Brilliant discussion! I've been following Pim's research for a number of years and great to see how much ground got covered here in simple easy to understand language. Well done guys!
Excellent all calm thinkers. With realistic thinking. Thanks for bringing out people like Pim for people like myself who would of never heard of him before. Keep up the great work guys this is time well spent.
This a very compelling case for low-risk equities. But looking at the rising valuation multiples (and premium over the broad market) of defensive sectors like consumer staples over the past decades, I have a question: is there a point at which conservative stocks become too expensive, and cyclicals are cheap enough?
My MO is building a baseline allocation in low-risk assets. I only give myself the freedom to invest in riskier assets when I feel like I have sizeable insurance in the form of highly stable assets. My philosophy is zero-sum risk in life. If I'm going to be doing risky things in my one aspect of my life, I take away risk from another. More risk in my career, less risk in my portfolio, for example.
sure. it is basically the concept of going on a great stretch of effort to experiment with different data selection and analysis methodologies. by doing this, you increase the chance of eventually finding something that shows a statistical relationship. hence, it is more likely that what is discovered in fact is a false positive. readers of such findings ought to be concerned with this, as a reader does not know the extend of prior attempts it took to get there. i.e. how much data mining was involved.
Risk is just part of life, you can be hit by lighting, car, tree or just drive by driver thrown something out of window. If we insure everything to reduce the risk to 0, then life will become very costly and unbearable.
Brilliant discussion! I've been following Pim's research for a number of years and great to see how much ground got covered here in simple easy to understand language. Well done guys!
Excellent all calm thinkers. With realistic thinking. Thanks for bringing out people like Pim for people like myself who would of never heard of him before. Keep up the great work guys this is time well spent.
I love these highly technical discussions! Lots of information to take in and I'm looking put it to good use.
Great discussion and Pim van Vliet is awesome at explaining a lot of conceps in a easy manner. I highly recommend his book.
Thanks a lot for returning to the "red meat". Really insightful interview.
red meat big big LOL
Wow, great discussion. Thank you!
Thank you for this insightful discussion!
This a very compelling case for low-risk equities. But looking at the rising valuation multiples (and premium over the broad market) of defensive sectors like consumer staples over the past decades, I have a question: is there a point at which conservative stocks become too expensive, and cyclicals are cheap enough?
I think I will read everything he has written
My MO is building a baseline allocation in low-risk assets. I only give myself the freedom to invest in riskier assets when I feel like I have sizeable insurance in the form of highly stable assets. My philosophy is zero-sum risk in life. If I'm going to be doing risky things in my one aspect of my life, I take away risk from another. More risk in my career, less risk in my portfolio, for example.
Risk is at the portfolio level, not the asset level. You're thinking about it wrong.
When they are mentioning data mining as a concern, what exactly does that mean? Can someone explain it to me?
sure. it is basically the concept of going on a great stretch of effort to experiment with different data selection and analysis methodologies. by doing this, you increase the chance of eventually finding something that shows a statistical relationship. hence, it is more likely that what is discovered in fact is a false positive. readers of such findings ought to be concerned with this, as a reader does not know the extend of prior attempts it took to get there. i.e. how much data mining was involved.
@@tonimeiners8945 Thank you so much!! So it's similar to overfitting and lack of generalization in models?
@@ericfang101 yes. rather, overfitting is one aspect and issue in data mining overall.
Risk is just part of life, you can be hit by lighting, car, tree or just drive by driver thrown something out of window. If we insure everything to reduce the risk to 0, then life will become very costly and unbearable.