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 Example say one has large cap from Indian Equity market it has 284 equities but for long term investors from historical reference 10% of them are black sheep.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.
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