Clarify Finance1K dataset limitations

README.md

@@ -65,12 +65,8 @@ The close and volume configurations can be read independently:
 
     from datasets import load_dataset
 
-    close = load_dataset(
-        "abel-lab/finance1k", "close", revision="v1.0.0"
-    )
-    volume = load_dataset(
-        "abel-lab/finance1k", "volume", revision="v1.0.0"
-    )
+    close = load_dataset("abel-lab/finance1k", "close")
+    volume = load_dataset("abel-lab/finance1k", "volume")
 
 The synchronized arrays must remain intact when evaluating cross-sectional
 metrics. Flattening ticker and timestamp axes changes the empirical task.
@@ -78,15 +74,18 @@ metrics. Flattening ticker and timestamp axes changes the empirical task.
 ## Known limitations
 
 The stored timeline is a regular calendar-day hourly grid rather than an
-exchange-session calendar. It includes weekend and market-holiday rows that
-become zero log changes after forward filling. The paper evaluates
-cross-sectional IC on the 1,455 trading-hour targets with nonzero
-cross-sectional spread. The ticker universe also requires long coverage and is
-stratified by volatility bucket. It is designed for predictability and ranking
-experiments and should not be interpreted as a representative market sample.
+exchange-session calendar. Weekend and market-holiday rows are filled with zero
+log changes.
+
+The ticker universe is limited to names with sufficiently long historical
+coverage to span the common evaluation window. Eligible tickers are divided
+into ten historical-volatility buckets, with 100 selected from each bucket, to
+balance the panel across volatility levels. This construction introduces
+survivorship and selection effects and should not be interpreted as a
+representative market sample.
 
 ## License
 
-Finance1K is licensed under the Creative Commons
+Finance1K is released under the Creative Commons
 Attribution-NonCommercial-ShareAlike 4.0 International license
 (CC BY-NC-SA 4.0).