As financial markets embrace artificial intelligence and algorithmic trading at an unprecedented scale, we face a critical turning point that requires careful consideration of the technological, ethical, and societal consequences. This article examines the growing challenges of markets increasingly driven by machines rather than humans.
Algorithmic bias and market fairness
The notion of financial markets as fair playing fields is increasingly challenged by advanced algorithms that can preserve or amplify existing biases. Trading algorithms, although perceived as objective and data-driven, ultimately reflect the values, priorities, and blind spots of their creators.
Key Concerns
- Data bias: Algorithms trained on historical market data inherit previous discriminatory patterns.
- Access disparities: High-frequency trading requires expensive infrastructure, which can concentrate benefits among a few resource-rich actors.
- Information asymmetry: Complex algorithms create new forms of insider knowledge.
Recent research from the Financial Supervisory Authority shows that algorithmic trading systems systematically discriminate against retail investors during volatility.
The challenge is not to resist the algorithmic transformation, but to shape it to preserve the social functions of markets.