Artificial Intelligence-powered tools, similar to ChatGPT, have the potential to revolutionize the efficiency, effectiveness and speed of the work humans do.

And that is true in financial markets as much as in sectors like health care, manufacturing and just about every other aspect of our lives.

I’ve been researching financial markets and algorithmic trading for 14 years. While AI offers a lot of advantages, the growing use of those technologies in financial markets also points to potential perils. A have a look at Wall Street’s past efforts to hurry up trading by embracing computers and AI offers necessary lessons on the implications of using them for decision-making.

Program trading fuels Black Monday

In the early Eighties, fueled by advancements in technology and financial innovations similar to derivatives, institutional investors began using computer programs to execute trades based on predefined rules and algorithms. This helped them complete large trades quickly and efficiently.

Back then, these algorithms were relatively easy and were primarily used for so-called index arbitrage, which involves attempting to benefit from discrepancies between the value of a stock index – just like the S&P 500 – and that of the stocks it’s composed of.

As technology advanced and more data became available, this type of program trading became increasingly sophisticated, with algorithms able to research complex market data and execute trades based on a big selection of things. These program traders continued to grow in number on the largey unregulated trading freeways – on which over a trillion dollars value of assets change hands every single day – causing market volatility to extend dramatically.

Eventually this resulted within the massive stock market crash in 1987 referred to as Black Monday. The Dow Jones Industrial Average suffered what was on the time the most important percentage drop in its history, and the pain spread throughout the globe.

In response, regulatory authorities implemented a variety of measures to limit using program trading, including circuit breakers that halt trading when there are significant market swings and other limits. But despite these measures, program trading continued to grow in popularity within the years following the crash.

This is how papers across the country headlined the stock market plunge on Black Monday, Oct. 19, 1987.
AP Photo

HFT: Program trading on steroids

Fast forward 15 years, to 2002, when the New York Stock Exchange introduced a totally automated trading system. As a result, program traders gave method to more sophisticated automations with rather more advanced technology: High-frequency trading.

HFT uses computer programs to research market data and execute trades at extremely high speeds. Unlike program traders that bought and sold baskets of securities over time to make the most of an arbitrage opportunity – a difference in price of comparable securities that might be exploited for profit – high-frequency traders use powerful computers and high-speed networks to research market data and execute trades at lightning-fast speeds. High-frequency traders can conduct trades in roughly one 64-millionth of a second, compared with the several seconds it took traders within the Eighties.

These trades are typically very short term in nature and should involve buying and selling the identical security multiple times in a matter of nanoseconds. AI algorithms analyze large amounts of knowledge in real time and discover patterns and trends that will not be immediately apparent to human traders. This helps traders make higher decisions and execute trades at a faster pace than could be possible manually.

Another necessary application of AI in HFT is natural language processing, which involves analyzing and interpreting human language data similar to news articles and social media posts. By analyzing this data, traders can gain priceless insights into market sentiment and adjust their trading strategies accordingly.

Benefits of AI trading

These AI-based, high-frequency traders operate very in another way than people do.

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

And, so, similar to most technologies, HFT provides several advantages to stock markets.

These traders typically buy and sell assets at prices very near the market price, which implies they don’t charge investors high fees. This helps be sure that there are all the time buyers and sellers out there, which in turn helps to stabilize prices and reduce the potential for sudden price swings.

High-frequency trading can even help to scale back the impact of market inefficiencies by quickly identifying and exploiting mispricing out there. For example, HFT algorithms can detect when a selected stock is undervalued or overvalued and execute trades to make the most of these discrepancies. By doing so, this type of trading can assist to correct market inefficiencies and be sure that assets are priced more accurately.

a crowd of people move around a large room with big screens all over the place
Stock exchanges was filled with traders buying and selling securities, as on this scene from 1983. Today’s trading floors are increasingly empty as AI-powered computers handle increasingly more of the work.
AP Photo/Richard Drew

The downsides

But speed and efficiency can even cause harm.

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

Additionally, HFT financial firms are in a position to use their speed and technology to achieve 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 after which recovered in a matter of minutes – erasing after which restoring about $1 trillion in market value.

Since then, volatile markets have grow to be the brand new normal. In 2016 research, two co-authors and I discovered 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 analyze the information mean that even a small change in market conditions can trigger a lot of trades, resulting in sudden price swings and increased volatility.

In addition, research I published with several other colleagues in 2021 shows that almost all high-frequency traders use similar algorithms, which increases the chance of market failure. That’s because because the variety of these traders increases within the marketplace, the similarity in these algorithms can result in similar trading decisions.

This implies that all the high-frequency traders might trade on the identical side of the market if their algorithms release similar trading signals. That is, all of them might attempt to sell in case of negative news or buy in case of positive news. If there isn’t any one to take the opposite side of the trade, markets can fail.

Enter ChatGPT

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

In general, humans, left to their very own devices, will are likely to make a various range of selections. But if everyone’s deriving their decisions from the same artificial intelligence, this could limit the variety of opinion.

Consider an extreme, nonfinancial situation through which everyone relies on ChatGPT to determine on the very best computer to purchase. Consumers are already very prone to herding behavior, through which they have a tendency to purchase the identical products and models. For example, reviews on Yelp, Amazon and so forth motivate consumers to select amongst just a few top selections.

Since decisions made by the generative AI-powered chatbot are based on past training data, there could be a similarity in the selections suggested by the chatbot. It is extremely likely that ChatGPT would suggest the identical brand and model to everyone. This might take herding to a complete recent level and may lead to shortages in certain products and repair in addition to severe price spikes.

This becomes more problematic when the AI making the selections is informed by biased and misinformation. 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 making factual errors.

In addition, since market crashes are relatively rare, there isn’t much data on them. Since generative AIs rely upon data training to learn, their lack of information about them could make them more prone to occur.

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

But I strongly imagine banks will eventually embrace generative AI, once they resolve concerns they’ve 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 worldwide economy and everybody are also great, so I hope they tread rigorously.

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