Araştırma Makalesi

Yıl 2024,
Cilt: 4 Sayı: 1, 44 - 58, 28.06.2024
### Öz

### Kaynakça

- A. Bylund, “What’s the difference between Alphabet’s stock tickers, GOOG and GOOGL?,” The Motley Fool, https://www.fool.com/investing/2022/07/27/whats-the-difference-between-goog-and-googl/ (accessed Aug. 16, 2023).
- A. Grinsted, J. C. Moore, and S. Jevrejeva, “Application of the cross wavelet transform and wavelet coherence to Geophysical Time Series,” Nonlinear Processes in Geophysics, vol. 11, no. 5/6, pp. 561–566, 2004. doi:10.5194/npg-11-561-2004
- B. F. Smith and B. Amoako-Adu, “Relative prices of dual class shares,” The Journal of Financial and Quantitative Analysis, vol. 30, no. 2, p. 223, 1995. doi:10.2307/2331118
- C. D. Rio and R. Santamaria, “Stock characteristics, investor type, and market myopia,” Journal of Behavioral Finance, vol. 17, no. 2, pp. 183–199, 2016. doi:10.1080/15427560.2016.1170682
- C. Erten, N. Chotai, and D. Kazakov, “Pair trading with an ontology of SEC Financial Reports,” 2020 IEEE Symposium Series on Computational Intelligence (SSCI), 2020. doi:10.1109/ssci47803.2020.9308384
- C. Li, D. Songand D. Tao, “Multi-task Recurrent Neural Networks and Higher-order Markov Random Fields for Stock Price Movement Prediction: Multi-task RNN and Higer-order MRFs for Stock Price Classification”, ACM, Jul. 2019. doi: 10.1145/3292500.3330983.
- C. Torrence and G. P. Compo, “A practical guide to wavelet analysis,” Bulletin of the American Meteorological Society, vol. 79, no. 1, pp. 61–78, 1998. doi:10.1175/1520-0477(1998)079<0061:apgtwa>2.0.co;2
- C. W. Holden and L. L. Lundstrum, “Costly trade, managerial myopia, and long-term investment,” Journal of Empirical Finance, vol. 16, no. 1, pp. 126–135, 2009. doi:10.1016/j.jempfin.2008.05.001
- C.-H. Chen, W.-H. Lai, and T.-P. Hong, “An effective correlation-based pair trading strategy using genetic algorithms,” Compu-tational Collective Intelligence, pp. 255–263, 2021. doi:10.1007/978-3-030-88081-1_19
- E. Hoseinzade and S. Haratizadeh, “CNNpred: CNN-based stock market prediction using a diverse set of variables,” Expert Sys-tems with Applications, vol. 129, pp. 273–285, 2019. doi:10.1016/j.eswa.2019.03.029
- E. Tokat and A. C. Hayrullahoğlu, “Pairs trading: Is it applicable to exchange-traded funds?,” Borsa Istanbul Review, vol. 22, no. 4, pp. 743–751, 2022. doi:10.1016/j.bir.2021.08.001
- H. Maqsood et al., “A local and global event sentiment based efficient stock exchange forecasting using Deep Learning,” Interna-tional Journal of Information Management, vol. 50, pp. 432–451, 2020. doi:10.1016/j.ijinfomgt.2019.07.011
- H. S. Sim, H. I. Kim, and J. J. Ahn, “Is deep learning for image recognition applicable to stock market prediction?,” Complexity, vol. 2019, pp. 1–10, 2019. doi:10.1155/2019/4324878
- H. Yang, Y. Zhu, and Q. Huang, “A multi-indicator feature selection for CNN-Driven Stock Index Prediction,” Neural Information Processing, pp. 35–46, 2018. doi:10.1007/978-3-030-04221-9_4
- I. Nyman, “Stock market speculation and managerial myopia,” Review of Financial Economics, vol. 14, no. 1, pp. 61–79, 2005. doi:10.1016/j.rfe.2004.06.002
- J. Eapen, D. Bein, and A. Verma, “Novel deep learning model with CNN and bi-directional LSTM for improved stock market index prediction,” 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC), 2019. doi:10.1109/ccwc.2019.8666592
- J. M.-T. Wu et al., “A graphic CNN-LSTM model for stock price prediction,” Artificial Intelligence and Soft Computing, pp. 258–268, 2021. doi:10.1007/978-3-030-87986-0_23
- J. P. Ramos-Requena, M. N. López-García, M. A. Sánchez-Granero, and J. E. Trinidad-Segovia, “A cooperative dynamic approach to pairs trading,” Complexity, vol. 2021, pp. 1–8, 2021. doi:10.1155/2021/7152846
