The publication of Michael Lewis' new book Flash Boys: A Wall Street Revolt has pushed discussion of high frequency trading (HFT) to the fore.
Noah Smith opines that nobody knows if HFT is really good or bad.
Do market-makers increase or decrease liquidity? Do front-runners increase or decrease it? What about informational efficiency of prices? What about volatility and other forms of risk, at various time scales? What about total trading costs? Good luck answering any of these questions. Actually, Stony Brook people are working on some of these, as are researchers at a number of other universities, but they are huge questions, and our data sets are incredibly limited (data is expensive, and a lot of stuff, like identities of traders, just isn't recorded). And keep in mind, even if we did know how each of these strategies affected various market outcomes, that wouldn't necessarily tell us how the whole ecosystem of those strategies affects markets - after all, they interact with each other, and these interactions may change as the strategies themselves evolve, or as the number and wealth of the people using each strategy changes.
Confused yet? OK, it gets worse. Because even if we did know how HFT affects markets, we don't really know if it's good or bad on balance. For example, HFT defenders often say HFT provides "liquidity". Is liquidity good for markets? How much is liquidity worth, are there different kinds of liquidity, and does it matter when the liquidity comes? If I have a bunch of totally random trading, that certainly makes markets liquid, but is that a good thing? Actually, maybe yes! In lots of models of markets, you need random, money-losing "liquidity traders" in order to overcome the adverse selection problem, thus inducing informed traders to trade, and getting them to reveal their information. But HFTs don't lose money, they make money - is their liquidity provision worth the cost?
To know that, even if we knew the impact of HFTs on informational quality of prices, we'd have to know the economic value of informational efficiency. Suppose the true worth of GE stock. according to the best information humanity has available, is $100. Suppose the price is $100.20. How bad is that? How much is it worth, in economic terms, to push the price from $100.20 to $100.00? Is it worth $0.20 per share? It depends on how GE's stock price affects the company's investment decisions. To know that, we need an economic model of corporate decision-making. We have many of these, but we don't have one over-arching one that we know works in all circumstances. Corporations are way more complicated than what you read in your intro corporate finance textbook!
(And this is all without thinking about weird things like behavioral effects of the humans who interact with HFTs...)
I don't know much about HFT other than a few articles I've read here or there, so count me among those who have no idea if it's positive or negative.