The DXY, the spot market for the US dollar, declined 7% in July. Stocks were up 7%. May was a good month for the DXY, which rose from 81 to 87, roughly. May crucified equities and gave us the Flash Crash on the heels of a surge in the value of the dollar.

Is it six of one, half a dozen of the other? The dollar in your pocket loses 7% of its purchasing power versus other currencies in July. Stocks appreciate 7%. Call me simple, but it seems that when a thing you buy is worth more because the thing you buy it with is worth less, that these sort of cancel each other out.

Which brings us to making and taking liquidity, the main method by which traded shares move today. In this “maker-taker” model, market centers pay participants to provide shares that attract customers, and charge customers to consume these offered shares. The spread is profit. At BATS Exchange the cost gap between consuming and providing shares is one penny per hundred shares. On the Nasdaq and the NYSE it’s about five or six cents, but narrows if you offer tens of millions of shares daily.

This is crucial to understand. If you conclude that your volume is buying and selling meeting up, that’s true only sometimes. Most of the time, buyers are consuming shares offered by other market participants whose principal job is to keep the liquidity flowing, for pay. This is why high-frequency trading exists, really. Technology and human ingenuity adapted to market structure built around incentives. So now, there are systems doing both the providing and consuming, and if the spread between the two prices is a penny, and your stock moves two pennies, why that’s riskless profit. Mind, that’s harder to do than it seems!

Where do shares come from that liquidity providers offer for sale? Somebody always has inventory. Sometimes it’s coming from major broker-dealers whose millions of retail and institutional account holders don’t realize that their positions are used to generate profits for market-making operations. This is the main reason why Citadel invested in E*Trade. In many other cases, shares simply move from place to place at high speed.

To do that, traders can arbitrage different structures. Most market centers now, like BATS, Nasdaq, Direct Edge, and so on, are “time-priority” models, where the first to show up at the best bid or offer gets to complete the trade. On the NYSE floor, it’s a “parity” model that gets apportioned to all parties priced at market. So if you’re fast enough, you can move shares from parity to time priority and back and forth. This constant replenishment generates revenues for the firms doing it and looks like massive volume. It’s often the same liquidity appearing again in different places.

What’s good about this? It keeps price spreads tight, and it ensures that vast numbers of securities, regardless of appeal, offer anyone wanting to transact in them an easy, ready market. If you’re asset allocation managers, these are great conditions. Think of it like swiping your credit card through a reader rather than needing the exact cash price each time you buy.

What’s bad about it? Number one, market centers are motivated to entice volume that isn’t real. They make money through transactions. More transactions, more data to monetize too. This is not the fault of exchanges. They are businesses producing returns for shareholders. But if parties matching your product with buyers and sellers are financially incented to attract middle men, in time your market is most appealing to intermediaries and least appealing to real buyers and sellers.

That’s what maker-taker models encourage. Transient intermediation. It’s the most reliable way to make money. If 80% of volume is moving from place to place, and you’re in the 20% buying and holding, what form of activity is more likely to produce a return on investment? Clearly, making and taking liquidity, not owning things.

But the biggest problem is the same one afflicting the US dollar. In stock markets now, the maker-taker model has removed the focus of market participants from the value of businesses to the supply or demand of shares. The study, manipulation, and maximization of liquidity movement have come dangerously near to disconnecting underlying business fundamentals from stock markets. Intermediaries trade stuff for spreads. They don’t own investments for their intrinsic value.

This is true of the dollar too. Its value bears no connection to underlying national productivity or assets. That’s the essence of “fiat” currencies, and we’re near now to having “fiat stocks” too. Movement of the dollar from place to place alters the value of all the goods and services denominated by it. In time, no one knows the value of either the goods and services or the currency in which these things are valued. Then, the data derived from transactions in it are incorrect or distorted, too.

Think about this: does the same thing happen in the global economy that we described with the DXY and stocks? What if it’s just yin and yang of currencies and goods and services, with no real change in economic output? That path would lead almost ineluctably to large national debts.

Oh. Hm.

We’d warned that markets showed excessive arbitrage. Arbs capture net spreads between opposing trades and care little about price appreciation. When it’s high in the broad markets (and in specific issues, too), it often points to impending switches in the direction of money, say from one mix of assets to another. Why? Traders apparently detect algorithmic activity and move to profit from it.

We’d seen the same thing in June and thought it might occur again. It didn’t, and the market removed all doubt that we were the fools we appeared to be.

Or so it seems. Are investors responsible for this nice recent run back to positive turf on the major market measures? We can only share general observations from data. The data are what they are. We observed two developments on 7/22. First, Nomura traded more than half our client base that day, a rarity. Nomura had become a primary dealer to the Spanish Treasury a few days earlier, and it’s also a primary dealer for the US Treasury. It owns the trading platform Instinet and Lehman’s Asian and European operations.

