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I am curious and want to do some personal research into alpha signals, but I couldn't find much relevant information. What I think will be the way to is to start with a return series, build a long- short portfolio (e.g. top/bottom decile or some more refined ML techniques), take those returns calculate z-scores and do s.th like

z-score * IC * volatility 

to get a real alpha signal that I can use in a portfolio optimisation context. Would be great to get more insight.

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  • $\begingroup$ Generally, the questions of type "How to make money?" are off topic here. $\endgroup$ – LazyCat Jun 4 '17 at 17:27
  • $\begingroup$ If the goal is to only understand how to generate an alpha signal in general terms that could be fed in something like Black and Litterman, I thinks its okay. Asking for a method to generate alpha for today's markets? Not so much. $\endgroup$ – Bob Jansen Jun 4 '17 at 17:54
  • $\begingroup$ I am interested in the concept, I thought that's fine as it's a big part of factor research. $\endgroup$ – ThatQuantDude Jun 4 '17 at 19:21
  • $\begingroup$ One paper I know that talks about how to incorporate empirical data signals into a portfolio optimisation is Brandt's well known paper on Parametric Portfolio Policies. But I don't think that paper talks about Alpha per se. $\endgroup$ – Alex C Jun 4 '17 at 19:52
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    $\begingroup$ The only 'practical' publication I could find was from MSCI titled 'Converting Scores into Alphas'. $\endgroup$ – ThatQuantDude Jun 4 '17 at 19:58
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I used to combine factors into an expected return per signal. I then used historical return variances to create a Sharpe-optimized weighted portfolio. In hindsight, I wish I had not used the Sharpe ratio to create the final alpha signal. I believe that more recent work in Stochastic Portfolio Theory and the Continuous Kelly Capital Growth Criterion provides better avenues for maximizing the long-run rate of return for a given amount of risk. I also regret using historical return variances since I think our data contained information that would have been useful in constructing forward-looking covariance matrices.

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