Ethical Conflicts: The Morality of Algorithmic Trading

Following the ethical framework and psychological observations in Psychology of Stocks in the Digital Age, the shift from human intuition to algorithmic dominance raises several critical ethical dilemmas.

LarsGöran Boström emphasizes that the digital age hasn’t just changed how we trade, but the morality of the market structure itself. See video about algorithmic trading on European trends

The “Fair Play” Gap: Information Asymmetry

The most prominent ethical concern is the erosion of the “level playing field.”

Speed as a Weapon: High-frequency trading (HFT) algorithms can see incoming orders and “front-run” them in microseconds. Boström suggests this creates a two-tiered market: the “Predators” (high-speed algos) and the “Prey” (retail and traditional institutional investors).

Co-location Ethics: Large firms pay for “co-location,” placing their servers physically inside the exchange buildings to gain a few nanoseconds of advantage. Ethically, this shifts the market from a contest of analysis to a contest of proximity and hardware.

The Erosion of Responsibility: “The Ghost in the Machine”

When an algorithm causes a flash crash or manipulates a stock via “spoofing” (placing fake orders to trick others), who is responsible?

Diffusion of Accountability: Developers often argue that the AI or algorithm “learned” the behavior on its own to optimize profit. Boström warns that this creates a “responsibility vacuum” where systemic damage can be done without a single human “actor” being held liable.

Algorithmic Bias: If an algorithm is trained on historical data that includes human panic or systemic crashes, it may ethically “misbehave” by reinforcing those negative patterns during times of crisis, effectively profiting from social or economic misery.

Market Manipulation and “Spoofing”

Digital psychology allows for new forms of deception that were physically impossible in the pit-trading era.

Layering and Spoofing: Algorithms can place thousands of buy orders and cancel them in a fraction of a second. This tricks human traders (and other algos) into thinking there is high demand, causing them to buy. The original algo then sells its actual position at the inflated price.

The “Shadow” Impact: Boström highlights that this creates a “hallucination” of liquidity. Humans believe they can sell their stocks easily, but when a real crisis hits, the algorithmic liquidity vanishes instantly, leaving humans holding the bag.

Social Stability vs. Profit Maximization

Finally, there is the macro-ethical question of the market’s purpose.

Decoupling from Reality: When algorithms trade based on “Nowcasting” and NLP keyword triggers, the stock market stops serving its original ethical purpose: funding productive businesses. Instead, it becomes a closed-loop system for extracting value from volatility.

The Human Cost: In a chaotic financial situation or during Middle Eastern conflicts, algorithms profit from the volatility caused by human suffering. While a human trader might pause due to the gravity of a situation, an algorithm sees only an increase in the $VIX$ (Volatility Index) and a “Buy/Sell” signal.

Summary of Ethical Conflicts

Conclusion:

Boström’s work suggests that the digital age requires a new “Digital Ethics.” Regulation currently struggles to keep up with code, and until “The Hidden Players” are held to the same psychological and legal standards as human traders, the market will continue to be a “Speculative Cycle on Steroids” where the fastest—not the smartest—wins.

See video about algorithmic trading on European trends

More blog posts on this subject:

Flash Crash, Panic Selling, and Presidential Tweets: How Algorithms Are Exploiting the Modern Stock Market