Pinging: Who is the Predator, and Who Is the Prey?

The debate over Lewis’s Flash Boys is generating more informed commentary than the book itself. One thing that is emerging in the debate is the identity of the main contending parties: HFT vs. the Buy Side, mainly big institutional traders.

One of the criticisms of HFT is that it engages in various strategies to attempt to ferret out institutional order flows, which upsets the buy side. But the issue is not nearly so clearcut as the buy side would have you believe.

The main issue is that not all institutional orders are alike. In particular, there is considerable variation in the informativeness of institutional order flow. Some (e.g., index fund order flow) is unlikely to be informed. Other order flow is more informed: some may even be informed by inside information.

Informed order flow is toxic for market makers. They lose on average when trading against it. So they try to determine what order flow is informed, and what order flow isn’t.

Informed order flow must hide in order to profit on its information. Informed order flow uses various strategies based on order types, order submission strategies, choice of trading venues, etc., to attempt to become indistinguishable from uninformed order flow. Uninformed order flow tries to devise in strategies to signal that it is indeed uninformed, but that encourages the informed traders to alter their strategies to mimic the uninformed.

To the extent that market makers-be they humans or machines-can get signals about the informativeness of order flow, and in particular about undisclosed flow that may be hitting the market soon, they can adjust their quotes accordingly and mitigate adverse selection problems. The ability to adjust quotes quickly in response to information about pending informed orders allows them to quote narrower markets. By pinging dark pools or engage in other strategies that allow them to make inferences about latent informed order flow, HFT can enhance liquidity.

Informed traders of course are furious at this. They hate being sniffed out and seeing prices change before their latent orders are executed. They excoriate “junk liquidity”-quotes that disappear before they can execute. Because the mitigation of adverse selection reduces the profits they generate from their information.

It can be frustrating for uninformed institutional investors too, because to the extent that HFT can’t distinguish perfectly between uninformed and informed order flow, the uninformed will often see prices move against them before they trade too. This creates a commercial opportunity for new trading venues, dark pools, mainly, to devise ways to do a better way of screening out informed order flow.

But even if uninformed order flow often finds quotes running away from them, their trading costs will be lower on average the better that market makers, including HFT, are able to detect more accurately impending informed orders. Pooling equilibria hurt the uninformed: separating equilibria help them. The opposite is true of informed traders. Market makers that can evaluate more accurately the informativeness of order flow induce more separation and less pooling.

Ultimately, then, the driver of this dynamic is the informed traders. They may well be the true predators, and the uninformed (or lesser informed) and the market makers are their prey. The prey attempt to take measures to protect themselves, and ironically are often condemned for it: informed traders’ anger at market makers that anticipate their orders is no different that the anger of a cat that sees the mouse flee before it can pounce. The criticisms of both dark pools and HFT (and particularly HFT strategies that attempt to uncover information about trading interest and impending order flow) are prominent examples.

The welfare impacts of all this are unknown, and likely unknowable. To the extent that HFT or dark pools reduce the returns to informed trading, there will be less investment in the collection of private information. Prices will be less informative, but trading will be less costly and risk allocation improved. The latter effects are beneficial, but hard to quantify. The benefits of more informative prices are impossible to quantify, and the social benefits of more informed prices may be larger, perhaps substantially so, than the private benefits, meaning that excessive resources are devoted to gathering private information.

More informative prices can improve the allocation of capital. But not all improvements in price efficiency improve the allocation of capital by anything near the cost of acquiring the information that results in these improvements, or the costs imposed on uninformed traders due to adverse selection. For instance, developing information that permits a better forecast of a company’s next earnings report may have very little effect on the investment decisions of that company, or any other company. The company has the information already, and other companies for which this information may be valuable (e.g., firms in the same industry, competitors) are going to get it well within their normal decision making cycle. In this case, incurring costs to acquire the information is a pure waste. No decision is improved, risk allocation is impaired (because those trading for risk allocation reasons bear higher costs), and resources are consumed.

In other words, it is impossible to know how the social benefits of private information about securities values relate to the private benefits. It is quite possible (and in my view, likely) that the private benefits exceed the social benefits. If so, traders who are able to uncover and anticipate informed trading and take measures that reduce the private returns to informed trading are enhancing welfare, even if prices are less informative as a result.

I cannot see any way of evaluating the welfare effects of financial trading, and in particular informed trading. The social benefits (how do more informative prices improve the allocation of real resources) are impossible to quantify: they are often difficult even to identify, except in the most general way (“capital allocation is improved”). Unlike the trade for most goods and services, there is no reason to believe that social and private benefits align. My intuition-and it is no more than that-is that the bulk of informed trading is rent seeking, and a tax on the risk allocation functions of financial markets.

It is therefore at least strongly arguable that the development of trading technologies that reduce the returns to informed trading are a good thing. To the extent that one of the charges against HFT-that it is better able to detect and anticipate (I will not say front-run) informed order flow-is true, that is a feature, not a bug.

I don’t know and I am pretty sure nobody knows or even can know the answers to these questions. Which means that strongly moralistic treatments of HFT or any other financial market technology or structure that affects the returns to informed trading is theology, not economics/finance. Agnosticism is a defensible position. Certitude is not.

About Craig Pirrong 228 Articles

Affiliation: University of Houston

Dr Pirrong is Professor of Finance, and Energy Markets Director for the Global Energy Management Institute at the Bauer College of Business of the University of Houston. He was previously Watson Family Professor of Commodity and Financial Risk Management at Oklahoma State University, and a faculty member at the University of Michigan, the University of Chicago, and Washington University.

Professor Pirrong's research focuses on the organization of financial exchanges, derivatives clearing, competition between exchanges, commodity markets, derivatives market manipulation, the relation between market fundamentals and commodity price dynamics, and the implications of this relation for the pricing of commodity derivatives. He has published 30 articles in professional publications, is the author of three books, and has consulted widely, primarily on commodity and market manipulation-related issues.

He holds a Ph.D. in business economics from the University of Chicago.

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