Reference: | 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. |