Very true.

The same is true about trading.

When deciding how much personal information to reveal on Google Buzz or Facebook, the correct answer will depend partly on who you are (there is a big generational element to this), how much information you have to protect, and what you want to achieve.

If you are 18, have very little private information, and want to socialize as much with as many people as possible, then you are likely to reveal a lot (maybe too much) on Facebook.

If you are an adult professional (with a lot more at stake) and you just want to know what your former college buddies are up to, you might reveal a lot less.

So with trading.

Every decision about trading tactics (i.e. how to get the trade done, not whether to buy or sell) should be driven by the investment strategy.  For example:

• If you are buying a small quantity of a highly liquid stock because you anticipate positive earnings, then you might well choose to cross the spread and pay the offering price to get your order filled quickly before (you hope) the stock spikes up.  Because your order is immaterial to daily trading volume, you have little to lose and (you hope) a lot to gain by showing your entire order to the market.

• On the other hand, if you are a large fund trying to accumulate a substantial portion of a small-cap (and probably illiquid) stock which you intend to hold for 2-3 years, you are more likely to let your order sit in a dark pool, go to a broker for capital commitment, slice your order up using an algorithm, etc..  In general you will be more passive and less likely to show your hand.  You certainly wouldn’t show the entire order on the exchange, because you have a lot to lose and little incentive to rush.

The European regulation that addresses “best execution” is MiFID.  MiFID recognizes that different investors have different strategies and therefore will make different trade offs in their trading tactics… They do not have a single/uniform definition of “best execution”, any more than Facebook has a single definition of “appropriate privacy.”  Instead, MiFID recognizes that best execution is not limited to execution price but also includes cost, speed, likelihood of execution, likelihood of settlement and any other factors deemed relevant.

In contrast, the US regulation that addresses “best execution” (RegNMS) defines it very narrowly as getting an order executed at or between the best bid and the best offer on a publicly displayed market.  Never mind that you may want to do 100,000 shares of a small cap name and there may only be 100 shares at the inside quote.

In practice, traders in the US markets need to make trade offs and answer a variety of questions, including:

• How important is certainty of execution
• How important is speed of execution
• What is an acceptable market impact
• How much of their hand to show
• etc..

One of the most important tradeoffs is between how much of your hand to show (resulting in market impact) versus certainty and speed of execution.  i.e. If you show your entire order to the market you have a much higher probability of getting it filled quickly, but you will move the market.

It is instructive to consider some of the recent innovations in the US equities markets (or innovations that, while not strictly recent, have become more popular in the last few years) in light of these trade offs.

Take algorithms.  Algorithms allow a trader to slice a large order into many small orders and spread them out over time, usually to be executed on displayed markets that only offer liquidity in small quantities.  The trader can thus comply with best execution obligations (i.e. each individual “child” order is executed at or inside the NBBO) while minimizing market impact and information leakage.  (If you were to look at the average price achieved by all child orders that make up a single parent order, the parent order may well be executed at an average price better or worse than the NBBO at the time the parent order was created.  So if the client were buying and the market rose dramatically over the life of the parent order, did the client get “best execution” just because each individual child order looks good?)

Dark pools provide another alternative.  In general they offer less certainty or speed of execution, but also lower market impact.  An order may sit for days or weeks in a dark pool until it is matched.  (Your mileage may vary…  On a pure institutional/block system like LiquidNet, the order is likely to sit until it is matched by a contra-side institutional order.  On Millennium, it may be executed incrementally as it is matched against algorithms, but exposure to such smaller orders also creates greater risk of information leakage and gaming.)

Now let’s take a look at flash orders.

In a nutshell, flash orders allow a client to send an order to an exchange, and optionally permit the exchange to briefly “flash” the order to liquidity providers who can fill the order at a better price.  This improves the certainty of execution, and may provide price improvement over the NBBO, but it increases information leakage.  Should the client care?  Well, if he is not concerned with information leakage (maybe he only had that one order) and he wants to “get done” now, then he is probably happy to use a flash order.  If on the other hand, his order is one of many child orders for a larger parent order, he may prefer to be more passive and decrease information leakage.

