STOCK MARKET FORECASTING: A REVIEW OF LITERATURE
(1) Research Scholar, Srinivas University, Professional 2 DXC Technology India
(2) Associate Professor, College of Engineering and Technology, Srinivas University, Mangaluru, Karnataka
Corresponding Author
Abstract
Prediction of stock markets is a complex and challenging task due to price data generated is huge in volume, changes every second, sensitive to human emotions (fear), actions (Wars) and natural disasters (floods, famine, earthquake). Many Methods have been used to predict the stock price like Technical Analysis, Time Series, Fundamental analysis, etc. Prediction of stock price provides knowledgeable information about the status of the stock price and will also help in decision making for the investors. Much research has been carried out in prediction of stock prices using different approaches of Machine Learning techniques, Deep Learning, Sentiment Analysis etc. This paper explores and reviews some of the recent works carried out in predicting stock prices.
Keywords
References
Hu, Yong, Kang Liu, Xiangzhou Zhang, Lijun Su, E. W. T. Ngai, and Mei Liu. “Application of evolutionary computation for rule discovery in stock algorithmic trading: A literature review,” Applied Soft Computing 36: 534–51.
Fama, E. F. “Random walks in stock market prices,” Financial Analysts Journal 21(5), 55–59. Reprinted in 1995 as Random Walks in Stock Market Prices, Financial Analysts Journal 51(1), 75–80.
Fama, E. F. “The behavior of stock-market prices,” Journal of Business,1965, 38(1), 34–105.
Fama, E. F. “Efficient capital markets: A review of theory and empirical work,” The Journal of Finance 1970,25(2), 383–417
Becket, M. (2002). How the Stock Market Works.
Rolf W. Banz, “The relationship between return and market value of common stocks,” Journal of Financial Economics, Volume 9, Issue 1, 1981, Pages 3-18, ISSN 0304-405X
Oberholzer, M. (2010).Share prices: critical perspective of the greater fool theory.
Thomas J. Kewley &Richard A. Stevenson “The Odd-Lot Theory for Individual Stocks: A Reply,” Financial Analysts Journal Volume 25, 1969 - Issue 1 Pages 99-104
Tversky, A., Kahneman, D. “Advances in prospect theory: Cumulative representation of uncertainty.,”J Risk Uncertainty 5, 297–323 (1992). https://doi.org/10.1007/BF00122574
Darrell Duffie, Nicolae Gârleanu, Lasse Heje Pedersen, “Securities lending, shorting, and pricing,” Journal of Financial Economics, Volume 66, Issues 2–3, 2002, Pages 307-339, ISSN 0304-405X
James C. Van Horne &George G.C. Parker,” The Random-Walk Theory: An Empirical Test, Financial Analysts Journal Volume 23, 1967 - Issue 6, Pages 87-92
Anthony C Greig, “Fundamental analysis and subsequent stock returns,” Journal of Accounting and Economics, Volume 15, Issues 2–3, 1992, Pages 413-442, ISSN 0165-4101
George E. P. Box, Gwilym M. Jenkins, Gregory C. Reinsel, Greta M. Ljung.” Time Series Analysis: Forecasting and Control”
Bovas Abraham, Johannes Ledolter (2009) Statistical Methods for Forecasting
Fu, Tak-chung, Fu-lai Chung, Robert Luk, and Chak-man Ng. 2005. “Preventing Meaningless Stock Time Series Pattern Discovery by Changing Perceptually Important Point Detection,” International Conference on Fuzzy Systems and Knowledge Discovery, Changsha, China, August 27–29.
S. N. Deepa and S. N. Sivanandam(2018) Principles of Soft Computing.
T. O. Sprenger, A. Tumasjan, P. G. Sandner, and I. M. Welpe. “Tweets and trades: The information content of stock microblogs,” European Financial Management, 20(5):926--957, 2014
Aditya Bhardwaj, Yogendra Narayan, Vanraj, Pawan, Maitreyee Dutta, “Sentiment Analysis for Indian Stock Market Prediction Using Sensex and Nifty,” Procedia Computer Science, Volume 70, 2015, Pages 85-91, ISSN 1877-0509, https://doi.org/10.1016/j.procs.2015.10.043
Rajendra N. Paramanik, Vatsal Singhal, “Sentiment Analysis of Indian Stock Market Volatility,” Procedia Computer Science,Volume 176, 2020,Pages 330-338, ISSN 1877-0509, https://doi.org/10.1016/j.procs.2020.08.035
Nayak, A., Pai, M. M. M., & Pai, R. M. “Prediction Models for Indian Stock Market,” Procedia Computer Science, 89, 441449. https://doi.org/10.1016/j.procs.2016.06.096
Mahdi Pakdaman Naeini,Hamidreza Taremian,Homa Baradaran Hashemi(2010),” Stock Market Value Prediction Using Neural Networks”, International Conference on Computer Information Systems and Industrial Management Applications (CISIM), pp: 132-136, IEEE.
Mehar Vijh, Deeksha Chandola, Vinay Anand Tikkiwal, Arun Kumar, Stock Closing Price Prediction using Machine Learning Techniques, International Conference on Computational Intelligence and Data Science (ICCIDS 2019), Procedia Computer Science, Volume 167, 2020, Pages 599-606, ISSN 1877-0509, https://doi.org/10.1016/j.procs.2020.03.326.
Borovkova, S., & Tsiamas, I. “An ensemble of lstm neural networks for high-frequency stock market classification,” Journal of Forecasting, 2019.
