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題名 基於卷積神經網路之型態與橫斷面股票報酬率
Image Pattern Based on Convolutional Neural Network and Cross-Sectional Stock Return
作者 鄧昱辰
Den, Yu-Chen
貢獻者 羅秉政
Kendro Vincent
鄧昱辰
Den, Yu-Chen
關鍵詞 卷積神經網路
股票價格型態
橫斷面報酬預測
技術分析
Convolutional neural network
Stock chart pattern
Cross-sectional return predictability
Technical analysis
日期 2024
上傳時間 1-Jul-2024 12:34:20 (UTC+8)
摘要 本論文探討使用卷積神經網路(CNN)應用於由股價和成交量生 成的影像，來預測股票報酬率的可能性。我們採用多類別分類模型預 測股票報酬，其表現優於二元分類模型。此外，我們的研究還考慮了 不同股票型態的可預測性，發現小市值股票通常較大市值股票具有較 高的年度夏普比率和更顯著的月超額報酬。我們的研究成果強調在股 票報酬預測中，考慮橫截斷效應和股票型態異質性的重要性，為投資 者和研究人員提供了新的見解。
This paper investigates the predictability of stock returns using Convolutional Neural Networks (CNNs) apply to images generated from stock prices and volumes. We employ multi-class classification models to predict stock returns that outperform binary classification models. Additionally, our study examines the predictability of different stock styles, revealing that small-capital stocks generally exhibit higher annual Sharpe ratios and more pronounced monthly excess returns than large-capital stocks. Our findings underscore the importance of considering cross-sectional effects and stock style heterogeneity in stock return predictions, providing valuable insights for investors and researchers alike.
參考文獻 Araci, D. (2019). Finbert: Financial sentiment analysis with pre-trained language models. arXiv preprint arXiv:1908.10063. Avramov, D., Cheng, S., and Metzker, L. (2023). Machine learning vs. economic restric- tions: Evidence from stock return predictability. Management Science, 69(5):2587– 2619. Bajgrowicz, P. and Scaillet, O. (2012). Technical trading revisited: False discoveries, persistence tests, and transaction costs. Journal of Financial Economics, 106(3):473– 491. Banz, R. W. (1981). The relationship between return and market value of common stocks. Journal of financial economics, 9(1):3–18. Barndorff-Nielsen, O. E. and Shephard, N. (2002). Econometric analysis of realized volatility and its use in estimating stochastic volatility models. Journal of the Royal Statistical Society Series B: Statistical Methodology, 64(2):253–280. Bianchi, D., Büchner, M., and Tamoni, A. (2021). Bond risk premiums with machine learning. The Review of Financial Studies, 34(2):1046–1089. Caporin, M., Ranaldo, A., and De Magistris, P. S. (2013). On the predictability of stock prices: A case for high and low prices. Journal of Banking & Finance, 37(12):5132– 5146. Chen, L., Pelger, M., and Zhu, J. (2023). Deep learning in asset pricing. Management Science. Chuang, C. and Yang, Y. (2022). Buy tesla, sell ford: Assessing implicit stock market preference in pre-trained language models. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 100– 105. Cooper, M. J., Gulen, H., and Schill, M. J. (2008). Asset growth and the cross-section of stock returns. the Journal of Finance, 63(4):1609–1651. Corsi, F. (2009). A simple approximate long-memory model of realized volatility. Journal of Financial Econometrics, 7(2):174–196. 51 Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929. Dwivedi, R., Singh, C., Yu, B., and Wainwright, M. (2023). Revisiting minimum descrip- tion length complexity in overparameterized models. Journal of Machine Learning Research, 24(268):1–59. Fama, E. F. and French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33(1):3–56. Fama, E. F. and French, K. R. (2015). A five-factor asset pricing model. Journal of Financial Economics, 116(1):1–22. Giglio, S., Kelly, B., and Xiu, D. (2022). Factor models, machine learning, and asset pricing. Annual Review of Financial Economics, 14:337–368. Glorot, X. and Bengio, Y. (2010). Understanding the difficulty of training deep feedfor- ward neural networks. In Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, pages 249–256. JMLR Workshop and Conference Proceedings. Gu, S., Kelly, B., and Xiu, D. (2020). Empirical asset pricing via machine learning. The Review of Financial Studies, 33(5):2223–2273. Guo, Y., Xu, Z., and Yang, Y. (2023). Is chatgpt a financial expert? evaluating language models on financial natural language processing. arXiv preprint arXiv:2310.12664. Han, Y., Zhou, G., and Zhu, Y. (2016). A trend factor: Any economic gains from using