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As your model is to predict stock returns via P/E, I suggest you try out all possible P/E's in a backtest and select the one with best forecast ability(lowest forecast error).


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I think you are confusing the goal with the means. The calculation of the PE is not the goal, the true goal is assessing whether a particular stock is an interesting investment opportunity (cheap) under an investment thesis (set of hypotheses). Therefore, there is an infinite number of ways to calculate PE ratios, as a results of a set of different ...


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Both approaches can be useful. For stocks, sorting into quantiles is popular because it's easy to understand and explain it's a simple matter to build factor portfolios and track or backtest their performance, while the translation from expected returns to a portfolio is a bit more involved more robust than a single-stock regression, because it is less ...


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I strongly recommend reading an undergraduate finance textbook like Investments by Bodie, Kane, and Marcus. Your methodology may be limited by your data. For example, using forward P/E requires next fiscal year's EPS estimates. NTM (next twelve months) requires quarterly EPS estimates. If you do not have estimates, the best method is TTM (trailing twelve ...


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The general problem of the investor is: $$ \max_{w\in[0,1]^n} U(\mu_p(w),\sigma_p(w))\quad s.t. \sum_{i=1}^n w_i=1$$ where $w$ being the portfolio weights, and $U$ utility function. CAPM assumes investors with concave utility function $U=\mu_p-\frac{1}{2}\sigma_p^2$, from which then follows that all investors mix the market portfolio with the riskfree ...


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A stock's returns are also correlated to the performance of the ETFs or Index Funds that include that stock; for example, if you look at returns for MSFT (Microsoft), you might want to look at the performance of QQQ as well.


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Recently I found a book on earnings trading but did not have time to read thoroughly. Trading on Corporate Earnings News - John Shon I also had spent some time to see earnings surprise effects and it is a quite interesting but not easy to use topic. There is certainly a jump if the estimates and announced earnings have a large mismatch but the magnitude ...


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Very often a stock's return is determined primarily by what the broad market, or perhaps the stock's sector, is doing: to see this, take a random stock, and it will be very hard to justify most moves - until you consider the market [e.g. S&P500] or sector [e.g. transportation]. Thus, I would include the beta coefficient, which measures the volatility in ...


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I will try to answer both of your questions together. First, regarding a standard for calculating price momentum I would say no, there is no universal standard. In addition to what is simply called the momentum indicator (which @chjortlund described in his answer), there are dozens of additional momentum indicators, and each puts an emphasis on ...


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To my knowledge do you just use the closing prices for the period you want to calculate the momentum for. M = CP - CPn Where: M = Momentum CP = Closing price in 'current' period. CPn = Closing price n periods (weeks in this case) earlier. The optimal time frame really depends on, your, or your algos, preferred time frame.


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It really depends what you're trying to achieve! What is the ultimate goal? What are your constraints? Which stocks are you looking at? Without the answers to the above, any partition is just arbitrary: why choose 30th and 70 percentile, vs 10th and 90th? Why choose (-2)*Std.Dev and (+2)*Std.Dev vs just -1 and +1? The selected (and perhaps only correct) ...


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You can download the time series of e.g. S&P500 prices from NYSE, then their dates should well represent approximately the real NYSE trading days.


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If you are investing an amount $M$, split over deals indexed by $i$ and with a weight $w_i$, then your dollar position in each share will be $w_i M$. The exposure to the index will be $\sum \beta_i w_i M$ You should realize that this will not hedge idiosyncratic risks. In general, the more deals you have, the better this type of hedge should work (assuming ...


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I think u can hedge using the description given in JC hull.. here he uses index futures. A detailed explanation is given for one stock. I think u can extend it to a portfolio. Also one can hedge by combining two or three stock indices. See page 33 in this link http://www2.fiu.edu/~dupoyetb/Financial_Risk_Mgt/lectures/Ch03.pdf


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There are a lot of ways of doing this and what a good way of doing this will be driven by your needs as well. Criteria such as whether the method needs to be (in)sensitive to outliers and whether or not your groups need to be of the same size will influence this. One way to do this would be sorting the volatilities and group them: in groups of equal size ...


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I'll answer as if the backtester design goals were driven by specific system in development or planning. There's a lot of data to process in the market and atleast for my own system development I like to focus on data that I think may provide value in the system development. For that reason I chose to capture data from my broker - they offer so much data ...


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One depository receipt corresponds to 5 shares (see here), so you pay ~19USD for ~225ARS which corresponds to an implied exchange rate of ~12ARS/USD.



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