The problem with pure research in this field is that your decisions will influence the market. How much you invest will influence your returns and how successful you are will influence the behaviour of others in the future.
1) Generate a few hypothesis algorithms, including one that invests at random.
2) Publish a cryptographic commitment for each algorithm.
3) Never actually invest any money. Alternatively: let someone else invest your money for you, without knowledge of your hypotheses.
4) Run your algorithms privately, without updating them at all. Capture the data the algorithms use (including random choices taken).
5) 5, 10 or 20 years later, publish all your algorithms, the data they had as input and their results, see if any of them would have predicted the actual performance of the market in an statistically meaningful way.
I imagine the main reason most researchers are unlikely to do that is the 5-20 years project requirement. It is a lot easier and faster to just take historical data from the market and then produce algorithms that would have predicted performance after year X, based on information before year X. Of course, the problem is that you run into over-fitting and survivor bias (in that only positive results are generally published).
Btw, having your algorithm be run by a fund and having that fund succeed, then publishing the algorithm, would also be susceptible to survivor bias.