How do those facts bear on equity trading when treasuries are a credit market? We can only speculate. But we believe banks can effect carry trades – borrowing and paying interest to earn higher interest on something else.

At the same time Nomura rippled through equity markets, we saw a surge in conventional program trading. Our measures differ from what the exchanges use. Different kinds mean different things. In this case, it was the biggest firms helping global institutions manage transitions from one asset class to another. While market volumes have been relatively light, this increase in market share by big program traders attracted a surge in mathematical trading, which identifies disparities in market structure and capitalizes on them.

Together these could represent a bait-and-switch, not fundamental investment. Banks with trading technologies and commitments to governments to help them fund operations could get money from overnight treasury or central-bank facilities at low cost and use it to trade in equity markets and attract demand. Profits from these operations provide funds for use in required bidding at government primary-dealer auctions. It also gives the bond market a head feint by pulling demand to equities to improve bond-market rates and prices.

We’re not saying this happened. But trading data show that confusion reigns in equity trading markets. That’s not a mark of rational investment. Money may respond sporadically to earnings, and we do certainly see that. But the broad behavior is a mix of discordant sentiments and reactions. We see that, too. Somebody buys in a dark pool, and programs react to it, and algorithms follow, and speculative traders and all the auto-quote systems (translation: “high frequency trading”) tag along, and none of it knows the worth or driver.

Following rules of deductive reasoning, we conclude that if it’s not rational, it’s something else. What else would it be? Well, what dominates the global financial agenda now? Government debt.

So be ye not lulled into a false sense of security, IR pros. Trouble lurks below the rapids. But for now, we’re rafting on big programs, which seem, right or wrong, to know more than you and me.

Speaking of conversations best had around adult beverages, no doubt many of you have laid awake nights wondering, “How does relative value arbitrage work, and should I care?”

First, arbitrage isn’t bad. It’s a path to both profit and protection. Universa, the hedge fund advised by The Black Swan author Nassim Taleb, follows relative-value arbitrage techniques to help clients offset risks (Incidentally, Nassim Taleb remarked about the May Flash Crash that when a bridge collapses, you don’t study the last truck that crossed it; you look for structural flaws.). So the first thing to know is that gaps, or spaces, between things offer chances to profit from spreads and opportunities to guard against an equal but opposite risk. And this stuff will at some inevitable point affect the price of your stock, so it’s best to know about it.

Any trading strategy that focuses on the spaces between things rather than the things themselves is a form of “relative value” arbitrage. Global Macro strategies dominated hedge-fund investment in the 1990s, accounting for roughly 80% of assets under management. It’s not new. And it’s a form of relative value arbitrage. Global Macro techniques are widely used today, except at high speed now. For instance, have a look at indices from structured-products broker Newedge for volatility and macro trading, and you’ll see that they’re predicated on fairly small returns – and losses. Lots of little returns at nominal risk make sense when uncertainty abounds. So it becomes common in stocks too.

Let’s use you as an example. Say your stock trades for $20 right now. A trader with a relative value arbitrage strategy might buy an option, either on your stock or for an index, and then sell your stock for a “long” volatility position. Buying your stock (the underlying asset) and selling the option instead is a “short” volatility position, because the trader is short the potential, or implied, volatility. The trader profits in a long volatility trade if your actual volatility is greater than the implied volatility of the option.

Now that sounds complicated, and it can be. But realize that it’s about the volatility, not your price or the price of the option. Think about it this way. If your volatility is 25% over the life of this trading plan, while the volatility of the option is 20%, that’s a profit, regardless of what caused the spread.

Now suppose the trader is a “liquidity provider” too, offering shares for sale. Now the trader might be able to “move” the price of your stock with high-speed trades, while also holding a volatility play. This is fairly common. If your stock moves from $20 to $18 and back in a day, that could be enough for the trader to profit. What’s more, say you trade five million shares a day and the trader sat between 300,000 shares of that volume with a machine. The trader might’ve pocketed $1,500 on this activity too, as gravy. Duplicate that in a portfolio trade of 30 liquid stocks and combine it with relative-value arbitrage plays, and pretty soon you’re talking real money that’s almost as easy as a government bailout.

So what do you do about this? No, the point isn’t what you do, but whether you understand what’s going on. If your stock’s price moves a great deal intraday but not a lot by the close, there’s a reasonable chance that traders are engaged in relative value arbitrage. And simply having that answer when the CFO stops you in the hall may be the most important thing for now.

Snot Mark is also a tempting description for what’s happening behind share prices and volume, at least at times. But Global Statistical Arbitrage is more accurate, and widespread.

