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Quantitative Portfolio Manager
By Gaurav Chakravorty
Over the last decade deep learning has had tremendous success in pushing state of the art in numerous domains such as computer vision, natural language processing, machine translation and speech recognition. All of these domains are characterized by large quantities of data. In finance, however, even 20 years of end-of-day data is merely 5,000 points and any data-driven trading strategy is only as good as the data itself. In order to leverage deep learning research to the fullest, many progressive asset managers are experimenting with different approaches to generate and use hypothetical data so that the models can learn what to do in scenarios that the markets haven't seen yet. In this webinar, we will discuss use of synthetic/hypothetical data that can potentially solve this problem.
