11 citations · 20 across the 2 of their papers we have counts for
5 papers
Controlling False Discovery Rates under Cross-Sectional Correlations
Junpei Komiyama, Masaya Abe, Kei Nakagawa +1
We consider controlling the false discovery rate for testing many time series with an unknown cross-sectional correlation structure. Given a large number of hypotheses, false and m…
RM-CVaR: Regularized Multiple -CVaR Portfolio
Kei Nakagawa, Shuhei Noma, Masaya Abe
The problem of finding the optimal portfolio for investors is called the portfolio optimization problem. Such problem mainly concerns the expectation and variability of return (i.e…
Cross-sectional Stock Price Prediction using Deep Learning for Actual Investment Management
Masaya Abe, Kei Nakagawa
Stock price prediction has been an important research theme both academically and practically. Various methods to predict stock prices have been studied until now. The feature that…
A Robust Transferable Deep Learning Framework for Cross-sectional Investment Strategy
Kei Nakagawa, Masaya Abe, Junpei Komiyama
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. St…
Deep Recurrent Factor Model: Interpretable Non-Linear and Time-Varying Multi-Factor Model
Kei Nakagawa, Tomoki Ito, Masaya Abe +1
A linear multi-factor model is one of the most important tools in equity portfolio management. The linear multi-factor models are widely used because they can be easily interpreted…