O'Shaughnessy: Publicly sharing quant findings produces stronger signals via feedback
Patrick O'Shaughnessy · Patrick O'Shaughnessy – O'Shaughnessy Asset Management (First Meeting, EP.01) · Jun 10, 2019 · at 42:23
Patrick O'Shaughnessy explains why OSAM openly shares its quantitative research insights instead of keeping all findings proprietary.
0:00 / 0:19exact quote · 19.7s
720p mp4 · rendered on demand · StarZero watermark
“In quant research, this is really interesting, which is that you actually get a stronger signal. We found when you share your general findings, not necessarily the code or the particulars, but the general findings with a broad audience, then all of a sudden you're stress testing this idea against like the most brilliant people that are just curious themselves that then give you feedback.”
quote is from the automated transcript, cleaned for reading: filler sounds and stutters are removed, nothing is rephrased. names can be misheard (the analysis reads context, assessments check outside sources). how →
More from Patrick O'Shaughnessy
O'Shaughnessy: High quality metrics do not generate excess returns; low quality destroys them
“In the quantitative sense, high ROEs or gross profitability or whatever your measure of quality is really isn't indicative of strong future excess returns. What's really useful about quality is that very bad quality, so really over levered balance sheets, comp…”
O'Shaughnessy: Momentum forecasts fundamental growth better than valuation multiples
“What fascinates me about this is momentum does a better job of forecasting future fundamental growth than price does. So the highest momentum decile has better future fundamental growth than the most expensive, let's say PE decile or free cashflow yield decile…”
O'Shaughnessy: Momentum Alpha Exhausts Within One Year and Reverts to Negative Returns
“So you earn all of your momentum, all of your alpha, rather, in the first one year holding period, and then you actually need to get the hell out, because it reverts. And typically, depending on where you're looking at the data, within two years or so, it's ac…”
O'Shaughnessy: Almost No Funds Have Pure Machine Learning Models in Live Production
“To be clear, I don't think almost anybody has a lot of that in production today, but those tools are becoming more and more useful.”
O'Shaughnessy: OSAM has almost zero portfolio overlap with AQR and LSV
“Our overlaps with other quants are shockingly low. Like we have this little cool program that we can show people this on the fly and they won't believe us. Like they'll ask about AQR and LSV and these other great managers, you know, really, really, really stro…”
O'Shaughnessy: Machine learning algorithms always fail at predicting stock returns directly
“You can feed all the best data in the world to whatever ML algorithm you choose. Let's say it's like a decision tree or something. And if you're trying to predict returns, it always falls apart. It never works.”