The intersection of artificial intelligence and finance has ushered in a new era of innovation. One of the most promising frontiers in this realm is the application of Generative AI to stock analysis. Generative models, such as Generative Adversarial Networks (GANs) and other sophisticated algorithms, offer a unique approach to understanding and predicting market behavior.
In this article, we will embark on a journey to explore the fundamentals of Generative AI in the context of stock analysis and check out a step-by-step guide on building a basic Generative AI model for stock analysis.
Understanding Generative AI
Generative AI is a branch of artificial intelligence focused on creating data rather than simply interpreting it. Unlike traditional AI models that rely on large datasets for training, generative models have the ability to generate synthetic data that closely mimics real-world examples. This ability makes them particularly powerful in applications like stock analysis.
Generative Adversarial Networks (GANs)
At the forefront of generative AI, GANs have gained prominence for their capability to create realistic data through a process of competition between two neural networks — a generator and a…
