Zobrazeno 1 - 10
of 40
pro vyhledávání: '"Junhuan Zhang"'
Publikováno v:
ACS Omega, Vol 9, Iss 6, Pp 6595-6605 (2024)
Externí odkaz:
https://doaj.org/article/c303f1eb05654bfcb3d5a142f3e26bb5
Publikováno v:
iScience, Vol 27, Iss 1, Pp 108509- (2024)
Summary: This study aims to comprehensively review a recently emerging multidisciplinary area related to the application of deep learning methods in cryptocurrency research. We first review popular deep learning models employed in multiple financial
Externí odkaz:
https://doaj.org/article/d79a3483a0934e75800d1d503467a4f7
Publikováno v:
智能科学与技术学报, Vol 5, Pp 104-112 (2023)
With the development of computer technology, especially the development of artificial intelligence, big data technology and blockchain technology, the transaction mode of traditional economic society and financial market has been changed.Quantitative
Externí odkaz:
https://doaj.org/article/e524439156ba473d93eb9c8b152594ee
Publikováno v:
PLoS ONE, Vol 17, Iss 6 (2022)
This paper presents a Long Short Term Memory Recurrent Neural Network and Hidden Markov Model (LSTM-HMM) to predict China’s Gross Domestic Product (GDP) fluctuation state within a rolling time window. We compare the predictive power of LSTM-HMM wit
Externí odkaz:
https://doaj.org/article/bb0af8295ff946acb0d76ba25f116b0a
Autor:
Jiaqi Wen, Junhuan Zhang
Publikováno v:
SSRN Electronic Journal.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b62216c0ceae48d3e39fd1fe015e2e89
https://doi.org/10.2139/ssrn.4259674
https://doi.org/10.2139/ssrn.4259674
We present a new Hawkes-Contact model that combines a Hawkes process and a finite range contact process in order to model the stock price movements, especially under the impact of news and other information flows that could lead to contagious effects
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2f3a8bccd87bd7be1bafd9f63ee00451
https://hdl.handle.net/10453/169064
https://hdl.handle.net/10453/169064
Publikováno v:
Physica A: Statistical Mechanics and its Applications. 520:161-177
This paper studies the systemic risk of China’s stock market during crashes in 2008 and 2015 using the 5-minute intraday transaction data. The results show that liquidity contracted significantly after the downtrend. The systemic risk was magnified
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fb07ace5eff9b80af02da1bd1c628416
https://doi.org/10.1016/j.physa.2019.01.006
https://doi.org/10.1016/j.physa.2019.01.006
Publikováno v:
Physica A: Statistical Mechanics and its Applications. 513:620-634
We simulate the asset pricing in the framework of information networks when the number of agents is constant and tends to infinity. When the number of agents is a constant, we find that a higher risk aversion coefficient, a lower information uncertai
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::81c9b178b2945d43f1ce1a457752060d
https://doi.org/10.1016/j.physa.2018.09.024
https://doi.org/10.1016/j.physa.2018.09.024
Publikováno v:
Physica A: Statistical Mechanics and its Applications. 514:838-854
The trading volume in stock markets is known as an important variable which reflects the liquidity of the financial markets and therefore is regarded to be greatly important for the measurement of market liquidity risk. In this work, a new concept ca
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::07ce36b678ec840f1a741b04579fa971
https://doi.org/10.1016/j.physa.2018.09.115
https://doi.org/10.1016/j.physa.2018.09.115
Publikováno v:
Journal of Information Science. :016555152210978
This article proposes a combination model, which is composed of latent Dirichlet allocation model, TF-IDF feature extraction algorithm and Euclidean distance measurement method, to identify and judge whether the similarities between multiple policy t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5d1389973550f9bae87184d601f97405
https://doi.org/10.1177/01655515221097858
https://doi.org/10.1177/01655515221097858