Harnessing Convolutional Neural Networks for Accurate Stock Price Prediction: A Case Study of Hellenic Telecommunications Organization (HTO.AT)
(1) * Giannis Tzoulis Mail (University of Patras, Greece)
*corresponding author

Abstract


This study presents a novel approach to stock price prediction by employing Convolutional Neural Networks (CNNs) to forecast the stock prices of the Hellenic Telecommunications Organization (HTO.AT). The CNN model demonstrated exceptional predictive performance, achieving a Root Mean Squared Error (RMSE) of 0.22859211 and a Mean Absolute Percentage Error (MAPE) of 1.2041852, indicating a high level of accuracy. By effectively capturing complex and non-linear patterns in historical stock price data, the model surpasses traditional forecasting methods, thus offering significant advantages for investors and financial analysts. This research emphasizes the importance of integrating external data and exploring alternative deep learning architectures, such as Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) networks, to further enhance prediction capabilities. Overall, the findings underscore the potential of CNNs as powerful tools in financial market analysis, providing actionable insights for more informed investment decisions.

   

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https://doi.org/10.33292/ijarlit.v2i2.39 		      

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References


L. Mao, “Analysis of Forecasting Stock Prices Using CNN Model”, doi: 10.25236/ajcis.2023.061012.

B. S. Rao, R. Bhattacharya, M. K. Tiwari, K. A. Kumari, M. Devmane, and K. Singh, “Innovative Deep Learning Model-based Stock Price Prediction using a Hybrid Approach of CNN and Gradient Recurrent Unit,” 2023, pp. 1304–1309. doi: 10.1109/icces57224.2023.10192634.

G. Jin and O. Kwon, “Impact of chart image characteristics on stock price prediction with a convolutional neural network,” PLoS One, vol. 16, no. 6, 2021, doi: 10.1371/JOURNAL.PONE.0253121.

Z. Xukuan, Y. Li, R. Li, X. Gu, O. Habimana, and H. Wang, “Stock Price Prediction Using Time Convolution Long Short-Term Memory Network,” 2018, pp. 461–468. doi: 10.1007/978-3-319-99365-2_41.

Y. Srivastava, A. S. Chauhan, R. Vaishnav, and K. Bharadwaj, “Forecasting Market Price of Stock Using Artificial Neural Network,” 2023, pp. 544–547. doi: 10.1109/icseiet58677.2023.10303500.

J. M.-T. Wu, Z. Li, G. Srivastava, G. Srivastava, M. Tasi, and J. C.-W. Lin, “A graphâ€based convolutional neural network stock price prediction with leading indicators,” Softw. - Pract. Exp., vol. 51, no. 3, pp. 628–644, 2021, doi: 10.1002/SPE.2915.

J. Readshaw and S. Giani, “Using company-specific headlines and convolutional neural networks to predict stock fluctuations,” Neural Comput. Appl., vol. 33, no. 24, pp. 17353–17367, 2021, doi: 10.1007/S00521-021-06324-9.

V. Agarwal and S. Praveena, “A Deep Convolutional Kernel Neural Network based Approach for Stock Market Prediction using Social Media Data,” 2023, pp. 78–82. doi: 10.1109/ICICCS56967.2023.10142522.

P. D. Soni, Y. Tewari, and D. Krishnan, “Machine Learning Approaches in Stock Price Prediction: A Systematic Review,” J. Phys., vol. 2161, no. 1, p. 12065, 2022, doi: 10.1088/1742-6596/2161/1/012065.

P. Ye and Z. Mengqin, “Research on Stock Prediction Model Based on Computer Long and Short Term Memory Network Time Convolution Network,” 2023, doi: 10.1109/icsece58870.2023.10263283.

P. Chen, “Using Machine Learning to Predict the Stock Market Trend,” 2024, doi: 10.62051/bgrbnh39.

P. V. Hiray, A. patankar, and P. Doke, “ML based stock prediction method for accurate future prediction of stock market,” Int. J. Heal. Sci., pp. 7139–7148, 2022, doi: 10.53730/ijhs.v6ns4.10154.

S. Rajaa and J. K. Sahoo, “Convolutional Feature Extraction and Neural Arithmetic Logic Units for Stock Prediction,” in arXiv: Learning, 2019. doi: 10.1007/978-981-13-9939-8_31.

T. Aamodt, “Predicting Stock Markets with Neural Networks.” 2015.

L. Di Persio and O. Honchar, “Artificial Neural Networks Approach to the Forecast of Stock Market Price Movements,” vol. 01. pp. 158–162, 2016.

P. Pandhare, P. Hedaoo, K. Kolte, T. Ambule, and N. V Bansode, “Decoding the Market Trends: Precision Forecasting of Stock Prices Using CNN Based Approach,” 2023, pp. 1–6. doi: 10.1109/iccubea58933.2023.10392036.

P. H. Gowda, S. P. -, A. Lavanya, and P. A. T. -, “Stock Price Trend Analysis and Prediction using Machine Learning,” Int. J. Multidiscip. Res., vol. 6, no. 3, 2024, doi: 10.36948/ijfmr.2024.v06i03.20284.

R. M. I. Kusuma, T.-T. Ho, W.-C. Kao, Y.-Y. Ou, and K.-L. Hua, “Using Deep Learning Neural Networks and Candlestick Chart Representation to Predict Stock Market.,” arXiv: General Finance. 2019. [Online]. Available: https://arxiv.org/pdf/1903.12258

M. Hiransha, E. A. Gopalakrishnan, V. K. Menon, and K. P. Soman, “NSE Stock Market Prediction Using Deep-Learning Models,” Procedia Comput. Sci., vol. 132, no. Iccids, pp. 1351–1362, 2018, doi: 10.1016/j.procs.2018.05.050.

J. M.-T. Wu, Z. Li, G. Srivastava, J. Frnda, V. G. Díaz, and J. C.-W. Lin, “A CNN-based Stock Price Trend Prediction with Futures and Historical Price,” 2020. doi: 10.1109/ICPAI51961.2020.00032.

L. Zhong, T. Mi, and P. Wei, “Stock Price Prediction Based on Deep Learning,” 2023, doi: 10.1109/icaica58456.2023.10405540.


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