Lakonishok, Josef, and Seymour Smidt, “Are Seasonal Anomalies Real?” The Review of Financial Studies, Vol 1, No 4 (1988), 403-425.
If researchers analyze data using 100 different hypotheses, then formulate a theory based on results and then test the theory using the same data, they are very likely to get significant results for the theory. This problem frequently arises due to the limited scope of stock return data (only a few standard sources). Phenomena that are actually just noise get reported as asset-pricing anomalies.
In addition, rational efficient-market economists don’t want to publish or read papers that claim the market is efficient and investors are rational. Therefore, a type of selection bias can occur when the majority of publications show anomalies, even if the majority of evidence argues against them.
This paper studies anomalies using “new” data to avoid the first problem. The data are the daily Dow Jones Industrial Average returns, from January 1, 1897 to June 11, 1986. Recent anomalies studies were done with post-1962 or post-1927 data; thus, using the DJIA since its inception adds 30-65 years of new data.
The 30 firms in the DJIA compose almost 25% of the entire NYSE. The stocks of these very large firms are highly liquid, and so are unlikely to suffer from issues of nonsynchronous trading, which makes the DJIA a good measure of short-term market activity. However, using the DJIA means that this study cannot test the January effect, which is observed in small stocks.
- Monday returns are significantly negative (-0.14%).
- Turn-of-month price increases are greater than the price increase for the entire month.
- Prices increase 1.5% between Christmas and New Year’s.
- Rates of return before holidays is 20x the normal rate of return.
- Most anomalies are quite small in magnitude.
- There is no consistent monthly pattern in stock returns.
- There is no significant evidence that returns in the first part of month are different from returns in the last part.