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Found 7 papers, 0 papers with code
Doubly Robust Mean-CVaR Portfolio
However, the instability associated with the input parameter changes and estimation errors can deteriorate portfolio performance.
Controlling False Discovery Rates under Cross-Sectional Correlations
We achieve superior statistical power to existing methods and prove that the false discovery rate is controlled.
RM-CVaR: Regularized Multiple $β$-CVaR Portfolio
In order to improve this problem, we propose RM-CVaR: Regularized Multiple $\beta$-CVaR Portfolio.
Cross-sectional Stock Price Prediction using Deep Learning for Actual Investment Management
We perform empirical analysis in the Japanese stock market and confirm the profitability of our framework.
A Robust Transferable Deep Learning Framework for Cross-sectional Investment Strategy
Stock return predictability is an important research theme as it reflects our economic and social organization, and significant efforts are made to explain the dynamism therein.
Deep Recurrent Factor Model: Interpretable Non-Linear and Time-Varying Multi-Factor Model
Specifically, we extend the linear multi-factor model to be non-linear and time-varying with LSTM.
Deep Learning for Forecasting Stock Returns in the Cross-Section
Many studies have been undertaken by using machine learning techniques, including neural networks, to predict stock returns.
