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Green Referential Data Base for the Indian Stock Market

by Krishna Kumar Singh, Priti Dimri, Soumitra Chakraborty
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 89 - Number 3
Year of Publication: 2014
Authors: Krishna Kumar Singh, Priti Dimri, Soumitra Chakraborty
10.5120/15480-4197

Krishna Kumar Singh, Priti Dimri, Soumitra Chakraborty . Green Referential Data Base for the Indian Stock Market. International Journal of Computer Applications. 89, 3 ( March 2014), 8-11. DOI=10.5120/15480-4197

@article{ 10.5120/15480-4197,
author = { Krishna Kumar Singh, Priti Dimri, Soumitra Chakraborty },
title = { Green Referential Data Base for the Indian Stock Market },
journal = { International Journal of Computer Applications },
issue_date = { March 2014 },
volume = { 89 },
number = { 3 },
month = { March },
year = { 2014 },
issn = { 0975-8887 },
pages = { 8-11 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume89/number3/15480-4197/ },
doi = { 10.5120/15480-4197 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:08:16.710960+05:30
%A Krishna Kumar Singh
%A Priti Dimri
%A Soumitra Chakraborty
%T Green Referential Data Base for the Indian Stock Market
%J International Journal of Computer Applications
%@ 0975-8887
%V 89
%N 3
%P 8-11
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

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.

References
  1. Aurore J. Kamssu, Brian J. Reithel, Jennifer L. Ziegelmayer, "Information Technology and Financial Performance: The Impact of being an Internet-Dependent Firm on Stock Returns", Information Systems Frontiers 5:3, pp. 279–288, 2003.
  2. Chien-Jen Huang, Peng-Wen Chen, Wen-Tsao Pan : "Using multi-stage data mining technique to build forecast model for Taiwan stocks", Springer-Verlag London Limited , DOI 10. 1007 / s00521 – 011 – 0628 – 0, 2011.
  3. Di WU, Gabriel Pui Cheong FUNG, Jeffrey Xu YU,QiPAN, "Stock Prediction: An event driven approach based on bursty keywords", Front. Comput. Sci. China, vol. 3(2), pp. 145–157, 2009.
  4. Eamonn Keogh,Jessica Lin, "Clustering of time-series subsequences is meaningless: implications for previous and future research", Knowledge and Information Systems, vol. 8, pp. 154–177, 2005.
  5. Edward TSANG, "Forecasting – where computational intelligence meets the stock market", Front. Comput. Sci. China vol. 3(1), pp. 53–63, 2009.
  6. Hsing-Wen Wang,"Intelligent forecasting models-selection system for the portfolio internal structure change", Springer-Verlag, DOI 10. 1007/s00500-007-0177-8, 2007.
  7. Hui Ma, Klaus-Dieter Schewe, Bernhard Thalheim, Qing Wang, "A theory of data-intensive software services", SOCA, vol. 3, pp. 263–283, 2009.
  8. Ji-Yong Seo & Sangmi Chai, "The role of algorithmic trading systems on stock market Efficiency", Inf Syst Front. Vol. 15, pp. 873–888, 2013.
  9. Masayuki ISHINISHI, Yuhsuke KOYAMA and Hiroshi DEGUCHI, Hajime KITA, "A Futures Market-based Model for Dynamic Network Resource Allocation", New Generation Computing, Vol. 23, pp. 43-56 Ohmsha, Ltd. and Springer, 2005.
  10. MICHAIL VLACHOS, PHILIP S. YU, VITTORIO CASTELLI: "Structural Periodic Measures for Time-Series Data", Data Mining and Knowledge Discovery, Vol. 12, pp. 1–28, 2006.
  11. PATRICK THOMAS, "A relationship between technology indicators and stock market performance", Jointly published by Kluwer Academic Publishers, Dordrecht Scientometrics, and Akadémiai Kiadó, Budapest, Vol. 51, No. 1, pp. 319–333, 2001.
  12. Piyanath Mangkorntong & Fethi A. Rabhi, "A Domain-Driven Approach for Detecting Event Patterns in E-Markets", World Wide Web, vol. 12, pp. 69–86, 2009.
  13. Shuo BAI, Shouyang WANG, Lean YU, Aoying ZHOU, "Financial information processing and development of emerging financial markets", Front. Comput. Sci. China, vol. 4(2), pp. 185–186, 2010.
  14. www. nseindia. com
Index Terms

Computer Science
Information Sciences

Keywords

Stock Market Forecasting Referential database Green methodologies.

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