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Competitive Models to Detect Stock Manipulation

Journal article titled "Competitive Models to Detect Stock Manipulation" published by John M. Pfau Library, California State University San Bernardino on 2018-03-20. No additional event details or findings were included in the supplied excerpt.

"a Pipeline for model training, compound screening, and hit validation. Several classification scores were used as performance metrics to determine the most suitable model for the computational screen. b Results from three machine learning models trained on 2523 compounds (Fig. 1a) and a reduced set of 165 features (Supplementary Fig. 1a); bar plots show average performance metrics computed in 5-fold cross-validation, with error bars denoting one standard deviation across folds. Mean ± s.d. are
Illustrative image: "a Pipeline for model training, compound screening, and hit validation. Several classification scores were used as performance metrics to determine the most suitable model for the computational screen. b Results from three machine learning models trained on 2523 compounds (Fig. 1a) and a reduced set of 165 features (Supplementary Fig. 1a); bar plots show average performance metrics computed in 5-fold cross-validation, with error bars denoting one standard deviation across folds. Mean ± s.d. are — Authors of the study: Vanessa Smer-Barreto, Andrea Quintanilla, Richard J. R. Elliott, John C. Dawson, Jiugeng Sun, Víctor M. Campa, Álvaro Lorente-Macías, Asier Unciti-Broceta, Neil O. Carragher, Juan Carlos Acosta & Diego A. Oyarzún/Wikimedia Commons, CC BY 4.0

Categories: economy-and-trade, technology

Generated scores

Scores are based on the cited reporting and use a 1–10 scale. Read the methodology.

Confidence
3/10
Geographic reach
1/10
Global importance
2/10
Impact magnitude
2/10
Positivity
7/10
Urgency
1/10

Why it matters

The article addresses methods to detect stock manipulation, which relates to financial market integrity.

Sources

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