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absent today. For these strategies in particular past performance is not a guide to future returns.
(Also if you are looking at a backtest of these strategies rather than a live track record there is a ton of "data mining alpha" but that's a separate topic)
Way too much data-mining alpha, tempted to say even Eugene Fama worried about this one.
Agree. You need to look at the track record of actual value investors (and tease out the value premium from their other exposures) or look at post-publication performance of trading strategies to avoid it.
It's really hard to explain to someone who hasn't done this a lot how Every. Single. Time. in-sample $/alpha are bigger than OOS. To the point where you yell "Dammit, I did everything right! I was a good little boy, super careful with data, etc!" And it's *still* overfit.
Then you see someone pitch you their new quant fund with some backtest/papertrade results and you just laugh (internally).
You feel like a jerk, because they really do believe their #s. And *they're* trying to be good too! Doing things right, honest, etc.
It's just *that* hard.
Providing liquidity can be a solid revenue stream, if executed well. Equity stat arb should work as long as market inefficiency exists — various investment horizons, risk constraint from mandates, geographical differences, etc
Equity stat arb is good with alpha in there
Overfitting ftw
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