  In quantitative investment, constructing characteristic-sorted portfolios is
a crucial strategy for asset allocation. Traditional methods transform raw
stock data of varying frequencies into predictive characteristic factors for
asset sorting, often requiring extensive manual design and misalignment between
prediction and optimization goals. To address these challenges, we introduce
Direct Sorted Portfolio Optimization (DSPO), an innovative end-to-end framework
that efficiently processes raw stock data to construct sorted portfolios
directly. DSPO's neural network architecture seamlessly transitions stock data
from input to output while effectively modeling the intra-dependency of
time-steps and inter-dependency among all tradable stocks. Additionally, we
incorporate a novel Monotonical Logistic Regression loss, which directly
maximizes the likelihood of constructing optimal sorted portfolios. To the best
of our knowledge, DSPO is the first method capable of handling market
cross-sections with thousands of tradable stocks fully end-to-end from raw
multi-frequency data. Empirical results demonstrate DSPO's effectiveness,
yielding a RankIC of 10.12\% and an accumulated return of 121.94\% on the New
York Stock Exchange in 2023-2024, and a RankIC of 9.11\% with a return of
108.74\% in other markets during 2021-2022.
