I've spend an unreasonable amount of time reading research related to finance for my web app Piglet:
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+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-...