Here is my portfolio -All are from Large cap and it is optimized for minimum variance. Hence it gives good resistance against fall. Its CAGR is 40% since jun 2021. Here are the weights .
ACC 0.174
Apollohosp 0.105
DivisLab 0.081
Gail 0.100
M&M 0.157
SBILife 0.382

Last 1 year cagr 44%

         Are you an Investor into Indian Equity market. Do you know how to measure your portfolio risk. I will give tabulated data for large cap at the end of this write-up, you can use it as a ready reckoner-You can safeguard your interest by diversifying and distributing your holding value as per Markowitz model-which I will discuss sometime later.

       Assume you are investing into large cap there are 284 listed large cap in NSE. If you look at historical data in last 10 years 10% of them have failed or its prices have crashed below 95% of its peak value.  That is approximately 28 scrips out of 284.  It means you can expect 28 black-sheep in 284 large cap, mid cap it is 30% and small cap it is 40%.

There is a distribution called hyper geometric distribution. We will go to game of cards, if a deck of 52 cards in distributed to 4 players , what is the probablity that you have ( as one player) has exactly one J, 2 J’s, 3 J’s and 4 J’s . This is hypergeometric distribution where I assume J as black sheep and total number of scrips you have is 13 out of total 52 scrips available.

Here is the computed probability table for large cap below, first row has two scrip, second one has 3 scrips—and first column is probability that exactly one of them is black sheep, column 2 is exactly 2 of them is black sheep. If you need to diversify you can think column where probability is less that 0.05 (5 %) right of which is what will survive. Worst scenario is left of these columns are the scrips you have invested most! This is the worst outcome you can expect-
Here is the table:

Very poor result…

EPS is almost 50% down (YOY) !

https://www.bseindia.com/xml-data/corpfiling/AttachLive/8ac59b98-bffc-4e26-af06-6266d3865e25.pdf

Hi, what you have written is very impressive. Each portfolio has risk, based on historical data one can estimate probability scores of risks. Example say one has large cap from Indian Equity market it has 284 equities but for long term investors from historical reference 10% of them are black sheep. If a user is having say 6 portfolios in large cap. From hyper geometric distribution one can find out exactly what is the probability of 1 being black-sheep, 2 being black sheep, 3 being black sheep like that. We can assume safety level of 2 Sigma or 95% level or 99% level that how many of them will crash. Second one, one need to consider is Markowitz Modern portfolio Theory to optimize portfolio based on minimum variance. Usually, global fund managers use this method (Modern method is slightly modified but basically derive from this) one can minimize the risk yet can have portfolio that give same return on investment as original portfolio just by tweaking number of units one buys in each scrip! quite impressive results one can get. I am tracing my portfolio since last 3 years, optimized portfolio (need a tool-connect with me to get web-based tool) is giving good results well above original portfolio, this is justifiable since minimum variance portfolio offers good resistance to fall when market corrects or crashes.

No listed peer. Maybe Pokarna since both the cos are dependent on the US housing market and deal.in quartz products. But Pokarna has different products.