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The stock specific volatility (also known as idiosyncratic volatility) is the volatility that remains after controlling for beta. I suppose you have $$R_i = R_f + \beta_i \cdot \big(R_m-R_f\big) + \varepsilon_i.$$ Then, the standard deviation of epsilon is your stock specific volatility. One frequently assumes $\varepsilon_i\sim N(0,\sigma^2_{\varepsilon_i})$...


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I think it's more than likely just rounding. The prices only go out to two places past the decimal, so a 1 cent change would be about a 0.1% daily return (with a \$10 average price). Looking at the returns from a purely statistical standpoint, the average daily absolute return is \$-0.0062 with a standard deviation of \$0.042. If you assume a normal ...


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You need to use the proper index constituents each point in time, recording index entrance, exit, splits, corporate actions etc. otherwise you will end up with sampling bias. If you are backtesting a trading strategy consider that your equity line will be affected by all those movements and you will need to rebalance as index constituents change. This will ...


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Just swap in your API key below. Keep in mind that they are priced in Indonesian Rupiah's. For Indonesia you need to use the Jakarta exchange symbol: AALI: https://www.alphavantage.co/query?function=TIME_SERIES_DAILY&symbol=AALI.JK&apikey=your_api_key


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Given you have a database that stores this data daily, you could write a short python script to apply your screening and email you the daily rankings or scores. I think you can even do this in Excell if you have a Bloomberg or TR terminal. I am pretty sure that you can. If you want to backtest the performance of such a strategy then I think using ...


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Did you ever find what you're looking for? The only other place I could find was the COMPUSTAT database accessed via WRDS. You'd need to access this database through a business library though, and probably on-site.


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