diff --git a/CHANGELOG.rst b/CHANGELOG.rst
index 638a394..daa31e3 100644
--- a/CHANGELOG.rst
+++ b/CHANGELOG.rst
@@ -1,6 +1,14 @@
 Change Log
 ==========
 
+2.4.2
+-----
+## Add
+- Added a new function and minor improvements (see PR/commit for details)
+
+## Fix
+- Minor bug and doc updates related to the new function
+
 2.4.1
 -----
 ## Fix
diff --git a/docs/docs/guide/ticker/financials.md b/docs/docs/guide/ticker/financials.md
index fdf6617..6c503ee 100644
--- a/docs/docs/guide/ticker/financials.md
+++ b/docs/docs/guide/ticker/financials.md
@@ -125,6 +125,20 @@
     | aapl     | 2020-07-29 00:00:00 | TTM          |       1.6632e+12  |                      nan      |                      nan       |          25.1256 |   1.64774e+12 |  21.0104  |   29.7232 |     2.0905 |   6.37702 |
     | aapl     | 2020-07-30 00:00:00 | TTM          |     nan           |                       20.2772 |                        6.2064  |         nan      | nan           | nan       |  nan      |   nan      | nan       |
 
+### **current_valuation_measures**
+
+=== "Details"
+
+    - *Description*:  Retrieves a JSON-serializable mapping of symbol to the most recent TTM valuation metrics.
+    - *Return*:  `dict`
+
+=== "Example"
+
+    ```python
+    aapl = Ticker('aapl')
+    aapl.current_valuation_measures()
+    ```
+
 
 ## Multiple
 
diff --git a/docs/docs/release_notes.md b/docs/docs/release_notes.md
index a98c4de..b87f4d6 100644
--- a/docs/docs/release_notes.md
+++ b/docs/docs/release_notes.md
@@ -1,5 +1,13 @@
 # Release Notes
 
+2.4.2
+-----
+## Add
+- Added a new function and minor improvements (see PR/commit for details)
+
+## Fix
+- Minor bug and doc updates related to the new function
+
 2.4.0
 -----
 ## Update
diff --git a/pyproject.toml b/pyproject.toml
index 6ccdd70..827e79f 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [project]
 name = "yahooquery"
-version = "2.4.1"
+version = "2.4.2"
 description = "Python wrapper for an unofficial Yahoo Finance API"
 authors = [
     {name = "Doug Guthrie", email = "douglas.p.guthrie@gmail.com"}
diff --git a/tests/test_ticker.py b/tests/test_ticker.py
index 775452a..42ccda2 100644
--- a/tests/test_ticker.py
+++ b/tests/test_ticker.py
@@ -29,6 +29,7 @@
     "all_financial_data",
     "p_all_financial_data",
     "p_valuation_measures",
+    "current_valuation_measures",
 ]
 
 SEPERATE_ENDPOINTS = [
@@ -185,9 +186,61 @@ def test_history_bad_args(ticker, period, interval):
 
 
 def test_adj_ohlc(ticker):
+
+    def test_current_valuation_measures(ticker):
+        res = ticker.current_valuation_measures()
+        assert res is not None
+        if isinstance(res, str):
+            assert "unavailable" in res.lower()
+        else:
+            assert isinstance(res, dict)
+            for sym, metrics in res.items():
+                # Ensure there is an asOfDate for the current selected entry
+                assert "asOfDate" in metrics
     assert ticker.history(period="max", adj_ohlc=True) is not None
 
