The best overview I have seen so far is this paper which lists 214 (!) factors (or anomalies if you like) on over one hundred (!) pages:
Harvey, Campbell R. and Liu, Yan and Zhu, Caroline, …and the Cross-Section of Expected Returns (February 3, 2015). Available at SSRN: https://ssrn.com/abstract=2249314 or http://dx.doi.org/10.2139/ssrn.2249314
Hundreds of papers and hundreds of factors attempt to explain the
cross-section of expected returns. Given this extensive data mining,
it does not make any economic or statistical sense to use the usual
significance criteria for a newly discovered factor, e.g., a t-ratio
greater than 2.0. However, what hurdle should be used for current
research? Our paper introduces a multiple testing framework and
provides a time series of historical significance cutoffs from the
first empirical tests in 1967 to today. Our new method allows for
correlation among the tests as well as publication bias. We also
project forward 20 years assuming the rate of factor production
remains similar to the experience of the last few years. The
estimation of our model suggests that today a newly discovered factor
needs to clear a much higher hurdle, with a t-ratio greater than 3.0.
Echoing a recent disturbing conclusion in the medical literature, we
argue that most claimed research findings in financial economics are
The authors now provide a datasheet with an exhaustive overview of all factors: https://tinyurl.com/y23ozzkc
The following chart is taken from the paper and summarizes its key results:
A new record! The following new paper lists and tests 452 (!) anomalies on more than 130 pages:
Hou, Kewei and Xue, Chen and Zhang, Lu, Replicating Anomalies (October 2018). Review of Financial Studies, forthcoming; Fisher College of Business Working Paper No. 2017-03-010; Charles A. Dice Center Working Paper No. 2017-10. Available at SSRN: https://ssrn.com/abstract=3275496
It indicates "that most published U.S. stock market anomalies are not replicable after reasonably demoting microcaps to a very minor role, and especially after raising the threshold for significance to account for data snooping."
Source and summary of the paper (behind a paywall):
Most anomalies fail to hold up to currently acceptable standards for
empirical finance. With microcaps mitigated via NYSE breakpoints and
value-weighted returns, 65% of the 452 anomalies in our data library,
including 96% of the trading frictions category, cannot clear the
single test hurdle of the absolute t-value of 1.96. Imposing the
higher, multiple test hurdle of 2.78 at the 5% significance level
raises the failure rate to 82.1%. Even for the replicated anomalies,
their economic magnitudes are much smaller than originally reported.
In all, capital markets are more efficient than previously recognized.