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I can't up vote this enough.. I've spend an unreasonable amount of time reading research related to finance for my web app Piglet: https://projectpiglet.com/
by lettergram 9y ago
I can't up vote this enough..
I've spend an unreasonable amount of time reading research related to finance for my web app Piglet:
https://projectpiglet.com/ https://projectpiglet.com/
Long story short, all "research" is pretty much B.S.
I used to assume this is because people want to make money, so they keep the good analysis secret. However, after working in the industry a few years; it's mostly because they just don't know how to apply the algorithms or if it's even possible.
I think my favorite example is the seminal paper on using twitter sentiment to predict stock movement[1]. They don't use a large enough data set, and more importantly they use granger causality to identify "casualty"[2] between sentiment and stock value. They then claim they found a specific range which has a p-value indicating they are correlated... Of course you'll find a correlation when you look at two normalized signals and try to match them up.
Now, if they had not use the DJIA (Dow Jones Industrial Average) and instead used 500 individual stocks, and found the sentiment on twitter correlated with stock value(s) 90% of the time between 5 and 10 days. I'd argue they probably have something.
However, because their method is literally a BFS on only two signals in an attempt to find a correlation, they must correct for the p-value. i.e. "look and you shall find"[3]
This is just one of the hundred issues I've found, but really sheds light on how bad that industry is.
[1] https://scholar.google.com/scholar?hl=en&q=twitter+stock+sentiment+analysis&btnG=&as_sdt=1%2C47&as_sdtp=&oq=twitter https://scholar.google.com/scholar?hl=en&q=twitter+stock+sen...
[2] Granger Causality offsets two signals in an attempt to find a correlation between them at an offset between two times. AKA find "causality" by finding correlation at an offset in time
[3] https://stats.stackexchange.com/questions/5750/look-and-you-shall-find-a-correlation https://stats.stackexchange.com/questions/5750/look-and-you-...
- mayank 9y ago> Long story short, all "research" is pretty much B.S. Easy there! When I start feeling cynical about things like almost every sentiment analysis paper, I go back and read some of my favorite papers, especially ones from the 70s and 80s (eg, [1]) Another useful heuristic is to read the best paper award winners from conferences in a particular area. It's not a perfect metric, but the signal/noise ratio is better. [1] the flajolet-martin algorithm: http://algo.inria.fr/flajolet/Publications/FlMa85.pdf http://algo.inria.fr/flajolet/Publications/FlMa85.pdf
- aagha 9y ago> Long story short, all "research" is pretty much B.S. I'm curious about this as it relates to Piglet. Are you monetizing on the idea that most people won't realize this and will pay for Piglet with the hopes that they signals they get there are somehow positively correlated to what's happening in the real world for a particular stock/idea/company/politics when in reality you don't think it really does?
- lettergram 9y agoFor Piglet, I'm actually going back and conducting research to find valuable signals and releasing everything in as clear a way as possible. Importantly, I'm providing access to signals first with explanation(s) of what each signal is / how it's useful today. I also have an investment model currently accomplishing on average 50%+ YoY (for the back testing on 8 years, live for 4 years). From there, I'm building further machine learning models on top of the signals. However, (as mentioned prior from the sentiment analysis paper) I'm doing much more robust research. For example, comparing all stocks as opposed to DJIA.
- kusmi 9y agoWhat are your thoughts on Renaissance Technologies?