information over investment horizons? Journal of Financial Economics, 122(2):352– 375. Ioffe, S. and Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. In International Conference on Machine Learning, pages 448–456. PMLR. Jegadeesh, N. and Titman, S. (1993). Returns to buying winners and selling losers: Im- plications for stock market efficiency. The Journal of finance, 48(1):65–91. Jiang, J., Kelly, B., and Xiu, D. (2023). (Re-) imag (in) ing price trends. Journal of Finance, 78(6):3193–3249. Kaczmarek, T. and Pukthuanthong, K. (2023). Animating stock markets. Working paper. Kelly, B. T., Malamud, S., and Zhou, K. (2022). The virtue of complexity everywhere. Available at SSRN. 52 LeCun, Y. (1998). The mnist database of handwritten digits. http://yann. lecun. com/exdb/mnist/. LeCun, Y., Bengio, Y., et al. (1995). Convolutional networks for images, speech, and time series. The handbook of brain theory and neural networks, 3361(10):1995. LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278–2324. Li, J., Wang, D., and Zhang, Q. (2022). Reading the candlesticks: An ok estimator for volatility. Review of Economics and Statistics, pages 1–45. Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021). Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF international conference on computer vision, pages 10012–10022. Makrehchi, M., Shah, S., and Liao, W. (2013). Stock prediction using event-based sen- timent analysis. In 2013 IEEE/WIC/ACM International Joint Conferences on Web In- telligence (WI) and Intelligent Agent Technologies (IAT), volume 1, pages 337–342. IEEE. Marquering, W. and Verbeek, M. (2004). The economic value of predicting stock index returns and volatility. Journal of Financial and Quantitative Analysis, 39(2):407–429. Murray, S., Xia, Y., and Xiao, H. (2024). Charting by machines. Journal of Financial Economics, 153:103791. Nair, V. and Hinton, G. E. (2010). Rectified linear units improve restricted boltzmann machines. In Proceedings of the 27th international conference on machine learning (ICML-10), pages 807–814. Nijman, T., Swinkels, L., and Verbeek, M. (2004). Do countries or industries explain momentum in europe? Journal of Empirical Finance, 11(4):461–481. Novy-Marx, R. (2013). The other side of value: The gross profitability premium. Journal of financial economics, 108(1):1–28. Obaid, K. and Pukthuanthong, K. (2022). A picture is worth a thousand words: Measuring investor sentiment by combining machine learning and photos from news. Journal of Financial Economics, 144(1):273–297. Qian, N. (1999). On the momentum term in gradient descent learning algorithms. Neural networks, 12(1):145–151. Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al. (2021). Learning transferable visual models from natural language supervision. In International conference on machine learning, pages 8748– 8763. PMLR. 53 Rosenberg, B., Reid, K., and Lanstein, R. (1985). Persuasive evidence of market ineffi- ciency. Journal of portfolio management, 11(3):9–16. Ruder, S. (2016). An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747. Rytchkov, O. (2010). Expected returns on value, growth, and hml. Journal of Empirical Finance, 17(4):552–565. Shynkevich, A. (2012). Short-term predictability of equity returns along two style dimen- sions. Journal of Empirical Finance, 19(5):675–685. Smilkov, D., Thorat, N., Kim, B., Viégas, F., and Wattenberg, M. (2017). Smoothgrad: removing noise by adding noise. arXiv preprint arXiv:1706.03825. Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R. (2014). Dropout: a simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 15(1):1929–1958. Sullivan, R., Timmermann, A., and White, H. (1999). Data-snooping, technical trading rule performance, and the bootstrap. Journal of Finance, 54(5):1647–1691. Sun, L., Najand, M., and Shen, J. (2016). Stock return predictability and investor senti- ment: A high-frequency perspective. Journal of Banking & Finance, 73:147–164. Wang, Y., Liu, L., Ma, F., and Diao, X. (2018). Momentum of return predictability. Journal of Empirical Finance, 45:141–156. Xu, J., Li, Z., Du, B., Zhang, M., and Liu, J. (2020). Reluplex made more practical: Leaky relu. In 2020 IEEE Symposium on Computers and communications (ISCC), pages 1–7. IEEE.
