International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

Foreseeing the Stock Movement Using Exponential Moving Average in NSE

Abstract

The target of the review is to find the legitimacy of the Exponential Moving Average (EMA) for transient speculations. Here we gathered the end costs information of Procter and Gamble LTD, Dabur India LTD, Colgate and Bajaj Consumers from National Stock Exchange (NSE) for the time of fifteenth September 2020 to 14 September 2021. Here we distinguished that the Exponential Moving Average (EMA) specialized instrument will ready to produce the dependable and legitimate outcomes which can assists with picking right value stock for momentary ventures. The review presumes that outstanding Moving Average (EMA) will skilful to produce the substantial outcomes however financial backer ought not just depends Exponential Moving Average (EMA) and furthermore checks with other specialized pointers.

Country : India

1 Dr. Nalla Bala Kalyan2 P. Viswanatha Reddy

  1. Associate Professor, Department of Management Studies, Sri Venkateswara College of Engineering, Tirupati-517 507, India
  2. Assistant Professor, Department of Management Studies, Sri Venkateswara College of Engineering, Tirupati-517 507, India

IRJIET, Volume 5, Issue 12, December 2021 pp. 11-16

doi.org/10.47001/IRJIET/2021.512003

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References

  1. Acheme David Ijegwa1, Vincent Olufunke Rebecca1, Folorunso Olusegun & Olusola Olasunkanmi Isaac (2014). A Predictive Stock Market Technical Analysis Using Fuzzy Logic. Computer and Information Science; Vol. 7, No. 3; ISSN 1913-8989.
  2. Alejandro Escobar, Juli´an Moreno and Sebasti´an M´unera(2013), A Technical Analysis indicator Based on Fuzzy Logic. Electronic Notes in Theoretical Computer Science, 292, 27–37.
  3. Brock, W., J. Lakonishok and B. Lebaron (1992). Simple Technical Trading Rules and the Stochastic Properties of Stock Returns. Journal of Finance 47, pp.1731–1764.
  4. Gabriel Dan I.Anghel (2015), Stock Market Efficiency and MACD. Evidence from countries around the world. Procedia Economics and Finance 32 (1414 – 1431.
  5. Jensen, M.C.; Benington, G.A. (1970) Random walk and technical theories: Some additional evidence. J. Financ., 25, 469–482.
  6. Mahavir Sing, Dr.Anita (2012), Moving Averages and Stock Market Behaviour. International Journal of Computing and Corporate Research, Volume 2, Issue 6, November 2012, ISSN 2249054X.
  7. Mills, T.C. (1997). Technical Analysis and the London Stock Exchange: Testing Trading Rules using the FT30. International Journal of Financial Economics 2, pp. 319-331.
  8. Murphy, J.J (1999). Technical Analysis of the Financial Markets: A Comprehensive Guide to Trading Methods and Applications; New York Institute of Finance: New York, NY, USA.
  9. Terence Tai-Leung Chong, Wing-Kam Ng and Venus Khim-Sen Liew. Revisiting the Performance of MACD and RSI Oscillators. Journal of Risk and Financial Management, ISSN 1911-8074.
  10. Wilder, J.W. (1978). New Concepts in Technical Trading Systems; Trend Research: Greensboro, NC, USA.

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