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Worst trade ever?

Worst trade ever?
Worst trade ever?


In an essay entitled Why AI will save the world“, Marc Andreessen, general partner of a16z, argues that contemporary panic over the harmful potential of artificial intelligences is exaggerated.

Basically, Andreessen argues, AI is a glorified toaster; it is composed of inputs, processes and outputs. In other words, he has no potential and will develop no desire to conquer the world on his own terms.

Basically, I agree with the principle. I think it is unlikely that AI will ever reach truly human levels of autonomy and decision-making. Is this an optimistic or pessimistic outlet? It often depends on where you work. The AI ​​debate focuses on different issues across sectors.

In my industry, stock trading and market making, the outlook is usually a mix of excitement and apprehension. High-frequency traders are excited by the prospect of applying even more sophisticated algorithms to break through the market. Others, and myself in this category, fear that AI will further exacerbate the negative externalities of algorithmic trading.

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For example, as I previously argued in the Wall Street Journal: Programmatic Trading Amplifies Black Swan Disruptions. Indeed, volatility is one of the most important elements in the predefined algorithms used to carry out computerized transactions. When real volatility hits the market, computers exacerbate the problems.

But another issue should be a clear cause for concern, highlighting a nuance missing from Andreessens' argument. Robots do not need to be human, autonomous, sentient, or evil to be inscrutable. In other words, there is a happy medium between a toaster and killer software and robots that will come to life and decide to murder the human race or otherwise ruin everything, to use the wording of Andreessens.

For stock trading purposes, this happy medium lies squarely in the realm of decision-making. There is no doubt that AI will reach a point of sophistication or imprecision at which humans will not be able to explain how or why a decision was made.

Many people in Silicon Valley already say they can't explain why large language models (LLMs) do or say much of what they do. Even if the decision was made, the outcome consisted only of inputs and processes. At this point, the crucial question arises: who do you blame for a bad decision?

This is fundamentally new territory for our two existing categories of decision-making, namely decisions made by humans and by computers whose parameters were clearly defined or errors obviously triggered by humans.

Select Vantage Inc (SVI), the proprietary trading company I run, falls into the first category. Ensuring accountability is never a problem because every business decision is made by a human being. We employ over 2,500 traders in over 50 countries around the world. Every day we can trade more than $4 billion on global stock markets. But if a trader makes a bad decision, they get less capital to trade and their losses are capped. It's easy to identify who made the trade, analyze their reasoning to understand where they went wrong, and learn from past mistakes.

The second category concerns human errors applied to computers. A typical example is Knight Capital, a market-making firm that in 2012 suffered a $440 million loss in less than an hour due to a glitch in its trading software.

Knight was the largest trader in American stocks, with a market share of approximately 17.3% on the New York Stock Exchange (NYSE) and 16.9% on the NASDAQ. Knights Electronic Trading Group (ETG) handled an average daily trading volume of over 3.3 billion transactions, or over $21 billion per day.

It took 17 years of hard work to build Knight Capital Group into one of Wall Street's leading trading houses. And it almost went up in smoke in less than 60 minutes.

What happened to Knight that day is every business corporation's worst nightmare. On August 1, 2012, new trading software contained a flaw that only became apparent after the software was activated when the NYSE opened that day. The flawed software sent Knight into a buying frenzy, snapping up 150 different stocks for a total cost of about $7 billion, all in the first hour of trading.

Although it was difficult to predict in advance, in hindsight it was clear that simple human error was involved.

Other episodes, however, were less clear. Two years earlier, during the Flash Crash of May 6, 2010, the Dow Jones Industrial Average experienced a rapid and unprecedented decline, losing more than 1,000 points (approximately 9% of its value) in just a few minutes before partially straighten up. This incident was one of the first major crises that brought the potential risks of algorithmic trading to the forefront of public and regulator attention.

Although the initial investigation by the United States Securities and Exchange Commission (SEC) and the Commodity Futures Trading Commission (CFTC) highlighted a complex interaction between high-frequency trading algorithms as a significant factor, it has proven difficult to identify specific fault lines.

The algorithms involved acted according to their programming, responding to market conditions in ways that their designers had not fully anticipated when combined at scale. To this day, regulators aren't sure what happened.

So what happens when we apply artificial intelligence to business decisions and deals go wrong, but we have no idea how they made their decisions? Financial markets cannot operate without accountability, but who or what is ultimately responsible in these circumstances?

The complexity of assigning responsibility for financial losses caused by AI extends to legal and ethical dimensions. Legally, current frameworks primarily hold the institution carrying out its activities accountable, as it is responsible for the actions of the tools and technologies it uses.

However, as AI systems become more autonomous, it becomes difficult, to say the least, to distinguish between software acting according to its programmed parameters and truly unpredictable consequences.

The challenge is not just theoretical; this has practical implications for the regulation and operation of financial markets. For example, the European Union's General Data Protection Regulation (GDPR) includes provisions on the right to explanation, under which individuals can request justification for automated decisions that affect them.

While this represents a step toward resolving the black-box nature of AI, translating these principles into the high-stakes domain of financial trading involves complex considerations of privacy, intellectual property, and the technical feasibility of providing understandable explanations for AI decisions.

The problem is becoming more and more urgent as algorithmic trading is booming. For example, algorithmic trading in the U.S. stock market accounts for approximately 60% to 75% of total trading volume, according to Quantified Strategies. With such a large share of trading activity driven by algorithms, the potential for systemic risks arising from opaque AI decision-making processes cannot be underestimated.

This point was highlighted in a Bank of England report last December. On AI-driven trading, Banking Governor Andrew Bailey said: “All of us who have used it have had the experience of a kind of hallucination, and that brings us sort of something you think: how the hell did that happen?

If you're going to use it for real world and financial services, you can't have this kind of thing happening. You need to have controls and understand how it works.

Financial regulators are grappling with these issues, ensuring that markets remain fair and transparent. For example, the United States Securities and Exchange Commission (SEC) is exploring ways to regulate AI and algorithmic trading to protect investors and maintain market integrity. This includes possible regulations regarding algorithmic trading practices and disclosures to ensure that investors are aware of the role of AI in their investments.

But in reality, what is more likely: that humans will learn to discern the black box just in time, before it is too late? Or that, as usual, we promise to learn from our mistakes long after the train has left the station?

In my opinion, the future of trading lies in a balanced approach that leverages the best of technology while preserving and enhancing the role of human insight and responsibility. Real trading will always be the prerogative of good traders.

Computers may have been there for the ride, but they were there for the long haul. We forget it at our peril.




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