Open Access
SHS Web Conf.
Volume 132, 2022
Innovative Economic Symposium 2021 – New Trends in Business and Corporate Finance in COVID-19 Era (IES2021)
Article Number 01012
Number of page(s) 9
Section New Trends in Business and Corporate Finance in COVID-19 Era
Published online 05 January 2022
  1. R. Gottwald, Optimal portfolio of chosen stocks of the Prague stock exchange. Littera Scripta, 7(1), 12–24 (2014) [Google Scholar]
  2. U. Khan, F. Aadil, M. A. Ghazanfar, S. Khan, N. Metawa, K. Muhammad, I. Mehmood, Y. Nam, A robust regression-based stock exchange forecasting and determination of correlation between stock markets. Sustainability, 10(10) (2018) [Google Scholar]
  3. Z. Yeze, W. Yiying, Stock price prediction based on information entropy and artificial neural network. 2019 5th International Conference on Information Management (ICIM), pp. 248–251 (2019) [CrossRef] [Google Scholar]
  4. S. Arivarasan, A. Kumaravel, Stock market price prediction by 6 datamining techniques anad final decision by comparison. International Journal of Applies Engineering Research, 9(22), 7173–7178 (2014) [Google Scholar]
  5. W. Lazonick, M. Mazzucato, O. Tulum, Apple’s changing business model: What should the world’s richest company do with all those profits? Accounting Forum, 249–267 (2013) [CrossRef] [Google Scholar]
  6. P. Domanizova, F. Milichovsky, K. Kuba, Business models, strategy and innovation in the new world of digization. Littera Scripta, 13(1), 17–31 (2020) [CrossRef] [Google Scholar]
  7. M. Al Aradi, N. Hewahi, Prediction of stock price and direction using neural networks: Datasets hybrid modeling approach. 2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), pp. 1–6 (2021) [Google Scholar]
  8. R. Gupta, M. Chen, Sentiment analysis for stock price prediction. 2020IEEE Conference on Multimedia Information Processing and Retrieval (MIPR), pp. 213–218 (2020) [CrossRef] [Google Scholar]
  9. M. Jaggi, P. Mandal, S. Narang, U. Naseem, M. Khushi, Text mining of stocktwits data for predicting stock prices. Applied System Innovation, 4(1) (2021) [Google Scholar]
  10. A. Chaudhari, P. Ghorpade, Forecasting a firm’s position based on Pitroski’s F-score using ARIMA. 2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), pp. 1–5 (2020) [Google Scholar]
  11. K. I. Inaba, Information-driven stock return comovements across countries. Research in International Business and Finance, 51 (2020) [Google Scholar]
  12. R. Dias, P. Alexandre, P. Heliodoro, Contagion in the LAC financial markets: The impact of stock crises of 2008 and 2010. Littera Scripta, 13(1), 32–45 (2020) [Google Scholar]
  13. A. Kaushal, P. Chaudhary, News and events aware stock price forecasting technique. In: 2017 International Conference on Big Data, IoT and Data Science (BID), pp. 8–13 (2017) [CrossRef] [Google Scholar]
  14. N. Rajesh, L. Gandy, CashTagNN: Exploiting the use of cashtags to predict stock market prices using convolutional networks. Proceedings of the 2020 4th International Conference on Algorithms, Computing and Systems, pp. 1–5 (2020) [Google Scholar]
  15. O. M. E. Ebadati, M. T. Mortazavi, An efficient hybrid machine learning method for time series stock market forecasting. Neural Network World, 41–55 (2018) [CrossRef] [Google Scholar]
  16. D. P. Gandhmal, K. Kumar, Systematic analysis and review of stock market prediction techniques. Computer Science Review, 34 (2019) [Google Scholar]
  17. M. Vochozka, J. Horak, T. Krulicky, Innovations in management forecast: Time development of stock prices with neural networks. Marketing and Management of Innovations, 2020(2), 324–339 (2020) [CrossRef] [Google Scholar]
  18. J. Horak, T. Krulicky, Comparison of exponential time series alignment and time series alignment using artificial neural networks by example of prediction of future development of stock prices of a specific company. SHS Web of Conferences: Innovative Economic Symposium 2018 - Milestones and Trends of World Economy, EDP Sciences (2019) [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.