Acceleration of automated trading algorithms. Much effort is being expended in this area of computing to accelerate trading algorithms that buy and sell financial securities. These financial securities include common stock, futures, options, bonds and other more esoteric derivatives and structured finance products. Large amounts of research capacity and investment capital in the field of accelerated computing are targeted at such trading algorithms. Initially, trading algorithms were limited to replacing repetitive trading operations that were previously carried out by human traders. An example is “market making”, where a trader in a particular security or class of securities would buy and sell on their own account, either to facilitate a liquid market, dampen market volatility or to profit on bid-ask spreads. Other algorithmic trades include transaction cost reduction and arbitrage. These examples of algorithmic trading can be considered as labor-saving uses of computing, and are not what I want to talk about.

Instead I want to focus on information-based algorithmic trading, where computers process information relevant to the market of a particular security or class of securities, and then buy or sell as a result.

Given the extent to which the electronics and computing industries are positioning themselves to serve this market it is important for us to ask questions about its viability.

It is important for us to cogitate on what has just happened in the financial world. The sub-prime financial crisis originated in a single logical fallacy. Financiers observed that the US median house prices had not decreased on an annual basis in modern history. Fallacious logical deduction led to the axiom: Median House Prices in the United States Do Not Decrease on an Annual Basis. Upon this axiom an entire industry was built. The mathematical models, and the rigorous logic deployed in their development, did nothing to correct the base axiom of the industry. No matter how rationally the people that implemented and obeyed the models behaved, the end result could never overcome the original fallacy of logic. The result was a lot of companies and individuals profiting short-term, and the economy and wider populace suffering in the long term. I fully expect that the net result to the world will be seen to be negative when the dust has finally settled.

A lack of critical thinking and a belief in easy money got us into that mess, and it’s a pattern we are likely to repeat, unless we have a serious think about what is and isn’t possible in the financial markets.

Our greed and hero worship mean we look to skypilots like Ray Kurzweil to find magical ways of turning advanced technology into money.

“Artificial intelligence is becoming so deeply integrated into our economic ecostructure that some day computers will exceed human intelligence,” Kurzweil tells a room of investors who oversee enormous pools of capital. “Machines can observe billions of market transactions to see patterns we could never see.

The idea is:

“A market theory” + clever people + computers = money

“These ideas are the future,” said David Atkinson, a private investor who attended another lecture later that day by Kurzweil. “I’m not really sure I understand them, but they’re making some folks rich.”

However, if the model is fundamentally flawed, then over time all information-based algorithmic trading can hope to achieve is a steady erosion of capital through the costs of trading: that is to say the per-trade commission, the costs of the hardware-software infrastructure and the human resource costs as well as other costs. Those receiving commissions, salaries and purchase orders from the information-based algorithmic trading industry are incentivized to promote such trading regardless of its efficacy. However, unless it’s directing people to better invest capital in more productive industries, then it is a negative-sum game for humanity, a cancer that will eat at our economies. Look at the example of Long-Term Capital Management, and then look at how John Meriwether has fared in his latest hedge fund, which is going through the hoop as I write. We have turned our economies over to witchdoctors, convinced that because these people are intelligent and influential that they are correct. Well, they’re not. There is no easy answer. An economy sinks or swims on how wisely it allocates its capital. There is no reason to think that running billions of routines per second to try to exploit trends in security and derivative pricing would aggregate to the intelligent allocation of capital.

Robert Shiller’s plot of the S&P Composite Real Price Index, Earnings, Dividends, and Interest Rates, from Irrational Exuberance, 2d ed.^[1] In the preface to this edition, Shiller warns that “[t]he stock market has not come down to historical levels: the price-earnings ratio as I define it in this book is still, at this writing [2005], in the mid-20s, far higher than the historical average. … People still place too much confidence in the markets and have too strong a belief that paying attention to the gyrations in their investments will someday make them rich, and so they do not make conservative preparations for possible bad outcomes.”

Shiller points to the increasing disassociation between the price people are paying for securities and the underlying fundamentals on which price should depend in a rational market. How else can this be viewed but as a Ponzi lunacy?

But, in the new world, who cares if a company is paying dividends or not? All you need do is predict trends!

Last year, funds co-managed by Karaali returned in excess of 20 percent by using nonlinear techniques, according to his company. Whereas older methods of stock analysis rely on certain assumptions – for instance, that market volatility always reverts to the mean – Karaali’s models calculate probabilities and generates [sic] assumptions on the fly, and might predict that during a panic, investors will sell Microsoft but, for seemingly irrational reasons, hold onto IBM.

“Only an elite group of people are using these ideas, but a lot of people are thinking about them,” said Stacy Williams, director of quantitative strategies at HSBC Global Markets. HSBC is working with Cambridge University in using models based on how viruses spread to forecast foreign currency markets.

“The downside with these systems is their black box-ness,” Williams said. “Traders have intuitive senses of how the world works. But with these systems you pour in a bunch of numbers, and something comes out the other end, and it’s not always intuitive or clear why the black box latched onto certain data or relationships.”

This is self-evidently absurd! The idea that automated trading algorithms can predict the behavior of a system and act accordingly when their actions will change the very nature of that system. Together with the hundreds of other machines that are attempting to do the very same thing.

Automated trading algorithms should be relegated to the realm of pseudoscience where they belong, with homeopathy and astrology. Besides, can you imagine a caveman society where the cavemen didn’t go out on the hunt, but instead stayed in the caves, gambling on the result of the hunt? Do you think such a society would ever have made it out of the caves?

We should not allow automated trading algorithms to shape our destinies. The computer and electronics industry should be putting its human and financial capital to bring humanity forward. The grand challenge problems of energy, environment and healthcare need to be solved. It is plain immoral to use our vast computing resources to destroy our capital markets with lunatic financial alchemy. We will need both to solve our real world problems.