Some teenagers expose all their photos and personal information to the world (including, ahem, college admissions officers), while others only permit their friends access to their information.

On the face of it, this is good.  Flash orders provide a useful tool that allows a knowledgeable investor to make a reasonable tradeoff.

But consider Google Buzz.  Google Buzz was rolled out last week as Google’s social network, its answer to Facebook and LinkedIn and MySpace.  Cleverly, Google leveraged users Gmail contacts to automatically create “followers” on Google Buzz, and (by default) allowed any user of Buzz to see any other users followers.  Google very quickly found out that many users don’t want the world to know who all of their Gmail contacts are.  Especially if that list includes doctors, lovers, bookies, or other relationships best kept private.

So how does this relate to Flash Orders?

Google’s Buzz appears to be a reasonably clever service, and it actually does allow users to control their privacy.  But Google erred by making the default privacy settings very non-private, and by making the privacy controls too difficult to find.  They made a decision on behalf of their customers to expose those customers information.  They made the trade off.

Likewise, the problem with flash orders is really not flash orders themselves.  The problem is that in many cases a broker chooses to submit an order as a flash order, with the client never realizing (never mind understanding) the trade off that the broker has made on the clients behalf.  Of course this is just a symptom of some more fundamental problem, namely:

• RegNMS dictates a very narrow definition of best execution, which has had the unintended consequence of encouraging innovation that helps the customer achieve their execution objectives despite this regulatory constraint.  Maybe we should have a broader definition of best execution that incorporates the clients trading objectives.

• Brokers incentives are not aligned with their client’s objectives.  Which is why brokers receive rebates for order flow, use flash orders, and generally do things that maximize profits for brokers rather than for customers (the phrase that comes to mind is “where are all the customers yachts?”)

• Even asset managers, who act as the proxy for most investors, have incentives (maximize assets under management) that are at cross-purpose to their client’s objectives (maximize risk adjusted return.)

But those issues are subject for another post.

Brennan

Why would Google announce that it is getting into the broadband business (see http://www.google.com/appserve/fiberrfi/public/overview)?

To understand that, you need to understand what is strategic to Google (for background, Chris Dixon sets the table very nicely at http://cdixon.org/2009/12/30/whats-strategic-for-google/)

Microsoft wants you to live in Windows and Office, hence anything that makes those less relevant that (see e.g. Netscape) is a threat.

Apple wants you to live in MacOS and iPhoneOS, buy your media through iTunes, etc…  (Apple is really a lot like Microsoft in that way, they just execute a lot better… and a lot of that has to do with the fact that they never thought of themselves as just a software company like Microsoft, they think of themselves as a total integrated experience company.)  Anything that disrupts or taks you away from that total integrated experience (as insanely greatly designed by Apple) is a threat (see e.g. flash).  This is why Apple bought Quattro Wireless (a mobile ad company):  so you can consume ads within apps on your iPhone/iPad instead of on the web… or more to the point, so that iPhone/iPad app developers have a way to monetize their apps and thus an incentive to develop for iPhone/iPad instead of for the web.

So what does Google want?  Google wants you to live on the web (unlike Apple or Microsoft which really want the web to be secondary to their platform) where they can deliver targeted search-based ads.  Let’s consider some strategic moves by Google:

• Google and Apple are inherently at odds with each other, since the more time you spend in (for example) iPhone OS and iPhone apps, the less time you spend in the browser (looking at Google delivered ads.)  Which is why Google is promoting Android, a web-centric phone OS.  When you use an iPhone app, you aren’t on the web consuming Google ads (the internet is there in most apps, it is just buried as a communications layer that you don’t see.)
• Google wants fast, ubiquitous, cheap, and open internet connectivity, which facilitates you spending time in web-centric applications (like Google search, Docs, Google mail, Google Apps…) and viewing ads delivered by Google.  This is why Google pushes net neutrality… That commoditizes internet  access and prevents ISPs from disintermediating Google.  That is also why Google bid for wireless spectrum… to push the market to provide open access over wireless.
• With Google voice, the web is the platform… Since I started using Google voice, I find myself spending much more time in Google contacts, placing calls via the web, etc…  Google voice has shifted my phone experience to the web.  Where it sells ads…

So why is Google “planning to build, and test ultra-high speed broadband networks” (which, by the way, will also be “open, non-discriminatory, and transparent”, i.e. an embodiment of net neutrality.)

