Відео

agree with second point. why not to consider the other factor

we have some great code examples of causalnet in the free trial, you can apply here :) causalens.com/decisionos-free-trial/

Simple google search is gonna tell you more than enough

The fund was released in 2019, though. So most of the data you used is back-tested. Since inception JPGL underperformed IWDA. It might not be the counter argument it was intended to be.

@@Affepaul All factor funds underperformed the last few years because factors other than the market one haven't done well during this period. The same thing happened in the late 90s, just before a decade of underperformance by cap weighted market funds. Also, my initial comment wasn't meant to say that "factor funds performed better during x period so they will continue to do so". It was simply a response to the first graph in the presentation.

@@antonisdee having a great backtest and rolling “out-of-sample” performance while underperforming or not providing consistent alpha once after inception is exactly why he says many of the factor investing strategies aren’t scientific

⁠@@antonisdeeanyone can data mine a few factors that provide superior performance vs some benchmark over a certain period of history. Anyone can say a strategy is cyclical-- sometimes they work, sometimes they don’t. But is it scientific? And are the investors dumb enough to buy it?

​​@@jonnyh.2167 Neither is what he proposed, though. I mean, to be clear, he didn't actually propose anything, he just introduced some basic causal inference concepts, and complained about the state of econometrics 10+ years ago. But in finance, the causal graph changes so quickly and there are so many potential variable that the graph is almost pointless to estimate (if you even can estimate it--good luck! Causal Discovery algorithms aren't even good on simulated data!). Moreover, Marcos clearly doesn't understand what reflexivity means, even though he tried to cover that up by making some rough and incorrect analogy with science. Most frameworks for causal inference don't allow for reflexivity (technically, this corresponds to mixed direction causal graphs which may contain cycles and bidirective edges) which is another main problem in their use in investing... And finally with causal inference you also have a massive multiple testing problem, because as Marcos says at the end, you may have hundreds of graphs which are reasonable causal assumptions, then you need to test them all. Marcos is a smart guy but this causal factor investing stuff is mostly charlatanism in my opinion.

Bro.... most quants know physics 😅

Here is a link to the presentation: www.causalens.com/wp-content/uploads/2022/11/What-is-Causal-AI-UCL-Jan-2022.pdf

@@causaLens Many thanks!

Thanks, Jacob! That's a great thing about Causal AI graphs - they make it so easy to follow the model's logic. Please subscribe, we'll be sharing more!