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A Strategy For January Calendar Anomaly

In this article we use a strategy to analyze a variant of the January calendar anomaly in large cap stocks. The results are interesting and suggest this market inefficiency may be present but under specific conditions.

For the backtests in this article I used Norgate data for S&P 500 index that include current and past constituents. I highly recommend this data service (I do not have a referral arrangement with the company.)

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We will look into whether buying a number of best or worst performing stocks from the previous year at the start of the following year and holding them for about two weeks is a profitable strategy and under what conditions.

General Description of the Strategy

Buy maximum N stocks at the open of the year,
based on a Rank Metric from the previous year and subject to conditions.
Sell the stocks on the open of the first trading day after January 14

The Rank Metric is the total return performance in the previous year. Our objective is to determine the combination of Rank Metric and conditions that produce the best results.

Backtest Details

The backtest starts in 1990 in an effort to minimize the sampling error. This type of backtest is possible when delistings and index constituent rebalancing are taken into account otherwise results may be biased.

The yearly S&P 500 chart below shows that since 1989 (first year for calculating the Rank Metric and any Conditions), there have been 24 up years and 9 down years in the S&P 500. There are 32 years after 1989 for generating the backtest results.

Case 1. Up to N = 5 top/bottom stocks according to Rank Metric. There are no additional conditions.

CAGR Total Return Avg. Yearly return % Winning Years
Top stocks 2.1% 96.3% 2.6% 62.5%
Bottom stocks 2.3% 105.9% 3.2% 65.6%

Case 1 results show there is a small edge when selecting the bottom 5 stocks instead of the top 5 but the difference may be due to sampling error. In addition, the average yearly return and percent of winning years indicate any edge is small and is probably not worth the risks.

Case 2. Up to N = 10 top/bottom stocks according to Rank Metric. There are no additional conditions.

CAGR Total Return Avg. Yearly return % Winning Years
Top stocks 1.5% 58.6% 1.7% 50.0%%
Bottom stocks 2.0% 82.2% 2.6% 71.9%

Case 2 results confirm there is a small bias when selecting the bottom 10 stocks instead of the top 10 but the average yearly return drops as compared to the Case 1. Although the percent winning years increased to about 72%, the results are still not satisfactory. If N is increased to 20, the same pattern as in Case 2 persists at about the same levels for the parameters.

Conclusion: The basic strategy shows a small edge in favor of buying bottom ranked stocks but performance is low and is not worth the added risks.

Below I investigate if there is a way to improve the results. The rules of the modified strategy are also included. This section can be accessed by subscribers with  Market Signals or All in One subscriptions.

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Disclaimer:  No part of the analysis in this blog constitutes a trade recommendation. The past performance of any trading system or methodology is not necessarily indicative of future results. Read the full disclaimer here.

Charting and backtesting program: Amibroker. Data provider: Norgate Data

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