ChatGPT-powered Wall Street

HFT: Program trading on steroids

Fast forward 15 years, to 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 analyse market data and execute trades at extremely high speeds.

Unlike program traders that bought and sold baskets of securities over time to take advantage of an arbitrage opportunity – a difference in price of similar securities that can be exploited for profit – high-frequency traders use powerful computers and high-speed networks tos market data and execute trades at lightning-fast speeds. High-frequency traders can conduct trades in approximately one 64-millionth of a second, compared with the several seconds it took traders in the 1980s.

These trades are typically very short term in nature and may involve buying and selling the same security multiple times in a matter of nanoseconds. AI algorithms analyse large amounts of data in real time and identify patterns and trends that are not immediately apparent to human traders. This helps traders make better decisions and execute trades at a faster pace than would be possible manually.

Another important application of AI in HFT is natural language processing, which involves analysing and interpreting human language data such as news articles and social media posts. By analysing this data, traders can gain valuable insights into market sentiment and adjust their trading strategies accordingly.

Benefits of AI trading

These AI-based, high-frequency traders operate very differently than people do.

The human brain is slow, inaccurate and forgetful. It is incapable of quick, high-precision, floating-point arithmetic needed for analysing huge volumes of data for identifying trade signals. Computers are millions of times faster, with essentially infallible memory, perfect attention and limitless capability for analysing large volumes of data in split milliseconds.

And, so, just like most technologies, HFT provides several benefits to stock markets.

These traders typically buy and sell assets at prices very close to the market price, which means they don’t charge investors high fees. This helps ensure that there are always buyers and sellers in the market, which in turn helps to stabilise prices and reduce the potential for sudden price swings.

High-frequency trading can also help to reduce the impact of market inefficiencies by quickly identifying and exploiting mispricing in the market. For example, HFT algorithms can detect when a particular stock is undervalued or overvalued and execute trades to take advantage of these discrepancies. By doing so, this kind of trading can help to correct market inefficiencies and ensure that assets are priced more accurately.

The downsides

But speed and efficiency can also cause harm.

HFT algorithms can react so quickly to news events and other market signals that they can cause sudden spikes or drops 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 beasts led to the so-called flash crash in May 2010, when stocks plunged and then recovered in a matter of minutes – erasing and then restoring about $1 trillion in market value.

Since then, volatile markets have become the new normal. In 2016 research, two co-authors and I found that volatility – a measure of how rapidly and unpredictably prices move up and down – increased significantly after the introduction of HFT.

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

In addition, research I published with several other colleagues in 2021 shows that most high-frequency traders use similar algorithms, which increases the risk of market failure. That’s because as the number of these traders increases in the marketplace, the similarity in these algorithms can lead to similar trading decisions.

This means that all of the high-frequency traders might trade on the same side of the market if their algorithms release similar trading signals. That is, they all might try to sell in case of negative news or buy in case of positive news. If there is no one to take the other side of the trade, markets can fail.

Enter ChatGPT

That brings us to a new world of ChatGPT-powered trading algorithms and similar programs. They could take the problem of too many traders on the same side of a deal and make it even worse.

In general, humans, left to their own devices, will tend to make a diverse range of decisions. But if everyone’s deriving their decisions from a similar artificial intelligence, this can limit the diversity of opinion.

Consider an extreme, nonfinancial situation in which everyone depends on ChatGPT to decide on the best computer to buy. Consumers are already very prone to herding behaviour, in which they tend to buy the same products and models. For example, reviews on Yelp, Amazon and so on motivate consumers to pick among a few top choices.

Since decisions made by the generative AI-powered chatbot are based on past training data, there would be a similarity in the decisions suggested by the chatbot. It is highly likely that ChatGPT would suggest the same brand and model to everyone. This might take herding to a whole new level and could lead to shortages in certain products and service as well as severe price spikes.

This becomes more problematic when the AI making the decisions is informed by 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 criticised for making factual errors.

In addition, since market crashes are relatively rare, there isn’t much data on them. Since generative AIs depend on data training to learn, their lack of knowledge about them could make them more likely to happen.

For now, at least, it seems most banks won’t be allowing their employees to take advantage of ChatGPT and similar tools. Citigroup, Bank of America, Goldman Sachs and several other lenders have already banned their use on trading-room floors, citing privacy concerns.

But I strongly believe banks will eventually embrace generative AI, once they resolve concerns they have with it. The potential gains are too significant to pass up – and there’s a risk of being left behind by rivals.

But the risks to financial markets, the global economy and everyone are also great, so I hope they tread carefully.

Pawan Jain is assistant professor of finance, West Virginia University

This article is republished from The Conversation under a Creative Commons licence. Read the original article.