arXiv subjects

Jang Ho Kim

Publications and source records attributed to Jang Ho Kim.

3 recordsLinked to original sources

arXiv2026

Constructing a Portfolio Optimization Benchmark Framework for Evaluating Large Language Models

This study introduces a benchmark framework for evaluating the financial decision-making capabilities of large language models (LLMs) through portfolio optimization problems with mathematically explicit solutions. Unlike existing financial benchmarks that emphasize language-processing tasks, the proposed framework directly tests optimization-based reasoning in investment contexts. A large set of multiple-choice questions is generated by varying objectives, candidate assets, and investment constraints, with each problem designed to include a unique correct solution and systematically constructed alternatives. Experimental results comparing GPT-4, Gemini 1.5 Pro, and Llama 3.1-70B reveal distinct performance patterns: GPT achieves the highest accuracy in risk-based objectives and remains stable under constraints, Gemini performs well in return-based tasks but struggles under other conditions, and Llama records the lowest overall performance. These findings highlight both the potential and current limitations of LLMs in applying quantitative reasoning to finance, while providing a scalable foundation for developing LLM-based services in portfolio management.

q-fin.PM↗

arXiv2026

Investor risk profiles of large language models

This paper investigates how large language models (LLMs) form and express investor risk profiles, a critical component of retail investment advising. We examine three LLMs (GPT, Gemini, and Llama) and assess their responses to a standardized risk questionnaire under varying prompts. In particular, we establish each model's default investment profile by analyzing repeated responses per model. We observe that LLMs are generally longterm investors but exhibit different tendencies in risk tolerance: Gemini has a moderate risk level with highly consistent responses, Llama skews more conservative, and GPT appears moderately aggressive with the greatest variation in answers. Moreover, we find that assigning specific personas such as age, wealth, and investment experience leads each LLM to adjust its risk profile, although the extent of these adjustments differs across the models.

q-fin.PM↗

arXiv2023

Universal Approximation of Parametric Optimization via Neural Networks with Piecewise Linear Policy Approximation

Parametric optimization solves a family of optimization problems as a function of parameters. It is a critical component in situations where optimal decision making is repeatedly performed for updated parameter values, but computation becomes challenging when complex problems need to be solved in real-time. Therefore, in this study, we present theoretical foundations on approximating optimal policy of parametric optimization problem through Neural Networks and derive conditions that allow the Universal Approximation Theorem to be applied to parametric optimization problems by constructing piecewise linear policy approximation explicitly. This study fills the gap on formally analyzing the constructed piecewise linear approximation in terms of feasibility and optimality and show that Neural Networks (with ReLU activations) can be valid approximator for this approximation in terms of generalization and approximation error. Furthermore, based on theoretical results, we propose a strategy to improve feasibility of approximated solution and discuss training with suboptimal solutions.

math.OC↗