RAGIC: Risk-Aware Generative Adversarial Model for Stock Interval Construction

arXiv:2402.10760v1 Announce Type: cross
Abstract: Efforts to predict stock market outcomes have yielded limited success due to the inherently stochastic nature of the market, influenced by numerous unpredictable factors. Many existing prediction approaches focus on single-point predictions, lacking the depth needed for effective decision-making and often overlooking market risk. To bridge this gap, we propose a novel model, RAGIC, which introduces sequence generation for stock interval prediction to quantify uncertainty more effectively. Our approach leverages a Generative Adversarial Network (GAN) …

abstract adversarial arxiv bridge construction cs.lg decision focus generative interval making nature prediction predictions q-fin.st risk stochastic stock success type