I don't know who needs to hear this today, but if you have two time series that both increase over time, they will especially visually show spurious correlation. This should be intuitive. Imagine I was looking at number of CS graduate students versus the number of arcades open.
So I fit a model to both, because that makes sense. In this example it shouldn't really matter if I take the totals of each, because we can obviously surmise both the total and rate of CS graduate students is increasing over time. We can't obviously say the same about arcades
without some data to back us up, but we can guess it probably is also increasing at a rate related to urbanization and normal population increase (arcades per capita, essentially). Here's what our graph looks like.
Fuck, that r^2 is sexy. Can I predict anything with this? Certainly not. Correlation doesn't imply causation, and statistically if you look hard enough to find random correlations you will find them. A commenter mentioned on my shared threads the same thing occurs when you
look at time series that have natural covariance - a good example is number of Bitcoin wallets (or rate) and price of a Bitcoin (or rate). Naturally over time we expect two things. As the price of Bitcoin goes up, more people will open Bitcoin wallets to capitalize on it.
Similarly, as the rate of Bitcoin price increase goes up, we expect that the rate of people looking to join the Bitcoin network should *also* go up. In this case, we really have two variables, x1 and x2, that depend on a latent variable x3 -- the popularity of Bitcoin itself.
That said, unlike our prior example (CS grads vs arcades open), it's also incorrect to say there isn't a real correlation here. It's quite likely the size of the network (Metcalfe's law) has some explanatory power on the current price of the Bitcoin, just perhaps less than
what basic linear regression would imply. A general rule of thumb I have with financial time series is except for obvious tautologies (e.g. VX and vs spot VIX, for ex.), if you see an r^2 > .5 or so, you're probably doing something wrong. There's a heavy noise component, and
r^2 of course is a simple linear model. How often do you see strong linear trends in real world data? Not very often, unless it is causative. It doesn't mean such models (Bitcoin network vs price) are useless or even wrong, it just is a good example of *lying with statistics*.
Be careful around charts, because the beautiful thing is a skilled data technician can tell you any story you want to hear. Without proper hypotheses, data is simply a quantitative way to lie to you. Fin.
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Okay, so I want to talk about some vol f**kery in the meme stocks, and how to play for fun and profit. That said, not investment advice and in the interest of not upsetting compliance, I'll leave off the actual stock names too.
So lately, things have been odd.
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There's this large company that we all know about that rallied like 50% in a month and pissed off a lot of people. This came at the same time as a larger sector trend rally, so it wasn't too unexpected. CEO might've merked it though.
But anyway, lots of people tried shorting.
In general, shorting these things is a bad idea directly, because of course everyone is buying puts thinking they're righteously clever, and of course the puts tend to be overpriced. In {insert company}'s case, calls were also generally broken, but I digress. So how do I short?
Hi, it's been a minute since I made a thread (I deleted a prior one two weeks ago). This thread will be about natural gas and the United Kingdom, mostly since I have a research post about it coming out probably soon. I am not a natural gas trader unless you count FCG.
I do not claim complete accuracy, and you're more than welcome to correct nicely if there's any misinformation, or get blocked otherwise. Anyhow --
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As I posted yesterday, something weird is going on with natural gas in the United Kingdom. Natural gas prices are going up worldwide from a combination of factors: industrial output returning after COVID-19, mismatched reserves to demand needs, climate factors in a few places 2/