Abstract
Predicting the direction of stocks is an important task for all investors to gain a desirable profit. Stock market prediction is very important since it allows investors to make profitable decisions about when to buy and sell stocks, which ultimately result in accurate price prediction of stocks. Stock price prediction using technical data analysis results in better results than the other prediction models. This work uses the Logistic Regression algorithm for Intraday Stock Prediction to predict the close price of the next day based on technical indicators. The metrics such as Accuracy, Mean Squared Error (MSE) and Root Mean Square Error (RMSE) are used to evaluate the model’s performance. The experiment is conducted on stocks that are traded globally. When compared to other machine learning (ML) models, the experimental results demonstrate that the suggested model performed better in terms of low MSE, RMSE, and high accuracy as well as in predicting close price on real data. In the future, rather than focusing only on the stock’s close price, the researchers can explore many different technical indicators to predict the high, open and low price of the stock.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Khan, W., Ali, M., Muhammad, G., Azam, A., Karami, A., Alyoubi, K.H.: Stock market prediction using machine learning classifiers and social media, news. J. Ambient Intell. Humaniz. Comput. (2020).https://doi.org/10.1007/s12652-020-01839-w
Hiransha M, Gopalakrishnan E.A., Vijay Krishna Menon, S.K.P.: NSE stock market prediction using deep-learning models. Procedia Comput. Sci. 132, 1351–1362 (2018). https://doi.org/10.1016/j.procs.2018.05.050
Zhang, K., Zhong, G., Dong, J., Wang, S., Wang, Y.: Stock market prediction based on generative adversarial network stock market prediction based on generative adversarial network. Procedia Comput. Sci. 147, 400–406 (2019). https://doi.org/10.1016/j.procs.2019.01.256
Agrawal, M., Shukla, P., Nair, R., Nayyar, A., Masud, M.: Stock prediction based on technical indicators using deep learning model. C. -Tech Sci. Press 70, 287–304 (2021). https://doi.org/10.32604/cmc.2022.014637
Kim, S., Ku, S., Chang, W., Song, J.A.E.W.: Predicting the direction of US stock prices using effective transfer entropy and machine learning techniques 8, (2020). https://doi.org/10.1109/ACCESS.2020.3002174
Lin, Y., Liu, S., Yang, H., Wu, H.: Stock trend prediction using candlestick charting and ensemble machine learning techniques with a novelty feature engineering scheme. IEEE Access 9, 101433–101446 (2021). https://doi.org/10.1109/ACCESS.2021.3096825
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
Khang, P.Q., et al.: Machine learning for liquidity prediction on Vietnamese stock market. Procedia Comput. Sci. 192, 3590–3597 (2021). https://doi.org/10.1016/j.procs.2021.09.132
Nabipour, M., Nayyeri, P., Jabani, H., Mosavi, A., Salwana, E., Shahab, S.: Deep Learning for Stock Market Prediction
Kelotra, A., Pandey, P.: Stock market prediction using optimized deep-ConvLSTM model. Big Data 8, 5–24 (2020). https://doi.org/10.1089/big.2018.0143
Bhandari, H.N., Rimal, B., Pokhrel, N.R., Rimal, R., Dahal, K.R., Khatri, R.K.C.: Predicting stock market index using LSTM. Mach. Learn. Appl. 9, 100320 (2022). https://doi.org/10.1016/j.mlwa.2022.100320
Henrique, B.M., Sobreiro, V.A., Kimura, H.: Stock price prediction using support vector regression on daily and up to the minute prices *. J. Financ. Data Sci. 4, 183–201 (2018). https://doi.org/10.1016/j.jfds.2018.04.003
Vijh, M., Chandola, D., Tikkiwal, V.A., Kumar, A.: Stock closing closing price price prediction prediction using using machine machine learning learning techniques techniques. Procedia Comput. Sci. 167, 599–606 (2020). https://doi.org/10.1016/j.procs.2020.03.326
Pang, X., Zhou, Y., Wang, P., Lin, W., Chang, V.: An innovative neural network approach for stock market prediction. J. Supercomput. (2018).https://doi.org/10.1007/s11227-017-2228-y
Chung, H., Shin, K.: Genetic algorithm-optimized long short-term memory network for stock market prediction (2018).https://doi.org/10.3390/su10103765
Nayak, R., et al.: Indian stock market prediction based on rough set and support vector machine approach (2021). https://doi.org/10.1007/978-981-15-6202-0_35
Subasi, A., Amir, F., Bagedo, K., Shams, A., Sarirete, A.: Stock market prediction using machine learning. Procedia Comput. Sci. 194, 173–179 (2021).https://doi.org/10.1016/j.procs.2021.10.071
Akhtar, M.M., Zamani, A.S., Khan, S., Shatat, A.S.A., Dilshad, S., Samdani, F.: Stock market prediction based on statistical data using machine learning algorithms. J. King Saud Univ. - Sci. 34, 101940 (2022). https://doi.org/10.1016/j.jksus.2022.101940
Selvamuthu, D., Kumar, V., Mishra, A.: Indian stock market prediction using artificial neural networks on tick data. Financ. Innov. 5, 16 (2019). https://doi.org/10.1186/s40854-019-0131-7
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Gangwani, P., Panthi, V. (2024). Design of Intraday Stock Price Prediction Model Using Machine Learning via Technical Indicators. In: Dehuri, S., Cho, SB., Padhy, V.P., Shanmugam, P., Ghosh, A. (eds) Machine Intelligence, Tools, and Applications. ICMITA 2024. Learning and Analytics in Intelligent Systems, vol 40. Springer, Cham. https://doi.org/10.1007/978-3-031-65392-6_12
Download citation
DOI: https://doi.org/10.1007/978-3-031-65392-6_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-65391-9
Online ISBN: 978-3-031-65392-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)