Perhaps the most vexing question hanging over financial regulators right now is this: Does computer-driven trading--high-frequency and algorithmic trading in particular--cause Flash Crash-like volatility? As of now, there is no firm evidence that it does so, though the emotions run high. Recall that the joint SEC-CFTC report on the Flash Crash of May 6, 2010 declined to strongly link high-frequency trading to the extreme drop.

But the issue is definitely worth additional study, which can shed light on the current markets, which strike many as rather opaque. Not because any of the major market entities are nefarious by any means, but because the role of technology and the mechanisms by which volatility can be created remains little so little understood. In that spirit, we welcome the U.K. government's Project Foresight report on computer-based trading and the effect on the markets.

The report makes clear that it has not produced any evidence that computer-based trading is the source of volatility. "However, in specific circumstances, a key type of mechanism can lead to significant instability in financial markets with computer based trading (CBT): self-reinforcing feedback loops (the effect of a small change looping back on itself and triggering a bigger change, which again loops back and so on) within well-intentioned management and control processes can amplify internal risks and lead to undesired interactions and outcomes."

Here's a look at possible feedback mechanisms:

• Risk feedback loop. High-frequency market makers may find themselves in a situation where they need to reduce risk in the face of a big market drop. So they sell securities, depressing prices across the board. Which leads more losses and amore heightened need to sell more securities. "The value-destruction in turn can lead banks to stop performing their intermediation role with adverse spillover effects on the real economy."
• Volume feedback loop. The SEC-CFTC report noted the tendency of some high-frequency traders to hold positions for extremely short periods of time. This generated lots of volume, though net positions stayed the same. This volume triggered certain algorithms that were coded to sell more securities when volume rises, under the theory that higher volume create conditions made for less market impact.
• Shallowness feedback loop. Some academics have posited that an initial increase in volatility, perhaps due to news, can lead to greater dispersion of bids and asks in the order books. "With everything else constant, incoming market orders (i.e., orders to buy or sell at the market's current best available price) are more able to move the market reference price and this increase in volatility in turn feeds back into yet more dispersed quotes."
• New feedback loop. More algorithms now take news feeds and online sentiment indicators into account. If high-frequency traders en masse make market moves that are reflected in these indicators, they could create a cycle that feeds on itself, as more "news" of more selling triggers more sell orders.
• Delay feedback loop. In times of volatility, the NYSE tape could lag a bit. "Since the market is falling, the delayed NYSE bids appear to be the most attractive to sellers, and all sales are routed to NYSE, regardless of the fact that actual bids were lower. Algorithmic momentum HFTs short those stocks and given the oddness, HFTs may sell inventories. A second feedback loop then reinforces the first one: as delays creep grow, as the increased flurry of activity arising from the previous feedback loop can cause further misalignments." This theory was proposed by Nanex.
• Index feedback loop. The SEC-CFTC report suggested that the volatility of individual component stocks spilled over into the ETF markets and led market makers to pause their activities. "In return, the illiquid and stub ETF prices for aggregates provide false systematic factor signals, feeding back into the pricing of individual securities." -Jim