# Tag Info

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Technically, anything can happen: stocks can rise in rising and falling rate environments. There is no rule that stocks can only rise when rates fall. Other times the market may rise if rates rise less than the expectation

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You're basically asking for books on fundamental analysis. The great ones that came in my mind were: The Intelligent Investor Security Analysis Common Stock and Uncommon Profit Stocks for the Long Run. The last one is probably the best in terms of quantitatively analyzing stock's past performances based on clearly defined metrics.

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This book by Shumway and Stoffer (two Pitt Stats profs) is excellent IMO: Time Series Analysis and Its Applications: With R Examples (Springer Texts in Statistics): 9781441978646 http://www.amazon.com/Time-Series-Analysis-Its-Applications/dp/144197864X

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This really depends on your methods. Earlier today in a different question I talked about confidence intervals using a very simplistic Gaussian model. I could reproduce that example to fit with your example: Select some time lag for your data. Calculate the rate of returns for each time step. Calculate the standard deviation and mean of the rate of ...

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To obtain the vola for the log returns is easy and you don't need itos lemma, since $$\log S'(t) = \log S_1(t) + \log S_2(t),$$ therefore $$var(S'(t)) = var(\log S_1(t)) + var(\log S_2(t)) + 2covar(\log S_1(t),\log S_2(t))\\ = \sigma_1t+\sigma_2t+2\rho \sigma_1\sigma_2.$$ However, to get the vola for the non-log stock price you indeed need to use the ...

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Use Ito's lemma on the function $f(x,y) = xy$ and then extract out the diffusion term.

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The subordinate return process for log returns is normal (or Gaussian). The kurtosis stems from the "activity rate" of events that move asset prices. When we measure in "clock time" we see kurtosis. However, when we measure in "event times" or "business times" the distribution is normal. The "event time" is a subordinator. Substitute "event time" for ...

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Perhaps an answer coming from a different angle and giving you some perspective: The typical approach taken by statistics is top-down: Just looking at the data and finding patterns and stylized facts (like excess volatility, volatility clustering, fat tails, no autocorrelation in returns but significant autocorrelation in absolute returns etc.) The problem ...

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At what scale do you see kurtosis? Daily data? Single stocks or indices? Let us not look a single stock data, because you always find crazy stocks whose price process breaks all rules. Talking about daily data of indices: they could be thought of the sum of hourly returns or other returns of high frequency (minute returns, milliseconds ...). What are the ...

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Generally Kurtosis measures the degree to which a distribution is more or less peaked than a normal distribution. Positive kurtosis indicates a relatively peaked distribution. Negative kurtosis indicates a relatively flat distribution. In time series we can encounter high kurtosis which is caused by "fat tails" (higher frequencies of outcomes) at the ...

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I think there are a few conflating ideas here. With respect to the sum of logs idea, I think you're thinking about infinitely divisible distributions (https://en.wikipedia.org/wiki/Infinite_divisibility_(probability)). These ideas are indeed used to build more complicated models (i.e. Levy processes) for asset returns. With regards to the Efficient ...

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The usual suspects maintain lists for each stock. Bloomberg for instance has OWN<GO> and HLDS<GO> but in those cases you need to iterate through each stock individually. These are done on a best efforts basis. I'm pretty sure this is a design choice to keep you using the terminal front end and not the api. Other than that, you are going to ...

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You're going to have to do a lot of guesswork, obviously, so it's best to keep things mathematically simple. First off, choose a "certainty level" as some quantile $q$, perhaps around 0.9, and the corresponding normal variate $z=N^{-1}(1-q)$. Start by figuring out how much time $T_i$ you think each position $N_i$ will take to liquidate if necessary. Then ...

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Generally if they are missing a completely at random data in few places, you do not have to be worried. I advice you to use one of the technics of imputation: - Previous value - cannot be used in this case - Educated Guessing - you have "knowledge" about the data, you can try to use some interpolation in your mind. - Common-Point Imputation - try to ...

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