Indian F&O data, and the evidence about what not to trade · educational
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🔬 Myth-Check: popular beliefs, tested against the archive

Every Indian trading group repeats the same handful of beliefs. Almost nobody checks them. We have 6.5 years of NSE data and a research process built to falsify things, so here are the answers, including the ones that were uncomfortable for us. Each test shows its sample size, the full distribution and a year-by-year breakdown, because a statistic that lives in one market episode is not a pattern.

Myth: “Selling options before results is easy money”

MOSTLY TRUE ON AVERAGE, BUT THE TAIL IS BRUTAL · 1,015 observations

Across 1,015 results events since 2024, the at-the-money straddle cost 4.8% on average and the stock actually moved 4.6%.

Average straddle cost (option buyer pays)4.77%
Average actual move to expiry4.60%
Average seller profit per event+0.17%
Share of events the seller won60%
Median seller profit+0.87%
WORST single seller loss-24.4%
Losses bigger than 3x the average win42 events

Year by year (the fragility check: a result that lives in one year is not a pattern)

2024
+0.39
2025
+0.15
2026
+0.02

The seller wins often and small, and loses rarely and large: the classic insurance payoff. One event at the wrong end of the distribution erases many wins, and this sample covers only 3 years without a severe market shock. Costs, slippage and margin are NOT deducted here; they subtract from the seller's edge.

Myth: “Stocks with high delivery percentage outperform”

BUSTED · 338 observations

Over 338 sampled days since 2020, the highest-delivery fifth of the F&O universe returned +2.07% over the next month versus +2.27% for the lowest fifth.

Top delivery quintile, next 21 days+2.07%
Bottom delivery quintile+2.27%
Difference-0.20%
Universe average+1.89%
Days the top quintile won48%

Year by year (the fragility check: a result that lives in one year is not a pattern)

2020
+0.76
2021
+0.54
2022
+0.04
2023
-1.47
2024
+0.43
2025
-0.55
2026
-1.74

Delivery percentage tells you ownership genuinely changed hands, which is real information about the past. It does not, on its own, tell you what happens next.

Myth: “F&O ban stocks bounce (or crash) predictably”

BUSTED · 1,534 observations

Across 769 ban entries and 765 ban exits since 2020, the next five days looked almost exactly like the market: +1.10% and +0.72% on average.

After entering the ban, next 5 days+1.10%
After exiting the ban, next 5 days+0.72%
Positive after entry55%
Worst single outcome after entry-40.7%
Spread of outcomes after entry (sd)7.0%

Year by year (the fragility check: a result that lives in one year is not a pattern)

2020
+1.12
2021
+0.80
2022
+0.31
2023
+2.15
2024
+1.13
2025
+0.50
2026
+1.32

Our pre-registered study of 1,028 ban episodes found the 'forced unwind' story was actually wrong-signed: the crowd tends to persist through the ban. The real cost of a ban is practical, not directional: you cannot open or add positions, only reduce.

Myth: “High VIX means it is time to get out”

BUSTED · 1,710 observations

Since 2020, the month after the highest-VIX days averaged +3.68% versus +1.49% after the calmest days.

Next 20 days after HIGH VIX days+3.68%
After MEDIUM VIX days+0.14%
After LOW VIX days+1.49%
Positive after high VIX76%
Spread of outcomes after high VIX (sd)6.6%

Year by year (the fragility check: a result that lives in one year is not a pattern)

2020
+5.28
2021
+4.04
2022
+0.68
2024
+6.26
2025
+5.92
2026
+3.73

High VIX days have historically been followed by ABOVE-average returns, not below. But look at the spread: outcomes after high-VIX days are far wider in both directions. The honest lesson is about position size, not about exit.

Myth: “Expiry week behaves differently from any other week”

BUSTED · 1,729 observations

Average daily return in expiry week was -0.010% versus +0.115% in all other weeks. The difference is noise.

Average daily return, expiry week-0.010%
Average daily return, other weeks+0.115%
Daily volatility, expiry week1.23%
Daily volatility, other weeks1.10%
Expiry-week days in sample402

Year by year (the fragility check: a result that lives in one year is not a pattern)

2020
+0.07
2021
-0.05
2022
-0.27
2023
-0.01
2024
+0.10
2025
+0.03
2026
+0.09

Our pre-registered expiry study went further: it compared expiry-week reversals against identical trades in placebo mid-month weeks, and the extra effect was statistically indistinguishable from zero (t = 1.57). Expiry feels dramatic because volume is loud, not because returns are different.

Myth: “Certain weekdays are reliably better than others”

BUSTED · 1,729 observations

Best day averaged +0.205% and worst -0.062% per day since 2020, a gap far smaller than a single day's typical swing.

Monday-0.062% (58% positive, n=346)
Tuesday+0.201% (59% positive, n=347)
Wednesday+0.205% (62% positive, n=346)
Thursday+0.071% (54% positive, n=345)
Friday+0.013% (51% positive, n=345)

Wednesday came out best and Monday worst in this sample, but daily returns vary by roughly 1% around these averages: the weekday pattern is a rounding error next to the noise, and it changes sample to sample.

Why we publish the busts: anyone can publish the studies that worked. Our own alpha research ran 22 pre-registered experiments and produced 22 negative results, which is exactly why the risk tools on this site exist rather than a tip service. If a belief you rely on is missing from this page, tell us and we will test it and publish the answer whatever it turns out to be.
Educational data reference: NOT investment advice. Historical statistics do not predict future results. We are not SEBI-registered advisers. All data from NSE public sources; figures may contain errors. Trading, especially leveraged F&O, can cause significant losses.
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