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CAPM is a model that assumes an efficient market and that the market prices securities based on the preferences of highly diversified investors. With regards to stock valuation, the usual approach is that we use some estimate of $\beta$ (the market correlated risk) to arrive at the $R_i$, which is the return on equity expected of this stock. This is the ...


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Metastock uses a binary format that you would have to convert to text before exporting it to a SQL table. Also, there is no Python library that could extract data directly from Metastock's end-of-day servers. You may consider quandl which allows Python developers to download pricing data as well as economic indicators. You can download CSV, JSON, or XML. If ...


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We consider the forward value, which can be employed to estimate the equity value. Let $T_1=0.5$ be the dividend payment time, and $T=1$. Moreover, let $r_1=5\,\%$ be the annualized interest rate to $T_1$, $r=6\,\%$ be the interest rate to $T$, and $d=5$ be the dividend payment. Then, the forward value, under the risk-neutral measure with the deterministic ...


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This has been driving me nuts as well! Thanks for providing the spreadsheet. Looking at MSN Money there is a discrepancy of over 11B in market cap between viewing GOOG and GOOGL shares! GOOGL has a market cap associated with 537B and GOOG has a market cap of 526B. I don't understand how one site can list two different market caps based on the class of ...


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The relationship between interest rates and equity prices being at best unstable and weak, I'll assume that the level of interest rate is irrelevant here. So the answer to your question (price of the equity in a year) is 95, everything else being equal. Of course it's unlikely that the equity will actually price at 95 in a year due to market movements, but ...


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The easiest way to think of this is as follows: Settlement Price - Price at which the exchange margins all accounts for those options. Closing Price - Mid/Bid/Ask of Active Market at the exchanges last trade time. E.g. for TY Contracts this is at 5pm EST vs. a Settle Time of 3pm EST. Last Trade Price - Not all options trade every day. This is the price ...


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Being a google finance user myself I was not able t figure out how it computes the beta. However, my best guess it's that is done in a way very similar to those of Yahoo and Bloomberg. I.e. SP500 and 36 or 60 monthly observations. In general, I would say that it does not really matter on how the beta is computed since the betas on google and yahoo are only ...


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Google uses the 1 factor CAPM model developed by Fama French (1974). Its a simple linear regression with the stock as dependent variable and the market portfolio as independent


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If certain broad assumptions are correct (eg, asset prices are continuous in time, markets are efficient) then asset returns must follow a Levy process. Both the Gaussian and Stable distributions are subsets of Levy processes. The question should not be whether Gaussian or Stable is better. Neither are adequate (in fact, many Stable distributions imply ...


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I think, the use of stable distributions in Finance (and, probably, in Economics) is a big mistake. It is essential that the intuitive fact that the stable distributed observations possess a large number of big deviations from empirical mean is not true (see, Lev B. Klebanov, Irina Volchenkova (2015) "Heavy Tailed Distributions in Finance: Reality or Mith? ...


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First it would help to know some more details about what you mean by maximum capacity. However here are a few things to consider. Do you have a simulator you use to simulate your strategy with market data? If answer to above is yes then you can clearly see the linear impact due to market liquidity constraints for your increased size. Now 2 is easy only if ...


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As soon as you're comfortable with Python, let's do this exercise in three steps: Download data and calculate cumulative returns (or value of your position as if you invested $1 in each of the stocks) Define function that will capture stock movements in excess of predefined threshold, 10% in this case. This function is going to be the "indicator" you ...


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You're not looking for volatility, at least not that measure alone. Imagine two stocks A and B, A is gaining a constant 2% every day whereas B is gaining 1% one day and -1% the other day. Then look at the return over the 10 days and the volatility of the two stocks.


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I found what I was looking for at Nasdaq.com. This information wasn't available months ago when I initially built my trading strategy. Basically Nasdaq.com provides a CSV file of all symbols that are ETFs. And its free. http://www.nasdaq.com/investing/etfs/etf-finder-results.aspx?download=Yes



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