# Month-of-year Seasonality Also known as: September weakness, return matrices. A Time, Sessions & Seasonality concept (Calendar effects) in the LuxAlgo Library, with 3 indicator implementations. ## What is Month-of-year Seasonality? Month-of-year seasonality is the tendency for average returns to differ by calendar month, measured by laying years of monthly returns into a matrix (years down the rows, months across the columns) and summarizing each column with an average and a hit rate. The equity folklore lives here: September's historically weak average in major US indexes, 'sell in May', the Santa Claus rally, the January effect in small caps. Some of these are visible in long samples; all of them are averages with wide dispersion around them. The study of calendar effects is nearly as old as index data. Sidney Wachtel flagged January strength in small stocks in a 1942 paper, Rozeff and Kinney's 1976 study made the January effect an academic fixture, and 'sell in May' descends from much older London market lore. The later chapters matter just as much: several celebrated effects weakened or vanished after publication, which is the standing warning attached to every seasonal average. Its value is as a base rate. A monthly average over twenty years rests on twenty observations, outlier years can dominate a column, and widely publicized effects have historically weakened after publication. That is why [seasonality tooling](https://www.luxalgo.com/library/concept/seasonality-tooling/) pairs each monthly average with a consistency score, and why the output is a tilt on top of price analysis rather than a standalone signal. Reading a matrix well is mostly sample discipline. An average means little without its hit rate (how many years the month closed the same way) and its dispersion (whether one crash year is doing the talking); a split sample, folklore-era versus the recent decade, shows whether the tilt still exists; and instrument specificity is non-negotiable, since commodity calendars follow harvests and inventories, and crypto's short history barely supports monthly columns at all. ## How to read a month-of-year seasonality matrix Seasonality tools render as return matrices or monthly bar profiles; the reading is sample-size skepticism applied twelve times. 1. Check the sample first: which years the matrix spans and how many observations each monthly column actually contains. 2. Read average and hit rate together: a +1% average month that closed higher in 6 of 20 years is an outlier artifact, not a tendency. 3. Scan the column's dispersion for dominating years; one crisis October can manufacture a phantom effect on its own. 4. Split the sample and compare: a tilt present in the full history but absent in the last decade is folklore in the process of expiring. 5. Keep the matrix instrument-specific: index folklore does not transfer to commodities or crypto, and each symbol earns its own columns. ## How traders use it - Tilting expectations: sizing more conservatively or demanding better setups in a symbol's historically weak months, without letting the calendar overrule live price action. - Building the matrix per instrument: commodities follow harvest and demand calendars, and crypto's four-year halving folklore belongs with [long-horizon calendar cycles](https://www.luxalgo.com/library/concept/long-horizon-calendar-cycles/); equity folklore does not transfer, so each market earns its own return matrix. - Auditing strategies: if a backtest's edge concentrates in one or two calendar months across the sample, that is concentration risk worth knowing before committing capital. - Stacking calendars: monthly tilts combine with [day-of-week effects](https://www.luxalgo.com/library/concept/day-of-week-effects/) and [fixed time cycles](https://www.luxalgo.com/library/concept/fixed-time-cycles/) into one calendar view, with each layer held to the same sample-size standard before it earns weight. - Scheduling around known catalysts: a month's tilt often traces to recurring [macro event days](https://www.luxalgo.com/library/concept/macro-event-days/) inside it (earnings seasons, fiscal year-ends, index rebalances), and knowing the mechanism turns a bare average into a testable hypothesis. ## Month-of-year seasonality vs other calendar reads - **Day-of-week Effects** (https://www.luxalgo.com/library/concept/day-of-week-effects/): Same matrix method on a faster clock: columns are weekdays instead of months, samples are hundreds of observations instead of tens. The statistics firm up while the effect sizes shrink, the usual trade in calendar work. - **Long-horizon Calendar