- J. Wu, A pairs trading strategy for GOOG/GOOGL using machine learning, https://cs229.stanford.edu/proj2015/028_report.pdf (accessed Aug. 16, 2023).
- J.-F. Chen, W.-L. Chen, C.-P. Huang, S.-H. Huang, and A.-P. Chen, “Financial time-series data analysis using deep convolutional neural networks,” 2016 7th International Conference on Cloud Computing and Big Data (CCBD), 2016. doi:10.1109/ccbd.2016.027
- K. Nakagawa, T. Uchida, and T. Aoshima, “Deep factor model,” ECML PKDD 2018 Workshops, pp. 37–50, 2019. doi:10.1007/978-3-030-13463-1_3
- K. Rydqvist, “Dual-class shares: A Review,” Oxford Review of Economic Policy, vol. 8, no. 3, pp. 45–57, 1992. doi:10.1093/ox-rep/8.3.45
- Ko, Ching-Ru, and Hsien-Tsung Chang. “LSTM-Based Sentiment Analysis for Stock Price Forecast.” PeerJ Computer Science, vol. 7, 11 Mar. 2021, p. e408, https://doi.org/10.7717/peerj-cs.408.
- L. Ni et al., “Forecasting of forex time series data based on Deep Learning,” Procedia Computer Science, vol. 147, pp. 647–652, 2019. doi:10.1016/j.procs.2019.01.189
- L. Zhang, “Pair trading with machine learning strategy in China Stock Market,” 2021 2nd International Conference on Artificial Intelligence and Information Systems, 2021. doi:10.1145/3469213.3471353
- L. Zingales, “The value of the voting right: A study of the Milan stock exchange experience,” Review of Financial Studies, vol. 7, no. 1, pp. 125–148, 1994. doi:10.1093/rfs/7.1.125
- M. Abe and H. Nakayama, “Deep learning for forecasting stock returns in the cross-section,” Advances in Knowledge Discovery and Data Mining, pp. 273–284, 2018. doi:10.1007/978-3-319-93034-3_22
- M. R. Horner, “The value of the corporate voting right,” Journal of Banking and Finance, vol. 12, no. 1, pp. 69–83, 1988. doi:10.1016/0378-4266(88)90051-9
- M. U. Gudelek, S. A. Boluk, and A. M. Ozbayoglu, “A deep learning based stock trading model with 2-D CNN trend detection,” 2017 IEEE Symposium Series on Computational Intelligence (SSCI), 2017. doi:10.1109/ssci.2017.8285188
- M. Wen, P. Li, L. Zhang, and Y. Chen, “Stock market trend prediction using high-order information of Time Series,” IEEE Access, vol. 7, pp. 28299–28308, 2019. doi:10.1109/access.2019.2901842
- N. Foysal Ahamed, and M. Mahmudul Hasan. “Predicting Stock Price from Historical Data using LSTM Technique.” Journal of Artificial Intelligence and Data Science 3.1: 36-49.
- N. Naik and B. R. Mohan, “Stock price movements classification using machine and deep learning techniques-the case study of Indian Stock Market,” Engineering Applications of Neural Networks, pp. 445–452, 2019. doi:10.1007/978-3-030-20257-6_38
- Niu, Hongli, et al. “A Hybrid Stock Price Index Forecasting Model Based on Variational Mode Decomposition and LSTM Net-work.” Applied Intelligence, vol. 50, no. 12, 17 July 2020, pp. 4296–4309, https://doi.org/10.1007/s10489-020-01814-0.
- P. Oncharoen and P. Vateekul, “Deep learning using risk-reward function for stock market prediction,” Proceedings of the 2018 2nd International Conference on Computer Science and Artificial Intelligence, 2018. doi:10.1145/3297156.3297173
- P. Patil, C.-S. M. Wu, K. Potika, and M. Orang, “Stock market prediction using ensemble of graph theory, machine learning and Deep Learning Models,” Proceedings of the 3rd International Conference on Software Engineering and Information Management, 2020. doi:10.1145/3378936.3378972
- P. Srivastava and P. K. Mishra, “Stock market prediction using RNN LSTM,” 2021 2nd Global Conference for Advancement in Technology (GCAT), 2021. doi:10.1109/gcat52182.2021.9587540
- R. C. Lease, J. J. McConnell, and W. H. Mikkelson, “The market value of control in publicly-traded corporations,” Journal of Financial Economics, vol. 11, no. 1–4, pp. 439–471, 1983. doi:10.1016/0304-405x(83)90019-3
