OpenAI's ChatGPT May Rival Flash Boys in Transforming Markets

tech.hindustantimes.com:

Large language models, such as ChatGPT, are threatening to disrupt most areas of life and work. Financial trading is no exception. The potential for LLMs to understand markets rather than just recognize patterns sets them apart from earlier versions of machine learning and artificial intelligence that have failed to achieve much notable trading success.

The basic problem is that financial prices are nearly all noise, they are very close to random walks. Lots of smart people and algorithms conspire to eliminate any signal that can be used for profit. It's like trying to understand text that is deliberately written to be misleading. Traditional AI is more successful when signals are stronger relative to noise.

Before we get deeper into what sets modern LLMs apart, let me lay out why you should care, even if you have no interest in computerized financial trading. Trading is the foundation of finance and even small changes in mechanisms exert profound effects on the market, which translate into profound economic consequences.

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High-frequency trading, introduced in the late 1990s, didn't just link end-buyers and end-sellers more quickly and efficiently. It vastly increased trading volumes, lowered transaction costs, and knocked humans out of the equity-trading business — a transformation Michael Lewis explored in Flash Boys. It led to zero-commission brokerages and zero-fee index funds — eliminating the revenues that brokers and asset managers had relied upon since they were created. It required a fundamental re-engineering of financial regulation. But HFT didn't just restructure two major financial businesses, challenge regulators and cut costs to end-investors, it gave us new phenomena like flash crashes.

In the last half-century other trading innovations have had similarly broad effects. The introduction of public futures and options traded on financial instruments in 1973 created the modern global derivatives economy, which vastly