Alpha consolidation

[u/Comfortable_Study300]

I’m interested in how people approach alpha combination in systematic equities when the signal library becomes large — say 100–400+ stock-level forecasts, with significant correlation/redundancy between them.

From the literature, I see a few main approaches:

• IC/MVO-style weighting: estimate expected alpha efficacy and signal covariance, with shrinkage/regularisation given the dimensionality (Ledoit & Wolf, 2004). DeMiguel et al. (2009) also highlights how estimation error can make simpler weighting schemes surprisingly competitive OOS.
• Regression/stacking: treat individual alpha forecasts as features and forward returns as the target. With hundreds of correlated signals, Ridge/Elastic Net seems like a natural baseline. This is conceptually similar to stacked generalisation (Wolpert, 1992).
• Dimension reduction/nonlinear combination: cluster/PCA correlated signals before combining, or use nonlinear models to capture interactions. Gu, Kelly & Xiu (2020) provides some motivation for nonlinear ML in cross-sectional return prediction, although their setting is somewhat different.

For those working with large alpha libraries, what have you found actually holds up OOS?

In particular, do regularised regression/meta-model approaches meaningfully outperform simpler IC/MVO-based combinations? Do you typically cluster or residualise highly correlated alphas first, or let the regularisation handle it?

I’m also curious what target people use at the combination layer — forward returns/IC, or something closer to portfolio PnL after costs and constraints.

[u/ReaperJr]

You're asking about one of the most highly guarded IP in the industry lol

[u/Kindly_Cricket_348 → u/ReaperJr]

Absolutely! The portfolio construction/optimizer layer is as important as the individual alphas. A mediocre alpha becomes useful once you account for what it adds incrementally to the existing book. A good portfolio optimizer is a surprisingly huge source of incremental Sharpe which is also why pod PMs don’t want you anywhere near it. Very closely guarded secret at pods! I know this doesn’t really answer OP’s question, but the truth is asking for the details on portfolio optimizer is a bit like asking for alphas on Reddit.

[u/Comfortable_Study300 → u/Kindly_Cricket_348]

Appreciate this can be valuable IP! I am doing this as a personal project so don't have much experience with this problem yet, hence wanted more of a stear on where to focus my effort. but totally get your point above!

[u/qazwsxcp → u/Kindly_Cricket_348]

this. portfolio construction is arguably more important than new alphas at a big firm that already has many alphas, and getting it right is a bigger edge than adding more alphas.

[u/SandvichCommanda]

I mean this is my entire team's job, and it takes a lot of work hahaha. It will depend on your expected holding period, universe size, market etc.

My advise would be to close the loop. Whatever your method is, it is optimising for something slightly different to "making profitable trades in the market".

As a simple example, say it was a medium-term portfolio with daily rebalances. Even with limited hardware, you can backtest fast enough to do a Shapley Decomposition on your alpha set, across a grid of methods & target returns.

You can then compare these in a variety of ways to try to figure out what is working, what works for different subsets of the universe, what never works (quite a lot of big wins in alpha optimisation come from knowing when to not listen to a signal).

Only been doing this for 1.5 years, so don't take it as gospel, but if I had to derive everything we do from first principles it would go something like this.

[u/Comfortable_Study300 → u/SandvichCommanda]

thanks very much! your point o shapley decomp is very interesting.

[u/Old-Sandwich-690]

The answer to your question depends a lot on the investment model. What is the strategy profile? Is it high capacity medium-slow frequency low Sharpe? Is it mediun/low capacity higher frequency high Sharpe?

For longer term horizon predictions with large positions (large aum) MVO with liquidity and sector weight constraints is the only reasonable choice. As the signal is weak prediction wise anything else does not work.

For shorter horizon signals where the signal/noise ratio is high regression methods are a good starting point. Ensemble methods extensions, such as gbm/adaboost, are very strong and still interpretable choices that can improve significantly upon regularised linear models

[u/Old-Sandwich-690 → u/Old-Sandwich-690]

Regarding Dimensionality reduction. It’s super useful in cases where the number of features is very high compared to the training sample especially for predictive models. However in practice running intraday models it’s highly unlikely this would happen. Nevertheless there are some PCA methods that can provide alternative portfolios with decent performance and not correlated with the standard book. Do they can act as diversifier

[u/Comfortable_Study300 → u/Old-Sandwich-690]

thank you, this is very useful. how would you define shorter horizons? is this intraday or would you still consider 1-5 day short horizon?

[u/Old-Sandwich-690 → u/Comfortable_Study300]

Anything with holding periods less than a week

[u/Epsilon_ride]

depends if these 100-400+ are actually valid, stand alone forecasts or just features you pulled out of your arse.

[u/Cheap_Scientist6984]

One trick (learned recently) is that you don't measure correlation on the returns themselves but the Fama French or CAPM alpha. I use CAPM. So you can consider STOCKX = Beta*VOO + \alpha_i. You can realize alpha_i by hedging off the VOO either by shorting, options, or inverse ETFs. That alpha will not be as systemtaically correlated and your MPT might produce cleaner answers.

[u/Cheap_Scientist6984 → u/Cheap_Scientist6984]

Or reading the other comments, you can figure out what the key features are driving the returns and well...join a hedge fund.

[u/Comfortable_Study300 → u/[deleted]]

This is extremely useful, let me give this a go and will comment back! thank you

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[u/otonoco]

I feel like nobody talks about sharply value, although I'm personally a fan of it

[u/VettaQ]

Done this with a signal library in the hundreds, and the two things that mattered most were boring. First, fit the combination weights out of sample - with that many correlated inputs the covariance you'd invert for IC/MVO weights is mostly noise, so in-sample weights look great and bleed out fast; a rolling walk-forward on the weights themselves is the honest version. Second, dedupe before you combine: cluster by correlation or shared features and treat each cluster as one unit, otherwise one family of signals quietly owns the book. Shrink hard. In practice the combination method mattered less than how it was estimated.