How can Multi-Agents AI Systems help Reduce Biases in Trading Algorithms?
Algorithmic trading is now the most common form of trading in financial markets, and it has been estimated that it accounts for 60-75% of the total trading volume in major markets. However, algorithmic trading is still accompanied by cognitive and algorithmic biases such as overconfidence, confir...
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| Main Author: | |
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| Format: | Article |
| Language: | English |
| Published: |
Editura ASE
2025-05-01
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| Series: | Revista de Management Comparat International |
| Subjects: | |
| Online Access: | https://www.rmci.ase.ro/no26vol2/10.pdf |
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| Summary: | Algorithmic trading is now the most common form of trading in financial markets,
and it has been estimated that it accounts for 60-75% of the total trading volume in major
markets. However, algorithmic trading is still accompanied by cognitive and algorithmic
biases such as overconfidence, confirmation bias, and anchoring effects that can result in
suboptimal decisions and higher levels of risk. These biases are due to the excess reliance on
certain kinds of data, historical overfitting, and the absence of mechanisms to adapt to
changing market environments. We propose in this paper, the use of multi-agent AI systems
(MAIS) to tackle these biases through collaboration, role differentiation, and learning. In this
manner, MAIS design various agents that perform specific tasks, for instance, fundamental
analysts, sentiment analysts, and technical analysts to ensure that the analysis is holistic yet
without concentrating on a single kind of data. Thus, debate protocols and risk management
teams ensure that the generation and evaluation of trading ideas are properly structured and
that overconfidence and groupthink are avoided. Furthermore, there are market observer
agents and reflective agents that provide online learning of model drift and offline learning
of historical performance, respectively. Our architecture framework was tested in a simulated
environment in which MAIS traded against human traders and rule-based algorithms using
historical market data. The results showed that there were great quantitative improvements
in the Sharpe ratios and drawdowns, which show that the system is good at improving riskadjusted returns and decreasing volatility. The last section of the paper contains a conclusion
and the suggestions for future research. |
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| ISSN: | 1582-3458 2601-0968 |