9 papers
Rethinking Synthetic Scenario Realism: Compatibility, Not Fidelity, Drives Hedging Performance
Ryuji Hashimoto, Masanori Hirano, Ryota Ozaki +1
Deep hedging is a data-driven approach to learn hedging strategies. It relies on synthetic price paths generator, as real market data is often limited for training. Existing approa…
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…