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Found 8 papers, 3 papers with code
Construction of Domain-specified Japanese Large Language Model for Finance through Continual Pre-training
After continual pre-training using the datasets and the base model, the tuned model performed better than the original model on the Japanese financial benchmarks.
Adversarial Deep Hedging: Learning to Hedge without Price Process Modeling
In this framework, a hedger and a generator, which respectively model the underlying asset process and the underlying asset process, are trained in an adversarial manner.
Efficient Learning of Nested Deep Hedging using Multiple Options
That is, we develop a fully-deep approach of deep hedging in which the hedging instruments are also priced by deep neural networks that are aware of frictions.
Uncertainty Aware Trader-Company Method: Interpretable Stock Price Prediction Capturing Uncertainty
Machine learning-based stock prediction methods including the TC method have been concentrating on point prediction.
Theoretically Motivated Data Augmentation and Regularization for Portfolio Construction
The task we consider is portfolio construction in a speculative market, a fundamental problem in modern finance.
No-Transaction Band Network: A Neural Network Architecture for Efficient Deep Hedging
Deep hedging (Buehler et al. 2019) is a versatile framework to compute the optimal hedging strategy of derivatives in incomplete markets.
Trader-Company Method: A Metaheuristic for Interpretable Stock Price Prediction
We show the effectiveness of our method by conducting experiments on real market data.
Deep Portfolio Optimization via Distributional Prediction of Residual Factors
In this study, we propose a novel method of constructing a portfolio based on predicting the distribution of a financial quantity called residual factors, which is known to be generally useful for hedging the risk exposure to common market factors.
