Researchers conclude that a market anomaly may be present if a change in the price of an asset or security cannot directly be linked to current relevant information known in the market or to the release of new information into the market.
In the widespread search for discovering profitable anomalies, many findings could simply be the product of a process called data mining, also known as data snooping.
Time-Series Anomalies
Two of the major categories of time-series anomalies that have been documented are
- 1) calendar anomalies and
- 2) momentum and overreaction anomalies.
Calendar Anomalies
January effect, has been observed in most equity markets around the world. This anomaly is also known as the turn-fo-the-year effect, or even often referred to as the “small firm in January effect” because it is most frequently observed for the returns of small market capitalisation stocks.
Momentum and Overreaction Anomalies
Momentum anomalies relate to short-term share price patterns. One of the earliest studies to identify this type of anomaly was conducted by Werner DeBondt and Richard Thaler, who argued that investors overreact to the release of unexpected public information.
Cross-Sectional Anomalies
Two of the most researched cross-sectional anomalies in financial markets are the size effect and the value effect.
Size Effect
The size effect results from the observation that equities of small-cap companies tend to outperform equities of large-cap companies on a risk-adjusted basis.
Value Effect
A number of global empirical studies have shown that value stocks, which are generally referred to as stocks that have below-average price-to-earnings (P/E) and market-to-book (M/B) ratios, and above-average dividend yields, have consistently outperformed growth stocks over long periods of time.