Not because Google wants to be a telco.

Partly because Google wants to create some competition to the incumbent cable companies and telecom companies.  But Google can’t create a material level of competition.

What Google can do is to create pressure, through the media and through regulators, on the cable companies and telecom companies.  That pushes those companies to provide high-speed, open broadband networks (which, not coincidentally, make it much easier to live on the web, happily using Gmail, Google Apps, and consuming Google ads.)  Imagine your congressman or city councilwoman asking the local cable company when they come up to renew their license “Why can Google offer an open internet service with 1 gigabit fiber-to-the-home connections when you only offer a crummy slow 5 megabit connection as part of a bundle with 6 movie channels????”  Watch the cableco executives squirm as they try to explain that one.

So does Google want to be a telecom company?  No, they want to offer proof points and create pressure for faster and more open internet connections, so you can live on the web and consume ads delivered by Google.

Not that there is anything wrong with that.

Brennan

More detail at: http://worldrg.com/showConference.cfm?confcode=FW10009

Brennan

http://www.cmconsortium.com/high-frequency-trading

http://www.cmconsortium.com/dark-pool

In general the confusion stems from the fact that computers play a role in all of these, and so they tend to all get lumped together.  In order to have a healthy debate on US equity market structure, we should all have a good understanding of what these things are, so I will use this post to explain some key terms.  Others may use these terms differently, but I have outlined below what I consider to be widely accepted (by actual practitioners) definitions.

One important distinction to understand with any computer based trading is the phase of the trading process that is being automated.  At a high level:

1. Pre-Trade: An investor (whether an individual investor, a portfolio manager at a fund company, or a computer program acting on behalf of an investor) performs some analysis that leads to a decision on whether to buy or sell a stock.  This is where “alpha” is created.
2. Trade Execution: Once that decision has been made, a trader (or a computer) is responsible for implementing that decision, and uses discretion to decide when to place an order to buy or sell, what type of order to place, where to place the order, and the size of the order.  It is important to understand at this phase of the cycle that the trader is not deciding whether to buy or sell, but how to do so in the way that best meets the investment objectives that led to the buy/sell decision.  (For example, if the investor decided to buy because he is hoping for a positive earnings report the next day, the trader may want to buy more aggressively to get into the market ahead of that news.  If the investor decided to buy a large block because he has a 5 year view on the companies outlook, the trader may take a slower and more passive approach to avoid bidding up the price of the stock.)  The ideal execution tactic should support the investment strategy.
3. Matching: Once the trader places his order, a variety of different mechanisms may be employed to actually match a buy order with a sell order.  Before the widespread adoption of computers, this matching function was performed by specialists and floor traders on the floor of physical exchanges, and by market makers and brokers over the telephone.

For each of the different techniques described below, I will identify where they fit in this simplified three-step process.  Since these different techniques evolved over time, I will describe them in (roughly) the historical sequence in which they appeared in the market.

Program Trading: Whether you call it Program Trading, Basket Trading, or List Trading, it is one of the oldest forms of trading using computer technology.  Often used as a term by the media to describe ALL forms of electronic trading, “program trading” best describes when a trader submits a list (or “basket”) of orders for simultaneous (or near simultaneous) execution.  Program trades can be used to achieve a number of investment objectives including transitions (when a plan sponsor moves assets from one money manager to another), rebalancing, moving funds into or out of an index, etc.. “Program trading” is fundamentally a mechanism to execute a series of trades across a portfolio of stocks.   The New York Stock Exchange defines program trading as “a wide range of portfolio trading strategies involving the purchase or sale of 15 or more stocks having a total market value of $1 million or more”.  While program trading is generally automated today through the use of computers, the fundamental strategies (e.g. of tracking an index) preceded widespread automation.  Automation just makes program trading faster and easier.  More important, program trading is used as a mechanism to implement an investment strategy.  It is not a strategy in itself, and therefore fits into phase #2 in the process outlined above.  Portfolio Insurance, commonly blamed for the market crash in 1987, is one (but only one) application of program trading.

