Zobrazeno 1 - 10
of 183
pro vyhledávání: '"Kiyoshi Izumi"'
Publikováno v:
Frontiers in Artificial Intelligence, Vol 5 (2022)
This article proposes a methodology to forecast the movements of analysts' estimated net income and stock prices using analyst profiles. Our methodology is based on applying natural language processing and neural networks in the context of analyst re
Externí odkaz:
https://doaj.org/article/03cbc9d06b6841d9a51a58699f66c7ec
Publikováno v:
Frontiers in Artificial Intelligence, Vol 5 (2022)
This study demonstrates whether analysts' sentiments toward individual stocks are useful for stock investment strategies. This is achieved by using natural language processing to create a polarity index from textual information in analyst reports. In
Externí odkaz:
https://doaj.org/article/f5e6041dca6e460b8e1b4e3fea90b687
Publikováno v:
Data Science and Engineering, Vol 5, Iss 2, Pp 180-192 (2020)
Abstract Although deep neural networks are excellent for text sentiment analysis, their applications in real-world practice are occasionally limited owing to their black-box property. In this study, we propose a novel neural network model called cont
Externí odkaz:
https://doaj.org/article/406f94228d3e4fca9cbd0b39cb4a69e6
Publikováno v:
Information, Vol 11, Iss 6, p 292 (2020)
This paper proposes and analyzes a methodology of forecasting movements of the analysts’ net income estimates and those of stock prices. We achieve this by applying natural language processing and neural networks in the context of analyst reports.
Externí odkaz:
https://doaj.org/article/db68e1083ce34be08b6edec14adc5f66
Autor:
Masanori Hirano, Hiroki Sakaji, Shoko Kimura, Kiyoshi Izumi, Hiroyasu Matsushima, Shintaro Nagao, Atsuo Kato
Publikováno v:
Information, Vol 10, Iss 3, p 102 (2019)
We propose an extended scheme for selecting related stocks for themed mutual funds. This scheme was designed to support fund managers who are building themed mutual funds. In our preliminary experiments, building a themed mutual fund was found to be
Externí odkaz:
https://doaj.org/article/589a5d01448245abaf4559e6333c9984
Publikováno v:
New Frontiers in Artificial Intelligence ISBN: 9783031291678
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::94d5746cd15985e803475e16bf90ef0c
https://doi.org/10.1007/978-3-031-29168-5_12
https://doi.org/10.1007/978-3-031-29168-5_12
Publikováno v:
2022 IEEE International Conference on Big Data (Big Data).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b6ab4c40aa66cc0138f6d3b3bea0ad66
https://doi.org/10.1109/bigdata55660.2022.10020445
https://doi.org/10.1109/bigdata55660.2022.10020445
Publikováno v:
2022 IEEE International Conference on Big Data (Big Data).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::425989e43fd2a6fc49d4d1266baf50f8
https://doi.org/10.1109/bigdata55660.2022.10020497
https://doi.org/10.1109/bigdata55660.2022.10020497
Publikováno v:
2022 IEEE International Conference on Big Data (Big Data).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7d98bb90248d0b69124066895d859539
https://doi.org/10.1109/bigdata55660.2022.10021066
https://doi.org/10.1109/bigdata55660.2022.10021066
Publikováno v:
2022 IEEE International Conference on Big Data (Big Data).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::75a9228d71e51327e05bbe98168273d0
https://doi.org/10.1109/bigdata55660.2022.10020220
https://doi.org/10.1109/bigdata55660.2022.10020220