Exploring Candlesticks and Multi-Time Windows for Forecasting Stock-Index Movements

Author:

Seo Kanghyeon1ORCID,Yang Jihoon1ORCID

Affiliation:

1. Sogang University, Seoul, Republic of Korea

Publisher

ACM

Reference42 articles.

1. Ryo Akita , Akira Yoshihara , Takashi Matsubara , and Kuniaki Uehara . 2016 . Deep learning for stock prediction using numerical and textual information . In 2016 IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS) . Okayama, Japan, 1--6. Ryo Akita, Akira Yoshihara, Takashi Matsubara, and Kuniaki Uehara. 2016. Deep learning for stock prediction using numerical and textual information. In 2016 IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS). Okayama, Japan, 1--6.

2. Sercan Ö. Arik and Tomas Pfister. 2021. TabNet: Attentive Interpretable Tabular Learning . Proceedings of the AAAI Conference on Artificial Intelligence 35 , 8 ( 2021 ), 6679--6687. Sercan Ö. Arik and Tomas Pfister. 2021. TabNet: Attentive Interpretable Tabular Learning. Proceedings of the AAAI Conference on Artificial Intelligence 35, 8 (2021), 6679--6687.

3. Dzmitry Bahdanau , Kyunghyun Cho , and Yoshua Bengio . 2014. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 ( 2014 ). Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014).

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5. Lukasz Banasiak . 2016. yahoo-finance 1.4.0·PyPI. WebPage. https://pypi.org/project/yahoo-finance/ Accessed : 10 Dec. 2021 . Lukasz Banasiak. 2016. yahoo-finance 1.4.0·PyPI. WebPage. https://pypi.org/project/yahoo-finance/ Accessed: 10 Dec. 2021.

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