LC

Short term you may see more pressure on PM/MO due to vaping: https://www.webmd.com/mental-health/addiction/news/20190826/as-vaping-injuries-grow-doctors-seek-answers

https://www.reuters.com/article/us-people-jeffrey-epstein-cameras/fbi-studies-two-broken-cameras-outside-cell-where-epstein-died-source-idUSKCN1VI2LC This would be believable if it was a ridiculous movie plot and not real life :-\

Well I mean most people already "work now get paid later" i.e. they are paid every two weeks. This is just another disguised form of a payday loan.

Average US median wage is 51k. For teachers it's 56K. I do not think a 10% premium is so extravagant.

Now in terms of the actual classroom experience: Teachers simply cannot impose discipline in their classes. This is an institutional problem and it requires a comprehensive solution. Teachers/administrators must be able to do certain things (suspend students, kick them out of classes, etc.) - but parents should also be enforcing the same. All too often this is lacking. I cannot tell you how many times I have heard stories from teachers about how the problem wasn't even the kid - it was that the parents simply did not care to enforce any work ethic or discipline at home, or would rather threaten to "sue the school" rather than teaching their child to behave himself. In short it is a societal problem with multiple factors. Casually blaming "teachers" in quotes as if they are leeches without a care for students just strikes me as a bit naive and looking for the easiest scapegoat.

According to that article 110K in NYC is "rich". Let me tell you that is simply not the case. Regardless, I simply cannot disparage teachers when the average salary for a US teacher is 58K. So no, I would say these people are riding some gravy train. Like I said, where are the yachts?

I always wonder when these types of criticism are made against a certain "rent-seeking" class: Where are the customer's yachts? These fat-cat teachers, where are their yachts? Do they keep them docked on the private island to make sure they can back the 2004 toyota corolla out of the driveway to work? Here's a pretty accurate representation of what teaching is really like (particularly in large minority urban areas): https://quillette.com/2019/02/10/public-educations-dirty-secret/

SD, the simple solution is to wait say 8 years out-of-school before being allowed to include student loan debt in bankruptcy. You have either built some life for yourself which you will be hesitant to lose in bankruptcy, or you have so few assets that this would be a “legitimate” case. Of course people can try to game this as well by spending 8 years stuffing all these assets in blind trusts or whatnot, but people play that same game with all types of other assets as well.

Drone footage of the FBI raid on Epstein's island:

If Uncle Warren really cared he'd buy me out of my portfolio ;D SNAP could be a decent acquisition. Hell, he could buy NIKE if he wanted.

I figured as much! I should have given you the benefit of the doubt, my bad! No hard feelings and of course I agree on both items (the "real risk" ;D) and the fact that the US does seem to be reaching a mature stage and this requires different incentives etc. Thanks for the link as well.

John, I think you’re right especially knowing Cigarbutt is never anything but cordial. The sentence just read so oddly to me that I had to ask! And more so, a fair point to make. To approve massive infrastructure spending, both parties need to buy in. Very difficult in today’s environment.

I would suggest those interested read the interview with Damodaran in the GS report you linked. Allison Nathan: But are these cash-rich companies engaging in buybacks just not looking hard enough for opportunities to innovate? Aswath Damodaran: You can look as hard as you want. You can make a reincarnated Steve Jobs the next CEO. But you can’t change many of these businesses. The fact of the matter is that many of the companies engaging in the largest buybacks are in the late stages of their lifecycles, and you can’t reverse that aging. Another good point is actually the question prior to this one, where AD talks about re-allocation of capital as Cardboard alluded to. However I am not sure I would agree with that point. A bit rude, no? :-[ I wanted to discuss the factors causing firms to drastically increase buybacks. Now of course we know where the cash comes from, as mentioned a lot of this excess cash comes from tax reform and the fact that many SP500 juggernauts are late-stage cash cows. But firms still have a choice how to spend this exesss cash, i.e. return to shareholders vs. invest in the company. Rate of capital investment does not appear to have changed trajectory over the past 5 years or so. Wouldn't one therefore conclude that internal investment opportunities have been essentially exhausted, hence the decision to return the majority of this excess cash to shareholders? So where are the best marginal opportunities for reinvestment, if not in the SP500 companies which have dominated the past decade? Financials, Real Estate, Consumer Staple/Discretionary, Autos...all seem "tapped out". Even tech appears closer to this end of the spectrum. One poster suggested infrastructure, this seems reasonable. But I think this is a real question that the US economy will have to face over the coming years. That is the question I wanted to underline.

