Seasonality refers to the predictable fluctuations in prices that occur around the same time each year. These changes can be linked to specific seasons, months, quarters, holidays, or off-peak times. This phenomenon is particularly common in the commodity market. For instance, demand for heating oil often rises in colder months, leading to higher prices, while demand decreases during warmer periods, causing prices to drop. Similarly, the supply of soybeans varies with the planting, growing, and harvesting seasons, impacting both price and production trends throughout the year.
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(Percentages updated for 2023). Reference: Markets Made Clear.
Seasonality can also be observed in other markets, including stocks, indices, and Forex, often driven by fundamental factors. Identifying seasonal patterns can help traders predict trends, refine trade ideas, or spot profitable opportunities, giving them a competitive edge. However, it's important to remember that individual years may differ, and seasonal patterns can change over time. Whether used alone or alongside other methods, seasonality is a valuable tool for technical traders. It represents recurring fluctuations or cycles that create identifiable trends. Various fundamental influences, many of which are seasonal, affect nearly all markets. Factors such as weather, fiscal calendars, crop cycles, and specific futures contract characteristics (like delivery and expiration dates) regularly impact certain markets each year. As markets respond to these recurring influences, seasonal price patterns tend to develop.
This heat map for currencies, stocks, bonds, and commodities shows the
average historical % change in price each month over the last 20 years.
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The Four-Year Presidential Election Cycle has a profound impact on the economy and the stock market, with a distinct pattern emerging over time. Notably, the four-year cycle has become a more significant driver of market behavior than the Decennial Cycle, except in extraordinary years such as those ending in five and eight. In recent decades, the US has experienced a period of unprecedented prosperity, with returns distributed relatively evenly across the decade.
Analyzing market performance by quarter reveals some fascinating and useful patterns. The fourth quarter consistently stands out as a period of exceptional gains, while the first quarter follows closely behind. This is largely due to heightened cash inflows, increased trading volume, and a general bias toward buying during these quarters. As the holiday season approaches, positive market sentiment reaches its peak and tends to persist into the spring. Professionals often drive the market upward as they adjust their portfolios to optimize year-end results, and bonuses are typically reinvested around the turn of the year.
The fourth quarter of the midterm year marks the start of the Four-Year Cycle's sweet spot. The second quarter of the pre-election year also performs well, ranking as the third-best quarter in the cycle, creating a strong three-quarter stretch from midterm Q4 to pre-election Q2. While quarterly strength tends to diminish in the latter half of the pre-election year, it remains notably positive throughout the election year. However, the first quarter of post-election years typically sees losses, along with the second and third quarters of midterm years.
In the Decennial Cycle fourth years, in particular, have tended to perform better than average. Looking back, the last six election years ending in four (2004, 1984, 1964, 1944, 1924, and 1904) the S&P 500 averaged a full-year gain of 14%. The 5th year is by far the best year of the decennial cycle. In the Dow Jones Industrial Average out of the last 14 "5th years", 12 were up averaging a return of 26% per year. The only two 5th years that have ever been negative in the history of the DJIA were 2005 (-0.61%) and 2015 (-2.2%).
The chart below is an attempt to marry the decennial cycle with the four-year presidential election cycle by creating a composite of all US presidential elections that took place since 1900 in the fourth year of a decade (1904, 1924, 1944, 1964, 1984, 2004):
Why Seasonals Work: The seasonal approach to markets aims to predict future price movements instead of constantly reacting to a flood of often conflicting news. While many factors influence the markets, certain conditions and events tend to recur annually. A clear example is the yearly weather cycle, transitioning from warm to cold and back again. Additionally, the calendar marks significant events, such as the April 15 deadline for U.S. income taxes. These annual occurrences create consistent cycles in supply and demand. For instance, grain supplies peak during harvest and gradually diminish throughout the year. Demand for heating oil typically increases as colder weather sets in, only to decrease as inventory levels rise. Similarly, monetary liquidity may fall when taxes are paid but increase as the Federal Reserve injects funds back into the economy.
