Deep Neural Networks in High Frequency Trading
Year: 2018
Type: preprint
Abstract: The ability to give precise and fast prediction for the price movement of stocks is the key to profitability in High Frequency Trading. The main objective of this paper is to propose a novel way of modeling the high frequency trading problem using Deep Neural Networks at its heart and to argue why Deep Learning methods can have a lot of potential in the field of High Frequency Trading. The paper goes on to analyze the model's performance based on it's prediction accuracy as well as prediction speed across full-day trading simulations.
Source: arXiv (Cornell University)
Authors: Prakhar Ganesh, Puneet Rakheja
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W2891388704