  In addition to being familiar with policies, high investment returns also
require extensive knowledge of relevant industry knowledge and news. In
addition, it is necessary to leverage relevant theories for investment to make
decisions, thereby amplifying investment returns. A effective investment return
estimate can feedback the future rate of return of investment behavior. In
recent years, deep learning are developing rapidly, and investment return
prediction based on deep learning has become an emerging research topic. This
paper proposes an embedding-based dual branch approach to predict an
investment's return. This approach leverages embedding to encode the investment
id into a low-dimensional dense vector, thereby mapping high-dimensional data
to a low-dimensional manifold, so that highdimensional features can be
represented competitively. In addition, the dual branch model realizes the
decoupling of features by separately encoding different information in the two
branches. In addition, the swish activation function further improves the model
performance. Our approach are validated on the Ubiquant Market Prediction
dataset. The results demonstrate the superiority of our approach compared to
Xgboost, Lightgbm and Catboost.
