Economic Forecasting using Generative Adversarial Networks
Sarvagya Srivastava, Vishwaas Khare, R. Vidhya · Zenodo (CERN European Organization for Nuclear Research) · 2021
Modern-day finance relies immensely on economic forecasting. Making wise decisions and maximizing growth is a function of being able to predict economic variables. Forecasting these variables is a very arduous job because of the complex ways in which different factors impact a given variable. Various time-series models have shown a proven record of success in the field of economic forecasting. They analyze historical patterns in data supplied to predict future values of any variable. In our paper, we propose the implementation of Generative Adversarial Networks to forecast variables of the financial market. This framework uses Gated Recurrent Units as a generator alongside Convolutional Neural Network used as a discriminator. We have used Yahoo Finance API to import stock data of six equities from the Nifty 50 index which include Hindalco, IOC, NTPC, ONGC, Powergrid, and Wipro.