A hybrid evolutionary dynamic neural network for stock market trend analysis and prediction using unscented Kalman filter

By Ranjeeta Bisoi, P. Dash. 2014

125 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Economics and Econometrics, Management Science and Operations Research

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Jingshuang Liu
2. Qiubin Liang
3. Zhang Xiong
4. Jiayi Zhang
5. Bing Yang
6. Wenqi Yang
7. Longbing Cao
8. Wenge Rong

In Economics and Econometrics

1. Bing Yang
2. Wenqi Yang
3. Longbing Cao
4. Zi-Jia Gong

In Management Science and Operations Research

1. Bing Yang
2. Wenqi Yang
3. Longbing Cao
4. Zi-Jia Gong

Citations in the corpus

• Restricted Boltzmann machine based stock market trend prediction (2017, background): “In most of articles, trend is up or down [4], [26], [27].”
• AI in Finance: Challenges, Techniques, and Opportunities (2021, background): “Examples include integrating expert systems and evolutionary computing for advising investment [131]; combining evolutionary computing and neural computing to generate evolutionary neural models to simulate and analyze a capital market [18]; integrating complexity science and game theory to model an”
• Stock market index prediction using deep neural network ensemble (2017, methodology): “[15] used the dynamic neural network, a variation of recurrent neural network, to predict the trend of closing price of several stock markets.”