Op-Ed: Will the next Skynet be an algorithmic bot?

By Ataberk Sevim, MEng ’22 (IEOR)

This op-ed is part of a series from E295: Communications for Engineering Leaders. In this course, Master of Engineering students were challenged to communicate a topic they found interesting to a broad audience of technical and non-technical readers. As an opinion piece, the views shared here are neither an expression of nor endorsed by UC Berkeley or the Fung Institute.

Photo by Thierry K on Unsplash

Everybody knows the futuristic movies about artificial intelligence or intelligent robots bringing chaos to the world, but what if the next Skynet is already with us?

SkyNet, from the Terminator franchise, is a “highly-advanced computer system possessing artificial intelligence. Once it became self-aware, it saw humanity as a threat to its existence due to the attempts of [scientists] to deactivate it once it had gained self-awareness. Hence, Skynet decided to trigger the nuclear holocaust: Judgment Day.”

In 2012, 85 percent of the daily volume of trading was done by algorithmic bots. Most of these bots are black box algorithms, which means that we only know the inputs and outputs but do not know why the input gives the output. Black box algorithms are fascinating because they are able to capture patterns that the human brain can not understand. They are frightening for the very same reason. In 2012, a company called Knight Capital launched a new algorithmic bot on the New York Stock Exchange. With the launch, they ended up losing $440 million according to the BBC. The scary part starts with the explanation of why they lost $440 million:

“We don’t know exactly what. They switched it on and immediately they started losing literally $10 million [£6.4m] a minute. It looks like they were buying high and selling low many, many times per second, and losing 10 or 15 dollars each time. And this went on for 45 minutes. At the end of it all, they wound up having lost $440 million [£281m].”

$440 million was vaporized from the economy in 45 minutes but nobody knows exactly why.

Photo by Markus Spiske on Unsplash

Before going into the depth of what causes this kind of behavior in the algorithmic bots, let’s first understand the different types of algorithmic bots. Algorithmic bots use trend-following strategies, arbitrage opportunities, mean reversion strategies, and high-frequency trading strategies.

Trend-following strategy bots look for the trends like moving averages and lines of lowest/highest prices to understand the future price movement and take a position using these insights.

Arbitrage opportunity bots check for the prices between different exchanges and take advantage of the arbitrage without taking any risks and balancing the market.

Mean reversion strategy bots assume that every asset price will go back to the mean of the past prices. Therefore, if the price is lower than the mean, they buy the asset and if the price is higher than the mean, they sell the asset.

High-frequency (HFT) strategy is the most effective and also the most unpredictable. To use HFT algorithms; you need to have an understanding of complex algorithms, strong computers, and also the fast infrastructure to send transactions quickly. In HFT algorithms, the fastest agent makes the most of the money.

Photo by Jamie Street on Unsplash

HFT algorithms bring up a nexus of complex ethical and technical questions.
There are 3 main concepts that cause the questions:

1. HFT algorithms are modeled to maximize revenue. Right now, algorithms do not consider ethical dilemmas or market imbalances; they only try to maximize revenue.
2. Even the creators of the algorithms cannot tell how the algorithms
maximize profit. The nature of the algorithms creates obstacles to regulations and foreseeing bad outcomes.
3. In general, HFT algorithms can make money because they are faster than the other players in the market. The slower players are generally people who don’t have access to high-performance computers and infrastructure — the normal individual investors.

These concepts bring up the question: should hedge funds make money
out of the individual investors just because individual investors do not have access to fancy computers? Also, another question is how far can the algorithms go in order to maximize revenues? Nobody has the answers. However, we can give some solutions in the form of regulations.

The easiest and most effective solution is regulations. HFT is a fairly new technology and regulating a new technology is a hard job since in order to regulate a concept, it must be first well-understood. According to the SEC, there is no clear-cut definition of HFT, however, there are only certain features of HFT algorithms.

Regulators are working on several solutions to decrease the negative impact of the HFT algorithms, there are still ongoing regulation drafts and most of the regulations are not accepted by all the countries. For instance, rules like minimum resting times (which would limit the time between order and the cancellation of the order) or minimum order to execution ratios (which would limit the number of orders without a trade) have decreased the manipulative effects of HFT algorithms.

In conclusion, there are some regulatory activities but they are not enough. Government agents and HFT firms should work together to understand the algorithms and decrease the negative impacts of the algorithms on retail investors. HFT firms should be transparent about their orders and file their transactions to government agents. Government agents like the SEC (The U.S. Securities and Exchange Commission) should oversee the HFT companies and fine the ones which do not comply with the regulations. If we do not work on regulations, it might be too late to stop the next economic crisis.

References:

“Skynet.” Terminator Wiki, https://terminator.fandom.com/wiki/Skynet.

Harford, Tim. “High-Frequency Trading and the $440M Mistake.” BBC News, BBC, 10 Aug. 2012, https://www.bbc.com/news/magazine-19214294.

Says, Tet Soon, et al. “Trend Following Trading Strategy Guide.” TradingwithRayner, 5 Oct. 2021, https://www.tradingwithrayner.com/trend-following/.

Seth, Shobhit. “Basics of Algorithmic Trading: Concepts and Examples.”
Investopedia, Investopedia, 8 Sept. 2021,
https://www.investopedia.com/articles/active-trading/101014/basics-algorithmic-trading-concepts-and-examples.asp.

Team, The Investopedia. “What Is High-Frequency Trading?” Investopedia,
Investopedia, 9 June 2021, https://www.investopedia.com/ask/answers/09/high-frequency-trading.asp.

Morelli, Michael. “Implementing High-Frequency Trading Regulation: A Critical Analysis of Current Reforms”. 6 MICH. BUS. & ENTREPRENEURIAL L. REV. 201. 2017.

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Edited by Danielle Valdez.

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