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An AI-based hyperautomation framework for algorithmic trading: forecasting automated trading strategy profitability from market data

Chun-Chia Lee et al.

Enterprise Information Systems2026https://doi.org/10.1080/17517575.2026.2628644article
AJG 2ABDC A
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https://doi.org/https://doi.org/10.1080/17517575.2026.2628644

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@article{chun-chia2026,
  title        = {{An AI-based hyperautomation framework for algorithmic trading: forecasting automated trading strategy profitability from market data}},
  author       = {Chun-Chia Lee et al.},
  journal      = {Enterprise Information Systems},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1080/17517575.2026.2628644},
}

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An AI-based hyperautomation framework for algorithmic trading: forecasting automated trading strategy profitability from market data

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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