- S. Basodi, C. Ji, H. Zhang, and Y. Pan, “Gradient amplification: An efficient way to train deep neural networks,” Big Data Mining and Analytics, vol. 3, no. 3, pp. 196–207, 2020. doi:10.26599/bdma.2020.9020004
- R. Zhang, Z. Yuan, and X. Shao, “A new combined CNN-RNN model for sector stock price analysis,” 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC), 2018. doi:10.1109/compsac.2018.10292
- S. Cai, X. Feng, Z. Deng, Z. Ming, and Z. Shan, “Financial News quantization and Stock Market Forecast Research based on CNN and LSTM,” Lecture Notes in Computer Science, pp. 366–375, 2018. doi:10.1007/978-3-030-05755-8_36
- S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Computation, vol. 9, no. 8, pp. 1735–1780, 1997. doi:10.1162/neco.1997.9.8.1735
- S. Liu, C. Zhang, and J. Ma, “CNN-LSTM neural network model for quantitative strategy analysis in stock markets,” Neural Information Processing, pp. 198–206, 2017. doi:10.1007/978-3-319-70096-0_21
- S. P. Chatzis, V. Siakoulis, A. Petropoulos, E. Stavroulakis, and N. Vlachogiannakis, “Forecasting stock market crisis events using deep and Statistical Machine Learning Techniques,” Expert Systems with Applications, vol. 112, pp. 353–371, 2018. doi:10.1016/j.eswa.2018.06.032
- S. Selvin, R. Vinayakumar, E. A. Gopalakrishnan, V. K. Menon, and K. P. Soman, “Stock price prediction using LSTM, RNN and CNN-sliding window model,” 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2017. doi:10.1109/icacci.2017.8126078
- Thorir Mar Ingolfsson, “Insights into LSTM architecture,” Thorir Mar Ingolfsson, https://thorirmar.com/post/insight_into_lstm/ (accessed Aug. 16, 2023).
- W. Chen, C. K. Yeo, C. T. Lau, and B. S. Lee, “Leveraging Social Media News to predict stock index movement using RNN-Boost,” Data & Knowledge Engineering, vol. 118, pp. 14–24, 2018. doi:10.1016/j.datak.2018.08.003
- W. Jiang, “Applications of deep learning in stock market prediction: Recent progress,” Expert Systems with Applications, vol. 184, p. 115537, 2021. doi:10.1016/j.eswa.2021.115537
- W. L. Megginson, “Restricted voting stock, acquisition premiums, and the market value of corporate control,” The Financial Re-view, vol. 25, no. 2, pp. 175–198, 1990. doi:10.1111/j.1540-6288.1990.tb00791.x
- X. Ding, Y. Zhang, T. Liuand J. Duan, “Deep learning for event-driven stock prediction”, AAAI Press, Jul. 2015.
- X. Sheng, S. Guo, and X. Chang, “Managerial myopia and firm productivity: Evidence from China,” Finance Research Letters, vol. 49, p. 103083, 2022. doi:10.1016/j.frl.2022.103083
- Y. Liu, Q. Zeng, H. Yang, and A. Carrio, “Stock price movement prediction from financial news with deep learning and knowledge graph embedding,” Knowledge Management and Acquisition for Intelligent Systems, pp. 102–113, 2018. doi:10.1007/978-3-319-97289-3_8
- Y. Song, J. W. Lee, and J. Lee, “A study on novel filtering and relationship between input-features and target-vectors in a deep learning model for stock price prediction,” Applied Intelligence, vol. 49, no. 3, pp. 897–911, 2018. doi:10.1007/s10489-018-1308-x
- Y. Zhao and M. Khushi, “Wavelet denoised-resnet CNN and LIGHTGBM method to predict forex rate of Change,” 2020 Interna-tional Conference on Data Mining Workshops (ICDMW), 2020. doi:10.1109/icdmw51313.2020.00060
- Z. Hu, Y. Zhao, and M. Khushi, “A survey of Forex and Stock Price Prediction using Deep learning,” Applied System Innovation, vol. 4, no. 1, p. 9, 2021. doi:10.3390/asi4010009
- Z. Zeng and M. Khushi, “Wavelet denoising and attention-based RNN- Arima model to predict forex price,” 2020 International Joint Conference on Neural Networks (IJCNN), 2020. doi:10.1109/ijcnn48605.2020.9206832

