The benefits and dangers of using artificial intelligence when trading stocks and other financial instruments

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Artificial intelligence based tools like ChatGPT have the potential to revolutionize the efficiency, effectiveness and speed of human work.

And that applies to financial markets as well as sectors like healthcare, manufacturing and pretty much every other aspect of our lives.

I have been researching financial markets and algorithmic trading for 14 years. While AI offers many benefits, the increasing use of these technologies in financial markets also points to potential dangers.

A look at Wall Street’s past efforts to accelerate trading through the use of computers and AI offers important insights into the implications of their use for decision-making.

Program trading fuels Black Monday

In the early 1980s, driven by advances in technology and financial innovations such as derivatives, institutional investors began using computer programs to execute trades based on predefined rules and algorithms.

This helped them handle large orders quickly and efficiently.

At the time, these algorithms were relatively simple and were primarily used for what is known as index arbitrage, in which attempts were made to profit from price differences between a stock index — such as the S&P 500 — and the price of the stocks that make it up.

As technology advanced and data became more available, this type of program trading became more sophisticated, with algorithms able to analyze complex market data and execute trades based on a variety of factors.

The number of these program traders on the largely unregulated trading highways, where over a trillion dollars worth of assets change hands every day, continued to proliferate, causing a dramatic increase in market volatility.

This finally led to the massive stock market crash in 1987, the so-called Black Monday.

The Dow Jones Industrial Average suffered what was then the largest percentage drop in its history, and the pain spread across the world.

In response, regulators have taken a number of measures to limit the use of program trading, including circuit breakers that halt trading during significant market swings and other restrictions.

But despite these measures, program trading continued to grow in popularity in the years following the crash.

HFT: steroid trading program

15 years later, until 2002, when the New York Stock Exchange introduced a fully automated trading system.

As a result, program traders gave way to more sophisticated automations with much more advanced technology: high-frequency trading.

HFT uses computer programs to analyze market data and execute trades at extremely high speeds.

Unlike program traders, who bought and sold baskets of securities over time to take advantage of an arbitrage opportunity — a price differential in similar securities that can be profitably exploited — high-frequency traders use powerful computers and high-speed networks to analyze market data and execute trades at lightning speed.

High-frequency traders can execute trades in about 64-millionths of a second, compared to the several seconds it took traders to do so in the 1980s.

These trades are typically very short-term in nature and can involve multiple purchases and sales of the same security within nanoseconds.

AI algorithms analyze large amounts of data in real time, identifying patterns and trends that are not immediately apparent to human traders.

This helps traders make better decisions and execute trades faster than would be possible manually.

Another important application of AI in the HFT is natural language processing, which involves analyzing and interpreting human language data such as news articles and social media posts.

By analyzing this data, traders can gain valuable insight into market sentiment and adjust their trading strategies accordingly.

Benefits of AI trading

These AI-based high-frequency traders work very differently than humans.

The human brain is slow, imprecise and forgetful. It is unable to perform fast, high-precision floating-point arithmetic required for analyzing large amounts of data to identify trading signals.

Computers are millions of times faster, have virtually infallible memory, perfect attention, and unlimited ability to analyze large amounts of data in fractions of a millisecond.

And so, just like most technologies, HFT offers several advantages to the stock markets.

These traders typically buy and sell assets at prices that are very close to the market price, which means they don’t charge investors hefty fees.

This helps ensure that there are always buyers and sellers in the market, which in turn helps stabilize prices and reduce the risk of sudden price fluctuations.

High frequency trading can also help reduce the impact of market inefficiencies by quickly identifying and exploiting mispricings in the market.

For example, HFT algorithms can identify when a particular stock is undervalued or overvalued and execute trades to take advantage of those discrepancies.

In this way, this type of trading can help correct market inefficiencies and ensure assets are valued more accurately.

The disadvantages

But speed and efficiency can also do harm.

HFT algorithms can react so quickly to news events and other market signals that they can cause sudden spikes or falls in asset prices.

Additionally, HFT financial firms are able to use their speed and technology to gain an unfair advantage over other traders, further distorting market signals.

The volatility created by these extremely sophisticated, AI-powered trading giants led to what is known as the Flash Crash in May 2010, in which stocks plummeted and then recovered within minutes – with the market value wiping out and then recovering about $1 trillion .

Since then, volatile markets have become the new normal. In a 2016 study, two co-authors and I found that volatility — a measure of how quickly and unpredictably prices rise and fall — increased significantly following the introduction of HFT.

The speed and efficiency with which high-frequency traders analyze the data means that even a small change in market conditions can trigger a large number of trades, resulting in sudden price swings and increased volatility.

Additionally, research I published in 2021 along with several other colleagues shows that most high-frequency traders use similar algorithms, increasing the risk of market failure.

Because as the number of these traders in the market increases, the similarity of these algorithms can lead to similar trading decisions.

This means that all high-frequency traders may be trading on the same side of the market if their algorithms are sending out similar trading signals.

That is, they all could try to sell on negative news or buy on positive news. When there is no one to take the other side of the trade, markets can fail.

Enter ChatGPT

This brings us into a new world of ChatGPT based trading algorithms and similar programs. They could compound the problem of too many traders being on the same side of a deal.

In general, when left to their own devices, people tend to make multiple choices. But if everyone derives their decisions from a similar artificial intelligence, this can limit the diversity of opinion.

Imagine an extreme, non-financial situation where everyone relies on ChatGPT to choose the best computer.

Consumers are already strongly inclined towards herd behavior, in which they tend to buy the same products and models. For example, reviews on Yelp, Amazon, etc. motivate consumers to choose between some top deals.

Since the decisions made by the AI-powered generative chatbot are based on previous training data, there would be a similarity in the decisions suggested by the chatbot.

It is very likely that ChatGPT would suggest the same make and model to everyone. This could take livestock farming to a whole new level, leading to shortages of certain products and services and significant increases in prices.

This becomes more problematic when the AI ​​making the decisions is based on biased and incorrect information.

AI algorithms can reinforce existing biases when systems are trained on biased, old, or limited data sets. And ChatGPT and similar tools have been criticized for factual errors.

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 about them could make them more likely to occur.

At least for now, it looks like most banks don’t allow their employees to use ChatGPT and similar tools.

Citigroup, Bank of America, Goldman Sachs and several other lenders have already banned their use in the trading room, citing privacy concerns.

But I firmly believe that banks will eventually embrace generative AI once they have addressed their concerns. The potential gains are too great to pass up – and there is a risk of being left behind by the competition.

But the risks to financial markets, the global economy and everyone else are also great, so I hope they will proceed cautiously.

Written by Pawan Jain. The conversation.

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