Price Discovery Thought Piece

April 11, 2015

Price Discovery Thought Piece

Here’s the riddle of institutional equities trading: experience and reason suggest block trades
have enormous potential to reduce transaction costs. However, block trades don’t happen in
the dark. And only a fool would display a large order in a lit market. So block trading falls short
of its potential.

But there’s an answer to this riddle. It’s called price discovery.

For a period of time algorithmic trading provided an attractive alternative to other methods.
Over several years the market lacked tools to interpret and exploit the trail of market data left
by large institutional algorithms. That era is over and has been for some time.
To be sure, block trades also leave a trail of market data. But large trades are inherently more
difficult for third parties to interpret as there are fewer of them. Plus with large trades, more of
your order is done before the market has a chance to figure you out.

Back to the riddle. The probability of a block trade occurring in the dark is pretty close to zero.
Yes there are examples, great ones, where traders received large fills in dark pools. But those
aren’t really trades. They are coincidences. Two particles colliding. In fact in the stock market
that’s all that ever happens in the dark – particles collide. When the particles are small and there
are lots of them, the probability of a coincidence is high. When the particles are large and few
the probability of coincidence is low.

That was the lesson of Optimark, the great noble failure that took the equity trading world by
storm fifteen years ago. The brainchild of the electronic trading pioneer and successful
innovator Bill Lupien, Optimark intended to solve the riddle by revolutionizing the price
discovery process. It offered the promise of a market‐wide optimization of supply, demand, and
price. All market participants would submit something called a satisfaction density profile –
your degree of happiness if you were to trade across a range of prices and sizes, up to your

Optimark had standard profiles for different types of traders. A market‐maker profile would be
downward‐sloping – satisfied buying larger quantities only at lower prices. An investor’s profile
might be the reverse: willing to pay higher prices, but only for more size. Optimark received
everybody’s profile and arrived at the prices and quantities that maximized satisfaction across
the market. At the time I wondered if traders were really interested in maximizing the mutual
satisfaction of all traders, as opposed to just maximizing their own.

Not unike IEX today, Optimark was all anyone talked about for a while. The publicity was
massive and expectations ran the gamut. In the end, a number of factors made it difficult for
Optimark to achieve critical mass. The product was pretty complicated. They faced formidable
political opposition from the New York Stock Exchange. Optimark’s expenses were high and
they couldn’t sustain their burn rate long enough to break through.

The big Optimark takeaway is that price discovery doesn’t happen in the dark. To produce more
than mere coincidence ‐‐ to produce a trade that adds value and reduces cost ‐‐ one side of the
trade has to make itself visible before the trade. That’s the lesson: one side has to go first and
make itself known. One trader has to take a risk, to stick his neck out. The other side aggresses.
That’s how a trade takes place. One trader offers a choice. Another trader chooses – paying the
spread and trading price for immediacy.

Not that Optimark had a hard time getting people to go first. A lot of firms participated over a
sustained period of time. The lesson of Optimark is not that it’s hard to get someone to submit
orders. The lesson of Optimark is that nobody aggresses in the dark.

This is not as obvious as it may appear. After all, why not submit super‐aggressive dark orders?
If you’re working an order with alpha and willing to pay up to trade in large size, just make each
order immediate‐or‐cancel with a large minimum fill. Either the trade gets done or no one
knows you tried. Where’s the harm in that?

The obstacle is psychological but it’s real. Traders expect aggressive orders will be filled. If a
trader fires the harpoon a few times he expects to hit something. Come away with nothing and
after awhile he gets tired of trying. Nothing more complicated than that happened with
Optimark. In the dark there’s no difference between a near miss and a far one. Aggressors got
tired of the misses. And they moved on.

So block trades reduce transaction costs but they require light, as traders only aggress against
block orders they can see. But light can trigger signaling if you don’t trade and can attract
adverse selection if you do. Isn’t that an intractable problem? Not necessarily. For instance, a
1991 paper on “Sunshine Trading” by Stanford professors Paul Pfleiderer and Anat Admati
argued that benefits may accrue to institutional traders and their counterparties when they
“pre‐announce” the intention to trade. Their assumption was that per‐announced merchandise
was perceived to be “informationless” – that no one would go public pre‐trade with the orders
with the most alpha. But in the real world even that isn’t clear.

In today’s market structure there are two precautions you can take to display block merchandise
safely: one, limit your display to a high‐value universe. Reg ATS provides the regulatory
framework to allow technology to be deployed that addresses this need. An alternative trading
system can qualify its participants on a need to know basis and only display to the most likely
potential counterparties. Two, use price both to mute the signal, and to compensate for the cost
of signaling and adverse selection. A low‐ball bid does not signal much urgency. Yet that bid can
provide an urgent counterparty with an opportunity to exchange price for time.

Yes, every once in awhile an urgent counterparty is going to run you over and inevitably those
will be the trades that the portfolio manager notices. But they will be balanced by trades that
go your way, and the net bias against you will be built into the spread you charge. Consider that
both parties are going to trade anyway. Imagine the contribution if in these situations you could
capture 15 basis points of negative slippage on even just 10% of the order.

This is where future innovation is likely to take place. In the years ahead much analytical work
will be devoted to determining pricing methods that encompass the risks and rewards of both
displaying and aggressing. These methods will be incorporated directly into algorithms and will
be deployed on the desktops of traders executing trades by mouse or by phone. In the process,
trading will become more efficient and costs will fall for both parties to the trade. That’s the
benefit of leaving a thinner trail of market data for third parties to analyze.

In short, dissatisfaction with algo trading as it exists today is making much of Bill Lupien’s vision
relevant again. You heard it here first.