Cannot argue with you but the more concerning aspect is not a buildup of balance sheet but it may signify a lack of reinvestment options.

https://www.cnbc.com/2019/07/29/buybacks-companies-increasingly-using-debt-to-repurchase-stocks.html The level of buybacks to free cash flow hit 104% for the 12 months ending in the first quarter of 2019, the first time that number has topped 100% during the economic recovery that started in 2009. In 2017, the level was 82%.

Haha, thankfully for my lifestyle that’s definitely not the case. ;D But I hope you keep compounding at that rate, I’m sure it would be useful! Gratefully I can put the time towards more leisurely uses.

Gerg, you can just ask for 50 bucks...

Spek ;D

Whats the reason for the switch? You see more upside with PayPal? Long term yes. Short term also capitulating to any bearish ideas. The original idea was to invest in some recession-resistant dividend paying companies with at least some pricing power. But growth has totally outperformed this idea. Mostly I think I need to move towards a more passive (indexing) option, at least for the near term (3-5 yrs I'd guess). Over the last year I haven't really had the time/energy/pleasure to actually do any investment research, and the results reflect it. Even worse, I haven't really cared... The reality is given my situation (age, net worth) my ROI is higher concentrating on my career than on my portfolio (ROI both in terms of amount and volatility of cash inflows). Something about whole-assing one thing vs. half-assing two things ;D ;D

Sold Verizon, bought Paypal

They are absolutely worth it. Silicon valley investors are a dime a dozen and add little differentiated value. Quality engineers on the other hand are a rare commodity. If your friends/family are truly the latter they should be out on the west coast getting paid.

Take it to the extreme: you only review and know the answer to 1 question. Is your skill level still very high?

But is this luck? To me this seems like pure skill. By applying enough skill you can guarantee a 100 on this test. No luck is required. Luck is applying zero skill, having the teacher get a better job at the school across the street and having the test cancelled.

Beautiful story! So happy for Chica and Max. Dogs are the best :D

IMHO and in my experience ML techniques are better utilized in the consumer space, particularly consumer credit. These models provide some lift over traditional logistic approaches BUT I would argue it is still requires more time to manage stability concerns. Even forgetting regulator concerns, boosted trees, neural network variants and such are so difficult to understand you do not know whether this lift is attributed towards additional intuitive patterns being exploited, or random chance or some other factor which may not be relevant. Interpretability is a real concern. On the trading side there is obviously much more opportunity, but this is why these models will IMHO have a more difficult time adding value here. One these markets are so picked-over it is ridiculous. Every intelligent statistician wants to design trading models. Two the majority of buyers are sophisticated. Compare the average derivative buyer (i.e. a highly educated bank/fund trader) to the average credit card buyer. Three, securitized financial products have more generally-accepted pricing mechanisms. (BS, Heston, etc.) What does the ML model add here? ML models excel at finding nonlinear patterns, and perhaps you can combine this with a traditional BS approach but you still run into the problem of interpretability. Obviously I am speaking in hindsight here but it has become clear why ML techniques are having a difficult time on the trading side of the business. In this sense, I think a lot of the folks who jumped into this business based on the glamor of combining their ML experience either from a digital or consumer credit space towards a trading landscape, they have found themselves in front of something of a brick wall and are withdrawing their claws and turning heel so to speak. Another area where ML approaches perform very well is in fraud use cases. Fraud techniques change rapidly (remember the old Nigerian Prince scam?) and fraudsters are adept at gaming basic systems. Easily you can see why the ML model excels here: it is highly adaptive and specializes in pattern seeking. As with all modelling you are not always looking for the best short-term fit but you are looking to match a particular methodology's strengths/weaknesses with your problem/use case. One big drawback of ML models is managing overfitting. These models have traditionally shown very real stability problems out-of-sample. Regularized learning intervals is one method used to manage such risk, however this leads to "momentum" as I alluded, as the model is regularly updated to take into account and weigh more heavily the most recent data. Curious what your thoughts are in terms of overfitting and managing stability in your particular use case, or prior use cases where you may have had experience.