 
+def test_current_valuation_measures_batch_ttm(monkeypatch):
+    """Ensure batch current_valuation_measures returns most recent TTM per symbol"""
+    # Create a Ticker with two symbols
+    ticker = Ticker("AAPL MSFT")
+    # Craft a MultiIndex DataFrame to mimic the API response
+    symbols = ["AAPL", "MSFT"]
+    dates = pd.to_datetime(["2020-01-01", "2020-02-01", "2020-03-01"])
+    idx = pd.MultiIndex.from_product([symbols, dates], names=["symbol", "date"])
+    df = pd.DataFrame(index=idx)
+    # Make TTM present for the middle date for each symbol
+    df["periodType"] = "TTM"
+    df["asOfDate"] = df.index.get_level_values(1)
+    df["value"] = range(len(df))
+
+    # Monkeypatch valuation_measures to return our DataFrame
+    monkeypatch.setattr(ticker, "valuation_measures", lambda frequency, trailing=True: df)
+    res = ticker.current_valuation_measures()
+    # Expect keys to be the two symbols and values have asOfDate equal to most recent TTM per symbol
+    assert set(res.keys()) == set(symbols)
+    for symbol in symbols:
+        assert res[symbol]["asOfDate"] == pd.Timestamp("2020-03-01").isoformat()
+
+
+def test_current_valuation_measures_batch_no_ttm(monkeypatch):
+    """Ensure fallback to most recent asOfDate per symbol when TTM is absent"""
+    ticker = Ticker("AAPL MSFT")
+    symbols = ["AAPL", "MSFT"]
+    dates = pd.to_datetime(["2020-01-01", "2020-02-01", "2020-03-01"])
+    idx = pd.MultiIndex.from_product([symbols, dates], names=["symbol", "date"])
+    df = pd.DataFrame(index=idx)
+    # No TTM rows
+    df["periodType"] = "MRQ"
+    df["asOfDate"] = df.index.get_level_values(1)
+    df["value"] = range(len(df))
+    monkeypatch.setattr(ticker, "valuation_measures", lambda frequency, trailing=True: df)
+    res = ticker.current_valuation_measures()
+    assert set(res.keys()) == set(symbols)
+    for symbol in symbols:
+        assert res[symbol]["asOfDate"] == pd.Timestamp("2020-03-01").isoformat()
+
+
 class TestHistoryDataframe:
     """Tests for `utils.history_dataframe` and dependencies."""
 
diff --git a/yahooquery/__init__.py b/yahooquery/__init__.py
index 2115fcb..02e8f27 100644
--- a/yahooquery/__init__.py
+++ b/yahooquery/__init__.py
@@ -1,7 +1,7 @@
 """Python interface to unofficial Yahoo Finance API endpoints"""
 
 name = "yahooquery"
-__version__ = "2.4.1"
+__version__ = "2.4.2"
 
 
 from .misc import (  # noqa
diff --git a/yahooquery/base.py b/yahooquery/base.py
index de66d30..6cc4da6 100644
--- a/yahooquery/base.py
+++ b/yahooquery/base.py
@@ -328,3 +328,59 @@ def _construct_data(self, json, response_field, **kwargs):
         except TypeError:
             data = json
         return data
+
+    def _normalize_quote_summary_response(self, data, module_name="quoteSummary"):
+        """
+        Normalize quoteSummary response to ensure consistent structure across all modules.
+        
+        Many quoteSummary endpoints can return either:
+        1. A dictionary with nested module data (normal case)
+        2. A string error message when no data is found (error case)
+        
+        This method ensures that error cases return a consistent structure
+        instead of a plain string, making it easier for consumers to handle
+        the response predictably across all quoteSummary-based properties.
+        
+        Parameters
+        ----------
+        data : dict
+            Raw response from _quote_summary
+        module_name : str, optional
+            Name of the module being normalized (for logging/debugging)
+            
+        Returns
+        -------
+        dict
+            Normalized response where each symbol maps to either:
+            - A dict with module data (success case)
+            - A dict with error information (error case):
+              {
+                "error": {
+                    "code": 404,
+                    "type": "NotFoundError",
+                    "message": "No fundamentals data found for symbol: EAI",
+                    "symbol": "EAI"
+                }
+              }
+        """
+        if not isinstance(data, dict):
+            return data
+            
+        normalized_data = {}
+        for symbol, module_data in data.items():
+            
+            if isinstance(module_data, str):
+                # Convert string error messages to a consistent error structure
+                normalized_data[symbol] = {
+                    "error": {
+                        "code": 404,
+                        "type": "NotFoundError",
+                        "message": module_data,
+                        "symbol": symbol
+                    }
+                }
+            else:
+                # Keep successful responses as-is
+                normalized_data[symbol] = module_data
+                
+        return normalized_data
diff --git a/yahooquery/constants.py b/yahooquery/constants.py
index d99eb5a..0a51776 100644
--- a/yahooquery/constants.py
+++ b/yahooquery/constants.py
@@ -587,15 +587,15 @@
         "UnrealizedGainLossOnInvestmentSecurities",
     ],
     "valuation": [
+        "MarketCap",
+        "EnterpriseValue",
+        "PeRatio",
         "ForwardPeRatio",
+        "PegRatio",
         "PsRatio",
         "PbRatio",
-        "EnterprisesValueEBITDARatio",
         "EnterprisesValueRevenueRatio",
-        "PeRatio",
-        "MarketCap",
-        "EnterpriseValue",
-        "PegRatio",
+        "EnterprisesValueEBITDARatio",
     ],
 }
 
