LYFT

Lyft Inc Seasonality

NASDAQ · 1,885 trading days · Updated September 25, 2026
Seasonal summary
At a glance

Across 8 years of monthly data (2019–2026), Lyft Inc's strongest month on average has been November (+16.4% average, higher in 5 of 7 years). Its weakest has been October (−5.3% average, higher in 3 of 7 years).

This month · September +1.8% avg Higher in 3 of 7 years (43%) · median −7.1% Best +35.7% · worst −16.6%
Next month · October −5.3% avg Higher in 3 of 7 years (43%) · median −7.0% Best +11.2% · worst −17.1%
  • Jan +2.0% Up 3/7
  • Feb −3.4% Up 3/7
  • Mar −2.6% Up 2/7
  • Apr −3.3% Up 4/8
  • May −5.1% Up 2/8
  • Jun +0.5% Up 6/8
  • Jul −0.9% Up 3/8
  • Aug −1.8% Up 4/8
  • Sep +1.8% Up 3/7
  • Oct −5.3% Up 3/7
  • Nov +16.4% Up 5/7
  • Dec +2.0% Up 3/7

A month's return is the close on its last trading day compared with the previous month's last close. Averages are historical and not a forecast; any single year can differ sharply.

Data coverage
Auto-updated
Trading days1,885Daily sessions in the dataset
CoverageJan 1970 – Sep 20269 calendar years
Latest closeSep 25, 2026Most recent session on file
Last refreshSep 27, 2026When the dataset last rebuilt

Updated This LYFT seasonality chart is built from 1,885 trading days of historical data, spanning from through — covering 9 years of market activity.

Lyft Inc · NASDAQ · Prices adjusted daily. Past seasonality is not a forecast.

Monthly trading activity
Table

Sessions traded per month for LYFT. Months with more sessions carry more weight in every seasonal average below.

Trading Days in Each Month Across The Years

This table provides a historical record of trading days per month for each year, offering a clear view of market activity over time. It helps identify seasonal fluctuations, market closures, and variations in trading schedules that may impact investment decisions. By analyzing this data, traders and analysts can spot trends such as months with fewer trading days due to holidays or regulatory changes and assess how these variations might influence liquidity, volatility, and overall market behavior throughout the years. This information is particularly useful for traders planning investment strategies, financial professionals analyzing market cycles, and researchers studying long-term trading patterns.

Annual trading summary
Table

Year-by-year performance for Lyft Inc, so a seasonal pattern can be read against the trend it sits inside.

Annual Summary Table of Trading Activities

This table provides a comprehensive yearly overview of trading activity, highlighting key metrics such as trading days, price fluctuations, and market trends. It includes the first and last trading days of each year, total trading days, and price movements, including the highest and lowest prices, opening and closing values. By analyzing this data, traders and analysts can track historical price patterns, assess market volatility, and gain insights into yearly stock performance to support strategic investment decisions.

Monthly cumulative returns
Heatmap

Intraday momentum. Each cell sums the daily open-to-close percent changes inside that month, excluding overnight gaps. It answers whether a month earned its return through steady sessions or one or two outliers.

Monthly Seasonality — Cumulative Daily Returns (%)

Each cell shows the sum of all daily percent changes within that month for the given year. This is the arithmetic cumulative return — the same methodology used in the seasonality charts above. A positive value means the stock gained ground across its trading days that month; negative means it lost ground. Colors reflect relative magnitude within each month across all years.

Monthly absolute returns
Heatmap

Held through the month. Each cell is the change from the first session open to the last session close — the return of entering on day one and exiting on the final bar.

Monthly Seasonality — Absolute Price Return (%)

Each cell shows the actual percent change from the first trading day's close to the last trading day's close of that month. This is the true price return an investor would have experienced holding the stock for the entire month. While similar to the cumulative table, small differences arise because the cumulative method sums daily changes arithmetically rather than compounding them.

Historical percent change
Chart

Daily percent change, January 1, 1970 to September 25, 2026

Close is plotted by default. Add open, high, low or volume from the legend, and drag across the chart to zoom into any period.

