The seasonal holiday pattern on Coca-Cola is one fantastic example of how seasons affect stocks. The pattern has a logical justification, which is the association of the brand with holidays built over decades. This consequently influenced consumer and investor behavior before this period.
Inspiration
The seasonal Coca-Cola holiday pattern is one fantastic example of how seasons can affect stock performance. The pattern has a logical explanation, which is the association of the brand with holidays built over decades. This consequently influenced consumer and investor behavior before this period.
The rules of the strategy are quite simple: buy in mid-October and sell just before Christmas Eve, precisely sell when common retail investors think that Coke is a Christmas-related drink and is worth investing in stocks.
If you think it's worth holding onto the stock even after the holidays, take a look at this backtest.
The pattern has confirmed effectiveness since at least 1963. However, in the article, we present backtest results from 1980 to 2023 due to data availability in SQX.
Key components
Exploration of a unique pre-Christmas pattern on stocks associated with Christmas.
The strategy is based solely on specific dates. We completely ignore trends, news, stock valuation, and the overall market situation
Entry rules
Entry occurs at the close of October 17th (market order), or the next business day if it is a weekend.
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Exit rules
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The closing takes place on December 23rd or the next day if it is a day off. Additional safety fuse of an exit after 50 days.
Backtest 1, $ Money Management
In this variant, we invest a constant amount of $100k.
We are testing the period of the last 42 years.
Equity chart for this test:
Test results:
*At the time of writing this article, the transaction in 2023 has not yet finished.
The average annual return was close to 7%, with only 18% of the capital being invested throughout the year.
Backtest 2, % Money Management
In this backtest, we invest 100% of the current capital in the strategy (with an initial capital of $100k). This means that as the capital increases or decreases, the value of the positions changes proportionally. The rest of the parameters remain unchanged.
The Equity Chart for this test looks as follows:
Basic statistics resulting from the test:
Max Drawdown was only 2.84%. However, please note that the Open Drawdown, which is shown on the chart, was about 15% in 1998.
The use of MM% and reinvesting all profits in the next transaction results in a multiple increase in strategy income over the years. The total profit from the invested $100k was $1.57M, which gave a CAGR of 6.77% annually, despite capital exposure for only about 18% of the time. Therefore, the annualized return is 37.6%.
Additional information about the strategy
SL & TP
The strategy does not use typical stop loss and take profit levels, although they can potentially be implemented. According to our tests, for most ETF and stock strategies, SL settings worsen the results (see why). The diversification within the strategy portfolio serves as protection against the strong impact of any price changes in individual stocks on the entire portfolio. However, the company KO is considered a defensive stock and is seen as a haven even in turbulent times.
Market regime
The strategy has been tested and has performed well in different market regimes, despite its simplicity.
Trading costs
The backtests take into account trading costs and slippage typical for the strategy, which were observed in real account tests with the Alpaca broker (detailed study). With a diversified portfolio of stocks and strategies, transaction costs can significantly affect your profit or loss, so take the time to thoroughly test and choose a broker.
Robustness
The strategy was not generated automatically or optimized automatically to fit the historical data.
The main concern with seasonal strategies is the number of trades. We cannot directly compare it to the number of trades obtained in backtests of stockpicker strategies.
Seasonal patterns, by their nature, will never have a large number of trades.
Recommended instruments
The primary instrument is KO. Similar results can be achieved by using PEP stocks, which is highly correlated to KO and can be used interchangeably.
Pattern Day Trader
The strategy is also suitable for use on smaller accounts. The holding period is more than a few business days, which does not meet the PDT requirements.
Correlation with other strategies
The strategy fits into the good endo of year period for stocks in general. Therefore, it will be correlated with strategies that trade during this period. However, the results show that KO is an exceptional instrument that is worth using. Visit the correlation page.
Summary & Strengths and weaknesses of the strategy
The KO Christmas Rally strategy is an excellent example of how seasonality works not only in nature but also in financial markets.
The results speak for themselves. The backtest you see above is from over 40 years, but we have personally tested it since 1960 and it worked even earlier.
The strategy is very easy to use, even manually. If you value your time and want to use more systems, we suggest familiarizing yourself with how they can work for you and entrusting this work to algorithms, so you don't have to worry about deadlines before Christmas.
The main concern one may have with seasonality strategies is the relatively small number of trades. Seasonal patterns by their nature will never have a large number of trades. What is more important in them is:
The pattern's alignment with nature, business patterns, or consumer behavior patterns.
A representative number of years - we consider a minimum of 15-20 years.
A longer period of the pattern. We use seasonal patterns that last at least a few weeks, preferably months.
There have been many interesting scientific studies on seasonal patterns in financial markets. They confirm that seasonality can often have more power than trends. We enjoy studying and exploring them for you. However, we assume that patterns can evolve over time. That is why we review and make any necessary adjustments to our seasonal strategies once a year. The next published versions will be available to you on this website.
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What you get in the package for this strategy:
An SQX file ready to be used on the Algocloud and StrategyQuant platforms.
Pseudocode that describes all the rules in an easy-to-understand way.
Disclaimer
The results obtained from historical data do not guarantee future outcomes. The effectiveness of a strategy can change over time. Backtesting is a tool that allows for the analysis and evaluation of an investment strategy based on historical data. Various factors, such as market changes or economic conditions, can influence the effectiveness of a strategy over time.
Investing always involves risk. This material is not investment advice. We share our experience and algorithms for educational purposes. We make efforts to ensure that our algorithms are error-free, but neither we nor the tools we use guarantee the absence of technical issues. Any decisions to use a particular strategy are made at your own risk and should be preceded by careful understanding and verification. You should always carefully consider your investment goals and risk tolerance before making investment decisions.
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The seasonal holiday pattern on Coca-Cola is one fantastic example of how seasons affect stocks. The pattern has a logical justification, which is the association of the brand with holidays built over decades. This consequently influenced consumer and investor behavior before this period.
