Does technological advancement (e.g., algorithmic trading) increase financial risk?

Okay, let's talk about this topic.

Imagine the old stock trading floors, where people in red vests crowded together, shouting into phones to buy and sell stocks. Now, most of that scene has moved into data centers, replaced by countless high-speed servers. This is the change brought about by technological progress, and "algorithmic trading" is the protagonist.

So, will this thing make financial markets more dangerous?

The answer is: It's a double-edged sword. In some ways, it reduces risk, but in others, it creates new risks we haven't seen before.

Why does it increase risk? (The "sharp edge" of the sword)

You can think of algorithmic trading as an extremely fast-reacting "person," but one without emotions, strictly executing code. When thousands of such "robots" trade simultaneously in the market, problems can arise.

1. Flash Crash

   • This is the most typical example. Imagine a trader accidentally placing a huge sell order (e.g., typing "billion" instead of "million"), or an algorithm misinterpreting a piece of news. Other algorithmic robots, seeing this massive sell order, will immediately conclude, "The market is going to crash!" and start frantically selling too. Within a second, thousands of robots follow suit, and stock prices can instantly plummet from $100 to $1. This is a "flash crash." The entire process might only take a few minutes, but its destructive power is immense. The US experienced a famous flash crash in 2010, where the Dow Jones Industrial Average plunged nearly a thousand points in minutes, then quickly rebounded. Human traders simply couldn't react in time.

2. Amplified Herd Effect

   • Many companies' trading algorithms may have similar underlying logic (e.g., all referencing a certain technical indicator). When a certain signal appears in the market, these algorithms will, as if by agreement, simultaneously make buy or sell decisions. This "machine herd" effect is much faster and more intense than the human herd effect, easily pushing prices to extremes in a short period.

3. "Black Box" Risk

   • Some current algorithms, especially those using artificial intelligence and machine learning, are extremely complex. Sometimes, even their designers cannot 100% predict how they will react in certain extreme market conditions. It's like a "black box" that we can't see through. If it goes wrong, it's hard to immediately find the cause and fix it.

4. Liquidity Illusion

   • Normally, algorithmic trading provides a large number of buy and sell orders, making the market appear very active, and it's easy to buy or sell (this is called "good liquidity"). But when a crisis hits, these algorithms might cancel all their orders in milliseconds, instantly "disappearing." The market's bid and ask orders suddenly become empty, and you can't sell the stocks you hold. This liquidity is like a mirage, beautiful to look at, but gone with the slightest disturbance.

Why might it also reduce risk? (The "safe handle" of the sword)

Of course, technology isn't purely evil. It also solves many old problems.

1. Reduces "Fat Finger" Errors and Emotional Trading

   • Humans make mistakes, tremble (e.g., placing the wrong order), panic, and get greedy. Programs don't. As long as the code is written correctly, it can execute trades precisely, avoiding many human errors. In times of market panic, people might sell irrationally, while a well-designed risk management algorithm will calmly operate according to preset rules.

2. Improves Market Efficiency

   • Algorithms have significantly reduced trading costs. In the past, when you bought and sold stocks, intermediaries charged considerable fees. Now, because of automated machine processing, costs are much lower. At the same time, it makes price discovery more efficient; the fair price of an asset can be found by the market much faster.

3. Refined Risk Management

   • Many algorithms are specifically designed for "stop-loss." For example, you can set a program that automatically sells your stock if it falls by more than 10%, helping you lock in losses and avoid greater losses due to indecision. This is a very useful tool for managing risk.

Conclusion

So, back to the original question: Does technological progress increase financial risk?

• It doesn't "increase" the total amount of risk, but it fundamentally "changes" the form of risk.

Old risks (like human operational errors) have decreased, but new risks (like flash crashes, algorithmic convergence) have emerged. The characteristics of these new risks are: extremely fast, highly correlated, and potentially very concentrated in their destructive power.

It's like we've replaced horse carriages with high-speed trains. High-speed trains are faster and more efficient, but it also means that if something goes wrong, the consequences could be far more severe than a carriage overturning.

Therefore, the key is not whether to use technology, but how we manage it. Regulators need to keep pace with technology, establish "circuit breakers" (pausing trading during market plunges to allow everyone to calm down), strengthen scrutiny of algorithms, and ensure that the financial system's "brakes" and "airbags" can keep up with the "engine's" speed.