4 citations · 6 across the 7 of their papers we have counts for
8 papers
Generative AI-enhanced Sector-based Investment Portfolio Construction
Alina Voronina, Oleksandr Romanko, Ruiwen Cao +2
This paper investigates how Large Language Models (LLMs) from leading providers (OpenAI, Google, Anthropic, DeepSeek, and xAI) can be applied to quantitative sector-based portfolio…
ChatGPT-based Investment Portfolio Selection
Oleksandr Romanko, Akhilesh Narayan, Roy H. Kwon
In this paper, we explore potential uses of generative AI models, such as ChatGPT, for investment portfolio selection. Trusting investment advice from Generative Pre-Trained Transf…
Gradient boosting for convex cone predict and optimize problems
Andrew Butler, Roy H. Kwon
Prediction models are typically optimized independently from decision optimization. A smart predict then optimize (SPO) framework optimizes prediction models to minimize downstream…
Efficient differentiable quadratic programming layers: an ADMM approach
Andrew Butler, Roy Kwon
Recent advances in neural-network architecture allow for seamless integration of convex optimization problems as differentiable layers in an end-to-end trainable neural network. In…
Data-driven integration of norm-penalized mean-variance portfolios
Andrew Butler, Roy H. Kwon
Mean-variance optimization (MVO) is known to be sensitive to estimation error in its inputs. Norm penalization of MVO programs is a regularization technique that can mitigate the a…
Data-driven distributionally robust risk parity portfolio optimization
Giorgio Costa, Roy H. Kwon
We propose a distributionally robust formulation of the traditional risk parity portfolio optimization problem. Distributional robustness is introduced by targeting the discrete pr…
Integrating prediction in mean-variance portfolio optimization
Andrew Butler, Roy H. Kwon
Prediction models are traditionally optimized independently from their use in the asset allocation decision-making process. We address this shortcoming and present a framework for…
Market Graph Clustering Via QUBO and Digital Annealing
Seo Hong, Pierre Miasnikof, Roy Kwon +1
Our goal is to find representative nodes of a market graph that best replicate the returns of a broader market graph (index), a common task in the financial industry. We model our…