12 Month Cycle in Cross-Section of Stocks Returns

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.

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Markets Traded
equities

Financial instruments
stocks

Confidence in anomaly's validity
Strong

Backtest period from source paper
1965-2002

Notes to Confidence in Anomaly's Validity

Indicative Performance
8.6%

Period of Rebalancing
Monthly

Notes to Indicative Performance

per annum, long short strategy, annualized monthly return for large cap stocks from table 7 (0.69%) for strategy which sort stocks based on returns 12 months ago


Notes to Period of Rebalancing

Estimated Volatility
12.2%

Number of Traded Instruments
1000

Notes to Estimated Volatility

estimated from t-statistic (4.19) from table 7


Notes to Number of Traded Instruments

more or less, it depends on investor’s need for diversification


Maximum Drawdown
0%

Complexity Evaluation
Complex strategy

Notes to Maximum drawdown

not stated


Notes to Complexity Evaluation

Sharpe Ratio
0.38

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

Not known - 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.

Source paper
Strategy's implementation in QuantConnect's framework (chart+statistics+code)
Other papers

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