Kimberly-Clark (KMB), a global leader in paper product manufacturing, experiences a very regular seasonal increase in stock value every November. Discover a strategy based on 36 years of data.
Inspirations
Seasonal strategies often seek justification. In our opinion, the recurring seasonal rise in Kimberly-Clark (KMB) stock prices in November may be due to increased demand for hygiene products during the pre-holiday period, suggesting to both consumers and investors that the company's shares are worth attention. Interestingly, this pattern usually begins after the company's October data release and develops on average regardless of whether the data exceeded or fell short of analysts' expectations.
This pattern fits into a broader trend where this period is generally good for stocks. We don't know the exact reasons, but we can collectively benefit from this phenomenon, which has over 80% effectiveness over the past 36 years.
Key Components
Exploration of the pre-holiday season as a very favorable period for Kimberly-Clark's stock during this time.
The strategy is based purely on dates. In the backtest, we completely ignore trends, news, company valuation, and the overall market situation.
Entry Rules
Entry occurs on October 25th or the next business day, with a market order at the end of the day.
Exit Rules
We are leaving in the fifth of December, no later than 30 working days from the moment of entry.
Backtest 1, Fixed $ Money Management
In this backtest, we consistently invest the same amount of $100k, testing over a period of 36 years (1987-2023).
Equity chart for this test:
Basic statistics and results month by month:
Backtest 2, % Money Management
In this backtest, we invest in a strategy with a constant 100% of the current capital (with an initial capital of $100k). This causes the position value to change proportionally with the increase or decrease in capital. The rest of the parameters remain unchanged.
The equity chart for this test looks as follows:
Basic statistics resulting from the test:
The maximum drawdown was 7.23%, and the open drawdown, shown below the chart, was approximately 9%.
Additional Information About the Strategy
SL & TP
The strategy does not use typical stop loss and take profit, although they can potentially be introduced. According to our tests, for most stock strategies, these settings worsen the results (read why).
The protection against the strong impact of a potential price change of a single stock on the entire portfolio is diversification within the strategy portfolio. TIP - trade seasonal strategies only within a portfolio of multiple stocks and preferably also a portfolio of multiple strategies with different mechanisms.
Market Regime
Despite the simplicity of its rules, the strategy performed well in various periods, both Bull and Bear Markets, although in our opinion, it is safest to use it during a Bull Market. You can read more about the market regime here.
Trading Costs
In the backtests, trading costs and slippage typical for the given strategy were taken into account, as observed on a real account in our tests with the broker Alpaca. With a diversified stock portfolio and strategy, transaction costs can determine your profit or loss, so take the time to thoroughly test and choose a broker.
Robustness
The strategy was neither generated automatically nor optimized automatically to select optimal Equity for historical data. Tests of other entry and exit days during this period yield stable similar results.
However, it should be noted that this strategy operates over a short period of 30 days. Personally, we prefer seasonal strategies with a 2-3 month horizon.
Due to its nature, a seasonal strategy will not have a large number of transactions, but in this case, one can expect some evolution of the pattern in the coming years.
Recommended Instruments
The primary instrument is KMB.
Pattern Day Trader
The strategy is also successfully applicable to smaller accounts. The holding period is approximately 30 days, which does not meet the PDT condition.
Correlation with Other Strategies
The strategy aligns with consumer behavior trends related to the pre-holiday and autumn period. It may, therefore, be correlated with others trading during this time.
Summary & Strengths and Weaknesses of the Strategy
The investment strategy based on the seasonality pattern involves entering the market at the end of October and exiting at the beginning of December. Tested over a 36-year history in various market conditions, it yields stable profits. The strategy does not use classic stop loss or take profit, and its effectiveness may stem from seasonal consumer trends.
Out of 36 years of history, the strategy achieved an winrate of 80.5% and an average annual profit of around 4%. These are solid results, especially considering that the strategy operates only for 30 days a year.
Low exposure, amounting to about 11%, and a low drawdown slightly below 7% are strengths of this strategy. On the other hand, its biggest weakness is the low number of transactions, which is obvious for seasonal patterns. Therefore, the strategy should, in our opinion, be used as part of a diversified portfolio, combined with strategies of different mechanics.
Scientific research indicates that seasonal patterns can sometimes be stronger than market trends. However, it should be remembered that such patterns may evolve over time.
What you get in the package for this strategy:
Ebook detailing the rules and results of the strategy
.SQX file ready to be applied on the Algocloud and StrategyQuant platform.
If you need the code for this strategy in formats: Tradestation (easylanguage), Multicharts, MT4, or MT5 (MQL), contact us in this topic.
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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