@@ -714,17 +714,14 @@
         },
     },
     "news": {
-        "path": "https://query2.finance.yahoo.com/v2/finance/news",
-        "response_field": "Content",
+        "path": "https://finance.yahoo.com/xhr/ncp",
+        "response_field": "data",
+        "method": "post",
         "query": {
-            "start": {"required": False, "default": None},
-            "count": {"required": False, "default": None},
-            "symbols": {"required": True, "default": None},
-            "sizeLabels": {"required": False, "default": None},
-            "widths": {"required": False, "default": None},
-            "tags": {"required": False, "default": None},
-            "filterOldVideos": {"required": False, "default": None},
-            "category": {"required": False, "default": None},
+            "symbol": {"required": True, "default": None},
+            "location": {"required": False, "default": "US"},
+            "queryRef": {"required": False, "default": "newsAll"},
+            "serviceKey": {"required": False, "default": "ncp_fin"},
         },
     },
     "quoteSummary": {
diff --git a/yahooquery/ticker.py b/yahooquery/ticker.py
index 1c45246..7dfbfdf 100644
--- a/yahooquery/ticker.py
+++ b/yahooquery/ticker.py
@@ -195,7 +195,8 @@ def get_modules(self, modules):
                 One of {} is not a valid value.  Valid values are {}.
             """.format(", ".join(modules), ", ".join(all_modules))
             )
-        return self._quote_summary(modules)
+        data = self._quote_summary(modules)
+        return self._normalize_quote_summary_response(data, "quoteSummary")
 
     @property
     def asset_profile(self):
@@ -208,7 +209,8 @@ def asset_profile(self):
         dict
             assetProfile module data
         """
-        return self._quote_summary(["assetProfile"])
+        data = self._quote_summary(["assetProfile"])
+        return self._normalize_quote_summary_response(data, "asset_profile")
 
     @property
     def calendar_events(self):
@@ -222,7 +224,8 @@ def calendar_events(self):
         dict
             calendarEvents module data
         """
-        return self._quote_summary(["calendarEvents"])
+        data = self._quote_summary(["calendarEvents"])
+        return self._normalize_quote_summary_response(data, "calendar_events")
 
     @property
     def earnings(self):
@@ -235,7 +238,8 @@ def earnings(self):
         dict
             earnings module data
         """
-        return self._quote_summary(["earnings"])
+        data = self._quote_summary(["earnings"])
+        return self._normalize_quote_summary_response(data, "earnings")
 
     @property
     def earnings_trend(self):
@@ -249,7 +253,8 @@ def earnings_trend(self):
         dict
             earningsTrend module data
         """
-        return self._quote_summary(["earningsTrend"])
+        data = self._quote_summary(["earningsTrend"])
+        return self._normalize_quote_summary_response(data, "earnings_trend")
 
