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This is a In Vitro Study study.

August 21, 2025Trends in Finance and EconomicsOpen Access

Algorithmic Trading with AI That Redefines Market Strategy Using GBR and SVR and RFR Regressions

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Support vector regression achieved the highest predictive accuracy with a test R² of 0.9478, outperforming gradient boosting and random forest regressions.
The study utilized a dataset of 20 observations covering 6 financial indicators often used in algorithmic trading.
A comparative evaluation of the three regression models was conducted, highlighting predictive accuracy via several metrics including mean absolute error and R².
Findings underscore the transformative role of AI in trading while cautioning against issues such as increased market volatility and systemic risks.

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Implication

The study reveals the performance of AI models in predicting trading signals in financial markets, indicating significant advances in algorithmic trading.

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A 2025 study studied this question.

synapsesocial.com/papers/68af5231ad7bf08b1eada4da https://doi.org/10.46632/tfe/3/2/3

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Implication

The study reveals the performance of AI models in predicting trading signals in financial markets, indicating significant advances in algorithmic trading.

Algorithmic Trading with AI That Redefines Market Strategy Using GBR and SVR and RFR Regressions

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Discussion

Implication

Cite This Study

Algorithmic Trading with AI That Redefines Market Strategy Using GBR and SVR and RFR Regressions

Key Points

Discussion

Implication

Cite This Study

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