Quick Decision Making To Achieve The Competitive Edge: Significance Of Decimated Data For Forecasting The Stock Market

Abstract

Since computers have been used to trade stocks on the trading floor, the stock markets have changed. Traditional bookmakers have been replaced by online portfolio management algorithms so that stocks and shares can be bought and sold quickly in competitive market. But because these algorithms need so much processing, it is hard to speed up the process and make computers profitable at the same time. By reducing the speed at which stock data is delivered to the computers that make quick decisions, the computational load can be reduced. Online algorithms for predicting the stock market and optimizing the portfolios may use decimated data if certain statistical conditions are met. We proposed decimated version of the algorithm to forecast the stock market prices for quick decision making and resource allocation. The proposed algorithm's performance is maintained if decimation rates are chosen in accordance with certain theoretical conditions derived from data analysis, and real-world stock market data supports the result.