ISSN: 2225-8329
Open access
Forecasting stock market is always a challenge task for the investors. This study aimed to develop a new approach for forecasting the price movements of e-commerce stocks. The signals emitted by the technical indicators are used as the features for two machine learning algorithms in predicting the stocks movements. The technical indicators used in this study were Moving Average (MA), Moving Average Convergence Divergence (MACD), Relative Strength Index (RSI) and Stochastic Oscillator (SO). Meanwhile, the machine learning algorithms used in this study were Random Forest (RF) and K-Neighbor Nearest (KNN). The findings of this study indicated that the inclusion of signals emitted by MA rule with 5-days short MA and 20-days long MA helps to reduce the error values for the prediction model. Besides that, this study also found that the signals from MA, MACD, RSI and SO fit the prediction model well. The investors are recommended to use machine learning algorithms to predict the price movements of e-commerce stocks. Lastly, investors are recommended to consider the signals from these four technical indicators, MA (5-days short MA & 20 long-MA), MACD, RSI and SO as the reference for their investment strategies in e-commerce stocks.
Ahmad, Z., Ibrahim, H., & Tuyon, J. (2017). Behavior of fund managers in Malaysian investment management industry. Qualitative Research in Financial Markets, 9(3), 205-239. https://www.emerald.com/insight/content/doi/10.1108/QRFM-08-2016-0024/full/html
Ampomah, E. K., Qin, Z., & Nyame, G. (2020). Evaluation of tree-based ensemble machine learning models in predicting stock price direction of movement. Information, 11(6), 332. https://doi.org/10.3390/info11060332
Ayala, J., Garcia-Torres, M., Noguera, J., Gomez-Vela, F., & Divina, F. (2021). Technical analysis strategy optimization using a machine learning approach in stock market indices. Knowledge-Based Systems, 225, 107119.
https://doi.org/10.1016/j.knosys.2021.107119
Barghouthi, S., & Ehsan, A. (2017). Market efficiency analysis of Amman stock exchange through moving average method. International Journal of Business and Society, 18, 531-544. http://www.ijbs.unimas.my/images/repository/pdf/Vol18-s3-paper8.pdf
Basak, S., Kar, S., Saha, S., & Dey, S. R. (2018). Predicting the direction of stock market prices using tree-based classifiers. The North American Journal of Economics and Finance, 47, 552-567. https://doi.org/10.1016/j.najef.2018.06.013
Gerritsen, D. F. (2016). Are chartists artists? The determinants and profitability of recommendations based on technical analysis. International Review of Financial Analysis, 47, 179-196. https://doi.org/10.1016/j.irfa.2016.06.008
Han, Y., Kim, J., & Enke, D. (2023). A machine learning trading system for the stock market based on N-period Min-Max labeling using XGBoost. Expert Systems with Applications, 211, 118581. https://doi.org/10.1016/j.eswa.2022.118581
Kang, B. K. (2021). Improving MACD technical analysis by optimizing parameters and modifying trading rules: Evidence from the Japanese Nikkei 225 Futures Market. Journal of Risk and Financial Management, 14, 37.
https://doi.org/10.3390/jrfm14010037
Khan, W., Ghazanfar, M. A., Azam, M. A., Karami, A., Alyoubi, K. H., & Alfakeeh, A. S. (2022). Stock market prediction using machine learning classifiers and social media, news. Journal of Ambient Intelligence and Humanized Computing, 13, 3433-3456. https://doi.org/10.1007/s12652-020-01839-w
Khand, S., Ahmad, V., & Qureshi, M. N. (2020). The predictability and profitability of simple moving averages and trading range breakout rules in Pakistan stock market. Review of Pacific Basin Financial Markets and Policies, 23, 1-38.