Nabipour M, Nayyeri P, Jabani H, Mosavi A, Salwana E, S. S. “Deep Learning for Stock Market Prediction,” Entropy. 2020; 22(8):840. https://doi.org/10.3390/e22080840
O. Hegazy, O. S. Soliman and M. Abdul Salam, "A machine learning model for stock market prediction," International Journal of Computer Science and Telecommunications, vol. 4, pp. 17-23, Dec 2013.
Gao, T., & Chai, Y. (2018). “Improving stock closing price prediction using recurrent neural network and technical indicators,” Neural Computation, 30 , 2833–2854. URL: https://doi.org/10.1162/neco_a_01124.
Shen, J., Shafiq, M.O. Short-term “Stock market price trend prediction using a comprehensive deep learning system,” J Big Data 7, 66 (2020). https://doi.org/10.1186/s40537-020-00333-6
Qiu M, Song Y. “Predicting the direction of stock market index movement using an optimized artificial neural network model,” PLoS ONE. 2016;11(5):e0155133.
Piramuthu S. “Evaluating feature selection methods for learning in data mining applications,” Eur J Oper Res. 2004;156(2):483–94. https://doi.org/10.1016/S0377-2217(02)00911-6.
Shunrong Shen, Haomiao Jiang, and Tongda Zhang, “Stock market forecasting using machine learning algorithms,” available at: http://cs229.stanford.edu/proj2012/ShenJiangZhang-StockMarketForecastingusingMachineLearningAlgorithms.pdf
Estember, Rene, and Michael Maraña,”Forecasting of Stock Prices Using Brownian Motion – Monte Carlo Simulation,” International Conference on Industrial Engineering and Operations Management,2016, Kuala Lumpur, Malaysia.
Xiao Zhong & David Enke , “Predicting the daily return direction of the stock market using hybrid machine learning algorithms,” Springer, https://doi.org/10.1186/s40854-019-0138-0
D. Lv, S. Yuan, M. Li, and Y. Xiang, ‘‘An empirical study of machine learning algorithms for stock daily trading strategy,’’ Math. Problems Eng., vol. 2019, Apr. 2019, Art. no. 7816154
Xiongwen Pang, Yanqiang Zhou, Pan Wang, Weiwei Lin, and Victor Chang. “An innovative neural network approach for stock market prediction,” The Journal of Supercomputing, January 2018.
Mathias Kraus and Stefan Feuerriegel. Decision support from financial disclosures with deep neural networks and transfer learning. Decision Support Systems, 104:38–48, December 2017.
Xingyu Zhou, Zhisong Pan, Guyu Hu, Siqi Tang, and Cheng Zhao. “Stock market prediction on high frequency data using generative adversarial nets,” Mathematical Problems in Engineering, 2018:1–11,2018
Leonardo dos Santos Pinheiro and Mark Dras. “Stock market prediction with deep learning: A character-based neural language model for event-based trading,” In Proceedings of the Australasian Language Technology Association Workshop 2017, pages 6–15, 2017.
Manna Majumder and MD Anwar Hussian, “Prediction of Indian stock market index using artificial neural network ,“
Hossain, Mohammad Asiful, Rezaul Karim, Ruppa K. Thulasiram, Neil D. B. Bruce, and Yang Wang. 2018. “Hybrid Deep Learning Model for Stock Price Prediction,”2018 IEEE Symposium Series on Computational Intelligence (SSCI), Bangalore, India, November 18–21.
Abidatul Izzah, Yuita Arum Sari, Ratna Widyastuti, Toga Aldila Cinderamata, “Mobile app for stock prediction using Improved Multiple Linear Regression”, 2017 IEEE International Conference on Sustainable Information Engineering and Technology (SIET), 2017
Awajan AM, Ismail MT, Wadi SA “Improving forecasting accuracy for stock market data using EMD-HW bagging,” PLoS One 13(7):1–20, 2018
Patil, P.; Wu, C.-S.; Potika, K.; Orang, M. “Stock market prediction using ensemble of graph theory, machine learning and deep learning models,” In Proceedings of the 3rd International Conference on Software Engineering and Information Management, Sydney, Australia, 12–15 January 2020
Sim, H.S.; Kim, H.I.; Ahn, J.J. “Is deep learning for image recognition applicable to stock market prediction? Complexity” 2019, 1–10
Checkley MS, Higón DA, Alles H. “The hasty wisdom of the mob: How market sentiment predicts stock market behavior,” Expert Systems with Applications. 2017;77:256-263
Wu GG, Hou TC, Lin JL. “Can economic news predict Taiwan stock market returns? “Asia Pacific Management Review. 2019;24(1):54-59
Maqsood, H., et al., “A local and global event sentiment based efficient stock exchange forecasting using deep learning,” International Journal of Information Management, 2020. 50: p. 432-451.
Selvin, S., et al. “Stock price prediction using LSTM, RNN and CNN-sliding window model,” in 2017 international conference on advances in computing, communications, and informatics (icacci). 2017. IEEE
Mohsen Mehrara, Ali Moeini, Mehdi Ahrari and Alireza Ghafari, “Using Technical Analysis with Neural Network for Prediction Stock Price Index in Tehran Stock Exchange,” Middle Eastern Finance and Economics, EuroJournals Publishing, Inc. 2010.
Tong-Seng Quah, “Using Neural Network for DJIA Stock Selection, “ Engineering Letters, 15:1, EL_15_1_19, 2007
Article Metrics
Abstract View
: 2145 times
Download : 234 times
DOI: 10.56327/ijiscs.v5i3.1064
Refbacks
- There are currently no refbacks.