描述 碩士
國立政治大學
金融學系
111352027
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0111352027
資料類型 thesis
dc.contributor.advisor 羅秉政zh_TW
dc.contributor.advisor Kendro Vincenten_US
dc.contributor.author (Authors) 鄧昱辰zh_TW
dc.contributor.author (Authors) Den, Yu-Chenen_US
dc.creator (作者) 鄧昱辰zh_TW
dc.creator (作者) Den, Yu-Chenen_US
dc.date (日期) 2024en_US
dc.date.accessioned 1-Jul-2024 12:34:20 (UTC+8)-
dc.date.available 1-Jul-2024 12:34:20 (UTC+8)-
dc.date.issued (上傳時間) 1-Jul-2024 12:34:20 (UTC+8)-
dc.identifier (Other Identifiers) G0111352027en_US
dc.identifier.uri (URI) https://ah.lib.nccu.edu.tw/item?item_id=171543-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 金融學系zh_TW
dc.description (描述) 111352027zh_TW
dc.description.abstract (摘要) 本論文探討使用卷積神經網路(CNN)應用於由股價和成交量生 成的影像，來預測股票報酬率的可能性。我們採用多類別分類模型預 測股票報酬，其表現優於二元分類模型。此外，我們的研究還考慮了 不同股票型態的可預測性，發現小市值股票通常較大市值股票具有較 高的年度夏普比率和更顯著的月超額報酬。我們的研究成果強調在股 票報酬預測中，考慮橫截斷效應和股票型態異質性的重要性，為投資 者和研究人員提供了新的見解。zh_TW
dc.description.abstract (摘要) This paper investigates the predictability of stock returns using Convolutional Neural Networks (CNNs) apply to images generated from stock prices and volumes. We employ multi-class classification models to predict stock returns that outperform binary classification models. Additionally, our study examines the predictability of different stock styles, revealing that small-capital stocks generally exhibit higher annual Sharpe ratios and more pronounced monthly excess returns than large-capital stocks. Our findings underscore the importance of considering cross-sectional effects and stock style heterogeneity in stock return predictions, providing valuable insights for investors and researchers alike.en_US
dc.description.tableofcontents 摘要 Abstract ii Contents iii List of Figures v List of Tables vi 1 Introduction 1 2 Literature Review 5 2.1 Machine Learning and Computer Vision Models in Finance 5 2.2 Cross-sectional Return Predictability 6 2.3 Technical Analysis as Factors 7 3 Data 8 3.1 Generation of image data 8 4 Methodology 12 4.1 Labeling Methods 12 4.2 Convolutional Neural Network 13 4.3 Architecture of Convolutional Neural Network 14 4.3.1 Convolutional layer 15 4.3.2 Max-Pooling layer 15 4.3.3 Activtion function 16 4.4 Calculate the dimension of layers 17 4.5 Optimizer 19 4.6 Parameter initialization and tuning 19 4.7 Fully connected layer 20 4.8 Return prediction and portfolio construction 20 5 Empirical Results 24 5.1 CNN model performance 24 5.2 Portfolio Performance Analysis 27 5.2.1 Performance across all models 27 5.2.2 Performance for a specific style of stocks 32 5.2.3 Portfolio Decile Performance 36 5.3 Channels of Predictability 43 6 Conclusions & Future Works 48 6.1 Conclusion 48 6.2 Future Works 49 Reference 51zh_TW
dc.format.extent 5165073 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0111352027en_US
dc.subject (關鍵詞) 卷積神經網路zh_TW
dc.subject (關鍵詞) 股票價格型態zh_TW
dc.subject (關鍵詞) 橫斷面報酬預測zh_TW
dc.subject (關鍵詞) 技術分析zh_TW
dc.subject (關鍵詞) Convolutional neural networken_US
dc.subject (關鍵詞) Stock chart patternen_US
dc.subject (關鍵詞) Cross-sectional return predictabilityen_US
dc.subject (關鍵詞) Technical analysisen_US
dc.title (題名) 基於卷積神經網路之型態與橫斷面股票報酬率zh_TW