https://doi.org/10.1142/S0219091520500010
Kumar, D., Sarangi, P. K., & Verma, R. (2022). A systematic review of stock market prediction using machine learning and statistical techniques. Material Today: Proceedings, 3187-3191. https://doi.org/10.1016/j.matpr.2020.11.399
Kumbure, M. M., Lohrmann, C., Luukka, P., & Porras, J. (2022). Machine learning techniques and data for stock market forecasting: A literature review. Expert Systems With Applications, 197, 116659. https://doi.org/10.1016/j.eswa.2022.116659
Lee, Y., Ha, B., & Hwangbo, S. (2022). Generative model-based hybrid forecasting model for renewable electricity supply using long short-term memory networks: A case study of South Korea’s energy transition policy. Renewable Energy, 200, 69-87. https://dx.doi.org/10.2139/ssrn.3986352
Ling, P. S., Ruzita, A. R., & Said, F. F. (2020). The effectiveness of technical strategies in Malaysian shariah vs conventional stocks. ISRA International Journal of Islamic Finance, 12(2), 195-215. http://dx.doi.org/10.1108/IJIF-08-2018-0092
Lohrmann, C., & Luukka, P. (2019). Classification of intraday S&P 500 returns with a random forest. International Journal of Forecasting, 35(1), 390-407.
https://doi.org/10.1016/j.ijforecast.2018.08.004
Ma, F., Lu, X., Liu, J., & Huang, D. (2022). Macroeconomic attention and stock market return predictability. Journal of International Financial Markets, Institutions & Money, 79, 101603. https://doi.org/10.1016/j.intfin.2022.101603
Mahmoodi, A., Hashemi, L., Jasemi, M., Laliberte, J., Millar, R. C., & Noshadi, H. (2022). A novel approach for candlestick technical analysis using a combination of the support vector machine and particle swarm optimization. Asian Journal of Economics and Banking, 1-23. https://doi.org/10.1108/AJEB-11-2021-0131
Mhadhbi, M., Gallali, M. I., Goutte, S., & Guesmi, K. (2021). On the asymmetric relationship between stock market development, energy efficiency and environmental quality: A nonlinear analysis. International Review of Financial Analysis, 77, 101840. https://doi.org/10.1016/j.irfa.2021.101840
Mndawe, S. T., Paul, B. S., & Doorsamy, W. (2022). Development of a stock price prediction framework for intelligent media and technical analysis. Applied Science, 12, 1-15. https://doi.org/ 10.3390/app12020719
Murphy, N. J., & Gebbie, T. J. (2022). Learning the dynamics of technical trading strategies. Expert Systems With Applications, 197, 116659.
https://doi.org/10.1080/14697688.2020.1869292
Picasso, S., Merelloa, S., Ma, L., Onetoa, L., & Cambria, E. (2019). Technical analysis and sentiment embeddings for market trend prediction. Expert Systems With Applications, 135, 60-70.
Sonenshine, R., & Erikson, B. O. (2022). Institutional determinants of emerging market returns and flows. Emerging Market Review, 51, 100888.
https://doi.org/10.1016/j.eswa.2019.06.014
Weng, B., Lu, L., Wang, X., Megahed, F. M., & Martinez, W. (2018). Predicting short-term stock prices using ensemble methods and online data sources. Expert Systems With Applications, 112, 258-273. https://doi.org/10.1016/j.eswa.2018.06.016
Tapa, A., Yean, S. C., & Ahmad, S. N. (2016). Modified moving average crossover trading strategy: Evidence in Malaysian equity market. International Journal of Economics and Financial Issues, 6(57), 149-153.
https://www.econjournals.com/index.php/ijefi/article/view/3598
Weng, B., Lu, L., Wang, X., Megahed, F. M., & Martinez, W. (2018). Predicting short-term stock prices using ensemble methods and online data sources. Expert Systems With Applications, 112, 258-273. https://doi.org/10.1016/j.eswa.2018.06.016
In-Text Citation: (Ming et al., 2022)
To Cite this Article: Ming, K. L. Y., Jais, M., & Soon, L. P. (2022). New Approach for E-Commerce Stock Prices Prediction: Combination of Machine Learning and Technical Analysis. International Journal of Academic Research in Accounting Finance and Management Sciences, 12(3), 653–665.
Copyright: © 2022 The Author(s)
Published by Human Resource Management Academic Research Society (www.hrmars.com)
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