Electronic Trading: While “programs” could be executed with minimal computer technology, and were around even when most trades were executed by specialists on the (physical) floor (or market makers in the over the counter markets), the next phase in automation was when computers were introduced into the actual process of matching buy and sell orders.  So the first proper use of the term “electronic trading”, and still its best definition, is the use of computers to  match orders, i.e. step #3 in the process above.  In the US, services such as Globex and Instinet were pioneers in electronic trading.  Exchanges in Europe were among the first to go electronic, eventually followed by the US exchanges (in part by merging with existing non-exchange based electronic markets.)  Some of these systems were and are continuous real-time systems, others are “point in time” matching systems (or “crossing networks”), and while they take different approaches, they are all fundamentally matching buy and sell orders electronically.  Today most trading of simple securities such as equities is electronic, with lower rates of technology adoption in markets where instruments are less well standardized (e.g. credit derivatives.)

Dark Pools: While the name sounds sinister, Dark Pools developed to automate the function of the block trading desks that used to mint large amounts of money for sell-side firms.  Originally called “Crossing networks” (early examples included Lattice, ITG Posit, the Instinet Cross, etc.) they matched (or “crossed”) large blocks of stock.  Today they include ITG Posit, Pipeline, Goldman Sachs Sigma-X, UBS PIN, Credit Suisse Crossfinder, NYFIX Millennium, and many more.  The appeal of dark pools is not that they allow traders to hide surreptitious or illegal activity, but that they allow traders to buy and sell large blocks of stock without moving the market.  Originally dark pools only matched large blocks of stock.  More recently dark pools opened up to algorithms (see below), which allows traders to expose large blocks of stock to the orders generated by algorithms and without moving the market (because the block of stock is not displayed, nobody will see a 10,000 share sell order suddenly on the NBBO and watch the price drop in reaction.)  They allow algorithms the opportunity to trade against the large blocks and benefit from price improvement (i.e. the ability to get a price in-between the best bid/offer).  Some dark pools are operated by independents (e.g. LiquidNet, Pipeline), others are vehicles for “internalization” by brokers (i.e. they allow brokers to trade their customers orders against each other and against their own inventory, providing opportunity for price improvement and reducing exchange fees.)  Dark pools implement the third step (matching) in the 3-step process described above.

Algorithmic Trading: Once the function of matching orders was automated, networks established to connect to these markets, and programmable interfaces such as FIX were developed (as opposed to dedicated screens which is how early electronic platforms were accessed), it became possible to automate the function of delivering orders to an electronic market.  One of the jobs of a trader is to manage the flow of orders into the market so as to achieve “best execution” (a topic for another post).  With electronic interfaces in place, it became a (relatively) straightforward process for programmers to develop automated systems that took an order from a customer or portfolio manager, sliced that order into smaller pieces (which would have less market impact) and send them into an execution venue.  This function is performed by computer algorithms, and came to be known as algorithmic trading.  Those purists with degrees in Computer Science may protest that ALL functions performed by computers are executed by algorithms, and an introductory course in algorithms is mandated in all Computer Science curriculums offered today.  Mathematicians would argue for an even broader application of the term algorithm, and they would be right.  But in the trading world, the term “algorithm” is generally understood to mean automation of the (very tactical) process of placing a (usually largish) order into the market, often by means of breaking it into smaller chunks and managing the timing of those “child orders” into the marketplace so as to achieve a particular objective.  (That objective is generally formulated as a benchmark to be tracked, such as Volume Weighted Average Price.)  In other words, algorithms are used to automate step #2 in the process above, and contrary to the assertion in the NY Times:

