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Let's focus on the technical note, but let's change the symbols for easy comparison. The note has two parts: Firstly, if we assume that the stock price is log normal with the following parameters: $S_t \sim \mathrm{LN}\left(\ln S_0+\mu t,\sigma^2 t\right)$ then by definition, its log is normally distributed: $\ln S_t \sim \mathrm{N}\left(\ln S_0+\mu t,\...


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In Hull's textbook, the stock price dynamics is lognormal: $S_T = S_0 \exp(\mu T - \frac{1}{2}\sigma^2T + \sigma W_T)$, where $W_t$ is a standard brownian motion. And so the mean of this is the mean of a lognormal random variable with the log mean as $\ln S_0 + \mu T - \frac{1}{2}\sigma^2T$ and the log standard deviation as $\sigma \sqrt{T}$, and so the ...


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See https://www.deutsche-boerse-cash-market.com/dbcm-en/instruments-statistics/statistics/listes-companies for an Excel. It is however an Excel, with each sector on a different tab


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My answer isn’t really “quant”. I don’t think that “there is a way” to do it. The board of directors has to decide on the dividend as well as the amount of the dividends. So this hypothetical number can be anywhere between 0 and (almost) the company’s earnings. For a company like Berkshire, incomes can come from underlying holdings’ dividends as you notice,...


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There isn't a single answer to this question. It strongly depends on your goals and why it is missing. If you have a long enough time-series, you will find large numbers of missing data points. The NYSE used to maintain a post for companies that did not trade weekly not so long ago. However, unless you have cause to believe there was a reason for it to ...


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