dc.title (題名) Image Pattern Based on Convolutional Neural Network and Cross-Sectional Stock Returnen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) Araci, D. (2019). Finbert: Financial sentiment analysis with pre-trained language models. arXiv preprint arXiv:1908.10063. Avramov, D., Cheng, S., and Metzker, L. (2023). Machine learning vs. economic restric- tions: Evidence from stock return predictability. Management Science, 69(5):2587– 2619. Bajgrowicz, P. and Scaillet, O. (2012). Technical trading revisited: False discoveries, persistence tests, and transaction costs. Journal of Financial Economics, 106(3):473– 491. Banz, R. W. (1981). The relationship between return and market value of common stocks. Journal of financial economics, 9(1):3–18. Barndorff-Nielsen, O. E. and Shephard, N. (2002). Econometric analysis of realized volatility and its use in estimating stochastic volatility models. Journal of the Royal Statistical Society Series B: Statistical Methodology, 64(2):253–280. Bianchi, D., Büchner, M., and Tamoni, A. (2021). Bond risk premiums with machine learning. The Review of Financial Studies, 34(2):1046–1089. Caporin, M., Ranaldo, A., and De Magistris, P. S. (2013). On the predictability of stock prices: A case for high and low prices. Journal of Banking & Finance, 37(12):5132– 5146. Chen, L., Pelger, M., and Zhu, J. (2023). Deep learning in asset pricing. Management Science. Chuang, C. and Yang, Y. (2022). Buy tesla, sell ford: Assessing implicit stock market preference in pre-trained language models. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 100– 105. Cooper, M. J., Gulen, H., and Schill, M. J. (2008). Asset growth and the cross-section of stock returns. the Journal of Finance, 63(4):1609–1651. Corsi, F. (2009). A simple approximate long-memory model of realized volatility. Journal of Financial Econometrics, 7(2):174–196. 51 Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929. Dwivedi, R., Singh, C., Yu, B., and Wainwright, M. (2023). Revisiting minimum descrip- tion length complexity in overparameterized models. Journal of Machine Learning Research, 24(268):1–59. Fama, E. F. and French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33(1):3–56. Fama, E. F. and French, K. R. (2015). A five-factor asset pricing model. Journal of Financial Economics, 116(1):1–22. Giglio, S., Kelly, B., and Xiu, D. (2022). Factor models, machine learning, and asset pricing. Annual Review of Financial Economics, 14:337–368. Glorot, X. and Bengio, Y. (2010). Understanding the difficulty of training deep feedfor- ward neural networks. In Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, pages 249–256. JMLR Workshop and Conference Proceedings. Gu, S., Kelly, B., and Xiu, D. (2020). Empirical asset pricing via machine learning. The Review of Financial Studies, 33(5):2223–2273. Guo, Y., Xu, Z., and Yang, Y. (2023). Is chatgpt a financial expert? evaluating language models on financial natural language processing. arXiv preprint arXiv:2310.12664. Han, Y., Zhou, G., and Zhu, Y. (2016). A trend factor: Any economic gains from using information over investment horizons? Journal of Financial Economics, 122(2):352– 375. Ioffe, S. and Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. In International Conference on Machine Learning, pages 448–456. PMLR. Jegadeesh, N. and Titman, S. (1993). Returns to buying winners and selling losers: Im- plications for stock market efficiency. The Journal of finance, 48(1):65–91. Jiang, J., Kelly, B., and Xiu, D. (2023). (Re-) imag (in) ing price trends. Journal of Finance, 78(6):3193–3249. Kaczmarek, T. and Pukthuanthong, K. (2023). Animating stock markets. Working paper. Kelly, B. T., Malamud, S., and Zhou, K. (2022). The virtue of complexity everywhere. Available at SSRN. 52 LeCun, Y. (1998). The mnist database of handwritten digits. http://yann. lecun. com/exdb/mnist/. LeCun, Y., Bengio, Y., et al. (1995). Convolutional networks for images, speech, and time series. The handbook of brain theory and neural networks, 3361(10):1995. LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278–2324. Li, J., Wang, D., and Zhang, Q. (2022). Reading the candlesticks: An ok estimator for volatility. Review of Economics and Statistics, pages 1–45. Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021). Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF international conference on computer vision, pages 10012–10022. Makrehchi, M., Shah, S., and Liao, W. (2013). Stock prediction using event-based sen- timent analysis. In 2013 IEEE/WIC/ACM International Joint Conferences on Web In- telligence (WI) and Intelligent Agent Technologies (IAT), volume 1, pages 337–342. IEEE. Marquering, W. and Verbeek, M. (2004). The economic value of predicting stock index returns and volatility. Journal of Financial and Quantitative Analysis, 39(2):407–429. Murray, S., Xia, Y., and Xiao, H. (2024). Charting by machines. Journal of Financial Economics, 153:103791. Nair, V. and Hinton, G. E. (2010). Rectified linear units improve restricted boltzmann machines. In Proceedings of the 27th international conference on machine learning (ICML-10), pages 807–814. Nijman, T., Swinkels, L., and Verbeek, M. (2004). Do countries or industries explain momentum in europe? Journal of Empirical Finance, 11(4):461–481. Novy-Marx, R. (2013). The other side of value: The gross profitability premium. Journal of financial economics, 108(1):1–28. Obaid, K. and Pukthuanthong, K. (2022). A picture is worth a thousand words: Measuring investor sentiment by combining machine learning and photos from news. Journal of Financial Economics, 144(1):273–297. Qian, N. (1999). On the momentum term in gradient descent learning algorithms. Neural networks, 12(1):145–151. Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al. (2021). Learning transferable visual models from natural language supervision. In International conference on machine learning, pages 8748– 8763. PMLR. 53 Rosenberg, B., Reid, K., and Lanstein, R. (1985). Persuasive evidence of market ineffi- ciency. Journal of portfolio management, 11(3):9–16. Ruder, S. (2016). An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747. Rytchkov, O. (2010). Expected returns on value, growth, and hml. Journal of Empirical Finance, 17(4):552–565. Shynkevich, A. (2012). Short-term predictability of equity returns along two style dimen- sions. Journal of Empirical Finance, 19(5):675–685. Smilkov, D., Thorat, N., Kim, B., Viégas, F., and Wattenberg, M. (2017). Smoothgrad: removing noise by adding noise. arXiv preprint arXiv:1706.03825. Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R. (2014). Dropout: a simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 15(1):1929–1958. Sullivan, R., Timmermann, A., and White, H. (1999). Data-snooping, technical trading rule performance, and the bootstrap. Journal of Finance, 54(5):1647–1691. Sun, L., Najand, M., and Shen, J. (2016). Stock return predictability and investor senti- ment: A high-frequency perspective. Journal of Banking & Finance, 73:147–164. Wang, Y., Liu, L., Ma, F., and Diao, X. (2018). Momentum of return predictability. Journal of Empirical Finance, 45:141–156. Xu, J., Li, Z., Du, B., Zhang, M., and Liu, J. (2020). Reluplex made more practical: Leaky relu. In 2020 IEEE Symposium on Computers and communications (ISCC), pages 1–7. IEEE.zh_TW