Fellow Houstonian Cameron Smith (of HFT shop Quantlab) and HFT gadfly Haim Bodek have an oped in the FT that makes recommendations on how to fix the US equity markets. (That’s a key right there. There’s HFT in futures, but it doesn’t generate near the heartburn as it does in equities.)
The recommendations are a mixture of good, bad, and ugly. The good are recommendations to fix RegNMS, specifically by allowing locked markets, and moving away from one-size-fits-all tick sizes. The bad/ugly are their recommendations on dark pools and especially on exchange policies regarding data access and pricing.
All of this is too much for one post, so I will defer discussions of RegNMS reform and dark pools. I will focus here on the data issues.
Here’s what they say about data access and pricing:
Make market data free
Free market data would eliminate the disparity between professionals and investors. It would also cut the $400m of revenues divided among exchanges – which essentially subsidises the creation of otherwise useless markets. At the same time, we must ensure that data disseminated by the public consolidator is synchronised with the private exchange data feeds so that all the data are received by investors at the same time, eliminating the perception of unfairness. A technology company should be dedicated to this task.
This is bad/ugly because overlooks the basic microeconomics of entry and investment into HFT. Let’s think through the implications of this recommendation.
The fundamental error is in the first sentence: making data free would not eliminate the disparity between professionals and investors. Nor would making it possible for all participants to access the data simultaneously by synchronizing the data feeds. To understand where Smith and Bodek err, it is necessary to think through the equilibrium effects of their recommendation.
There would still be disparities because access to data is a necessary but not sufficient condition to eliminate them. HFT firms take the private data feed they get from exchanges, and also make additional investments in hardware and software in order to use that data to drive their trading strategies. Without these complementary investments, the data is useless in implementing HFT-type strategies. Given the cost of private data feeds, there is investment in hardware and software and other supporting resources to implement HFT. In a reasonably competitive market, entry and investment in these other resources will proceed to the point where for the marginal HFT firm, risk adjusted profits cover its cost of capital. We’ve seen that process in action: HFT profits were high in 2008-2009, but have subsequently fallen substantially as entry and investment into this business has occurred. This is the way that competitive markets work.
Note that not everybody decides to make the investments in the resources necessary to implement HFT. Even many big institutional investors eschew doing so. Certainly individual investors do. This is because the returns on the investment in hardware and software (where returns depend on the costs of data) do not cover the related capital costs. This is why disparities exist. The disparities in speed and strategies are the result of maximizing choices made by myriad market participants, and these maximizing decisions reflect the costs of engaging in various market activities.
Understanding this, let’s consider the economic effects of mandating free access to data and synchronizing access. To a first approximation, data charges are a fixed cost. Therefore, making data free would reduce the fixed costs of becoming an HFT firm. Reducing fixed costs will induce entry into HFT: costs are just covered by the marginal firm when data must be paid for, meaning that when data is free all existing firms at existing scale will earn profits above the cost of capital. This economic profit induces entry. Entry means there will be more HFT activity when data is free. (If lowering data charges also reduce the marginal costs of HFT, existing HFT firms will expand, reinforcing this effect.)
Again, entry will occur to the point where the profits of the marginal HFT firm cover the cost of capital. Moreover, many market participants will choose not to make the additional investments required to engage in HFT. There will still be disparities. Some firms will be faster than others (i.e., the firms that make the investments necessary to engage in HFT will be faster than “investors” who don’t make the investments in hardware and software and people necessary to engage in HFT.) Moreover, there will be more HFT activity, for the simple reason that the cost of engaging in HFT has gone down.
In other words: if you want to want to reduce disparities and discourage entry into HFT, don’t make data free, tax it. Smith and Bodek’s policy recommendation will have the exact opposite effect from what they intend.
There are other things to consider here. Data revenues represent a substantial source of income for exchanges. Forcing them to forego these revenues will affect their economics. It is conceivable that the loss in revenue will induce some exchanges to exit, reducing competition which would tend to result in an increase in fees paid by investors. Even if exit doesn’t happen, the loss of revenue may affect exchange decisions on other margins: they may choose, for instance, to invest less in systems or technology. I just raise this as a possibility: the effects of the loss of data revenues on these other decision margins are likely to be complex and subtle, and I don’t pretend to understand them, and to do so would require considerable research and thought. (Moreover, given my agnosticism about the welfare effects of financial trading generally, the effects of adjustments on these other margins on welfare are even more complex and mysterious.)
This analysis brings out a general point. You need to think through the equilibrium implications of policy changes, taking into account how market participants will respond on all margins. Making data free reduces the costs of engaging in HFT. This induces entry into HFT, and leads to more of it, not less.
In other words, in analyzing HFT and market structure generally, not just microstructure is important. Microeconomics 101 is too.