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Evolving Systems

, Volume 10, Issue 4, pp 621–627 | Cite as

Neutrosophic soft set decision making for stock trending analysis

  • Sudan Jha
  • Raghvendra Kumar
  • Le Hoang SonEmail author
  • Jyotir Moy Chatterjee
  • Manju Khari
  • Navneet Yadav
  • Florentin Smarandache
Original Paper

Abstract

In this paper, we point out a major issue of stock market regarding trending scenario of trades where data exactness, accuracy of expressing data and uncertainty of values (closing point of the day) are lacked. We use neutrosophic soft sets (NSS) consisting of three factors (True, Uncertainty and False) to deal with exact state of data in several directions. A new approach based on NSS is proposed for stock value prediction based on real data from last 7 years. It calculates the stock price based on the factors like “open”, “high”, “low” and “adjacent close”. The highest score value retrieved from the score function is used to determine which opening price and high price decide the closing price from the above mentioned four factors.

Keywords

Neutrosophic soft sets Soft sets Stock trending Stock parameters Open Close High Low Adjacent close 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that they do not have any conflict of interests. This research does not involve any human or animal participation. All authors have checked and agreed the submission.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Computer EngineeringKIIT UniversityBhubaneswarIndia
  2. 2.Department of Computer Science and EngineeringLNCT CollegeBhopalIndia
  3. 3.VNU Information Technology InstituteVietnam National UniversityHanoiVietnam
  4. 4.School of Computer Science and EngineeringGD-RCETBhilaiIndia
  5. 5.Computer Science and Engineering DepartmentAIACTRNew DelhiIndia
  6. 6.Electronics and Communication Engineering DepartmentMaharaja Agrasen Institute of TechnologyNew DelhiIndia
  7. 7.University of New MexicoGallupUSA

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