• They don’t “execute millions of orders a second” (they generally spread the execution of an order out over hours, or even days and weeks, placing “child orders” into the markets with intervals of minutes or hours.)
• Just like human traders, they do “scan dozens of public and private marketplaces simultaneously” both to assess the amount of liquidity in the market (e.g. to avoid placing orders too large or too frequently and thus cause prices to move) and to determine where best to place the order.
• They do try to detect “trends before other investors can blink”, but primarily to avoid getting poor executions, e.g. to avoid accidentally buying at the peak of a transient spike in price (not to scalp investors).
• They generally don’t “change orders and strategies within milliseconds”, and while they might change orders (e.g. if the market starts moving against them) they do so to implement a clearly defined strategy (e.g. “buy 100,000 shares passively without moving the market” or “sell 10,000 shares at the daily volume weighted average price” or “buy 20,000 shares at as close a price to now as you can.”)

Algorithmic trading came into place for four reasons:

• First, algorithms are simply a way of automating what traders already did.  That is to say, looking at multiple markets and determining where best to place an order (called “smart order routing” when computers do it), and breaking large orders into smaller chunks that can be released into the market at the optimal time.
• Second, as the trading process has come under the microscope to be measured, and as benchmarks such as VWAP have been widely adopted, algorithms provide a simple and low cost way to execute against a benchmark.
• Third, as the buy side has assumed more responsibility for its own trading, algorithms provide a low-cost way to execute trades across multiple brokers without hiring large trading staffs.
• Finally, RegNMS has imposed “best execution” rules that require trades to be executed at the National Best Bid/Offer, which has resulted in smaller orders being shown at the NBBO, and as a consequence has driven traders to slice institutional orders up into retail sized chunks (to match the orders at the best bid/offer.)

Strategy or Black Box Trading: While algorithms are focused on the tactics of trading (i.e. given a decision to buy or sell a quantity of a security, how is that decision best effected), strategies or black box trading systems are one step higher in the food chain.  Such systems continually scan streams of market data, analyze them for patterns, and make decisions on whether and how much of a security (or usually a set of securities) to buy and/or sell.  They fit into step #1 of our 3-step process.  This includes strategies such as “High Frequency Trading” and statistical arbitrage (which may or may not be high frequency).  They are quantitatively driven techniques, implemented using high-speed computers.

High Frequency Trading: High frequency trading, very simply, encompasses a range of trading strategies (and therefore fits into step #1, i.e. pre-trade) that involved the rapid buying and selling of securities (and often the rapid posting and cancellation of orders as well.)  Broadly there are three classes of strategies pursued.  These strategies are not exclusively high-frequency, although they are used by high frequency traders (among others):

1. Automated market making, where the HFT trader posts buy and sell orders simultaneously, makes some money (maybe) on the spread, and makes some money on rebates paid by exchanges in return for posting orders.  Like the market makers of old, HFT firms make money on some trades, lose on others, but expect to make a net profit across a large number of trades.
2. Predictive traders, where the HFT employs software that does try to “spot trends before other investors can blink” and like all momentum traders, try to buy before the price has run up and sell out before it crashes back down.  In a way they are like the many day traders in 1999 who bought internet stocks in the expectation that prices would run up, and tried to sell them before everyone headed for the exits.  While this time the game is measured in milliseconds, the winners are still those who bet right, and get out early enough.  Of course some are stuck holding the bag after the price has collapsed.  And this time it is all done using computers.
3. Arbitrage traders, who look for short-lived inefficiencies in the markets, buy the (relatively) undervalued asset while simultaneously selling the (relatively) overvalued asset, and unwind the trade when prices come back into an equilibrium position.  Simple examples are pairs trades (i.e. a simple pair of securities where some price relationship should hold such as options with differing durations, two different share classes of the same stock, etc).  More complex examples are statistical arbitrage, where there is a relationship between complex baskets of securities.

Unlike algorithmic trading, where computerized techniques are used to establish or exit from a long or short position, and where portfolio turnover ranges from high to low (with average holdings potentially multi-year), high frequency strategies are neither long nor short but market neutral, portfolio turnover is extremely high short (average holding periods measured in seconds or milliseconds), and the strategies aim to end the day “flat”.

