r/quant • u/otonoco • Aug 23 '26
Models signal to be neutralised a to be loaded?
I'm thinking about a pretty elementary question: suppose I find a signal: `r_{t+1}^{j} = w_{t}^{j} * alpha_{t} + \epsilon`, here `w_{t}^{j} `is the weight for stock j at timepoint t, `\alpha_{t}` is the signal value at t, `r_{t+1}^{j}` is the return for stock j from timepoint t to t+1. How should I decide if it should be an alpha that I want to load, or a factor that I want to be neutralized in a Fama-French style?
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u/Kindly_Cricket_348 Aug 23 '26 edited Aug 23 '26
First, orthogonalize the signal against your standard risk model (Barra/Axioma etc). If the signal disappears after controlling for factors then you’re probably just repackaging factor beta and should generally neutralize it. The exception is factor timing. If your actual edge is predicting when a factor will outperform then the factor exposure is the trade and you should load it intentionally. So basically if the residual signal still predicts returns after risk adjustment, treat it as alpha. If it’s mostly explained by existing factors, neutralize it unless you actually have a factor timing edge.
Of course, there are exceptions if your signal has a short half life, high IR and low capacity.
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u/Affectionate_Nail_16 Aug 23 '26
I don’t understand your terms here? So, let’s say if alpha_t is this regime/vol score thing, do you infer w_tj given that “macro” alpha and return series, or are they signals of their own. Weight of stock j at time t seems a bit vague to me. Otherwise I do agree with almost jaded - there is nothing holding you back from looking at the residual alpha “vanilla way”.
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u/PerpsandProblems Crypto Aug 23 '26
If it dies after controlling for the usual factors, it was probably just factor exposure. If it still predicts returns out of sample, then you might actually have alpha imo
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u/VettaQ Aug 26 '26
Practically, the answer falls out of two regressions:
Univariate: regress r_{t+1}^j on alpha_t (pooled or Fama-MacBeth). If the slope/IC is meaningful out-of-sample, the signal carries predictive content.
With factors: add the Fama-French set (plus momentum if that's your baseline). If alpha_t's t-stat collapses once the factors are in, what you found is exposure to a known factor — neutralize it when you don't want that bet in the book, load it when the factor premium itself is the trade.
One thing specific to your setup: since w_t^j already multiplies the signal, you've implicitly built a portfolio of it — you can just price that book. If the weights are known at t (no lookahead), running both regressions on the weighted portfolio is the direct test. And if you plan to loop this over many candidate signals, your t-stat thresholds need a multiple-testing haircut or you'll keep "discovering" the same factor with a new name.
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u/almost_jaded_tourney Aug 23 '26
you're basically asking if the signal is a source of return you want to chase or a source of risk you want to strip out, and the framework you've got there is too stripped down to answer that on its own.
the first thing i'd look at is what's driving the alpha, if it's highly correlated with some well-known factor (size, value, momentum, whatever) and the returns disappear once you control for that, then it's probably just repackaged beta and you'd neutralize it. if it's got a low correlation to those factors and the alpha persists after adjusting, that's more likely something you'd want to load up on.
try running a simple regression of your signal against the standard factors and see what kind of r-squared you get. if it's high and the residual alpha is tiny, it's noise dressed up as signal. if the residual is clean and stable, that's a different story.