests/pipeline/test_events.py
index fbcc1105..81394962 100644
--- a/tests/pipeline/test_events.py
+++ b/tests/pipeline/test_events.py
@@ -6,6 +6,7 @@ import itertools
 from itertools import product
 
 import blaze as bz
+from nose_parameterized import parameterized
 import numpy as np
 import pandas as pd
 from pandas.util.testing import assert_series_equal
@@ -462,31 +463,44 @@ class BlazeEventsLoaderTestCase(EventsLoaderTestCase):
 
 
 class EventLoaderUtilsTestCase(ZiplineTestCase):
-    dates = [pd.Timestamp('2013-01-04 3:00:00'), pd.Timestamp('2013-01-24'),
-             pd.Timestamp('2013-04-04'), pd.Timestamp('2013-04-21')]
-    combos = list(itertools.permutations(dates))
-
-    def test_normalize_to_query_time(self):
-        # Order matters in pandas 18.0.2. Prior to that, using tz_convert on
-        # a DatetimeIndex with DST/EST timestamps mixed resulted in some of
-        # them being an hour off (1 hour past midnight).
-        expected = pd.Series(
-            [pd.Timestamp('2013-01-04'),
+    dates = [pd.Timestamp('2013-01-04 3:00:00'),
              pd.Timestamp('2013-01-24'),
+             pd.Timestamp('2013-01-31 20:00:00'),
              pd.Timestamp('2013-04-04'),
              pd.Timestamp('2013-04-21')]
-        ).reset_index(drop=True)
+    combos = list(itertools.permutations(dates))
+
+    expected_us = pd.Series(
+        [pd.Timestamp('2013-01-04'),
+         pd.Timestamp('2013-01-24'),
+         pd.Timestamp('2013-02-01'),
+         pd.Timestamp('2013-04-04'),
+         pd.Timestamp('2013-04-21')]
+    )
+
+    # Russia's TZ offset is +3
+    expected_russia = pd.Series(
+        [pd.Timestamp('2013-01-04'),
+         pd.Timestamp('2013-01-24'),
+         pd.Timestamp('2013-01-31'),
+         pd.Timestamp('2013-04-04'),
+         pd.Timestamp('2013-04-21')]
+    )
+
+    # Test with timezones on either side of the meridian
+    @parameterized.expand([(expected_us, 'US/Eastern', time(8, 45)),
+                           (expected_russia, 'Europe/Moscow', time(16, 45))])
+    def test_normalize_to_query_time(self, expected, tz, query_time):
+        # Order matters in pandas 0.18.2. Prior to that, using tz_convert on
+        # a DatetimeIndex with DST/EST timestamps mixed resulted in some of
+        # them being an hour off (1 hour past midnight).
         for combo in self.combos:
             df = pd.DataFrame({"timestamp": combo})
-            tz = 'US/Eastern'
-            query_time = time(8, 45)
             result = normalize_timestamp_to_query_time(df,
                                                        query_time,
                                                        tz,
                                                        inplace=False,
                                                        ts_field='timestamp')
             result = result.sort("timestamp").reset_index(drop=True)
-            assert_series_equal(result['timestamp'], expected)
-
-
-
+            assert_series_equal(result['timestamp'], expected,
+                                check_names=False)
diff --git a/zipline/pipeline/loaders/utils.py b/zipline/pipeline/loaders/utils.py
index 50781d4e..77a9f447 100644
--- a/zipline/pipeline/loaders/utils.py
+++ b/zipline/pipeline/loaders/utils.py
@@ -237,12 +237,15 @@ def normalize_timestamp_to_query_time(df,
         _midnight,
         include_end=False,
     )
-    # for all of the times that are greater than our query time add 1
-    # day and truncate to the date
+    # For all of the times that are greater than our query time add 1
+    # day and truncate to the date.
+    # We normalize twice here because of a bug in pandas 0.16.1 that causes
+    # tz_localize() to shift some timestamps by an hour if they are not grouped
+    # together by DST/EST.
     df.loc[to_roll_forward, ts_field] = (
         dtidx_local_time[to_roll_forward] + datetime.timedelta(days=1)
-    ).normalize().tz_localize(None).tz_localize('utc').normalize()  # cast
-    # back to utc
+    ).normalize().tz_localize(None).tz_localize('utc').normalize()
+
     df.loc[~to_roll_forward, ts_field] = dtidx[~to_roll_forward].normalize()
     return df