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Found 9 papers, 1 papers with code
From attention to profit: quantitative trading strategy based on transformer
In traditional quantitative trading practice, navigating the complicated and dynamic financial market presents a persistent challenge.
Unleashing the potential of prompt engineering in Large Language Models: a comprehensive review
This survey elucidates foundational principles of prompt engineering, such as role-prompting, one-shot, and few-shot prompting, as well as more advanced methodologies such as the chain-of-thought and tree-of-thoughts prompting.
Simultaneous upper and lower bounds of American option prices with hedging via neural networks
In this paper, we introduce two methods to solve the American-style option pricing problem and its dual form at the same time using neural networks.
Versatile and Robust Transient Stability Assessment via Instance Transfer Learning
To support N-1 pre-fault transient stability assessment, this paper introduces a new data collection method in a data-driven algorithm incorporating the knowledge of power system dynamics.
Deep neural network for optimal retirement consumption in defined contribution pension system
In this paper, we develop a deep neural network approach to solve a lifetime expected mortality-weighted utility-based model for optimal consumption in the decumulation phase of a defined contribution pension system.
Explicit approximations of option prices via Malliavin calculus in a general stochastic volatility framework
We obtain an explicit approximation formula for European put option prices within a general stochastic volatility model with time-dependent parameters.
Closed-form approximations with respect to the mixing solution for option pricing under stochastic volatility
We consider closed-form approximations for European put option prices within the Heston and GARCH diffusion stochastic volatility models with time-dependent parameters.
Deep neural networks algorithms for stochastic control problems on finite horizon: numerical applications
This paper presents several numerical applications of deep learning-based algorithms that have been introduced in [HPBL18].
Deep neural networks algorithms for stochastic control problems on finite horizon: convergence analysis
This paper develops algorithms for high-dimensional stochastic control problems based on deep learning and dynamic programming.
