Quantpedia is The Encyclopedia of Quantitative Trading Strategies
We've already analyzed tens of thousands of financial research papers and identified more than 1000 attractive trading systems together with thundreds of related academic papers.
- Unlock Screener & 300+ Advanced Charts
- Browse 1000+ uncommon trading strategy ideas
- Get new strategies on bi-weekly basis
- Explore 2000+ academic research papers
- View 800+ out-of-sample backtests
- Design multi-factor multi-asset portfolios
The best known seasonal effect in stocks is the January effect that says that stocks perform exceptionally well in the first month of the year. But let's take a better look inside this anomaly. We may realize that stocks that performed very well in last year's January will perform well also in this year's January. Academic research shows this effect is not confined only to January (although the first month of the year is the strongest month for this new anomaly). Still, it also holds for other months of the year - stocks with high historical returns in a particular calendar month tend to have high future returns in that same calendar month.
This seasonal effect is independent of, and its power is comparable to other known effects like momentum, mean reversion, the earnings announcement effect, or value effect. The effect holds well not only in the US but also in other countries. It is strong in each size group, but we present results for the large-cap stocks (as a real-world implementation of every anomaly is always easier with larger companies).
Fundamental reason
Academic research shows that the seasonal pattern of liquidity may help explain part of the expected returns. Other explanations attribute returns to compensation for systematic risk or to behavioral theories of investing.
Get Premium Strategy Ideas & Pro Reporting
- Unlock Screener & 300+ Advanced Charts
- Browse 1000+ unique strategies
- Get new strategies on bi-weekly basis
- Explore 2000+ academic research papers
- View 800+ out-of-sample backtests
- Design multi-factor multi-asset portfolios
Market Factors
Confidence in Anomaly's Validity
Period of Rebalancing
Number of Traded Instruments
Notes to Number of Traded Instruments
Complexity Evaluation
Financial instruments
Backtest period from source paper
Indicative Performance
Notes to Indicative Performance
Estimated Volatility
Notes to Estimated Volatility
Maximum Drawdown
Notes to Maximum drawdown
Sharpe Ratio
Regions
Simple trading strategy
The top 30% of firms based on their market cap from NYSE and AMEX are part of the investment universe. Every month, stocks are grouped into ten portfolios (with an equal number of stocks in each portfolio) according to their performance in one month one year ago. Investors go long in stocks from the winner decile and shorts stocks from the loser decile. The portfolio is equally weighted and rebalanced every month.
Hedge for stocks during bear markets
Unknown – Source and related research papers don't offer insight into the correlation structure of the proposed trading strategy to equity market risk; therefore, we do not know if this strategy can be used as a hedge/diversification during the time of market crisis. The strategy is built as a long-short, but it can be split into two parts. The long leg of the strategy is surely strongly correlated to the equity market; however, the short-only leg can be maybe used as a hedge during bad times. Rigorous backtest is, however, needed to determine return/risk characteristics and correlation.
Out-of-sample strategy's implementation/validation in QuantConnect's framework(chart, statistics & code)
Related picture
Source paper
Heston, Sadka: Seasonality in the Cross-Section of Expected Stock Returns
Abstract: This paper introduces seasonality into a model of expected stock returns. We confirm previous findings that there is no evidence for cross-sectional variation in expected stock returns when we restrict the means to be constant throughout the year. Yet, we show there is substantial variation when considering each month of the year separately. Applying a seasonal structure we estimate an annualized standard deviation of 13.8%. There is strong evidence stocks have distinct expected returns in January, February, ... December. The estimated seasonal variation in expected returns is positive in every calendar month and especially high during October, December, and January. This structure is independent of industry, size, and earnings announcements. These results support the inclusion of seasonal structure into asset-pricing models.
Other papers
Heston, Sadka: Common Patterns of Predictability in the Cross-Section of International Stock Returns
Abstract: This paper studies the performance of international stock strategies based on historical returns. Stocks that outperform the local market in a particular month continue to outperform the local market in future years in that same calendar month. This effect lasts for 10 years and the same pattern appears in Canada, Japan, and twelve European countries. This return pattern is independent of country, currency effects, and market capitalization. These strategies are not highly correlated across countries; this indicates they do not reflect pervasive international risk. Instead this common seasonal structure in international stocks suggests countries share similar segmented return mechanisms.
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.
Atilgan, Demitras, Gunaydin, Kirli: Mood Seasonality Around The Globe
Abstract: This paper examines the existence of mood seasonality, documented by Hirshleifer et al. (2020, JFE) for the cross-section of US equity returns, in an international setting. First, we confirm the results of the original study. Next, we extend these findings to non-US markets and show that a stock’s relative historical seasonal returns are positively correlated with its relative future seasonal returns during similar or congruent mood periods and negatively related with its relative future seasonal returns during dissimilar or non-congruent mood periods. Moreover, both regression and portfolio analyses indicate that mood beta, the sensitivity of equity returns to aggregate investor mood, helps explain these mood seasonality effects.