  Factor strategies have gained growing popularity in industry with the fast
development of machine learning. Usually, multi-factors are fed to an algorithm
for some cross-sectional return predictions, which are further used to
construct a long-short portfolio. Instead of predicting the value of the stock
return, emerging studies predict a ranked stock list using the mature
learn-to-rank technology. In this study, we propose a new listwise
learn-to-rank loss function which aims to emphasize both the top and the bottom
of a rank list. Our loss function, motivated by the long-short strategy, is
endogenously shift-invariant and can be viewed as a direct generalization of
ListMLE. Under different transformation functions, our loss can lead to
consistency with binary classification loss or permutation level 0-1 loss. A
probabilistic explanation for our model is also given as a generalized
Plackett-Luce model. Based on a dataset of 68 factors in China A-share market
from 2006 to 2019, our empirical study has demonstrated the strength of our
method which achieves an out-of-sample annual return of 38% with the Sharpe
ratio being 2.
