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What is the relation between "Z-score" and "alpha "?

I understand that the z score is the number of standard deviations that a data point is away from the mean of the population and that alpha is the return of the strategy after adjusting for market risk factors. [This post][1] (line 157 ) seems to claim that alpha is defined by the negative of z-score.

# Our objective is to maximize alpha, where 'alpha' is defined by the negative of  
# recent_returns_zscore factor.  
objective = opt.MaximizeAlpha(-context.recent_returns_zscore)  

I couldn't find any mention of a relation between the z-score and alpha when I tried to search for it.

How is the alpha defined by the z-score ?

Is it a function of the z score ?

Are there any sources where I can read about this?

[1]: https://www.quantopian.com/algorithms/5cb813755669f147e3c616e3

1 response

There is absolutely no universal relationship between a negative z-score and alpha. The algorithm isn't making a 'claim' but rather making an 'hypothesis' stating there may an inverse relationship between 'recent returns' and alpha (ie excess gains). In this specific case (and I emphasize specific case) there is some basis that short term returns are inversely proportional to future gains. This is broadly termed 'short term reversal' and is one of the style exposures which Quantopian computes exposure in backtesting.

Again, there isn't a general relationship between z-core and alpha. Additionally, the 'z-score' isn't meaningful without stating 'z-score of what'. There probably isn't a relationship between the 'z-score of temperatures in New Zealand' and alpha for stocks in the US. However, there may be a relationship between the 'z-score of capex spending' and alpha.

Hope that makes sense.

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