Can Use Simple Messages Information to Make a Remuneration Profit? –Evidence from Taiwan Stocks
DOI:
https://doi.org/10.47941/ijf.798Keywords:
Correlation analysis method, Grey relational analysis, Day trader, Taiwan StocksAbstract
Purpose: The goal of this research is to use the correlation analysis method (CAM) to find out the investors can invest with simple messages.
Results: This research's empirical results indicate the following: (1) Based on our empirical results obtained from using the correlation analysis method (CAM) method, the highest price, lowest price, and closing price can affect the opening price, and there is a significant and positive relationship. Moreover, the stock trading volume is an insignificant positive correlation, and the rate of price spread is an insignificant negative correlation. (2) This research assumes no external interference and government protection. And that stock investors do not have any technical analysis and other conditions. Whether the company can make a profit can reflect the value of bonds and stocks through public information. Therefore, investors can invest based on simple information. The grey relational analysis (GRA) research shows that that both the highest price and the lowest price displays were significant. The closing price was strong. In contrast, the study found that the stock trading volume and price spread displays are weak, and the analysis results showed almost no effect.
Unique contribution to theory, policy and practice: Therefore, if you play day trader it is feasible to use the high and low stock prices to make a remuneration profit.
Downloads
References
Akinwale Adio, T., Arogundade, O. T., and Adekoya Adebayo, F. (2009). Translated Nigeria stock market prices using artificial neural network for effective prediction. Journal of theoretical and Applied Information technology, 1, 36-43.
Agarwal, P., Bajpai, S., Pathak, A., and Angira, R. (2017). Stock market price trend forecasting using. Int J Res Appl Sci Eng Technol,5, 1673-1786.
Anbalagan, T., and Maheswari, S. U. (2015). Classification and prediction of stock market index based on fuzzy metagraph. Procedia Computer Science, 47, 214-221.
Ahmadi, E., Jasemi, M., Monplaisir, L., Nabavi, M. A., Mahmoodi, A., and Jam, P. A. (2018). New efficient hybrid candlestick technical analysis model for stock market timing on the basis of the Support Vector Machine and Heuristic Algorithms of Imperialist Competition and Genetic. Expert Systems with Applications, 94, 21-31.
Barber, B. M., Lee, Y. T., Liu, Y. J., and Odean, T. (2014). The cross-section of speculator skill: Evidence from day trading. Journal of Financial Markets, 18,1-24.
Checkley, M. S., Higón, D. A., and Alles, H. (2017). The hasty wisdom of the mob: How market sentiment predicts stock market behavior. Expert Systems with applications, 77, 256-263.
Conroy, R. M., and Harris, R. S. (1999). Stock splits and information: The role of share price. Financial Management, 28(3), 28–40.
Coyne, S., Madiraju, P., and Coelho, J. (2017, November). Forecasting stock prices using social media analysis. In 2017 IEEE 15th Intl Conf on Dependable, Autonomic and Secure Computing, 15th Intl Conf on Pervasive Intelligence and Computing, 3rd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC/PiCom/DataCom/CyberSciTech) (pp. 1031-1038). IEEE.
Dunne, M. (2015). Stock market prediction. University College Cork Cork.
Ghaznavi A., Aliyari M., and Mohammadi, M. R. (2016). Predicting stock price changes of tehran artmis company using radial basis function neural networks. Int Res J Appl Basic Sci, 10(8), 972–978.
Gyan, M. K. (2015). Factors influencing the patronage of stocks, Knu. Kwame Nkrumah University of Science & Technology (KNUST), Kumasi.
Guresen, E., Kayakutlu, G., and Daim, T. U. (2011). Using artificial neural network models in stock market index prediction. Expert Systems with Applications, 38(8), 10389-10397.
Ghaznavi, A., Aliyari, M., and Mohammadi, M. R. (2016). Predicting stock price changes of tehran artmis company using radial basis function neural networks. Int Res J Appl Basic Sci, 10(8), 972-978.
Rather, A. M., Agarwal, A., and Sastry, V. N. (2015). Recurrent neural network and a hybrid model for prediction of stock returns. Expert Systems with Applications, 42(6), 3234-3241.
Lin, Z. (2018). Modelling and forecasting the stock market volatility of SSE Composite Index using GARCH models. Future Generation Computer Systems, 79, 960-972.
Sankar, C. P., Vidyaraj, R., and Kumar, K. S. (2015). Trust based stock recommendation system–a social network analysis approach. Procedia Computer Science, 46, 299-305.
Seong, N., and Nam, K. (2021). Predicting stock movements based on financial news with segmentation. Expert Systems with Applications, 164(5), 1-12.
Taiwan Stock Exchange (2021). http://www.tse.com.tw//.
Tsai, M. F., and Wang, C. J. (2017). On the risk prediction and analysis of soft information in finance reports. European Journal of Operational Research, 257(1), 243-250.
Ticknor, J. L. (2013). A Bayesian regularized artificial neural network for stock market forecasting. Expert systems with applications, 40(14), 5501-5506.
Tsai, C. F., & Hsiao, Y. C. (2010). Combining multiple feature selection methods for stock prediction: Union, intersection, and multi-intersection approaches. Decision Support Systems, 50(1), 258-269.
Thanh, D. V., Minh, H. N., and Hieu, D. D. (2018). Building unconditional forecast model of stock market indexes using combined leading indicators and principal components: application to Vietnamese stock market. Indian J Sci Technol, 11(2), 1-13.
Nassirtoussi, A. K., Aghabozorgi, S., Wah, T. Y., and Ngo, D. C. L. (2014). Text mining for market prediction: A systematic review. Expert Systems with Applications, 41(16), 7653-7670.
Nti, I. K., Adekoya, A. F., and Weyori, B. A. (2019). A systematic review of fundamental and technical analysis of stock market predictions. Artificial Intelligence Review, 1-51.
Wanjawa, B. W., and Muchemi, L. (2014). ANN model to predict stock prices at stock exchange markets. ArXiv e-prints, abs/1502.
Wanjawa, B. W. (2016). Predicting Future Shanghai Stock Market Price using ANN in the Period 21-Sep-2016 to 11-Oct-2016. arXiv e-prints, abs/1609.
Wang, J. J., Wang, J. Z., Zhang, Z. G., and Guo, S. P. (2012). Stock index forecasting based on a hybrid model. Omega, 40(6), 758-766.
Xu, X., He, X., Ai, Q., and Qiu, R. C. (2015). A correlation analysis method for power systems based on random matrix theory. IEEE Transactions on smart grid, 8(4), 1811-1820.
Zhang, X., Hu, Y., Xie, K., Wang, S., Ngai, E. W. T., and Liu, M. (2014). A causal feature selection algorithm for stock prediction modeling. Neurocomputing, 142, 48-59.
Zhou, X., Pan, Z., Hu, G., Tang, S., and Zhao, C. (2018). Stock market prediction on high-frequency data using generative adversarial nets. Mathematical Problems in Engineering, 2018, 1-12.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 Chien-Wen Hsiao
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution (CC-BY) 4.0 License that allows others to share the work with an acknowledgment of the work’s authorship and initial publication in this journal.