Yıl 2024,
Cilt: 4 Sayı: 1, 44 - 58, 28.06.2024
### Öz

### Anahtar Kelimeler

### Kaynakça

Kardemir Karabuk Iron Steel Industry Trade & Co. Inc. is the 24th largest industrial company in Turkey with three stocks listed in the Borsa Istanbul: KRDMA, KRDMB, and KRDMD. While the only difference be-tween these three stocks is about voting power, prices of these stocks have exhibited significant divergence for a considerable period. In this paper, I examine the divergence patterns between these three stock prices between Jan-2001 and Jul-2023. There is no evidence supporting the efficiency of dual-class stocks as predictors of each other despite a strong coherence between them. Finally, I propose a novel training set selection rule for LSTM models incorporating a rolling training set and demonstrate its significant superiority in predicting future stock prices compared to conventional use of LSTM models employing large training sets.

Dual-class stock long short-term memory stock price prediction wavelet analysis

- A. Bylund, “What’s the difference between Alphabet’s stock tickers, GOOG and GOOGL?,” The Motley Fool, https://www.fool.com/investing/2022/07/27/whats-the-difference-between-goog-and-googl/ (accessed Aug. 16, 2023).
- A. Grinsted, J. C. Moore, and S. Jevrejeva, “Application of the cross wavelet transform and wavelet coherence to Geophysical Time Series,” Nonlinear Processes in Geophysics, vol. 11, no. 5/6, pp. 561–566, 2004. doi:10.5194/npg-11-561-2004
- B. F. Smith and B. Amoako-Adu, “Relative prices of dual class shares,” The Journal of Financial and Quantitative Analysis, vol. 30, no. 2, p. 223, 1995. doi:10.2307/2331118
- C. D. Rio and R. Santamaria, “Stock characteristics, investor type, and market myopia,” Journal of Behavioral Finance, vol. 17, no. 2, pp. 183–199, 2016. doi:10.1080/15427560.2016.1170682
- C. Erten, N. Chotai, and D. Kazakov, “Pair trading with an ontology of SEC Financial Reports,” 2020 IEEE Symposium Series on Computational Intelligence (SSCI), 2020. doi:10.1109/ssci47803.2020.9308384
- C. Li, D. Songand D. Tao, “Multi-task Recurrent Neural Networks and Higher-order Markov Random Fields for Stock Price Movement Prediction: Multi-task RNN and Higer-order MRFs for Stock Price Classification”, ACM, Jul. 2019. doi: 10.1145/3292500.3330983.
- C. Torrence and G. P. Compo, “A practical guide to wavelet analysis,” Bulletin of the American Meteorological Society, vol. 79, no. 1, pp. 61–78, 1998. doi:10.1175/1520-0477(1998)079<0061:apgtwa>2.0.co;2
- C. W. Holden and L. L. Lundstrum, “Costly trade, managerial myopia, and long-term investment,” Journal of Empirical Finance, vol. 16, no. 1, pp. 126–135, 2009. doi:10.1016/j.jempfin.2008.05.001
- C.-H. Chen, W.-H. Lai, and T.-P. Hong, “An effective correlation-based pair trading strategy using genetic algorithms,” Compu-tational Collective Intelligence, pp. 255–263, 2021. doi:10.1007/978-3-030-88081-1_19
- E. Hoseinzade and S. Haratizadeh, “CNNpred: CNN-based stock market prediction using a diverse set of variables,” Expert Sys-tems with Applications, vol. 129, pp. 273–285, 2019. doi:10.1016/j.eswa.2019.03.029