Natural Market Rhythms: These annual cycles in supply and demand lead to seasonal price phenomena, which can vary in intensity and timing. A consistent pattern of changing conditions can result in a corresponding pattern of price responses each year. Therefore, seasonality can be understood as a market's natural rhythm—an established tendency for prices to move in the same direction at similar times annually. This makes it a valid principle that can be objectively analyzed across different markets.
The term "pattern" suggests a degree of predictability. Future prices fluctuate in anticipation of changes and adjust when those changes occur. When these changes are annual, a recurring cycle of anticipation and realization develops. This phenomenon is central to the seasonal trading approach, which aims to anticipate, engage with, and capitalize on recurring trends as they emerge, exiting once those trends are fully realized.
The first step is to identify a market's seasonal price pattern. Traditionally, analysts relied on weekly or monthly high and low prices for relatively basic studies. For example, they might find that cattle prices in April are higher than in March 67% of the time, and higher than in May 80% of the time. Nowadays, computers can analyze daily price data over several years to create a detailed seasonal price pattern. When constructed properly, this pattern offers valuable insights into the market's annual price cycle.
However, market behavior doesn't always align perfectly with these seasonal patterns; markets are dynamic and patterns evolve. Macro-economic factors create bull and bear trends that can present significant trading opportunities, often leading to opposite behaviors. By breaking down seasonal behavior into two distinct patterns—one bullish and one bearish—we gain additional perspectives to identify emerging trends and anticipate market behavior more accurately.
However, market behavior doesn't always align perfectly with these seasonal patterns; markets are dynamic and patterns evolve. Macro-economic factors create bull and bear trends that can present significant trading opportunities, often leading to opposite behaviors. By breaking down seasonal behavior into two distinct patterns—one bullish and one bearish—we gain additional perspectives to identify emerging trends and anticipate market behavior more accurately.
Further behavioral analysis can explore both bullish and bearish market patterns. To qualify for either category, a contract year must adhere to a strict mathematical criterion: the absolute value of the slope from a linear regression of its closing price scatterplot must exceed a specified threshold. Years with a neutral bias are categorized separately to help isolate bullish and bearish behaviors.
These patterns can then be scrutinized for specific timeframes critical to breakouts or trend definitions. The aim of analyzing Bull/Bear patterns is to gain insights into when bull years are likely to be strongest and bear years weakest, as well as to identify periods when bull years may be weak or bear years may be strong.
These patterns can then be scrutinized for specific timeframes critical to breakouts or trend definitions. The aim of analyzing Bull/Bear patterns is to gain insights into when bull years are likely to be strongest and bear years weakest, as well as to identify periods when bull years may be weak or bear years may be strong.
Basic Pattern Dynamics: Consider the seasonal pattern for January Heating Oil. Demand—and consequently prices—are usually low in July, the hottest month of the year. As the industry starts to anticipate cooler weather, demand for future inventory rises, putting upward pressure on prices. This price rally often peaks before the coldest weather arrives, as anticipated demand materializes, refineries prepare to meet this demand, and the market shifts focus to inventory liquidation.
In contrast, August Gasoline follows a different, yet still weather-driven, demand cycle. Prices typically dip during the winter months when driving conditions are less favorable. However, as the summer driving season approaches, demand for future inventory increases, leading to higher prices. By Memorial Day, the official start of the driving season, refineries have sufficient incentive to ramp up production to meet this demand.
Seasonal "Pegs": Seasonal patterns based on daily prices seldom manifest as perfect cycles. Even when there are clear seasonal highs and lows, the trends in between can be influenced by various, sometimes conflicting, forces. For instance, while cattle prices typically decline from March/April to June/July, they often experience a strong rally in early May as grocery stores stock up for Memorial Day barbecues. Similarly, soybean prices usually decrease from June/July until the October harvest, but by Labor Day, the market often braces for a potential frost scare.
On the other hand, seasonal rallies are often interrupted by brief dips. For example, upward trends can be disrupted by artificial selling pressure related to First Notice Day for nearby contracts. This liquidation, aimed at avoiding delivery, can create opportunities to take profits or to enter or reestablish positions.
Thus, a seasonal pattern created from daily prices can illustrate not only the four major components of seasonal price movement but also highlight particularly reliable segments of broader seasonal trends. Recognizing fundamental events that align with these fluctuations can further enhance confidence in the pattern.