It’s a term that can induce instant narcolepsy too, so we’ll make it interesting. The Nasdaq can be up, the Dow down. Your stock is up, your nearest peer, down. Overnight the Asian markets are up on “renewed enthusiasm,” while by mid-afternoon the following day Europe is down on “rising pessimism.”

It’s statistical arbitrage on a global scale. It can be confused for other things, such as investing, which it is not. Suppose you were buying and selling Robert Graham shirts and doing the same with off-the-rack Macy’s brand clearance shirts. Most times, the price difference between the two asset classes is constant, but slight differences in shirts, fabrics, times of day, and customer interest produce little gaps. It’s on those that you make your money.

To the observer, it would appear that a brisk business is being done. The Robert Graham shirts are really moving and that discount rack keeps clearing out. Ah, but little actual buying and selling is occurring since most times you’re procuring and dispensing the same shirts over and over, with little risk. I’m reminded of what a sharp Israeli client once said, no doubt borrowing it from a time-tested lexicon of smart observations: “We don’t confuse busy with productive.”

Arbitrage often gets people to mistake busy for productive. Arbitrage is the former. Traders weave currencies, futures and options, and global equities into an arbitrage model to capture small, quick price gaps. European banks are doing it. Classic institutional money managers are doing it. Broker portfolio trading schemes are doing it. The catalyst for the explosion of the high-frequency version is monetary intervention over the past two years. It distorts prices – creating a “Trader’s Paradise,” to borrow and twist that old rap song sung by Coolio and penned by Stevie Wonder.

By contrast, arbitrage is risky in markets without price controls or monetary intervention. If there’s no best bid or offer, no mandated one-penny spread between price points, how do you assess your arbitrage risk? You can’t. Yet the Synthetic Market Rip on May 6 is likely leading to more controls, more intervention. Arbitrage opportunity, and therefore risk of another synthetic rupture – if your market is dominated by intermediaries you don’t know its real value – increases. It’s going to happen again.

Solutions are simple. Remove price controls. Expand the supply of currency only when saved capital increases significantly, if at all. That way, the medium of exchange isn’t being used to correct gross failure but instead to match investment capital with opportunity.

And wait! There’s immediate good news beyond simple solutions. One upshot to arbitrage markets is that they winnow some wheat from chaff. We see stark market-structure differences between companies with tight messages and IR outreach adapted to market structure, and those doing the same old things the same old way.

Price is not the measure of solid IR. Sometimes great stories have extended gaps between the rational price and the noise from intermediaries. But real value returns as fulcrum. Knowing what’s productive in your trading and what’s just busy makes you cool in crowds of intermediaries. Alas, there’s a lot of busy right now, and not much productivity. Forewarned is forearmed.

When I was a fresh-faced goofy college kid, my Logic professor, who was Greek and credible with his Hellenic accent, would say that a thing was theory only until you provided examples in the real world.

We hear that notion regularly. “So what exactly do you see with market structure? Give us some examples.” You asked. We’re doing it. We’ll aim to give you them regularly. Each will be an actual, real-world example, though the confidential nature of the data we track may preclude names.

As background, we’re clustering trading volumes behaviorally. It’s not quantitative analysis, which follows price and volume. It’s sorting out volumes with different time horizons and purposes to see which kind is prevailing. Often, price and volume change little, while behind the scenes tumult ensues among parties trading for different time horizons and purposes.

Here’s an example: Two large technology companies engaged in a bidding war for a third company. One of the two bidders was our client. There are three basic dimensions to market structure: trade executions driven by rational investment theses; trades for risk-management purposes based on changing market information, economic data or portfolio risk; and speculation in which traders take the other sides of trades, or sit between buyers and sellers to capitalize on short-term price movement or liquidity fluctuation.

Speculators are the truth-tellers about rumors and deals. If outcomes are uncertain, the parties most likely to know are the ones whose whole business it is to figure such things out. They’re not always right, but their batting average in our experience is over 80%. We pay attention.

In this instance, we saw 100% uniformity in speculators’ conclusions about the deal. How? They all did exactly the same thing. Literally no arbitrager questioned the prevailing sentiment.

For the IRO, that’s powerful data to present to boards and management teams. “Market structure indicates that the market universally expects us to prevail.” That says two things: Either your business and resources are more potent than your competitor’s, or you had better prevail or be prepared to suffer a high price. As a kicker, the IR team also could tell the Board and management that stock price was unlikely to change much at conclusion of the deal. Such was the outcome.

These are the things we see every day with market structure analytics.

Speaking of derivatives, the dollar retreated today, and US equities rebounded. We all want it to be about investing. Commentary everywhere today polished bullishness to an economic sheen. But that won’t make it reflect reality. Money keeps buying short-term love because the direction of the dollar is like a blacksmith’s bellows on equities.