- E. Tokat and A. C. Hayrullahoğlu, “Pairs trading: Is it applicable to exchange-traded funds?,” Borsa Istanbul Review, vol. 22, no. 4, pp. 743–751, 2022. doi:10.1016/j.bir.2021.08.001
- H. Maqsood et al., “A local and global event sentiment based efficient stock exchange forecasting using Deep Learning,” Interna-tional Journal of Information Management, vol. 50, pp. 432–451, 2020. doi:10.1016/j.ijinfomgt.2019.07.011
- H. S. Sim, H. I. Kim, and J. J. Ahn, “Is deep learning for image recognition applicable to stock market prediction?,” Complexity, vol. 2019, pp. 1–10, 2019. doi:10.1155/2019/4324878
- H. Yang, Y. Zhu, and Q. Huang, “A multi-indicator feature selection for CNN-Driven Stock Index Prediction,” Neural Information Processing, pp. 35–46, 2018. doi:10.1007/978-3-030-04221-9_4
- I. Nyman, “Stock market speculation and managerial myopia,” Review of Financial Economics, vol. 14, no. 1, pp. 61–79, 2005. doi:10.1016/j.rfe.2004.06.002
- J. Eapen, D. Bein, and A. Verma, “Novel deep learning model with CNN and bi-directional LSTM for improved stock market index prediction,” 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC), 2019. doi:10.1109/ccwc.2019.8666592
- J. M.-T. Wu et al., “A graphic CNN-LSTM model for stock price prediction,” Artificial Intelligence and Soft Computing, pp. 258–268, 2021. doi:10.1007/978-3-030-87986-0_23
- J. P. Ramos-Requena, M. N. López-García, M. A. Sánchez-Granero, and J. E. Trinidad-Segovia, “A cooperative dynamic approach to pairs trading,” Complexity, vol. 2021, pp. 1–8, 2021. doi:10.1155/2021/7152846
- J. Wu, A pairs trading strategy for GOOG/GOOGL using machine learning, https://cs229.stanford.edu/proj2015/028_report.pdf (accessed Aug. 16, 2023).
- J.-F. Chen, W.-L. Chen, C.-P. Huang, S.-H. Huang, and A.-P. Chen, “Financial time-series data analysis using deep convolutional neural networks,” 2016 7th International Conference on Cloud Computing and Big Data (CCBD), 2016. doi:10.1109/ccbd.2016.027
- K. Nakagawa, T. Uchida, and T. Aoshima, “Deep factor model,” ECML PKDD 2018 Workshops, pp. 37–50, 2019. doi:10.1007/978-3-030-13463-1_3
- K. Rydqvist, “Dual-class shares: A Review,” Oxford Review of Economic Policy, vol. 8, no. 3, pp. 45–57, 1992. doi:10.1093/ox-rep/8.3.45
- Ko, Ching-Ru, and Hsien-Tsung Chang. “LSTM-Based Sentiment Analysis for Stock Price Forecast.” PeerJ Computer Science, vol. 7, 11 Mar. 2021, p. e408, https://doi.org/10.7717/peerj-cs.408.
- L. Ni et al., “Forecasting of forex time series data based on Deep Learning,” Procedia Computer Science, vol. 147, pp. 647–652, 2019. doi:10.1016/j.procs.2019.01.189
- L. Zhang, “Pair trading with machine learning strategy in China Stock Market,” 2021 2nd International Conference on Artificial Intelligence and Information Systems, 2021. doi:10.1145/3469213.3471353
- L. Zingales, “The value of the voting right: A study of the Milan stock exchange experience,” Review of Financial Studies, vol. 7, no. 1, pp. 125–148, 1994. doi:10.1093/rfs/7.1.125
- M. Abe and H. Nakayama, “Deep learning for forecasting stock returns in the cross-section,” Advances in Knowledge Discovery and Data Mining, pp. 273–284, 2018. doi:10.1007/978-3-319-93034-3_22
