Diwali Moves Every Year: Your Sales Comparison Should Too

Diwali-related sales should be compared using a consistent festival window as well as calendar months. When the festival shifts between October and November, one month’s growth can reflect displaced timing rather than stronger or weaker underlying demand.

That is particularly relevant in 2026. The NSE calendar places Dussehra on 20 October and Diwali Laxmi Pujan on 8 November. Its 2025 calendar placed those holidays on 2 October and 21 October, respectively. These are exchange-calendar anchors, not a claim that every regional celebration or sales campaign follows identical dates. NSE 2026 circular, NSE 2025 circular.

This guide is about measuring seasonal business activity. It does not predict share-price movements or recommend festival trades. No completed 2026 festive outcome is being reported.

A calendar month can capture different parts of the season

Think about a business that prepares stock before a festival, sells during it and processes returns afterwards. The same October reporting period can include all three stages in one year but only preparation and early sales in another.

The date shift also affects the prior-year base. A strong October last year may leave this October looking weak even if customers spend the same amount across the full season. November may then look unusually strong against a quieter comparison.

Those are possible timing effects, not explanations to accept automatically. The researcher still needs evidence about when orders, deliveries and net sales occurred.

A simple example with no change in total demand

Suppose an imaginary retailer records the following net sales across October and November. All figures are hypothetical, in ₹ lakh, with unchanged stores and prices.

Period	Earlier festival year	Later festival year	Change
October	120	80	−33.33%
November	80	120	+50%
Combined	200	200	0%

The two-month total did not change. Yet separate monthly headlines could describe an October collapse followed by a November boom. The percentage changes differ because their starting bases differ.

Hypothetical ₹ lakh. Early and late refer to festival timing, not observed years. Both two-month totals equal ₹200 lakh.

A combined window is helpful, but it is not always enough. It may still miss buying brought forward into September or delayed into December. Choose a window that matches the business process, then disclose it before interpreting the result.

Compare the same distance from the festival

For a daily series, one possible method is to compare a fixed span from 28 days before the calendar anchor through 13 days afterwards. That is 42 days inclusive. This is an illustrative research convention, not FADA’s prescribed window or a universal optimum.

Call the anchor day zero. Keep day minus 28 comparable with day minus 28 in the prior year. Preserve the start date, end date, daily coverage and any missing observations.

Different sectors may need different windows. A retailer’s delivered sales and a manufacturer’s channel dispatches follow different clocks. Extending the window until the conclusion looks attractive is not a defensible adjustment.

Where only monthly data is available, do not fabricate a daily festival alignment. Use a disclosed wider calendar window and label the remaining timing problem.

Published windows provide a useful cross-check

FADA’s completed 2025 release reported 40.53% year-on-year growth for total October vehicle retail and 21.10% for its defined 42-day festive period. Different windows produced different rates. The comparison does not isolate the calendar’s causal effect: other conditions also changed. FADA, 7 November 2025.

Read the release’s own window definition. Do not relabel an independently chosen 42-day span as an industry association’s official festive period. The auto dispatch versus retail guide explains why the stage being counted matters too.

Calendar alignment cannot remove every difference

A matched festival window can still contain a different number of weekends, changes in promotional dates, weather disruptions or different tax rules. Outlet additions, product availability and reporting coverage can also alter the comparison.

Dividing by days produces an average, not a complete seasonal adjustment. If one year contains more peak shopping days, average daily sales can still mislead. Working-day adjustments are more useful for some industrial activities than for businesses that trade on holidays.

Keep the unadjusted data visible beside any adjusted view. Explain what the adjustment changes and what it cannot resolve. An adjustment should clarify evidence, not conceal inconvenient observations.

Separate business dates from publication dates

The period in which activity occurred is different from the date its release became available. A researcher reviewing what was known in early October cannot use a November publication as if it were already known.

For each observation, keep an activity period, publication date and retrieval date. Preserve revisions rather than silently replacing the original vintage. This is especially important for point-in-time research and historical comparisons.

Use the same clock throughout the review

Altys’s research workflow brings dated evidence and company disclosures together so analysts can check which period a claim actually covers. Source availability varies, and a quarterly filing cannot create daily granularity that the company never reported.

Request access to Altys to explore source-linked company research and monitoring. Keep calendar views, festival-relative views and reported financial periods distinct.

Read next: Diwali sales value versus quantities and post-festival inventory and cash.

Scope: factual calendar references and hypothetical teaching examples. Dates were checked on 5 October 2026; check current official notices for changes. Altys Labs is not a SEBI-registered Research Analyst or Investment Adviser. This article contains no security recommendation or return forecast.