![]() To accurately predict the upcoming equity prices, the model needs to be trained thoroughly with stock data and various economic indicators. To date, various implementations of machine learning like the SVM, ARIMA, LSTM, & GRU have been used to build a model capable enough to predict time-series data up to an extent of certainty. To achieve promising performance, many of these methods require careful selection of flexible input, configuration professional financial forecasting model, and adopting various mathematical methods for arbitrage analysis, making it difficult for people outside the financial field to use these methods to predict prices. Many researchers from different parts of the world have studied historical patterns in the financial time series as well suggested various ways to predict stock prices. In addition, the stock market is affected by several factors such as political events, firm policies, general economic conditions, investor expectations, institutional investors 'choices, other stock market movements, and investors' mental performance, etc. Predicting stock market returns is a challenging and growing research task with the availability of new data sources, markets, financial instruments, and algorithms. This is where the idea of stock market predictions comes from.Įquity shares and other financial indicators are related to each other which gives us the ability to predict future numbers.įorecasts help to develop risk trading strategies and assess portfolio pressures. ![]() However, there is a question How would an individual know where and how much to invest in a stock. This later began to be known as stock market investment. ![]() Consequently, clusters of patrons pooled their investments and grew into commercial associates and co-holders with separate shares in their businesses to form joint-stash firms. While many pioneer wholesalers aspired to start vast commerce, this required ample aggregates of assets that no solo wholesalers could foster unaided. Stock markets ushered in when nations in the New World initiated trading with each other. The ability to predict these economic indices helps as a guide in decision-making to accelerate growth. They are directed at the process of predicting financial variables through which individuals and institutions, both private and governments, can make their decisions regarding liability, employment, expenditure, trading, investing, and important policies that make the economic activity possible. KeywordsGANs, GRU, CNN, time series prediction.Įconomic forecasts play a vital role in today's financial system. 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. This framework uses Gated Recurrent Units as a generator alongside Convolutional Neural Network used as a discriminator. In our paper, we propose the implementation of Generative Adversarial Networks to forecast variables of the financial market. They analyze historical patterns in data supplied to predict future values of any variable. ![]() Various time-series models have shown a proven record of success in the field of economic forecasting. Forecasting these variables is a very arduous job because of the complex ways in which different factors impact a given variable. Making wise decisions and maximizing growth is a function of being able to predict economic variables. Economic Forecasting using Generative Adversarial NetworksĬomputer Science & Engineering SRM Institute of Science & TechnologyĪbstract Modern-day finance relies immensely on economic forecasting.
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