Statistical Assumptions Behind IPO Allotment
Introduction
IPO allotment, especially in the retail category, is governed by probabilistic and statistical assumptions derived from SEBI’s allotment framework. While the actual allotment is executed by registrars, many analytical tools rely on simplified statistical models to estimate chances.
This document explains the core statistical assumptions commonly used when analyzing or estimating IPO allotment probabilities.
Assumption 1: All Valid Applications Are Treated Equally
The most fundamental assumption is that every valid retail application has an equal chance of receiving an allotment.
SEBI mandates lottery-based allotment when demand exceeds available retail lots, implying no preference based on timing, broker, or bank.
Assumption 2: One Application Equals One Lottery Ticket
Retail probability calculations assume one valid PAN-based application equals one independent entry in the allotment lottery.
Multiple applications using the same PAN are invalidated and excluded, reinforcing this one-to-one statistical mapping.
Assumption 3: Applications Can Be Approximated from Demand
Since exchanges publish shares bid rather than application counts, models assume that most retail investors apply for exactly one lot.
This allows analysts to approximate the number of retail applications by dividing retail demand by lot size.
Assumption 4: Random Selection Is Uniform
Lottery-based allotment is assumed to follow a uniform random distribution, where each application has identical probability.
This mirrors classical probability models such as drawing winners without bias from a fixed pool.
Assumption 5: No Strategic Bias by Registrars
All statistical models assume registrars strictly follow SEBI-approved algorithms and do not introduce discretionary bias.
The presence of audits and exchange oversight supports this assumption, even though the exact algorithms are not publicly disclosed.
Assumption 6: Invalid Applications Are Proportionally Distributed
Models typically assume that invalid or rejected applications are evenly distributed across the applicant pool.
This allows analysts to ignore small rejection rates without materially impacting probability estimates.
Assumption 7: Cut-Off Price Selection Is Universal
Retail probability calculations assume that all retail applicants choose the cut-off price when bidding.
Applicants bidding below cut-off may be excluded if the final price is higher, which can slightly distort estimates.
Assumption 8: Oversubscription Is Fully Allocated by Lottery
When retail demand exceeds available lots, it is assumed that allotment is purely lottery-based with one lot per successful applicant.
Proportional allotment only applies when demand is less than or equal to available supply.
Limitations of These Statistical Assumptions
- Exact number of applications is not publicly disclosed during the IPO
- Bid modifications and cancellations can distort live data
- Registrar-level reconciliation can change final eligible counts
- SME IPOs often deviate due to small sample sizes
How These Assumptions Are Used in Practice
Despite limitations, these assumptions provide a reasonable probabilistic framework for estimating IPO allotment chances using publicly available subscription data.
They are best used for comparison and expectation-setting, not as guarantees of allotment.
Important Disclaimer
All statistical IPO allotment models are approximations. Final allotment is determined solely by the registrar as per SEBI regulations, and actual outcomes may differ from any probability estimate.