January Effect in Stocks

The January effect is a calendar anomaly connected with the stocks with small market capitalization. The anomaly itself is old (Keim,1983), therefore it has been widely researched and is well-known in the academic world or between practicioners. The anomaly says that returns of the small cap stocks are particulary strong in the January. Although the strategy based on this anomaly is well-documented, the strategy is connected with certain problems. Firstly, there were unusually high January returns during the 1960s and 1970s or the years which formed the sample examined by Keim (1983) in his seminal study and which are frequently used as a benchmark in studies of the temporal behavior of the anomaly. However, this paper has found that the magnitude of the effect quite similar to what it was before the spike. Moreover, evidence does not suggest that investors are learning of the effect and are arbitraging it away. Additionaly, the effect should be present at all the major stocks exchanges (NYSE, AMEX and NASDAQ - if we would consider the small stocks). But in the last several years, the returns were very low.

In the recent period, the January effect was so small that the transaction costs make it impossible to trade this anomaly and the anomaly has become unprofitable.This could be quite confusing, because on one hand (according to some papers), the January effect should be alive and well, and should continue to present a daunting challenge to the Efficient Market Hypothesis. On the other hand, this anomaly is starting to look more like the result of data mining than a real market anomaly and should not be traded at all.

Fundamental reason

The most common explanation of this phenomenon is connected with the tax-sensitive individual investors (to income taxes). If those investors disproportionately hold small stocks, they tend to sell those stocks for tax reasons at the end of the year (to claim a capital loss) and reinvest during the first month of the next year. This paper overally states that this anomaly should work and is significant, however, if we would consider also another reasearch, the conclusions become mixed. For example, Haug and Hirschey in the "The January Effect" say that: "The January effect in small cap stock returns is remarkably consistent over time, and does not appear to have been affected by passage of the Tax Reform Act of 1986. This finding adds new perspective to the traditional tax-loss selling hypothesis, and suggests the potential relevance of behavioral explanations. After a generation of intensive study, the January effect is alive and well, and continues to present a daunting challenge to the Efficient Market Hypothesis."

But the research of Anthony Yanxiang Gu - "The Declining January Effect: Evidences from the U.S. Equity Markets" says that: "The January effect exhibits a pronounced declining trend for both large and small firm stock indices since 1988 and the effect is disappearing for the Russell indices. The downward trend is more apparent for indices containing small stocks than for indices of large stocks.". Connecting the aforementioned with the transaction costs leads to a conclusion, that in the recent period, the January effect is becoming impossible to trade.

Markets traded
equities
Confidence in anomaly's validity
Moderately Strong
Notes to Confidence in anomaly's validity
Period of rebalancing
Monthly
Notes to Period of rebalancing
Number of traded instruments
1
Notes to Number of traded instruments
Complexity evaluation
Simple strategy
Notes to Complexity evaluation
Financial instruments
ETFs, funds, futures, CFDs
Backtest period from source paper
1947-2007
Indicative performance
12.70%
Notes to Indicative performance
per annum, data from table 1, January alpha from 2,7% for stocks from middle market deciles (average market capitalization 85.5 mil. USD to 141,3 mil. USD) to 6.7% alpha for stocks from lowest market decile (average market capitalization 7,9 mil. USD) plus average ~10% return for large cap stocks
Estimated volatility
not stated
Notes to Estimated volatility
Maximum drawdown
not stated
Notes to Maximum drawdown
Sharpe Ratio
not stated

Keywords:

seasonality, stock picking

Simple trading strategy

Invest into small cap stocks at the beginning of each January. Stay invested in large cap stocks for rest of the year.

Hedge for stocks during bear markets

No - Strategy invests long-only into equity market factor therefore is not suitable as a hedge/diversification during market/economic crises.

Source Paper

Easterday, Sen, Stephan: The Persistence of the Small Firm/January Effect: Is it Consistent With Investors' Learning and Arbitrage Efforts? http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1166149
Abstract:
Using improved methodology and an expanded research design, we examine whether the small firm/January effect (Keim 1983) is declining over time due to market efficiency. First, we find that January returns are smaller after 1963-1979, but have simply reverted to levels that existed before that time. Second, we show that the January effect is not limited to mature markets but also appears in firms trading on the relatively new NASDAQ exchange in the 1970s. Third, trading volume for small firms in December and January is not different from other months, implying that traders are not actively arbitraging the anomaly. Together, our results suggest that this anomaly continues to defy rational explanation in an efficient market.

Hypothetical future performance

Strategy's implementation in QuantConnect's framework (chart+statistics+code)

Other Papers

Haug, Hirschey: The January Effect
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=831985
Abstract:
This paper uses broad samples of value-weighted and equally-weighted returns to document the fact that abnormally high rates of return on small-cap stocks continued to be observed during the month of January. The January effect in small cap stock returns is remarkably consistent over time, and does not appear to have been affected by passage of the Tax Reform Act of 1986. This finding adds new perspective to the traditional tax-loss selling hypothesis, and suggests the potential relevance of behavioral explanations. After a generation of intensive study, the January effect is alive and well, and continues to present a daunting challenge to the Efficient Market Hypothesis.

Zhang, Jacobsen: Are Monthly Seasonals Real? A Three Century Perspective
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1697861
Abstract:
Over 300 years of UK stock returns reveal that well-known monthly seasonals are sample specific. For instance, the January effect only emerges around 1830, which coincides with Christmas becoming a public holiday. Most months have had their 50 years of fame, showing the importance of long time series to safeguard against sample selection bias, noise, and data snooping. Only - yet undocumented - monthly July and October effects do persist over three centuries, as does the half yearly Halloween, or Sell-in-May effect. Winter returns - November through April - are consistently higher than (negative) summer returns, indicating predictably negative risk premia. A Sell-in-May trading strategy beats the market more than 80% of the time over 5 year horizons.

Hirschleifer, Jiang, Meng: Mood Beta and Seasonalities in Stock Returns
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2880257
Abstract:
Existing research has documented cross-sectional seasonality of stock returns - the periodic outperformance of certain stocks relative to others during the same calendar month, weekday, or pre-holiday periods. A model based on the differential sensitivity of stocks to investor mood explains these effects and implies a new set of seasonal patterns. We find that relative performance across stocks during positive mood periods (e.g., January, Friday, the best-return month realized in the year, the best-return day realized in a week, pre-holiday) tends to persist in future periods with congruent mood (e.g., January, Friday, pre-holiday), and to reverse in periods with non-congruent mood (e.g., October, Monday, post-holiday). Stocks with higher mood betas estimated during seasonal windows of strong moods (e.g., January/October, Monday/Friday, or pre-holidays) earn higher expected returns during future positive mood seasons but lower expected returns during future negative mood seasons.