On the other hand, seasonal rallies are often interrupted by brief dips. For example, upward trends can be disrupted by artificial selling pressure related to First Notice Day for nearby contracts. This liquidation, aimed at avoiding delivery, can create opportunities to take profits or to enter or reestablish positions.
Thus, a seasonal pattern created from daily prices can illustrate not only the four major components of seasonal price movement but also highlight particularly reliable segments of broader seasonal trends. Recognizing fundamental events that align with these fluctuations can further enhance confidence in the pattern.
Inherent Strengths / Weaknesses: Trading patterns don’t repeat with absolute certainty. Like any methodology, seasonal analysis has its limitations. For traders, timing and contra-seasonal price movements are immediate concerns, as fundamentals can fluctuate both daily and over the long term. Some summers may be hotter and drier than others, significantly impacting market dynamics. Even the most consistent seasonal trends should be approached with common sense, basic technical indicators, and an understanding of current fundamentals to improve the timing of entries and exits.
When it comes to statistical samples, larger samples are generally better, but sometimes focusing on "modern" history is more practical. For example, Brazil's rise as a major soybean producer in the 1980s significantly altered the trading patterns established in the 1970s. Relying solely on disinflationary trends from 1981 to 1999 could be misleading in a new inflationary environment.
As fundamental conditions change, trading patterns will evolve. Analyzing cash markets can help mitigate some of these effects, but certain futures-specific patterns, like those related to delivery or expiration, may be overlooked. Thus, both the size and nature of the sample used should be suitable for its intended purpose, ideally determined by a user aware of the implications of their choices.
Another important issue is the use of statistics to project future trends. While statistics can confirm past patterns, they don’t inherently predict future outcomes. For instance, the correlation between Super Bowl winners and stock market performance is a coincidence, lacking a cause-and-effect relationship. This raises questions about the relevance of discoveries made through raw data analysis. Does the fact that a pattern has repeated in 14 out of the last 15 years automatically validate it?
Patterns driven by known fundamentals offer more confidence, but it’s impractical to know all relevant fundamentals for every market. Well-constructed seasonal patterns can highlight trends that have consistently recurred during specific timeframes, reducing the likelihood of statistical anomalies. Identifying a "cluster" of reliable trends with similar entry and exit points not only minimizes risks but also suggests that recurring fundamental conditions are likely to reemerge and impact the market in a timely manner.
Ultimately, a seasonal pattern illustrates the established trajectory a market tends to follow. It is this consistency that underpins the effectiveness of seasonal analysis.
When it comes to statistical samples, larger samples are generally better, but sometimes focusing on "modern" history is more practical. For example, Brazil's rise as a major soybean producer in the 1980s significantly altered the trading patterns established in the 1970s. Relying solely on disinflationary trends from 1981 to 1999 could be misleading in a new inflationary environment.
As fundamental conditions change, trading patterns will evolve. Analyzing cash markets can help mitigate some of these effects, but certain futures-specific patterns, like those related to delivery or expiration, may be overlooked. Thus, both the size and nature of the sample used should be suitable for its intended purpose, ideally determined by a user aware of the implications of their choices.
Another important issue is the use of statistics to project future trends. While statistics can confirm past patterns, they don’t inherently predict future outcomes. For instance, the correlation between Super Bowl winners and stock market performance is a coincidence, lacking a cause-and-effect relationship. This raises questions about the relevance of discoveries made through raw data analysis. Does the fact that a pattern has repeated in 14 out of the last 15 years automatically validate it?
Patterns driven by known fundamentals offer more confidence, but it’s impractical to know all relevant fundamentals for every market. Well-constructed seasonal patterns can highlight trends that have consistently recurred during specific timeframes, reducing the likelihood of statistical anomalies. Identifying a "cluster" of reliable trends with similar entry and exit points not only minimizes risks but also suggests that recurring fundamental conditions are likely to reemerge and impact the market in a timely manner.
Ultimately, a seasonal pattern illustrates the established trajectory a market tends to follow. It is this consistency that underpins the effectiveness of seasonal analysis.
