An Algorithm to Find Out Risk Free Share to Invest in Stock Market

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2017 by IJETT Journal
Volume-53 Number-1
Year of Publication : 2017
Authors : Md. Shahadat Hossain, Md. Abdul Hamid, A. F. M. Saifuddin Saif
DOI :  10.14445/22315381/IJETT-V53P205

Citation 

Md. Shahadat Hossain, Md. Abdul Hamid, A. F. M. Saifuddin Saif "An Algorithm to Find Out Risk Free Share to Invest in Stock Market", International Journal of Engineering Trends and Technology (IJETT), V53(1),19-22 November 2017. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group

Abstract
The stock market is interesting and beneficial for investors. The Stockholder did the analysis of share before buy and also used different types of algorithm to predict on the specific share. But the small investors are investing stock market without doing an analysis. Traders and Stockholder are waiting for this opportunity. They are passing the fake information to the small investors who does not have any knowledge about the share. As a result, Small investors buy those items to make short term profit at over price. When a large number of small investors are buying the share at over price, Traders and Stockholder sold their item. Therefore, the Small investors could not able to make any profit. In this paper, we are proposing an algorithm. It finds and extracts those share which has dramatic negative change on the market based on time frame. We analysed and found that those shares will give a positive change in between next 3 months to 6 months. So, It may give the profit to those investors who can wait at least 3 months. It can be decreased after buy and investors need not to panic, wait at most 6 months.

Reference
[1] M. Saini and A.K.Singh, “Forecasting stock exchange market and weather using soft computing,” International Journal of Advanced Research in Computer Science and Software Engineering, vol. 4, no. 5, 2014.
[2] V. Rajput and S. Bobde, “Stock market forecasting techniques: Literature survey,” International Journal of Computer Science and Mobile Computing, vol. 5, no. 6, pp. 500 – 506, june 2016.
[3] S. Saha, “Stock market crash of bangladesh in 2010-11: Reasons and roles of regulators,” Degree Thesis International Business, 2012.
[4] A. Gupta and D. S. D. Sharma, “A survey on stock market prediction using various algorithms,” International Journal of Computer Technology and Applications, vol. 5, pp. 530–533, 2014.
[5] C. S. Vui, G. K. Soon, C. K. On, R. Alfred, and P. Anthony, “A review of stock market prediction with artificial neural network (ann),” IEEE International Conference on Control System, vol. Computing and Engineering, 2013.
[6] M. Billah, S. Waheed, and A. Hanifa, “Stock market prediction using an improved training algorithm of neural network,” Electrical, Computer and Telecommunication Engineering (ICECTE), 2017.
[7] F. Mithani, S. Machchhar, and F. Jasdanwala, “A modified bpn approach for stock market prediction,” Computational Intelligence and Computing Research (ICCIC), 2017.
[8] S. Shen, H. Jiang, and T. Zhang, “Stock market forecasting using machine learning algorithms,” 2012.
[9] S. Kogan, D. Levin, B. R. Routledge, J. S. Sagi, and N. A. Smith, “Predicting risk from financial reports with regression,” Proceeding NAACL ’09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pp. 272–280, 2009.
[10] S. Wang, Z. Zhe, Y. Kang, H. Wang, and X. Chen, “An ontology for causal relationships between news and financial instruments,” Expert Systems with Applications, no. 35, pp. 569–580, 2008.
[11] N. Fern´andez, D. Fuentes, L. S´anchez, and J. A. Fisteus, “The news ontology: Design and applications,” Expert Systems with Applications, vol. 37, no. 12, pp. 8694–8704, 2010.
[12] L. Siming and W. Huaiqing, “Ontologies for stock market manipulation,” International Conference on ICCE, vol. AISC - 112, pp. 1–9, 2011.
[13] M. Usmani, S. H. Adil, K. Raza, and S. S. A. Ali, “Stock market prediction using machine learning techniques,” Computer and Information Sciences (ICCOINS), 2016.

Keywords
Stock Market Analysis, Financial Data Analysis, Risk Analysis, Data Mining, Prediction Algorithm, Dhaka Stock Exchange.