LYFT

Same date, every year

Day of year·Method 1 of 3

Every calendar date — say 2 January — is collected across all 9 years on file and averaged. Dates with no session, such as weekends and market holidays, are skipped rather than filled. This is the view that surfaces date-anchored effects: index rebalances, quarterly expiries, tax-driven flows and recurring earnings windows.

Daily percent change by date
Table
Non-cumulative — each cell is that single session, not a running total. One column per year, plus the multi-year average.

Seasonality: Same Date Across the Years > Cumulative Percent Change

This table tracks the daily percentage change in closing prices for each day of the year across multiple years, helping traders analyze historical market performance and identify seasonal trends. Each row represents a specific date, with columns showing the corresponding percentage change for different years. The Seasonal Average column calculates the average percent change for each day, highlighting recurring patterns and potential trading opportunities. By comparing daily movements over time, traders and analysts can spot trends, assess market volatility, and refine investment strategies based on historical price behavior.

Seasonal pattern by calendar date
Chart

Day-of-year seasonality for LYFT

One line per year plus the multi-year average. Recent years load first — switch to all years from the buttons above the chart, or toggle any single year in the legend.

Seasonality: Same Date Across the Years > Cumulative Percent Change

This table tracks the daily percentage change in closing prices for each day of the year across multiple years, helping traders analyze historical market performance and identify seasonal trends. Each row represents a specific date, with columns showing the corresponding percentage change for different years. The Seasonal Average column calculates the average percent change for each day, highlighting recurring patterns and potential trading opportunities. By comparing daily movements over time, traders and analysts can spot trends, assess market volatility, and refine investment strategies based on historical price behavior.

LYFT

Weekday and week of year

Day of week·Week 1–53·Method 2 of 3

Each session is aligned to its weekday and to its week number, so 2020 week 14 lines up with 2024 week 14 even though the dates differ. Use it for weekday effects — soft Mondays, strong Fridays — and for the way those effects drift across the calendar year.

Loading the current session…
Weekly seasonality by weekday
Table

Weekly Seasonality View by Day of Week and Week of Year

Cumulative weekly pattern
Chart

Cumulative percent change by weekday and week number

Each line is one year building through the weeks. Where lines cluster, the pattern repeated; where they scatter, that stretch of the calendar was noise.

Weekly Seasonality View by Day of Week and Week of Year

LYFT

Trading day of the month

Trading day index·Method 3 of 3

Sessions are numbered by their position in the month: day 1 is the first open, day 2 the second, regardless of date. Most months run 19 to 23 sessions. Aligning every month this way exposes flow-driven effects that calendar dates blur — payroll inflows at the start, fund rebalancing at the close.

Cumulative return by trading day
Chart

The shape of a typical month for LYFT

Cumulative percent change from trading day 1 through the end of the month, one line per year, with the average showing the repeatable shape.

Seasonality Chart

LYFT

How to read this page

Methodology·1,885 sessions
What is LYFT seasonality?

Seasonality is the tendency of a security to behave differently at particular points in the calendar. This page measures that tendency for Lyft Inc three ways — by calendar date, by weekday and week number, and by trading day within the month — across 1,885 sessions from January 1, 1970 to September 25, 2026.

Why do the three methods disagree sometimes?

They align the data differently. A calendar date can fall on any weekday and at any point in the trading month, so an effect that is really about month-end flows shows up cleanly in method 3 and only faintly in method 1. Agreement across all three is the stronger signal.

How current is the data?

The dataset is refreshed automatically. The most recent session on file is September 25, 2026, and every table and chart on this page is rebuilt from that same source.

Can seasonality be traded directly?

Not on its own. These are historical averages over a finite sample, and any single year can diverge from them completely. Treat a seasonal pattern as context for a position you have another reason to hold, and size it accordingly.

Data last refreshed September 25, 2026. Seasonality figures are historical averages drawn from 1,885 trading sessions and are not a forecast. Nothing on this page is investment advice.