Always visualize first. Human 'eyballing' is a good pattern detector.
Linear correlation is just one pattern the data can have.
Unfortunately many social science publications have reviewers who know only the basics and can't judge or accept statistically valid analysis that is outside their competence. Fit it into line or nothing.
>Third, Glen Weyl-style economic arguments have convinced me that, in the presence of superlinear returns to scale, the optimal policy is actually NOT Rothbard/Mises-style strict property rights. Rather, the optimal policy does involve some nonzero amount of more actively pushing projects to be more open than they otherwise would be.
(read the rest of his reasoning for this philosophical shift).
Many European countries have /the principle of the prohibition of enrichment/ within the law of damages. The injured party should receive compensation for the actual loss or damage suffered, but should not end up financially better off. The goal is to make the victim whole. Damages are not punitive.
There is separate case in EU against Google that can result severe penalty.
Maximums are high: For severe GDPR violations the penalty can be up to 20 million euros, or up to 4 % of total global turnover of the preceding fiscal year, whichever is higher. For less severe violations fines of up to 10 million euros, or up to 2% of its entire global turnover of the preceding fiscal year, whichever is higher. Turnover is from _undertaking_, meaning every corporation or natural person who is engaged in the activity (you can't avoid penalty with financing or corporate structure).
As the old saying goes, the market can stay irrational longer than you can stay liquid. So shorting a clearly overvalued stock can be a dangerous move.
In a world where seed and A stage AI startups are getting $100mm rounds and half that money's going to NVIDIA ... eh it's probably not too overvalued. I think there's more oomph left in the bubble.
When the valuation is something like "it looks like it's assuming they'll continue to sell $X billion a year" it can be reasonablish.
When the valuation requires "they will continue to grow sales X% a year" is when it quickly becomes impossible, and for a much smaller X than you might realize.
> Last summer, when I valued Nvidia in this post, I found it over valued at a price of $450, and sold half my holdings, choosing to hold the other half. Now that the price has hit $680, I plan to repeat that process, and sell half of my remaining holdings.
He bought it before me at least, so he *made* money. Doesn't matter if didn't predicted the top. He saw a stock that he thought will increase in value, bought some and sold at a higher price. That's what it matters
yeah, but he could have waited a little bit to see signs of a slowdown instead of taking such a radical contrarian view and leaving ~80% upside on the table.
better to risk it going down from $450 to $400 before selling than to miss out on the ride to $800
Aswath Damodaran looks at valuations with a very one-dimensional lens because he is not an innovator, engineer, or even a business person. Ever since Amazon was in its early days, he has said that Amazon was overvalued and he has always been wrong because Amazon has always found new verticals to build and create more value with.
Damodaran makes detailed analysis with assumptions in the open. Put your numbers to the Exel sheet and calculate valuations using your own numbers.
>Ever since Amazon was in its early days, he has said that Amazon was overvalued and he has always been wrong because Amazon has always found new verticals to build and create more value with.
Amazon has been overvalued multiple times.
Amazon stock had negative return 10 years between 1999 - 2009.
> Amazon stock had negative return 10 years between 1999 - 2009.
It did not. You will have to cherry pick very specific days in this time frame (top of the dot com bubble and bottom of the GFC) to get negative returns. But how about 1998-2008 or 2000-2010?
Here is how $10K invested in AMZN performed in 10 years [1]:
Jan 5, 1999 to Dec 28, 2009, AMZN had an 8.9% annual return.
Jan 5, 1999 to Dec 28, 2008 was -0.52% annual return.
Jan 5, 2000 to Dec 28, 2009 was 6.88% annual return.
But why give a crap about returns during a specific 10 year period? Almost nobody is buying something today to liquidate all of it at a single point in time in the future.
A forecasting system in aircraft autopilot that can accurately forecast when the plane hits the mountain is always wrong.
Forecasting when the forecast depends on the actions of agents that can be informed by the forecast changes the game. If the Fed model forecasts recession and the Fed takes action to prevent it from happening, it changes everything.
Only a forecasting model that is not observed/believed by policy makers can predict without intervention.
Layman's idea of forecasting: Predict what happens in the future.
Economic forecasting: Forecast is input for actions. Predict what happens in the future, using this model, these variables, and everything else stay the same. You can check afterward if the model is an accurate forecaster by removing the changes caused by variables outside the model.
It is always harder to accurately forecast actual recession, than it is to forecast the predictions of the Fed model. You don't need an information edge there, just information parity.
When the Fed takes action, it is usually a very rational action, with a clear-defined goal of long-term economic health. This makes their actions easier to predict than other market participants.
So you went the hard route, forecasting the highly complex system directly, but then "variables outside the model" caused the "accurate" model to not perform well? You don't buy anything with that, since you live in a world with outside variables which mess up your predictions. The solution is to make your model actually accurate, by incorporating these "variables outside the model": Predict what others will predict.
>'Putin isn't Hitler!' 'Trump isn't Hitler!' Wow, congratulations! You
have failed the most basic lesson of learning from history in order
not to repeat it!
>I wrote about this fallacy extensively in Winter is Coming and
elsewhere, but am happy to recap it here. Of course Putin isn't
Hitler, and Donald Trump isn't even Putin—no matter how much he would
like to be. The point is that no one is making comparisons to the
monster Adolf Hitler became in the 1940s. But in the 20s, even for
most of the 30s, Hitler wasn't Hitler either! There could be no more
important lesson to understand than how a race-baiting demagogue came
to power in an educated and liberal country like Germany and how he
transformed that nation into a fascist death machine capable of World
War and unimaginable acts.
Linear correlation is just one pattern the data can have.
Unfortunately many social science publications have reviewers who know only the basics and can't judge or accept statistically valid analysis that is outside their competence. Fit it into line or nothing.