Cycles** (https://www.luxalgo.com/library/concept/long-horizon-calendar-cycles/): Presidential cycles, decennial patterns, and crypto's halving rhythm live at multi-year scale with single-digit sample sizes. Monthly seasonality is the middle clock: thin samples by statistical standards, rich ones by comparison. - **Fixed Time Cycles** (https://www.luxalgo.com/library/concept/fixed-time-cycles/): Fixed cycles count bars from anchors and project rhythmic turn windows regardless of the calendar. Month-of-year work is strictly calendar-aligned averaging, no anchors, no projection, just conditional base rates per column. ## FAQ ### Is September really the worst month for stocks? In long-run averages of major US indexes, September has historically shown the weakest mean monthly return, which is why the 'September effect' persists in folklore. The average conceals wide variance: plenty of Septembers close higher, and the effect is neither stable enough nor large enough to trade as a standalone signal. ### What does 'sell in May and go away' mean? It is the adage that equity returns from May through October have historically lagged November through April, so investors should lighten exposure over summer. The evidence varies by market and by decade, transaction costs and missed dividends erode the naive version, and most studies treat it as a curiosity to verify rather than a rule. ### What is the January effect? The historical tendency for small-capitalization stocks to outperform in January, first flagged in the 1940s and studied heavily since, with tax-loss selling in December and year-turn rebalancing as the usual explanations. It is also the standard example of publication decay: the anomaly weakened substantially after it became famous, and its modern existence is debated. ### How many years of data does monthly seasonality need? More than most markets can offer. Twenty years gives each month twenty observations, enough to notice a tendency and nowhere near enough to certify one; conventional statistical significance for monthly effect sizes wants samples few instruments possess. The practical stance is humility: treat monthly averages as weak priors, weight hit rate and stability over the raw mean, and never let a column overrule live structure. ### Do seasonal effects survive being published? The record says: often not intact. The January effect is the canonical case of post-publication shrinkage, and studies of calendar anomalies broadly find effects weakening once they become common knowledge, consistent with arbitrage eroding them. That does not make matrices useless; it makes them base rates that need a mechanism and out-of-sample stability before they deserve capital. ### Does crypto have month-of-year seasonality? The honest answer is that the sample barely exists: major assets have roughly a decade of clean monthly data, so each column holds around ten observations dominated by two or three cycle phases. Patterns quoted from such data (strong Octobers, weak Septembers) are descriptions of a short history, not established tendencies. Crypto's more discussed calendar structure is the halving rhythm, which belongs to long-horizon cycle analysis. ## Implementations in the Library - Seasonality Chart (LuxAlgo): https://www.luxalgo.com/library/indicator/seasonality-chart/ - Seasonality Widget (LuxAlgo): https://www.luxalgo.com/library/indicator/seasonality-widget/ - Market Performance by Yearly Seasons (LuxAlgo): https://www.luxalgo.com/library/indicator/market-performance-by-yearly-seasons/ ## Related concepts - Day-of-week Effects: https://www.luxalgo.com/library/concept/day-of-week-effects/ - Turn-of-month Effects: https://www.luxalgo.com/library/concept/turn-of-month-effects/ - January Effect: https://www.luxalgo.com/library/concept/january-effect/ - Buyback Blackout Windows: https://www.luxalgo.com/library/concept/buyback-blackout-windows/ - Macro Event Days: https://www.luxalgo.com/library/concept/macro-event-days/ - Pre-holiday Drift: https://www.luxalgo.com/library/concept/pre-holiday-drift/ - Santa Claus Rally: https://www.luxalgo.com/library/concept/santa-claus-rally/ - sell-in-May: https://www.luxalgo.com/library/concept/sell-in-may/ - Quarterly Earnings Season Phases: https://www.luxalgo.com/library/concept/quarterly-earnings-season-phases/ - Tax-loss Selling Season: https://www.luxalgo.com/library/concept/tax-loss-selling-season/ --- Source: https://www.luxalgo.com/library/concept/month-of-year-seasonality/ (LuxAlgo Library, the encyclopedia of trading & technical analysis). Free to use with attribution: https://www.luxalgo.com/library/license/