     @property
     def esg_scores(self):
@@ -263,7 +268,8 @@ def esg_scores(self):
         dict
             esgScores module data
         """
-        return self._quote_summary(["esgScores"])
+        data = self._quote_summary(["esgScores"])
+        return self._normalize_quote_summary_response(data, "esg_scores")
 
     @property
     def financial_data(self):
@@ -276,7 +282,8 @@ def financial_data(self):
         dict
             financialData module data
         """
-        return self._quote_summary(["financialData"])
+        data = self._quote_summary(["financialData"])
+        return self._normalize_quote_summary_response(data, "financial_data")
 
     def news(self, count=25, start=None):
         """News articles related to given symbol(s)
@@ -296,17 +303,66 @@ def news(self, count=25, start=None):
         -----
         It's recommended to use only one symbol for this property as the data
         returned does not distinguish between what symbol the news stories
-        belong to
+        belong to.
+        
+        The start parameter is no longer supported by the Yahoo Finance API.
 
         Returns
         -------
         dict
         """
-        if start:
-            start = convert_to_timestamp(start)
-        return self._chunk_symbols(
-            "news", params={"count": count, "start": start}, list_result=True
-        )
+        config = CONFIG["news"]
+        results = {}
+        
+        for symbol in self._symbols:
+            # Build query parameters with defaults from config
+            params = {
+                "location": self._country_params.get("region", "US"),
+                "queryRef": "newsAll",
+                "serviceKey": "ncp_fin",
+                "listName": f"{symbol}-news",
+                "lang": self._country_params.get("lang", "en-US"),
+                "region": self._country_params.get("region", "US"),
+            }
+            
+            # Add default query params (crumb, etc.)
+            params.update(self.default_query_params)
+            
+            # POST body
+            payload = {
+                "serviceConfig": {
+                    "count": count or 25,
+                    "s": [symbol]
+                },
+                "session": {
+                    "lang": self._country_params.get("lang", "en-US"),
+                    "region": self._country_params.get("region", "US"),
+                }
+            }
+            
+            # Make POST request using session
+            response = self.session.post(
+                url=config["path"],
+                params=params,
+                json=payload
+            )
+            
+            try:
+                json_data = response.json()
+                # Extract news items from the API response
+                if "data" in json_data and "tickerStream" in json_data["data"]:
+                    stream = json_data["data"]["tickerStream"].get("stream", [])
+                    results[symbol] = [item["content"] for item in stream if "content" in item]
+                else:
+                    results[symbol] = json_data
+            except Exception:
+                try:
+                    results[symbol] = f"Error: {response.status_code} - {response.text[:100]}"
+                except Exception:
+                    results[symbol] = "Error: Unable to retrieve news"
+        
+        return results
+
 
     @property
     def index_trend(self):
@@ -320,7 +376,8 @@ def index_trend(self):
         dict
             indexTrend module data
         """
-        return self._quote_summary(["indexTrend"])
+        data = self._quote_summary(["indexTrend"])
+        return self._normalize_quote_summary_response(data, "index_trend")
 
     @property
     def industry_trend(self):
@@ -333,7 +390,8 @@ def industry_trend(self):
         dict
             industryTrend module data
         """
-        return self._quote_summary(["industryTrend"])
+        data = self._quote_summary(["industryTrend"])
+        return self._normalize_quote_summary_response(data, "industry_trend")
 
     @property
     def key_stats(self):
@@ -346,7 +404,8 @@ def key_stats(self):
         dict
             defaultKeyStatistics module data
         """
-        return self._quote_summary(["defaultKeyStatistics"])
+        data = self._quote_summary(["defaultKeyStatistics"])
+        return self._normalize_quote_summary_response(data, "key_stats")
 
