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Asset Selection for Stock Market Investment Using Bonferroni OWA-Based Penalty Function

Fabio Blanco-Mesa et al.

Economic Computation and Economic Cybernetics Studies and Research2026https://doi.org/10.24818/18423264/60.1.26.07article
ABDC C
Weight
0.50

What the paper says

In the decision-making process, it is common to encounter disagreements that can undermine the importance of information through penalties.This article proposes a new penalty function based on Bonferroni means and ordered weighted averaging (OWA), referred to as Bonferroni OWA-based penalties (BP-OWA).Likewise, this operator can be extended with induced variables, heavy weights, and weighted averages.This approach's main advantage is that it captures the importance, preferences, expectations, and attitudes associated with penalty gradation, the interrelationships among arguments, weighting vectors, and the reordering process, which considers approximate reasoning in decisionmaking.Finally, new methods are applied to asset selection for stock market investment using Yahoo Finance, with different penalty weights for stocks, generating new return expectations to be considered in the decision of which stock to include in the portfolio.

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https://doi.org/https://doi.org/10.24818/18423264/60.1.26.07

Or copy a formatted citation

@article{fabio2026,
  title        = {{Asset Selection for Stock Market Investment Using Bonferroni OWA-Based Penalty Function}},
  author       = {Fabio Blanco-Mesa et al.},
  journal      = {Economic Computation and Economic Cybernetics Studies and Research},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.24818/18423264/60.1.26.07},
}

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Asset Selection for Stock Market Investment Using Bonferroni OWA-Based Penalty Function

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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.