- M. R. Horner, “The value of the corporate voting right,” Journal of Banking and Finance, vol. 12, no. 1, pp. 69–83, 1988. doi:10.1016/0378-4266(88)90051-9
- M. U. Gudelek, S. A. Boluk, and A. M. Ozbayoglu, “A deep learning based stock trading model with 2-D CNN trend detection,” 2017 IEEE Symposium Series on Computational Intelligence (SSCI), 2017. doi:10.1109/ssci.2017.8285188
- M. Wen, P. Li, L. Zhang, and Y. Chen, “Stock market trend prediction using high-order information of Time Series,” IEEE Access, vol. 7, pp. 28299–28308, 2019. doi:10.1109/access.2019.2901842
- N. Foysal Ahamed, and M. Mahmudul Hasan. “Predicting Stock Price from Historical Data using LSTM Technique.” Journal of Artificial Intelligence and Data Science 3.1: 36-49.
- N. Naik and B. R. Mohan, “Stock price movements classification using machine and deep learning techniques-the case study of Indian Stock Market,” Engineering Applications of Neural Networks, pp. 445–452, 2019. doi:10.1007/978-3-030-20257-6_38
- Niu, Hongli, et al. “A Hybrid Stock Price Index Forecasting Model Based on Variational Mode Decomposition and LSTM Net-work.” Applied Intelligence, vol. 50, no. 12, 17 July 2020, pp. 4296–4309, https://doi.org/10.1007/s10489-020-01814-0.
- P. Oncharoen and P. Vateekul, “Deep learning using risk-reward function for stock market prediction,” Proceedings of the 2018 2nd International Conference on Computer Science and Artificial Intelligence, 2018. doi:10.1145/3297156.3297173
- P. Patil, C.-S. M. Wu, K. Potika, and M. Orang, “Stock market prediction using ensemble of graph theory, machine learning and Deep Learning Models,” Proceedings of the 3rd International Conference on Software Engineering and Information Management, 2020. doi:10.1145/3378936.3378972
- P. Srivastava and P. K. Mishra, “Stock market prediction using RNN LSTM,” 2021 2nd Global Conference for Advancement in Technology (GCAT), 2021. doi:10.1109/gcat52182.2021.9587540
- R. C. Lease, J. J. McConnell, and W. H. Mikkelson, “The market value of control in publicly-traded corporations,” Journal of Financial Economics, vol. 11, no. 1–4, pp. 439–471, 1983. doi:10.1016/0304-405x(83)90019-3
- S. Basodi, C. Ji, H. Zhang, and Y. Pan, “Gradient amplification: An efficient way to train deep neural networks,” Big Data Mining and Analytics, vol. 3, no. 3, pp. 196–207, 2020. doi:10.26599/bdma.2020.9020004
- R. Zhang, Z. Yuan, and X. Shao, “A new combined CNN-RNN model for sector stock price analysis,” 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC), 2018. doi:10.1109/compsac.2018.10292
- S. Cai, X. Feng, Z. Deng, Z. Ming, and Z. Shan, “Financial News quantization and Stock Market Forecast Research based on CNN and LSTM,” Lecture Notes in Computer Science, pp. 366–375, 2018. doi:10.1007/978-3-030-05755-8_36
- S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Computation, vol. 9, no. 8, pp. 1735–1780, 1997. doi:10.1162/neco.1997.9.8.1735