Direct/Sponsored/Naked Access: As trading has become more electronic, many buy-side institutions have chosen to take on the trading function that was historically performed by sell-side traders.  In some cases this is to better control the execution of their trades, in other cases it is simply to reduce costs (or for the broker to reduce costs by pushing clients from full-service to a low-touch model.)

• The first, and still most pervasive form of this is direct market access, where the broker provides the buy-side institution with some combination of a terminal (execution management system) and connectivity, the client sends orders electronically to the broker, where they pass through the brokers order management, risk management, and compliance systems, and on to the exchange for execution.
• As clients became more sensitive to speed and latency, many brokers offered their clients sponsored access, where the client connected directly to the exchange, using the brokers membership and clearing through the broker, but bypassing networks that route from the client through the brokers data center and on into the exchange.  Instead the clients either collocated with the exchange or connected directly.  In this model, the client still sends their orders through the brokers risk management and compliance systems, either through software written by the broker (e.g. Lime brokerage, which also provides the hosting/collocation) or in many cases by specialized vendors such as FTEN.  This software is deployed at the same location as the buy-side institutions servers (typically collocated at an exchange).
• Finally, there are some clients who have an intense need for speed, and who have built systems that are as lean and fast as possible.  These institutions use “Naked Access”, where they collocate their servers at an exchange, connect directly into the exchange using the brokers sponsorship and clearing, and where there is no brokerage system/software in-between performing risk management or compliance functions.

Flash Orders: Probably the most provocative innovation in 2009 (although the concept is not new, and has existed in derivatives markets well before being adopted by the cash equities markets) is the flash order.  The idea of a flash order is very simple:  A trader can (optionally) send his order to an exchange or ECN and specify that the order is a flash order.  When the exchange receives the order, if it cannot be immediately matched, it is “flashed” (shown) electronically for a very brief period of time to firms that have signed up to receive flash orders.  Those firms have a brief amount of time in which to respond to that order with a matching order, which allows the original order to get a better price than it might otherwise have received.  If none reply in time, the order is routed out to another exchange.  The firms that receive flash orders are typically High Frequency Trading firms pursuing an “automated market making” strategy (see above).  It is important to note that the firms sending flash orders are typically not high-frequency traders, and only some high frequency traders choose to receive and respond to flash orders.  Flash orders are an interesting example of where one firm (the firm submitting the flash order) is focused on the tactics of trade execution (#2 in the 3 step process) and seeking a good quality execution, and is interacting with another firm that is trading in the market as an inherent component of their strategy (i.e. the HFT firm).

Hopefully this brief explanation provides a framework for understanding and assessing the various technology innovations that are stirring debate on our public markets.

(Disclosure:  I sit on the board of Marketcetera, which offers products to the strategy trading marketplace, and has relationships with the NYSE, Lime Brokerage, and others.)

Over the last 2 months there has been a noticeable “up tick” in attention to the structure of the US securities markets, calls for banning certain practices regulating others and investigating everything from specific practices to the overall structure of the stock markets.

The last two years have brought us some of the most turbulent markets in history, and the last year has seen a surge in populism and a tilt away from a view that unfettered free markets and towards regulation.  So the focus on the structure and regulation of the US securities markets is not surprising.  Nor is it a bad thing to take a step back and review the structure of our markets and consider whether they can be improved.

While I am a strong advocate of free markets, even the most ardent Chicago-schooled economist will admit that for markets to function effectively certain conditions must be met.  Reaching back into the depths of my undergraduate economics, the neoclassical definition of an efficient market includes:

• Large numbers of buyers and sellers, that are free to enter and exit the market
• Goods are identical
• Information is perfect (accurate, widely disseminated, and timely)
• Transaction costs are zero
• Participants act independently

Of course we live in the real world, these conditions don’t always exist, externalities do exist, and so there is an appropriate role for regulation to ensure that real markets function effectively, to ensure fairness (which doesn’t necessarily equal a level playing field for all) and to ensure that whatever the rules are, they are clear and stable.  In particular, in a world where banks have been bailed out with public funds and subsidized by cheap central bank money, it is time to stop kidding ourselves that our financial markets are pure free markets and shouldn’t be subject to reasonable regulation.

