Patterns that repeat themselves after a period of days, weeks, months, or quarters can be a useful way to track volatility and predict market behavior. If you have heard the term “sell in May and go away,” you may already understand how this principle can be used to help you make investing decisions. A pattern that repeats itself after a period of weeks, months, or quarters is sometimes called a seasonal pattern.
The most common seasonal pattern is the January effect which refers to the tendency for stocks to rise in the first month of the year. This pattern has been observed since the mid-1800s and has been linked to the tendency of individual investors to make New Year’s resolutions to invest and to sell stocks they bought at the end of the previous year at a loss (called year-end tax loss harvesting).
Other seasonal patterns that repeat themselves after a period of weeks, months, or quarters are the tendency for stocks to rise in the summer months, the tendency for stocks to rise in the fourth quarter of the year, and the tendency for stocks to rise on the third Friday of the month.
By identifying patterns that repeat themselves after a period of days, weeks, months, or quarters, you can improve your odds of making profitable trades. For example, if you understand that stocks tend to rise on the third Friday of the month, you can create a rule to buy stocks when stocks rise on the third Friday of the month.
The following table shows the average returns for the S&P 500 in various time periods.
Average Return Days Weeks Months Quarters Years 1.4% 0.4% 1.8% 1.5% 6.8%
Example of a Rule Based on a Data Pattern That Repeats Itself After a Period of Weeks
The chart below shows the daily closing prices for the SPDR S&P 500 ETF (SPY) between May 1, 2017 and March 29, 2018.
The following rule is an example of a rule based on a data pattern that repeats itself after a period of weeks.
Rule: When the SPY closes at a new high for four consecutive days, buy SPY.
The rule is based on the data pattern that the SPY tends to rise after it closes at a new high for four consecutive days.
