Embedding-based neural network for investment return prediction

TL;DR

This paper introduces an embedding-based dual branch neural network for investment return prediction, effectively encoding high-dimensional data and decoupling features, validated on a market dataset with superior performance.

Contribution

The paper presents a novel dual branch neural network leveraging embeddings and Swish activation for improved investment return prediction.

Findings

Outperforms Xgboost, Lightgbm, and Catboost on the Ubiquant dataset.

Effectively encodes investment IDs into dense vectors.

Decouples feature representations for better prediction accuracy.

Abstract

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.