핵심 개념
RVRAE is a groundbreaking dynamic factor model that combines dynamic factor modeling principles with the variational recurrent autoencoder (VRAE) to predict stock returns more accurately.
초록
RVRAE is introduced as a dynamic factor model for stock returns prediction.
The model combines dynamic factor modeling with VRAE to address temporal dependencies and noise in market data.
RVRAE uses a prior-posterior learning method to fine-tune the model's learning process.
The model excels at risk modeling in volatile stock markets and predicting returns.
Empirical tests show RVRAE's superior performance compared to established baseline methods.
The paper discusses related work on dynamic factor models, RNN, and VAE.
Methodology involves training an optimal factor model and enforcing prior factors to approximate posterior factors.
Empirical results show RVRAE outperforms other machine learning-based dynamic factor models.
Financial performance evaluation using Sharpe Ratio indicates RVRAE's effectiveness.
Robustness analysis shows RVRAE outperforms other methods when certain stocks are missing from the training data.
통계
RVRAE는 주식 수익률 예측을 위한 동적 요인 모델입니다.
인용구
RVRAE는 주식 시장 데이터의 시간 의존성과 소음을 처리하기 위해 동적 요인 모델링과 VRAE를 결합합니다.