Open Access
E3S Web Conf.
Volume 214, 2020
2020 International Conference on Energy Big Data and Low-carbon Development Management (EBLDM 2020)
Article Number 02019
Number of page(s) 6
Section Machine Learning and Energy Industry Structure Forecast Analysis
Published online 07 December 2020
  1. DeFusco RA., McLeavey D.W., Pinto J.E., et al. Quantitative investment analysis[M]. John Wiley & Sons, 2015. [Google Scholar]
  2. Lakonishok J, Shleifer A., Vishny R.W. Contrarian investment, extrapolation, and risk[J]. The journal of finance, 1994, 49(5): 1541-1578. [Google Scholar]
  3. Malkiel BG., Fama E.F. Efficient capital markets: A review of theory and empirical work[J]. The journal of Finance, 1970, 25(2): 383-417. [Google Scholar]
  4. Grossman SJ., Stiglitz J.E. On the impossibility of informationally efficient markets[J]. The American economic review, 1980, 70(3): 393-408. [Google Scholar]
  5. Goodwin RM.. Chaotic economic dynamics[M]. Oxford University Press: Oxford, 1990. [CrossRef] [Google Scholar]
  6. encay R. Non-linear pprediction of security returns with moving average rules[J]. Journal of Forecasting, 1996, 15(3): 165-174. [Google Scholar]
  7. Zetsche DA., Buckley R.P., Arner D.W., et al. From FinTech to TechFin: The regulatory challenges of data-driven finance[J]. NYUJL & Bus., 2017, 14: 393. [Google Scholar]
  8. Heaton JB., Polson N.G., Witte J.H. Deep learning for finance: deep portfolios[J]. Applied Stochastic Models in Business and Industry, 2017, 33(1): 3-12. [Google Scholar]
  9. Lo AW., Mamaysky H., Wang J. Foundations of technical analysis: Computational algorithms, statistical inference, and empirical implementation[J]. The journal of finance, 2000, 55(4): 1705-1765. [Google Scholar]
  10. Thira Chavarnakul, David Enke. Intelligent technical analysis based equivolume charting for stock trading using neural networks[J]. Expert Systems with Applications, 34(2): 1004-1017. [Google Scholar]
  11. Kourentzes N, Barrow D.K., Crone S.F. Neural network ensemble operators for time series forecasting[J]. Expert Systems with Applications, 2014, 41(9): 4235-4244. [Google Scholar]
  12. Qin Q, Wang Q.G., Li J., et al. Linear and nonlinear trading models with gradient boosted random forests and application to Singapore stock market[J]. Journal of Intelligent Learning Systems and Applications, 2013, 5(01): 1. [Google Scholar]
  13. Sun X, Liu M., Sima Z. A novel cryptocurrency price trend forecasting model based on LightGBM[J]. Finance Research Letters, 2018. [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.