Tomorrow starts three days of expirations. We saw speculative trading spike last week, while European money returned on Feb 10. Traders had already discounted Greece’s woes and had gone searching for alpha. There are too many reasons for traders to pursue global statistical arbitrage (and regulators keep giving them more). Translating, that means it’s fun trading similar instruments in opposing fashion in different places to profit on spreads and timing, and not much fun investing in stuff.

What changes that? Funny you ask. We want to hate banks. We’d like it all to be Goldman Sachs’s fault that our economy is bloated on derivatives. Those blasted Wall Streeters and their nefarious schemes, like borrowing money at 15 basis points from the Fed, then using it to buy Treasuries paying 350 points of interest. Taking TARP cash and trading with it. Manufacturing reserves and using them to float atmospheric notional value swaps for obscene fees.

All that may be true. But banks don’t set interest rates on Fed money. Banks don’t decide, “Hey, let’s go con the Fed out of some cash.” Banks don’t determine the size of the money supply. Banks don’t write reserve rules. No, while we’re all tarring and feathering sellside CEOs, the folks spraying gasoline on everything and playing toss-the-match can be found at the Federal Reserve.

If we don’t want our stock markets to behave like roulette wheels, we must stop playing with Federal Reserve house money, which is cheap and artificial – or shall we say, derivative. Primary dealers from Barclays to BofA march as the Fed orders. Don’t think those folks who OK’d 100-cents-on-the-dollar payouts to AIG counterparties are aw-shucks hayseeds who got jobbed. They knew what they were doing. We should be asking why, rather than railing at the foot soldiers. We may not like the answer, but it’ll be true.

Speaking of money, I personally put cursor to Excel workbook and tallied 2009 earnings before non-cash items and taxes for seven of the primary dealers – the eighteen big US, European and Japanese banks commissioned by the Federal Reserve to make orderly markets in dollar-denominated US obligations – and came up with $185 billion. With a ‘b.”

From whence came these profits? Who deployed cash in the capital markets? It wasn’t institutions and individuals. The US government deployed about $3 trillion. Lord only knows how much came from other global central banks.

Lesson of the day: Governments create money. Goldman Sachs is not responsible for it. Banks work with what they’re given. If they get a gigantic mountain of cash, you can bet they’ll work with it.

If we want more investing and less trading, we might dump all that snow on the east coast atop the Federal Reserve and put the fire out. Unless Whistler offers a better deal.

The NYSE and Charles Schumer were talking today about rules for dark pools. The NYSE is partnered with dark-pool operator Liquidnet and is building a massive high-speed trading facility in New Jersey. The Nasdaq meanwhile plans to launch an exchange next year that will give priority to orders of size, to compete with the size advantage dark-pool operators offer.

What’s going on here? Politics, mostly. We’ve said plenty about this stuff. But the regulators – and IR folks too we fear – continue to misunderstand the central issue. The IR profession is about supporting capital formation and fostering productive, creative enterprises. At the rate we’re going, none of us will have jobs. If trading things is an end unto itself, why bother with all that work to start and run companies? Take your idea to a broker, have them issue an exchanged traded note representing your idea, hire an accounting firm to handle regulatory and financial reporting, and that’s all you need. Traders, have fun!

We’re being obtuse. But dark pools are like black markets. Black markets form in response to price controls. We can go back to the order-handling rule in 1996 in which the SEC set out to “create better pricing opportunity.”

Come forward to Reg NMS. It was a legitimate effort to minimize market arbitrage, but it in effect is a gigantic price control. It says that all trades (there are exceptions but stay with us here) must execute at the best national bid or offer. That’s like pouring Niagara Falls through a funnel. You have literally millions of different prices trying to match up for securities, but trades can only execute, simplistically, at that one best price. That supposes that all buyers and sellers have only one thing in mind: price. If that were the case with cars, we’d all drive Tata Nanos.

Dark pools formed to serve audiences that wanted something more than the best price at this split second in time without regard to supply. Who uses them? Mostly big institutions wanting to move sizeable amounts of shares without interference by parties with other objectives such as speculation, rebate-capture, high-frequency trading and risk-management.

What’s the response from regulators? To clamp down on dark pools.

We’re oversimplifying. And we have good friends running high-frequency trading platforms. We mean no offense to anyone. But the problem in our equity markets is that they’re efficient for parties that want the best price and which don’t want to commit capital and own things.

But they’re very inefficient for capital formation. In 1996, 675 companies IPO’d in US markets with prices over $5. In 2008, 21 such companies debuted here. Money has shifted to private equity by the trillions, and to international markets with fewer price controls.

This had better matter to us more than anyone else. This is our profession. Let’s defend it, rather than slice our collective noses off to spite our faces.