12 citations · 30 across the 16 of their papers we have counts for
17 papers
Rethinking Synthetic Scenario Realism: Compatibility, Not Fidelity, Drives Hedging Performance
Ryuji Hashimoto, Masanori Hirano, Ryota Ozaki +1
Deep hedging has emerged as a data-driven approach to optimizing hedging strategies. Since real market data are often limited, synthetic price paths play a crucial role in training…
Uncovering Residual Factors in Financial Time Series via PCA and MTP2-constrained Gaussian Graphical Models
Koshi Watanabe, Ryota Ozaki, Kentaro Imajo +1
Financial time series are commonly decomposed into market factors, which capture shared price movements across assets, and residual factors, which reflect asset-specific deviations…
PLaMo 2 Technical Report
Preferred Networks, :, Kaizaburo Chubachi +24
In this report, we introduce PLaMo 2, a series of Japanese-focused large language models featuring a hybrid Samba-based architecture that transitions to full attention via continua…
A Judge-free LLM Open-ended Generation Benchmark Based on the Distributional Hypothesis
Kentaro Imajo, Masanori Hirano, Shuji Suzuki +1
Evaluating the open-ended text generation of large language models (LLMs) is challenging because of the lack of a clear ground truth and the high cost of human or LLM-based assessm…
Financial Fine-tuning a Large Time Series Model
Xinghong Fu, Masanori Hirano, Kentaro Imajo
Large models have shown unprecedented capabilities in natural language processing, image generation, and most recently, time series forecasting. This leads us to ask the question:…
Enhancing Financial Domain Adaptation of Language Models via Model Augmentation
Kota Tanabe, Masanori Hirano, Kazuki Matoya +3
The domain adaptation of language models, including large language models (LLMs), has become increasingly important as the use of such models continues to expand. This study demons…
PLaMo-100B: A Ground-Up Language Model Designed for Japanese Proficiency
Preferred Elements, :, Kenshin Abe +18
We introduce PLaMo-100B, a large-scale language model designed for Japanese proficiency. The model was trained from scratch using 2 trillion tokens, with architecture such as QK No…
The Construction of Instruction-tuned LLMs for Finance without Instruction Data Using Continual Pretraining and Model Merging
Masanori Hirano, Kentaro Imajo
This paper proposes a novel method for constructing instruction-tuned large language models (LLMs) for finance without instruction data. Traditionally, developing such domain-speci…
Construction of Domain-specified Japanese Large Language Model for Finance through Continual Pre-training
Masanori Hirano, Kentaro Imajo
Large language models (LLMs) are now widely used in various fields, including finance. However, Japanese financial-specific LLMs have not been proposed yet. Hence, this study aims…
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…
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…