- S. Liu, C. Zhang, and J. Ma, “CNN-LSTM neural network model for quantitative strategy analysis in stock markets,” Neural Information Processing, pp. 198–206, 2017. doi:10.1007/978-3-319-70096-0_21
- S. P. Chatzis, V. Siakoulis, A. Petropoulos, E. Stavroulakis, and N. Vlachogiannakis, “Forecasting stock market crisis events using deep and Statistical Machine Learning Techniques,” Expert Systems with Applications, vol. 112, pp. 353–371, 2018. doi:10.1016/j.eswa.2018.06.032
- S. Selvin, R. Vinayakumar, E. A. Gopalakrishnan, V. K. Menon, and K. P. Soman, “Stock price prediction using LSTM, RNN and CNN-sliding window model,” 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2017. doi:10.1109/icacci.2017.8126078
- Thorir Mar Ingolfsson, “Insights into LSTM architecture,” Thorir Mar Ingolfsson, https://thorirmar.com/post/insight_into_lstm/ (accessed Aug. 16, 2023).
- W. Chen, C. K. Yeo, C. T. Lau, and B. S. Lee, “Leveraging Social Media News to predict stock index movement using RNN-Boost,” Data & Knowledge Engineering, vol. 118, pp. 14–24, 2018. doi:10.1016/j.datak.2018.08.003
- W. Jiang, “Applications of deep learning in stock market prediction: Recent progress,” Expert Systems with Applications, vol. 184, p. 115537, 2021. doi:10.1016/j.eswa.2021.115537
- W. L. Megginson, “Restricted voting stock, acquisition premiums, and the market value of corporate control,” The Financial Re-view, vol. 25, no. 2, pp. 175–198, 1990. doi:10.1111/j.1540-6288.1990.tb00791.x
- X. Ding, Y. Zhang, T. Liuand J. Duan, “Deep learning for event-driven stock prediction”, AAAI Press, Jul. 2015.
- X. Sheng, S. Guo, and X. Chang, “Managerial myopia and firm productivity: Evidence from China,” Finance Research Letters, vol. 49, p. 103083, 2022. doi:10.1016/j.frl.2022.103083
- Y. Liu, Q. Zeng, H. Yang, and A. Carrio, “Stock price movement prediction from financial news with deep learning and knowledge graph embedding,” Knowledge Management and Acquisition for Intelligent Systems, pp. 102–113, 2018. doi:10.1007/978-3-319-97289-3_8
- Y. Song, J. W. Lee, and J. Lee, “A study on novel filtering and relationship between input-features and target-vectors in a deep learning model for stock price prediction,” Applied Intelligence, vol. 49, no. 3, pp. 897–911, 2018. doi:10.1007/s10489-018-1308-x
- Y. Zhao and M. Khushi, “Wavelet denoised-resnet CNN and LIGHTGBM method to predict forex rate of Change,” 2020 Interna-tional Conference on Data Mining Workshops (ICDMW), 2020. doi:10.1109/icdmw51313.2020.00060
- Z. Hu, Y. Zhao, and M. Khushi, “A survey of Forex and Stock Price Prediction using Deep learning,” Applied System Innovation, vol. 4, no. 1, p. 9, 2021. doi:10.3390/asi4010009
- Z. Zeng and M. Khushi, “Wavelet denoising and attention-based RNN- Arima model to predict forex price,” 2020 International Joint Conference on Neural Networks (IJCNN), 2020. doi:10.1109/ijcnn48605.2020.9206832

Toplam 55 adet kaynakça vardır.

Birincil Dil | İngilizce |
---|---|

Konular | Makine Öğrenme (Diğer), Veri Madenciliği ve Bilgi Keşfi |

Bölüm | Research Articles |

Yazarlar | |

Yayımlanma Tarihi | 28 Haziran 2024 |

Gönderilme Tarihi | 3 Ekim 2023 |

Yayımlandığı Sayı | Yıl 2024 Cilt: 4 Sayı: 1 |