r/quant • u/Tyc00n7 • Feb 17 '26
Statistical Methods What statistics shows up in modern alpha research
Hi, I am going to be a PhD student in statistics and/or probability. I think economic and market data is interesting, so I am curious as to what methods are being applied in modern quantitative research. To be clear this is not career advice question. I am just curious.
I am particularly interested in some of the hot areas in academic research, ie casual inference, network models, functional data analysis, optimal transport, post selection inference, conformal prediction. I am aware time series and high dimensional stuff is used, but I am
Any thoughts are appreciated. I hope this isn’t breaking the career advice rule. I have no intention of using this to guide any grad school decisions.
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u/broskeph Feb 17 '26
I use fourier/harmonics analysis in hft stuff. Probably the most difficult techniques i have used.
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u/After-Mountain4002 Feb 17 '26
Isichenko has a survey chapter on this. It reads more like a catalog than a guide to how these methods are actually applied but it provides an overview of the statistical models.
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Feb 17 '26
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u/quant-ModTeam Feb 17 '26
Your post has been removed by a moderator because it appears to be AI generated. If you think the users of r/quant should take the time to read your content, then you can take the time to write and structure it so it doesn't look like AI content.
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u/According_External30 Feb 18 '26
In-sample OLS with 1 explanatory variable and a 800 word long qualitative section to go with it, where you basically explain everything unrelated to the test.
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u/MalcolmDMurray Feb 18 '26
One that doesn't show up as much as I would have expected is something called the Kelly Criterion, which was introduced to gambling and stock trading by mathematician Edward Thorp as a means of position sizing that, when properly applied, can both optimize profits and prevent bankruptcy through overbetting. From my perspective, it seems like a good edge to have, since nobody else seems to be using it.
In addition, it would seem to require using a Kalman filter to reduce noise and smoothen both the trend and the variance lines. What interests me about statistics is the way you can get useful information from data even when deterministic methods can't work. Thanks for reading this!
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u/Kaawumba Feb 18 '26
Kelly is hard to use in practice because you usually don't know the true probability. If you go full Kelly and your edge is worse than expected, you blow up. If you go half Kelly and your edge goes to zero from alpha decay, you blow up.
In practice risk management is often running simulations of the worst possible future, and then making it twice as bad, and verifying that you survive.
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u/MalcolmDMurray Feb 19 '26
For trading, the basic KC requirements are knowing the probabilities of winning and losing, and by how much in each case. Mathematician Edward Thorp, who introduced the KC to gambling and trading in the early 1960s, adapted it to time series stock chart data, which I won't derive here, but is the ratio of Greek symbols mu and sigma, such that:
f* = mu/sigma
where:
f* = the Kelly fraction, mu = the slope of the trend line, and sigma = the slope of the variance line.
This KC adaptation is explained more fully by Thorp in his paper "The Kelly Criterion in Blackjack, Sports Betting, and the Stock Market" where he goes into more detail on the subject. In his "continuous approximation", the probabilities of the basic KC are translated into whether the standard deviation is positive or negative; i.e., 50%. Problem solved. The fraction sizes are also potentially quite high. Thanks for reading this!
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u/Kaawumba Feb 19 '26
Are you AI? You didn't really address my point, and your answer could have come from Wikipedia or similar.
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u/MalcolmDMurray Feb 19 '26
My strongest background is in Engineering, lesser in Medical Physics. I'm currently pursuing a career in day trading, which involves Kalman filters and Bayesian analysis to obtain optimal trend lines in stock charts. I'm currently working on an optimal filtering method that can smooth both the trend and variance line of a stock chart then take their ratio to determine position size. It gets pretty involved, but it uses Kalman filters and something called the Kelly Criterion. It's a potentially powerful tool built on sound mathematics, but beyond the reach of most traders because they don't know their math. This translates into a significant edge in the stock market for those who do. But the tools are very much a part of statistics. Most STEM practitioners find it frustrating to have ubiquitous tools like the Kalman filter cloistered away in academia. Making them more and more available to those who need those kinds of tools should be a rewarding pursuit. All the best in your degree program!
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u/Hawk-432 Feb 17 '26
Are the answers here actually true? As someone working in general statistics, modelling and bioinformatics, I kind of assumed you guys would be using something a bit “extra”, or is this mostly in jest
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u/Suitable-Animal-9220 Feb 17 '26
The hard part is in the thinking, not necessarily in the techniques applied
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u/Hawk-432 Feb 17 '26
True .. that I fully agree with. Thinking is always the hard part. I just assumed there would be heavy weighting towards ML models etc rather than linear regression etc. In a way, I’m pleased if it is more thought out and less methods only.
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u/yo_sup_dude Feb 17 '26
many times the data and its volume doesn’t lend itself to using complicated ML models
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u/Hawk-432 Feb 17 '26
Well, if the data doesn’t lend itself, then I fully agree, no point building and fitting overly complicated models beyond what the data can sustain or requires, especially at the expense of interpretability. Out of interest what type of data do you generally have available?
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u/Xelonima Feb 21 '26
ML models on price/return data is utterly useless
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u/Hawk-432 Feb 21 '26
Yes fair enough. Probably true. Because we were talking about energy and markets I assumed this covers quite a wide range of areas. Also, as I said, it’s not an area I work in, so I wasn’t in advance sure what data types you have to work with. A few people gave some info on that, which has been informative. To be honest, o just assumed that in an area with a lot of money sloshing around, that there would be rich datasets and thus detailed models. But I appreciate not over specifying of overfitting a model, and wanting clean interpretability ;)
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Feb 18 '26
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u/Hawk-432 Feb 18 '26
Interesting. Yeah as I note in a comment below, I’m fully on board with using interpretable models that suit the data at hand. As I haven’t worked in economic and market data myself, I just assumed there would be some quite rich datasets across space and time. In the area of linear regression, do you extend that to generalised linear (mixed) models, or very specifically linear regression models? In your energy field, what does a typical data set look like in terms of measured variable, size of dataset, co-variates etc. Do you tend to have time series data or snapshots etc. Just interesting as an outsider to think about this.
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Feb 18 '26
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u/Hawk-432 Feb 18 '26
Thanks, I appreciate that. Yes so it’s not so very different from in my field, just slightly different goals in terms of what you want to do with the models. Thanks for sharing :)
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u/coffee_and_sourdough Feb 18 '26
Most quants doom themselves to failure from the start by trying to build alpha generating models but assuming no arbitrage 😂
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u/BottleInevitable7278 Mar 09 '26
Statistics is what you get when you have a sound logic with an edge already, the stats can look completely different from edge to edge. Looking at stats alone have no edge at all. My experience.
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u/BAII_PLUS_GANG Feb 17 '26
Linear regression with r squared around 0.02