Fetching the paper…
Reading the bibliography…
Traditional approaches to financial asset allocation start with returns forecasting followed by an optimization stage that decides the optimal asset weights.
Markowitz, H.: Portfolio selection. The Journal of Finance 7
1952
Earlier work this paper cites.
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9
1997
Earlier work this paper cites.
Moody, J., Wu, L., Liao, Y., Saffell, M.: Performance functions and reinforcement learning for trading systems and portfolios. Journal of Forecasting 17
1998
Earlier work this paper cites.
Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. In: A field guide to dynamical recurrent neural networks, chap. 14, pp. 237–374. IEEE Press (2001)
2001
Earlier work this paper cites.
Tino, P., Schittenkopf, C., Dorffner, G.: Financial volatility trading using recurrent neural networks. IEEE Transactions on Neural Networks 12
2001
Earlier work this paper cites.
Michaud, R.O., Michaud, R.O.: Efficient asset management: a practical guide to stock portfolio optimization and asset allocation. Oxford University Press (2008)
2008
Earlier work this paper cites.
2014
Earlier work this paper cites.
2015
Earlier work this paper cites.
Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016)
2016
Cited alongside, same era.
Chen, T., Guestrin, C.: XGBoost: A scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. pp. 785–794 (2016)
2016
Cited alongside, same era.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 770–778 (2016)
2016
Cited alongside, same era.
Dauphin, Y.N., Fan, A., Auli, M., Grangier, D.: Language modeling with gated convolutional networks. In: International Conference on Machine Learning. pp. 933–941 (2017)
2017
Cited alongside, same era.
2019
Later among the works it cites.
Zhang, Z., Zohren, S., Roberts, S.: Deep learning for portfolio optimization. The Journal of Financial Data Science 2
2020
Later among the works it cites.
Chen, W., Zhang, H., Mehlawat, M.K., Jia, L.: Mean-variance portfolio optimization using machine learning-based stock price prediction. Applied Soft Computing 100
2021
Later among the works it cites.
Kisiel, D., Gorse, D.: A meta-method for portfolio management using machine learning for adaptive strategy selection. In: International Conference on Computational Intelligence and Intelligent Systems 2021. pp. 67–71. ACM (2021)
2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2017
Cited alongside, same era.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N.: Attention is all you need. Advances in Neural Information Processing Systems 30
2017
Cited alongside, same era.
Shen, G., Tan, Q., Zhang, H., Zeng, P., Xu, J.: Deep learning with gated recurrent unit networks for financial sequence predictions. Procedia Computer Science 131
2018
Cited alongside, same era.
2019
Cited alongside, same era.
Lim, B., Arık, S.Ö., Loeff, N., Pfister, T.: Temporal fusion transformers for interpretable multi-horizon time series forecasting. International Journal of Forecasting 37
2021
Later among the works it cites.
2021
Later among the works it cites.
Xu, K., Zhang, Y., Ye, D., Zhao, P., Tan, M.: Relation-aware transformer for portfolio policy learning. In: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence. pp. 4647–4653 (2021)
2021
Later among the works it cites.