Pricing Anomalies: Calendar, Momentum and Overreaction Anomalies
Efficient markets reflect any new information in the prices of the securities that are traded. However, there are from time to time anomalies in the system that leads to a mispricing of the securities. They are not one off phenomenon but a persistent pattern that buckles the tenants of an efficient market system. For an anomaly to be termed such it has to occur over a long period of time.
Typically researchers develop a hypothesis and test it against a set of data. However in testing market anomalies a process called data mining is conducted. In this method historical data is analyzed for any deviation in patterns and the recurrence of these patterns over a period of time is studied.
Market pricing anomalies can be classified into three broad groups. They are Time Series Anomalies. In this past data is reviewed to identify pricing anomalies. Cross sectional anomalies are arrived at by assessing a cross section of companies. Still other anomalies are identified using methods such as event studies.
Time Series Anomalies can be further categorized into two groups. They are calendar anomalies and momentum and over-reaction anomalies. Let us look at what they mean.
Anomalies were noticed in the first five days of January of each year by a group of researchers in the 80s. Stock market returns during this time of the year far exceeded the rest of the year. This effect was termed January effect or turn of the year effect. This was not accompanied by any new news regarding stocks as the case should have been for an efficient market. A number of explanations have been given for this effect.
One explanation is that investors sell their loser stocks at the end of the year to off-set any capital gains. These loser stocks typically are highly volatile small cap stocks. This leads to a fall in the prices of such stocks which the investor then picks up early in January at attractive prices.
Another explanation attributes this anomaly to a kind of window dressing. Portfolio managers are expected to present their annual reports on December 31st. They drop their riskier stocks during December to make the portfolio look more stable. Then in January the risky stocks are picked back up to earn higher returns.
There are other calendar anomalies such as day of the week, turn of the month, weekend effect, holiday effect and time of the day effect. Most of these effects have stopped being anomalies over time as arbitrageurs have utilized these and the security prices have been readjusted.
In the turn of the month effect there is a spike in the returns at the end of the month and the first three trading days of the next month.
Returns tended to be negative and lower on Monday as compared to the averages of the four other days of the week. This was known as turn of the day effect.
In the weekend effect the returns were lower on an average on weekends rather than those on the weekdays. When the returns on the day prior to a market holidays were higher than other days it was known as the holiday effect.
Momentum and Over-reaction Anomalies
The impact of un-anticipated news on share prices was negative on the short term share price movements and such anomalies were classified under momentum anomalies. A study conducted by Werner DeBondt and Richard Thaler stated that stocks reacted negatively to unexpected news from that company in the short term. Even if the news were positive, since it was unexpected shares reacted badly. This effect was known as over-reaction effect. After studying this anomaly they came up with a method to pick stocks. They termed them as winner and loser stocks. They studied the returns of stocks in both the categories for a time period of three years. The loser stocks it was seen after the initial losses outperformed the market, while winner stocks underperformed the market.
If abnormal profits have been earned in stock markets using this method over a long period of time, it is a contradiction of the weak efficient market, since these profits have been made using historical data.