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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 practitioners. The anomaly says that returns of the small-cap stocks are particularly strong in 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, the magnitude of the effect quite similar to what it was before the spike.
Moreover, the evidence does not suggest that investors are learning of the effect and are arbitraging it away. Additionally, 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 the 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 overall states that this anomaly should work and is significant; however, if we would also consider another research, 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 the passage of the Tax Reform Act of 1986. This finding adds a 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: Evidence 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.
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Keywords
Market Factors
Confidence in Anomaly's Validity
Period of Rebalancing
Number of Traded Instruments
Complexity Evaluation
Financial instruments
Backtest period from source paper
Indicative Performance
Notes to Indicative Performance
Notes to Estimated Volatility
Maximum Drawdown
Notes to Maximum drawdown
Regions
Simple trading strategy
Invest in small-cap stocks at the beginning of each January. Stay invested in large-cap stocks for the rest of the year.
Hedge for stocks during bear markets
No – The strategy invests long-only into the equity market factor; therefore, it is not suitable as a hedge/diversification during market/economic crises.
Out-of-sample strategy's implementation/validation in QuantConnect's framework(chart, statistics & code)
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Source paper
Easterday, Sen, Stephan: The Persistence of the Small Firm/January Effect: Is it Consistent With Investors' Learning and Arbitrage Efforts?
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.
Other papers
Haug, Hirschey: The January Effect
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
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
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.
Mohamed, Hussein: Time-Based Trading Patterns
Abstract: The research paper investigates the influence of various calendar anomalies on stock market returns across multiple indices, including the S&P 500, S&P 400, S&P 600, Russell 1,000 Growth, and Russell 1,000 Value. The study provides a comprehensive analysis of both individual and interaction effects of several well-known calendar anomalies, challenging the conventional understanding that these anomalies occur in isolation. The calendar effects studied include the Federal Open Market Committee (FOMC) meetings, Options Expiration Dates, the Holiday Effect, Sports Events Effect, Halloween Effect, Turn-of-the-Month Effect, January and September Effects, as well as Friday and Monday Effects. The research applies advanced regression techniques, incorporating an ARMA (1,1) model and an EGARCH (1,1) model with a t-distribution to account for both autocorrelation and volatility clustering in stock returns. The ARMA model, with exogenous variables representing the calendar anomalies and their interactions, captures the linear dependencies in return series. The EGARCH model further analyses the persistence and asymmetry in volatility, revealing how negative market shocks tend to increase volatility more than positive shocks. These models effectively highlight the impact of calendar anomalies on both the returns and volatility of the studied indices. The findings reveal that the Halloween Effect was the most significant anomaly observed, particularly impacting indices such as the S&P 500, S&P 400, S&P 600, and Russell 1,000 Value. These results suggest that this anomaly can be strategically leveraged by investors to optimize returns. While the research highlights the potential for using calendar anomalies as part of a time-based trading strategy, it also notes that the effects may vary across different indices, reflecting the unique characteristics of each market segment. The study contributes to the broader discourse on market efficiency by offering practical insights for investors and suggesting that these calendar-based anomalies may present exploitable opportunities within financial markets.