Wondering if there is a method to estimate stock downside other than adjusting / sensitizing on estimates in a DCF analysis (or relying on only special situations where you can see the price change before and after the reveal of some upcoming specific event). Ie. upside can be estimated from both DCF models or comparative analysis, but can comparative analysis or something analogous to it be used to calculate downside risk?

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    Pretend we know the future. XYZ is announcing earnings next week. If it misses estimates by 5 cents, it drops 2% but if it misses by 20 cents it drops 10%. So how do you estimate the drop, not knowing what the earnings will be? When there is bad news, they rush for the door. If the entire herd panics, the stock gets pummeled. DCF cannot estimate panic in advance. Commented Aug 10, 2019 at 13:45

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Your question can be answered by the field of risk management. The good news is that this field has had a lot of attention in the past few decades and lots of research and tools have come out of that. The bad news is that those tools are not crystal balls and only offer some measure of outcomes based on historical data and/or simulations given current assumptions about the future.

One such simple tool is the Value-at-Risk (VaR), which, simply put, is a quantile on the loss distribution. For example, if you calculate the historical one-day 99% VaR this will inform you on losses you may expect in 1% of once every 100 trading days. Do note that as this is a quantile and that there are worse values than 1% (e.g. 0.5% ...), the value of VaR is a lower bound on losses you can expect, not a higher bound.

Also, the range of historical data you use will impact your results. For example, if your data includes only a generally good year, then you will understate the possible losses and vice versa if your data includes crisis-years.

Another measure is expected shortfall (ES), which is essentially the average of all values above and including the VaR. By definition ES is higher than VaR but is also more involved in terms of calculations.

More simple measures include simple statistics such as maximum loss over a period, standard deviation of returns, etc.

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