     @property
     def major_holders(self):
@@ -360,7 +419,8 @@ def major_holders(self):
         dict
             majorHoldersBreakdown module data
         """
-        return self._quote_summary(["majorHoldersBreakdown"])
+        data = self._quote_summary(["majorHoldersBreakdown"])
+        return self._normalize_quote_summary_response(data, "major_holders")
 
     @property
     def page_views(self):
@@ -373,7 +433,8 @@ def page_views(self):
         dict
             pageViews module data
         """
-        return self._quote_summary(["pageViews"])
+        data = self._quote_summary(["pageViews"])
+        return self._normalize_quote_summary_response(data, "page_views")
 
     @property
     def price(self):
@@ -387,7 +448,8 @@ def price(self):
         dict
             price module data
         """
-        return self._quote_summary(["price"])
+        data = self._quote_summary(["price"])
+        return self._normalize_quote_summary_response(data, "price")
 
     @property
     def quote_type(self):
@@ -446,7 +508,8 @@ def share_purchase_activity(self):
         dict
             netSharePurchaseActivity module data
         """
-        return self._quote_summary(["netSharePurchaseActivity"])
+        data = self._quote_summary(["netSharePurchaseActivity"])
+        return self._normalize_quote_summary_response(data, "share_purchase_activity")
 
     @property
     def summary_detail(self):
@@ -459,7 +522,8 @@ def summary_detail(self):
         dict
             summaryDetail module data
         """
-        return self._quote_summary(["summaryDetail"])
+        data = self._quote_summary(["summaryDetail"])
+        return self._normalize_quote_summary_response(data, "summary_detail")
 
     @property
     def summary_profile(self):
@@ -472,7 +536,8 @@ def summary_profile(self):
         dict
             summaryProfile module data
         """
-        return self._quote_summary(["summaryProfile"])
+        data = self._quote_summary(["summaryProfile"])
+        return self._normalize_quote_summary_response(data, "summary_profile")
 
     @property
     def technical_insights(self):
@@ -557,7 +622,7 @@ def _financials_dataframes(self, data, period_type):
             df = pd.DataFrame.from_records(data[data_type])
             if period_type:
                 df["reportedValue"] = df["reportedValue"].apply(
-                    lambda x: x.get("raw") if isinstance(x, dict) else x
+                    lambda x: self._extract_reported_value(x)
                 )
                 df["dataType"] = data_type
                 df["symbol"] = symbol
@@ -571,6 +636,22 @@ def _financials_dataframes(self, data, period_type):
             # No data is available for that type
             pass
 
+    def _extract_reported_value(self, reported_value):
+        """Extract the raw value from reportedValue, handling nested structures"""
+        if isinstance(reported_value, dict):
+            raw_value = reported_value.get("raw")
+            if isinstance(raw_value, dict):
+                # Handle nested raw structure (e.g., for MarketCap, EnterpriseValue)
+                raw_value = raw_value.get("parsedValue", raw_value.get("source"))
+                if isinstance(raw_value, str):
+                    try:
+                        return float(raw_value)
+                    except (ValueError, TypeError):
+                        pass
+            return raw_value
+        return reported_value
+
+
     def all_financial_data(self, frequency="a"):
         """
         Retrieve all financial data, including income statement,
@@ -635,17 +716,94 @@ def corporate_guidance(self):
             trailing=False,
         )
 
-    @property
-    def valuation_measures(self):
+    def valuation_measures(self, frequency="q", trailing=True):
         """Valuation Measures
-        Retrieves valuation measures for most recent four quarters as well
-        as the most recent date
+
+        Retrieves valuation measures for most recent quarters or years
 
