20 citations · 49 across the 13 of their papers we have counts for
13 papers
Adversarial Deep Hedging: Learning to Hedge without Price Process Modeling
Masanori Hirano, Kentaro Minami, Kentaro Imajo
Deep hedging is a deep-learning-based framework for derivative hedging in incomplete markets. The advantage of deep hedging lies in its ability to handle various realistic market c…
Efficient Learning of Nested Deep Hedging using Multiple Options
Masanori Hirano, Kentaro Imajo, Kentaro Minami +1
Deep hedging is a framework for hedging derivatives in the presence of market frictions. In this study, we focus on the problem of hedging a given target option by using multiple o…
Uncertainty Aware Trader-Company Method: Interpretable Stock Price Prediction Capturing Uncertainty
Yugo Fujimoto, Kei Nakagawa, Kentaro Imajo +1
Machine learning is an increasingly popular tool with some success in predicting stock prices. One promising method is the Trader-Company~(TC) method, which takes into account the…
Power Laws and Symmetries in a Minimal Model of Financial Market Economy
Liu Ziyin, Katsuya Ito, Kentaro Imajo +1
A financial market is a system resulting from the complex interaction between participants in a closed economy. We propose a minimal microscopic model of the financial market econo…
Unified Perspective on Probability Divergence via Maximum Likelihood Density Ratio Estimation: Bridging KL-Divergence and Integral Probability Metrics
Masahiro Kato, Masaaki Imaizumi, Kentaro Minami
This paper provides a unified perspective for the Kullback-Leibler (KL)-divergence and the integral probability metrics (IPMs) from the perspective of maximum likelihood density-ra…
Contrastive Representation Learning with Trainable Augmentation Channel
Masanori Koyama, Kentaro Minami, Takeru Miyato +1
In contrastive representation learning, data representation is trained so that it can classify the image instances even when the images are altered by augmentations. However, depen…
Theoretically Motivated Data Augmentation and Regularization for Portfolio Construction
Liu Ziyin, Kentaro Minami, Kentaro Imajo
The task we consider is portfolio construction in a speculative market, a fundamental problem in modern finance. While various empirical works now exist to explore deep learning in…
No-Transaction Band Network: A Neural Network Architecture for Efficient Deep Hedging
Shota Imaki, Kentaro Imajo, Katsuya Ito +2
Deep hedging (Buehler et al. 2019) is a versatile framework to compute the optimal hedging strategy of derivatives in incomplete markets. However, this optimal strategy is hard to…
Trader-Company Method: A Metaheuristic for Interpretable Stock Price Prediction
Katsuya Ito, Kentaro Minami, Kentaro Imajo +1
Investors try to predict returns of financial assets to make successful investment. Many quantitative analysts have used machine learning-based methods to find unknown profitable m…
Deep Portfolio Optimization via Distributional Prediction of Residual Factors
Kentaro Imajo, Kentaro Minami, Katsuya Ito +1
Recent developments in deep learning techniques have motivated intensive research in machine learning-aided stock trading strategies. However, since the financial market has a high…
The equivalence between Stein variational gradient descent and black-box variational inference
Casey Chu, Kentaro Minami, Kenji Fukumizu
We formalize an equivalence between two popular methods for Bayesian inference: Stein variational gradient descent (SVGD) and black-box variational inference (BBVI). In particular,…
Smoothness and Stability in GANs
Casey Chu, Kentaro Minami, Kenji Fukumizu
Generative adversarial networks, or GANs, commonly display unstable behavior during training. In this work, we develop a principled theoretical framework for understanding the stab…
Estimating Piecewise Monotone Signals
Kentaro Minami
We study the problem of estimating piecewise monotone vectors. This problem can be seen as a generalization of the isotonic regression that allows a small number of order-violating…