Following up on the high frequency trading (HFT) theme of my last blog, I came across yet another New York Times piece on the evils of HFT. This one was penned by Paul Wilmott, a financial consultant and the founder of the quant journal Wilmott.

In the Times commentary Wilmott focuses his attention on what HFT advocates claim that the technology is offering the financial system: market liquidity. Basically liquidity represents how easy an asset can be bought or sold. HFT purports to increase market liquidity by speeding up price discovery, thanks to the magic of quant codes, HPC hardware and optical communications. Wilmott questions the value of making the market so liquid, inasmuch as real live traders would probably be better off if transactions were executed on a more human scale.

This points to the disparity of goals between silicon-based HFT versus corporeal-based trading. In the case of the former, the aim is to go after narrow margins (pennies per share) with rapid fire trades. But long-term investors aren’t concerned with pricing determined in the last millisecond. They’re looking to reap much larger margins over the space of months, years or even decades. Can these two approaches live amicably?

Wilmott at least suggests the possibility that they cannot. The problem is that the supercomputers aren’t performing price discovery as some sort of simulation exercise. The machines are executing real trades — that’s the whole point. But in doing so, they’re affecting the price.

He illustrates the effect with the example of the October 1987 stock market crash. During that episode, the Standard & Poor 500 index lost over 20 percent of its value in a single day. He attributes this in part to the widespread use of dynamic portfolio insurance, a type of algorithm used to progressively sell off shares as market prices fall. Wilmott contends that since so much trading was done using the same algorithmic approach, the effect of a natural stock market dip was greatly amplified.

The implication is that many of these super-secret HFT algorithms used by various financial institutions are similar enough to exert this amplifier effect on market behavior. Wilmott sums it up thusly:

A rise in price begets a rise. (Think bubbles.) And a fall begets a fall. (Think crashes.) Volatility rises and the market is destabilized. All that’s needed is for a large number of people to be following the same type of strategy. And if we’ve learned only one lesson from the recent financial crisis it is that people do like to copy each other when they see a profitable idea.

Since no one really knows what all the algorithms look like, it’s impossible to say for sure if this is happening. But there’s little doubt that HFT is pumping up the volume (literally) in the financial markets. As I mentioned in my previous post, the TABB Group estimates more than 70 percent of all US market trades are executed via HFT.

The fundamental problem seems to be allowing financial vehicles like stocks and bonds to be used for short-term speculation.  Stocks and bonds represent investments that are designed to be held for relatively long time periods, not to be traded at sub-second timescales in order to squeeze out micro-profits. If high frequency trading makes the market too volatile, this will tend to scare off the long-term investors. This may or may not be a sustainable model, but I have a feeling we will shortly find out.