Turn-of-the-month refers to the pattern of a stock’s value rising on the last day of each trading month, with the price momentum continuing for the first three days of the next month.
Historically, the outsized gains at the turn of each month have a higher combined return than all 30 days in the month. There is little agreement about whether this is just a coincidence of random behaviour, or the result of positive business news being more likely to be announced at the end of the month.
The January effect describes the pattern of increased trading volume, and subsequently higher share prices, in the last week of December and the first few weeks of January.
While it is also known as the turn-of-the-year effect, the term ‘January anomaly’ is more commonly used to refer to the tendency of small-company stocks to outperform the market in the first two to three weeks of January.
It is believed that the January effect is caused by the turn of the tax calendar. Typically, according to this theory, prices drop in December when investors sell off their assets in order to realise capital gains. And, the increases in January are caused by traders rushing back into the market.
The holiday effect, or pre-holiday effect, is a calendar anomaly that describes the tendency for the stock market to gain on the final trading day before a public holiday.
The most frequently cited explanation for this is that people are naturally more optimistic around holidays, which can translate into positive market movement. An alternative explanation is that short-sellers are more likely to close their positions prior to holidays.
The holiday anomaly can also be attributed to expectations that there will be volatility at these times – the holiday effect becomes self-fulfilling, as traders buy or sell around the same historical anomalies.
The post-earnings-announcement drift is the name given to the pattern of stock returns continuing to move in the direction of surprise earnings. This anomaly follows a company announcement and is caused by the market gradually adjusting to new information.
In theory, if markets were entirely efficient, then company earnings announcements would cause an immediate shift in prices as the report is instantly factored into the market price. However, in practice, it can take up to approximately 60 days for markets to adjust – with a positive earnings announcement causing an upward drift, and a negative earnings announcement causing a negative earnings drift.
The most widely accepted reason for this delay is that markets under-react to earnings reports, and so it takes a period of time before the information gets absorbed into the stock’s price.
The momentum effect is based on historical technical analysis that suggests recent stock market ‘winners’ are more likely to continue to outperform the ‘losers’ – or that stocks with a strong upward trajectory are likely to continue to rise in the short to medium-term.
The momentum anomaly suggests that traders can take advantage of these price movements by going long on winners and shorting the losers.
One of the popular explanations for the momentum effect is that markets do not immediately price in new information, but do so more gradually.
Let’s say a company releases good news, but buyers under-react and take a while to flood the market, the price increase would be more gradual. This makes it appear that the winners are taking consistent gains.
Perhaps one of the most well-known fundamental anomalies is the value effect. This anomaly refers to the tendency of stocks with below-average balance sheets to outperform growth stocks on the market, due to investor belief in companies’ potential.
Normally, if the market value is higher than the book value per share, a stock is considered overvalued, while a stock with higher book value than market value is often thought of as undervalued. While this would usually prompt the market to correct, the value effect sees traders behaving counter to accepted practice and buying shares that are technically overvalued.
Although there is increased risk in investing in low-book-value stocks – as they could fall into financial distress – it is weighed up against the potential for superior returns.
What are the behavioural-finance explanations of market anomalies?
Behavioural finance is the opposing model to ‘conventional’ finance theories, including EMH. They assume that market participants are rational and predictable.
Although there are no sure-fire explanations for market anomalies, behavioural-finance theory does suggest a number of likely underlying factors:
- Conservatism: the preference of remaining attached to old sets of beliefs, rather than adjusting strategies to new information
- Overconfidence: the tendency of investors to overestimate their abilities and the accuracy of their information. This can lead to less risk-averse behaviour and irrational choices.
- Biased self-attribution: the human inclination to acknowledge events that confirm your existing beliefs, while ignoring those that disprove them
- Attention bias: the increased likelihood that market participants will pay attention to big companies and news outlets, often at the expense of lesser-known companies
Although these explanations go some way in explaining market patterns, this does not mean that conventional financial theory has no value. Both can provide clarification on market and trader behaviour.
Trading market anomalies
Even if you do not intend to trade market anomalies directly, it is important that you understand them so that you are not caught unawares by surprising market movements – and can adjust your trading strategy accordingly.
It is unlikely that anyone can consistently profit from market anomalies, and so traders need to have risk management strategies in place to deal with instances when these patterns fail.
As market anomalies demonstrate, an efficient market is a fluid concept. However, so are the anomalies themselves. Although their recurrence can build confidence in the likelihood of profiting from the discrepancies, traders should be aware that historical patterns aren’t always a reliable indicator of future performance.