18 citations · 56 across the 8 of their papers we have counts for
9 papers
Application of time-series quantum generative model to financial data
Shun Okumura, Masayuki Ohzeki, Masaya Abe
Despite proposing a quantum generative model for time series that successfully learns correlated series with multiple Brownian motions, the model has not been adapted and evaluated…
Doubly Robust Mean-CVaR Portfolio
Kei Nakagawa, Masaya Abe, Seiichi Kuroki
In this study, we address the challenge of portfolio optimization, a critical aspect of managing investment risks and maximizing returns. The mean-CVaR portfolio is considered a pr…
A New Initial Distribution for Quantum Generative Adversarial Networks to Load Probability Distributions
Yuichi Sano, Ryosuke Koga, Masaya Abe +1
Quantum computers are gaining attention for their ability to solve certain problems faster than classical computers, and one example is the quantum expectation estimation algorithm…
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…
Deep Learning for Forecasting Stock Returns in the Cross-Section
Masaya Abe, Hideki Nakayama
Many studies have been undertaken by using machine learning techniques, including neural networks, to predict stock returns. Recently, a method known as deep learning, which achiev…