Open Access
| Issue |
SHS Web Conf.
Volume 170, 2023
2023 International Conference on Digital Economy and Management Science (CDEMS 2023)
|
|
|---|---|---|
| Article Number | 02012 | |
| Number of page(s) | 4 | |
| Section | Economic Innovation and Talent Development Technology | |
| DOI | https://doi.org/10.1051/shsconf/202317002012 | |
| Published online | 14 June 2023 | |
- Rather A. M., Agarwal A., Sastry V. N. Recurrent neural network and a hybrid model for prediction of share returns[J]. Expert Systems with Applications, 2015, 42(6):3234–3241. [CrossRef] [Google Scholar]
- Li H. Y. Research on share Price Prediction Based on PCA-GA-BPNN Model[D]. Xi’an: Xi’an University of Technology, 2018. [Google Scholar]
- Baek Y. J., Kim H. Y. ModAugNet: A new forecasting framework for share market index value with an overfitting prevention LSTM module and a prediction LSTM module[J]. Expert Systems with Applications, 2018, 113: 457-480. [CrossRef] [Google Scholar]
- Kim H. Y., Won C. H. Forecasting the volatility of share price index: A hybrid model integrating LSTM with multiple GARCH-type models[J]. Expert Systems with Applications, 2018, 103: 25-37. [CrossRef] [Google Scholar]
- Tan Z., Yan Z. Q., Zhu G. G. Share selection with random forest: An exploitation of excess return in the Chinese share market[J]. Heliyon, 2019, 5(8):1–10. [Google Scholar]
- Cine R. C. Do industry returns predict the share market: A reprise using the random forest[J]. The Quarterly Review of Economics and Finance, 2018, 72:152-158. [Google Scholar]
- Lohrmann C., Luukka P. Classification of intraday S &P500 returns with a random forest[J]. International Journal of Forecasting, 2019, 35(1) : 390-407. [CrossRef] [Google Scholar]
- Lin N. N., Qin J. T. Forecast of A-share share change based on random forest[J]. University of Shanghai for Science and Technology, 2018, 40(3):267-273 [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.