A traditional database methodology has been used in the Indian stock market. Forecasting of the market is not only based on the prices of the stocks but also on other integrated information like socio-economic factors, prices, politics etc. Changing data format and its behavior requires a new methodology to handle and integrate. Investments solely depend upon efficiency and accuracy of the data. Data required for the stock market decision making process is generated from every event and all events have its own impact on the market. Irrespective of the nature of the event and its format, market requires data integration of all kind. Because of acute scarcity of natural resources, processing of the stock market data requires green methodologies which contribute to save energy, power, time, space etc. Fractal behavior of the market shows repetition of the stock prices again and again. Large amount of space, time, power etc have been utilized to store and process these repetitive data. Referential data base is one of the answers to this problem. This paper proposes referential data base for the stock market prices without compromising efficiency and accuracy for market forecasting methods.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.