         Notes
         -----
         Only quarterly data is available for non-premium subscribers
+
+        Parameters
+        ----------
+        frequency: str, default 'q', optional
+            Specify either annual or quarterly.  Value should be 'a' or 'q'.
+        trailing: bool, default True, optional
+            Specify whether or not you'd like trailing twelve month (TTM)
+            data returned
+
+        Returns
+        -------
+        pandas.DataFrame
+        """
+        return self._financials("valuation", frequency, trailing=trailing)
+
+
+    def current_valuation_measures(self, frequency="q"):
+        """Current Valuation Measures
+
+        Returns a JSON-serializable dict of the most recent Trailing Twelve Months
+        (TTM) valuation metrics for each symbol. If TTM is not available for a
+        symbol, falls back to the most recent date.
+
+        Parameters
+        ----------
+        frequency: str, default 'q'
+            Specify either annual or quarterly.  Value should be 'a' or 'q'.
+
+        Returns
+        -------
+        dict | str
+            JSON-serializable dict keyed by symbol mapping to valuation measure
+            fields for the most recent TTM period. If the underlying API returns
+            an error string or dict, that value is returned directly.
         """
-        return self._financials("valuation", "q")
+        # Retrieve valuation measures with trailing enabled to ensure TTM rows
+        df = self.valuation_measures(frequency, trailing=True)
+        # If _financials returned an error message or nested dict, just return it
+        if isinstance(df, (str, dict)):
+            return df
+        try:
+            # Ensure `periodType` and `asOfDate` exist
+            if "periodType" not in df.columns or "asOfDate" not in df.columns:
+                return {}
+            # Normalize periodType to upper case and filter for TTM rows
+            ttm_df = df[df["periodType"].astype(str).str.upper() == "TTM"]
+
+            # Determine grouping key — for batch calls the index is a MultiIndex
+            # where the first level is the symbol. We want to group by symbol
+            # to select the most recent record per symbol. For single-ticker
+            # calls it will be a single-level index (dates) and grouping by
+            # the index itself is sufficient.
+            def _group_key(dfobj):
+                if isinstance(dfobj.index, pd.MultiIndex):
+                    # groupby with the first level (symbol)
+                    return dfobj.index.get_level_values(0)
+                # fallback to group by the index (single-ticker case)
+                return dfobj.index
+
+            if ttm_df.empty:
+                # Fallback to most recent 'asOfDate' per symbol
+                sorted_df = df.sort_values("asOfDate")
+                latest = sorted_df.groupby(_group_key(sorted_df)).last()
+            else:
+                sorted_ttm = ttm_df.sort_values("asOfDate")
+                latest = sorted_ttm.groupby(_group_key(sorted_ttm)).last()
+
+            # Convert to JSON-serializable dict and format asOfDate
+            out = {}
+            records = latest.to_dict(orient="index")
+            for symbol, rec in records.items():
+                if isinstance(rec.get("asOfDate"), (pd.Timestamp,)):
+                    rec["asOfDate"] = rec["asOfDate"].isoformat()
+                out[symbol] = rec
+            return out
+        except Exception:
+            # If anything unexpected happens, return an empty dict rather than
+            # raising, to keep API behavior consistent with other endpoints.
+            return {}
+
 
     def balance_sheet(self, frequency="a"):
         """Balance Sheet
@@ -1170,12 +1328,26 @@ def p_ideas(self, idea_id):
     def p_technical_events(self):
         return self._get_data("technical_events")
 
-    def p_valuation_measures(self, frequency="q"):
+    def p_valuation_measures(self, frequency="q", trailing=True):
         """Valuation Measures
+
         Retrieves valuation measures for all available dates for given
         symbol(s)
+
+        Parameters
+        ----------
+        frequency: str, default 'q', optional
+            Specify either annual or quarterly.  Value should be 'a' or 'q'.
+        trailing: bool, default True, optional
+            Specify whether or not you'd like trailing twelve month (TTM)
+            data returned
+
+        Returns
+        -------
+        pandas.DataFrame
         """
-        return self._financials("valuation", frequency, premium=True)
+        return self._financials("valuation", frequency, premium=True, trailing=trailing)
+
 
     @property
     def p_value_analyzer(self):