Opinion: ChatGPT brings huge benefits and risks to Wall Street

Editor’s note: In the financial world, with the development of technology and technology, transactions have become more complex and high-frequency. History has proved that the more advanced the technology, the greater the market volatility. In this process, there are beneficiaries and there are victims. This article is from compilation, I hope to inspire you.

Image source: Generated by Unbounded AI tool

AI-powered tools, such as ChatGPT, have the potential to revolutionize the efficiency, effectiveness, and speed of human work.

This is true in financial markets, but it is also true in healthcare, manufacturing, and just about every other aspect of our lives.

I have studied financial markets and algorithmic trading for 14 years. While artificial intelligence offers many benefits, the growing ubiquity of these technologies in financial markets also brings potential dangers. Looking at Wall Street’s past attempts to speed up trading by embracing computers and artificial intelligence, we can spot some important lessons about using these technologies for decision-making.

1. Programmatic trading gave birth to “Black Monday”

In the early 1980s, spurred by technological advances and financial innovations such as derivatives, institutional investors began using computer programs to execute trades based on pre-set rules and algorithms. This helps investors complete large transactions quickly and efficiently.

At the time, these algorithms were relatively simple and were mainly used for so-called index arbitrage, which is to profit from the difference between the price of “a stock index such as the S&P 500” and the “stocks that make up the index”.

As technology advances and more data becomes available, this programmatic trading becomes more sophisticated and algorithms begin to analyze complex market data and execute trades based on various factors. The number of these programmatic traders continues to grow on the largely unregulated trading highway, with more than $1 trillion worth of assets changing hands every day, leading to a sharp increase in market volatility.

Ultimately, this led to the massive stock market crash of 1987, known as Black Monday. The Dow Jones Industrial Average suffered its worst drop ever, and the pain spread across the globe.

In response, regulators have implemented a series of measures to limit the use of programmatic trading, including circuit breakers and other restrictions that suspend trading during major market fluctuations. But despite these steps, programmatic trading has continued to gain popularity in the years following the crash.

2. High Frequency Trading (HFT)

Fifteen years later, in 2002, the New York Stock Exchange launched a fully automated trading system. As a result, programmatic traders gave way to more sophisticated automated trading and a more advanced technique: high-frequency trading.

High frequency trading uses computer programs to analyze market data and execute trades at extremely high speeds. Unlike program traders, who take advantage of arbitrage opportunities by buying and selling baskets of securities over long periods of time, high-frequency traders use powerful computers and high-speed networks to analyze market data and execute trades at lightning speed. High-frequency traders can make trades in about 64 millionths of a second, compared with the seconds it took traders in the 1980s.

These trades are typically very short-term and may involve buying and selling the same security multiple times within nanoseconds. AI algorithms are able to analyze large amounts of data in real time and identify patterns and trends that human traders cannot see instantly. This helps traders make better decisions and execute trades faster than manually.

Another important application of artificial intelligence in high-frequency trading is natural language processing, which involves analyzing and interpreting data in human language, such as news articles and social media posts. By analyzing this data, traders can gain insights into market sentiment and adjust their trading strategies accordingly.

3. Benefits of AI Trading

These artificial intelligence-based high-frequency transactions operate very differently from human transactions.

The human brain is sluggish, inaccurate, forgetful, and incapable of the fast, high-precision floating-point arithmetic that is a skill required to analyze large amounts of data to identify trading signals. But computers are millions of times faster than the human brain, with impeccable memory, perfect focus, and an unlimited ability to analyze vast amounts of data in milliseconds.

So, like most technologies, high-frequency trading brings several benefits to the stock market.

High-frequency traders typically buy and sell assets very close to market prices, which helps ensure that there are always buyers and sellers in the market, which in turn helps stabilize prices and reduce the chance of sudden price swings.

High-frequency trading can also help reduce the impact of market inefficiencies by quickly identifying and exploiting mispricing in the market. For example, high-frequency trading algorithms can detect when a particular stock is undervalued or overvalued and execute trades to take advantage of those differences. Such transactions can help correct market inefficiencies and ensure that assets are more accurately priced.

4. Disadvantages of artificial intelligence trading

But speed and efficiency can also hurt markets.

High-frequency trading algorithms can react very quickly to news events and other market signals, causing sudden spikes or drops in asset prices.

In addition, high-frequency trading financial firms are able to use their speed and technology to gain an advantage over other traders, further distorting market signals. The volatility created by these extremely sophisticated AI-powered trades led to the so-called “flash crash” in May 2010, when stocks plunged and then recovered within minutes, wiping out roughly $1 trillion in market value and then And quickly recovered.

Since then, volatile markets have become the new normal. In a 2016 study, two co-authors and I found that volatility (a measure of the speed and unpredictability of price rises and falls) increased significantly after the introduction of high-frequency trading.

The speed and efficiency with which high-frequency traders analyze data means that even small changes in market conditions can trigger massive trade volumes, leading to sudden price swings.

Furthermore, research published in 2021 by myself and several other colleagues showed that most high-frequency traders use similar algorithms, which increases the risk of market failure. This is because the similarity of these algorithms leads to similar trading decisions as the number of traders in the market increases.

This means that all high-frequency traders are likely to be trading on the same side of the market if their algorithms emit similar trading signals. That is, they are all likely to try to sell on negative news and buy on positive news. If no one is on the other side of the trade, then the market will fail.

5. Enter the ChatGPT era

Artificial intelligence has brought us into a new world of trading algorithms powered by ChatGPT and similar programs. And these techniques can make the “too many traders on the same side of the trade” problem worse.

In general, humans tend to make a variety of decisions if they let nature take its course. But if everyone bases their decisions on similar AI, that could limit diversity of opinion.

Consider an extreme, non-financial situation where everyone relies on ChatGPT to decide on the best computer to buy. At this time, consumers are already very inclined to herd behavior, and they tend to buy the same product and model. For example, reviews on sites like Yelp, Amazon, etc., prompt consumers to choose among several best options.

Since the decisions made by a chatbot powered by generative AI are based on past training data, the decisions proposed by the chatbot will have similarities. Chances are ChatGPT will recommend the same make and model to everyone. This could take the “herd effect” to an even higher level and could lead to shortages of certain products and services, as well as serious price spikes.

This becomes even more problematic when AI makes decisions based on biased and incorrect information. When systems are trained on biased, stale or limited data sets, AI algorithms reinforce existing biases. ChatGPT and similar tools have been widely criticized for making factual mistakes.

Also, since market crashes are relatively rare, there isn’t much data on them. Since generative AIs rely on data training to learn, their lack of knowledge of this could make crashes more likely.

Most banks, at least for now, don’t appear to be allowing employees to use ChatGPT and similar tools. Citigroup, Bank of America, Goldman Sachs and several other banks have banned the use of the tools from their trading floors, citing privacy concerns.

But I firmly believe that once banks address their concerns about generative AI, they will eventually embrace generative AI. Because the potential gains are too great to miss, and you risk being left behind by your competitors.

But there are also significant risks for the financial markets, the global economy and everyone, so I would like them to proceed with caution.

Translator: Jane