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Artificial intelligence in behavioural finance using a sophisticated decision-tree algorithm

Shirley Leo Pereira et al.

International Journal of Electronic Finance2025https://doi.org/10.1504/ijef.2025.145248article
ABDC C
Weight
0.50

What the paper says

Financial market statistics have been an essential part of the national economy due to their capacity to represent the state of the economy. Due to economic impact, the market failed. This paper introduces an automatic classification approach that detects cyber-bullying without requiring a high-dimensional space. We designed a text classification engine that pre-processes tweets, filters out environmental noise and extraneous information, recovers the chosen features, and classifications without overfitting the data. Due to this limitation, we proposed a sophisticated decision-tree algorithm (SDTA) for analysing the behaviour of financial marketing strategies by employing the artificial intelligence (AI) technique. The Morgan Stanley Capital International (MSCI) World Index-based data is initially gathered. Furthermore, the data is pre-processed using the normalisation technique. Then SDTA is proposed for predicting the behaviour of financial marketing. Moreover, optimisation is employed by utilising the particle swarm optimisation technique (PSO). The proposed method was compared to assess system efficiency. For this goal, feature extraction with the networking model may be suggested.

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https://doi.org/https://doi.org/10.1504/ijef.2025.145248

Or copy a formatted citation

@article{shirley2025,
  title        = {{Artificial intelligence in behavioural finance using a sophisticated decision-tree algorithm}},
  author       = {Shirley Leo Pereira et al.},
  journal      = {International Journal of Electronic Finance},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1504/ijef.2025.145248},
}

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Artificial intelligence in behavioural finance using a sophisticated decision-tree algorithm

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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

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.