Relevant and even prescient commentary on news, politics and the economy.

Liquidity, Markets, and Pricing: A Contemporary Example

A lot of trading in the Fixed Income (and especially FX) market is done for “liquidity” purposes. There is often an underlying goal involved (e.g., push prices higher with small lots, sell large ones at the elevated prices) and frequently such strategies are discussed as “algorithmic trading.” (Example: the algorithm estimates that you will need to buy 5 $100MM lots of JPY at incrementally higher rates to be able to sell $1B USD at the higher JPY level.)

The liquidity of the “markets” is facilitated by algorithmic trading: the seller for the first five trades in the above example doesn’t care about the purpose of the counterparty’s trade, just that the price bid is agreeable.

Then there are the times when algorithmic pricing goes terribly wrong:

Eisen began to keep track of the prices until he caught on to what was happening: The two sellers of that particular book — bordeebook and profnath — were adjusting their product prices algorithmically based on competitors:

Once a day profnath set their price to be 0.9983 times bordeebook’s price. The prices would remain close for several hours, until bordeebook “noticed” profnath’s change and elevated their price to 1.270589 times profnath’s higher price. The pattern continued perfectly for the next week.

The biologist continued to watch the prices grow higher and higher until they hit a peak price of $23,698,655.93 on April 19. On that day “profnath’s price dropped to $106.23, and bordeebook soon followed suit to the predictable $106.23 * 1.27059 = $134.97.” This means that someone must’ve noticed what was happening and manually adjusted the prices. [italics mine]

As a mathematical exercise, the shift from $106.23 to $23,000,000 and change is clear: one dealer must price their copy higher than the other dealer. (If both do so, you get to the same point or higher even quicker.) Similarly, if both dealers price at a fraction below 1.000 of the other, the price will converge toward $0.00 as the algorithm progresses.

Consider the implication for a potential third seller, though. Depending on when they check, they may believe they have a book that will make them (if and when sold) rich. But the “market” they see is two computers offering against each other—there is no bid-side shown, and pricing “to sell” (say, $850K when both of the others are offered at around $1.7MM) implies that the third potential seller is carrying that asset at an inflated value.

Market transactions do not require two entities to like each other, or even to understand what the other is trying to do. Indeed, if your alogirthm is buying at 85.3 JPY/USD and mine is selling at that level, neither of us necessarily cares why the other is transacting. And the rest of the market sees an actual trade against which they can adjust their pricing.

It’s only when the algorithms are trying to do the same thing that $23MM+ books are offered.

The implication for mark-to-market valuation seems obvious, and is left as an exercise to the reader.

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Derivatives are useful for Asset-Liability Management. Nu?

I was going to post something a couple of days ago on Greece’s derivatives deal, but knew I was missing a key piece.

It became prominent yesterday, and Felix’s summary today gets it spot on:

So while it’s entirely fair to blame Greece for trying to hide its debt, and to blame Eurostat for letting it do so, I think that blaming Goldman is harder. It was surely not the only bank involved in these transactions, and the swaps were simple enough to be shopped around a few different banks to see which one could provide the best deal. Structuring swaps transactions is one of those things which investment banks do. If countries like Greece buy swaps in order to hide their true fiscal status, then that’s the country’s fault, not the banks’. No self-respecting bank would decline such a transaction because they felt it was unfair to Eurostat.

Yes, I’m sure that Goldman put a team of people onto the Eurostat rules and made that team available to the Greeks. But let’s not blame the advisers here, for structuring something entirely legal and which the Greeks and Italians clearly wanted to be able to do all along. This is a failure of European transparency and coordination; Goldman is a scapegoat. [emphases mine]

In the “good old days,” some corporate treasurers would use swaps because they were an off-balance sheet way to bet on the movement of Treasuries. But the good ones were using them for asset-liability management: reducing their cost of funds and/or the risks associated with that funding.

Greece is Asset-Liability Management Writ Large—and they made certain that the Eurostat agreements specifically permitted them to do it. Only an economist would call the result an unintended consequence; the finance world will be surprised if they were the only ones.

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Heavy Flow (not an iPad post)

Was 2009 a great year to be a bank? The headlines all say so. (The 140 U.S. banks that were closed by the FDIC last year may disagree some.) But, as Isabelle Kaminska of Alphaville notes, very little of the gains posted for last year came from anything related to talent:

Deutsche Bank reported net income of €5bn for the year 2009 on Thursday, compared to a €3.9bn loss in 2008.

This, we would say, is a pretty impressive turnaround in anyone’s business….

Deutsche attributes much of that growth to the successful re-orientation of its business towards customer business and liquid, ‘flow’ products. While it’s not broken out within the results, we’re willing to bet that a large slice of that re-orientation was therefore focused on managing flow emanating from the group’s ever growing synthetic exchange-traded-product and foreign exchange businesses — both of which happen to do very well when spreads are wide, and volatility is high.

When I first started working in the investment side of the banking industry, 20-some years ago, the traders and marketers were especially careful to distinguish themselves from the “retail” side of banking. Indeed, the retail bankers were described as “9-6-3” people: lend at 9%, take deposits at 6%, and be on the golf course by 3:00.

Now that that same type of effort is producing all those record profits, is it time to decide that the legendary “management skills” of Jimmy Cayne, Vikram Pandit, and Neutron Jack (who turned GE from a products company into a finance company) might not have been all that different from that of a polyester-suited small-town bank manager?

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