While a “comprehensive, independent zero-based regulatory review of a broad range of market structure issues, analyzing the current market structure from the ground-up” (as urged by Sen. Edward E. Kaufman in a letter this week to the Securities and Exchange Commission) could be beneficial to the functioning of the US markets, any review should be based on a thorough understanding of the investment process and how trading is used to support that process.

Unfortunately much of the attention to financial markets in recent months has reached hysterical levels, is based on a poor understanding of how financial markets work, and has conflated different trading tactics (such as high frequency trading and “flash orders”) and unfairly demonized legitimate and beneficial aspects of our market structure.

As our elected and appointed officials consider the shape of future regulation for the securities markets, I would encourage them to consider some of the following points:

• We cannot look at the process of trading in isolation from the broader investment process.  Trading ultimately serves to support an investment strategy, and the goals of that investment strategy should dictate the trading tactics used to achieve it.  A large fund management company may form a fundamental, long-term view of a company from considering research, meeting with and assessing the companies management, the markets it serves, etc., and may want to own a large holding in the stock.  An index fund may need to buy a stock because it has been added to the target index.  A hedge fund may need to sell a stock simply because it that stock is highly liquid, the fund has experienced large redemptions and needs to raise cash quickly.  An individual may want to buy a stock because he believes that recent news is positive for the stock and he wants to get in before the price runs up.  A trader may buy or sell a stock because he detects short-term imbalances in the market.  All of these are different strategies, with different sensitivities to timing, price, quantity, and certainty of execution.

• Since different people have different objectives when they trade, those objectives are not always in conflict.  i.e. Trading does not have to be a zero sum game where one party wins at the expense of the other.  For example, many professional traders buy and sell securities using strategies that either arbitrage across multiple markets or securities, or attempt to predict the movement of securities, in order to capture pennies in profit.  The accusation leveled at these traders is that they are, in effect, stealing pennies (lots of them) from small investors who can’t afford the investment in technology and who lack the access of the big boys.  That would be true if the small investor’s objective was to profit by making pennies on every trade.  But it isn’t.  The small investor is typically buying or selling because of a longer-term view of the prospects for the security, or because they need to raise cash, or for some other reason.  The small investor who makes a decision to buy or sell 1,000 shares of a small cap stock is more interested in the certainty of getting his trade done, and whether trade is done at $1.63 or $1.64 is (relatively) less important than the fact that the stock has traded as high as $3.75 and as low as $0.40 in the last year.  In this situation the small investor benefits from the liquidity and the improved price discovery provided by the institutional traders, and in effect may “pay up” a penny but in return benefit from the liquidity provided by the professionals.

• The same is true for institutions as well, i.e. professional money managers (pension fund managers, mutual fund managers, etc.) are all consumers of liquidity, not natural providers of liquidity.  This is why electronic systems such as Instinet, which was originally a platform solely for buy-side institutions, didn’t establish meaningful traction until they opened up to market makers, the sell-side, and other providers of liquidity.  Likewise LiquidNet has built a nice business matching “natural buyers” with “natural sellers”, but that segment of its business appears to have reached inherent growth limits.  So while buy-side traders might like the idea of trading with their buy-side counterparties, they recognize that to get a trade done they frequently will have to go to a broker or an exchange or some other system that allows them to interact with active traders.  (Instinet used to have an “Institution Only” order type to limit exposure of orders only to other buy side firms, but most traders learned that if they wanted to get done now, they needed to expose their orders to the broader market.)

• This is not so dissimilar to other markets we are all familiar with.  If I am selling a used car, I can probably get a better price from another individual than I will get from a used car dealer.  But I may not be able to find someone who wants exactly my car, I may need to negotiate with multiple potential buyers, make myself and my car available to visit in my spare time, etc..  So I am willing to accept a lower price from a dealer in return for the convenience the dealer provides.  The dealer makes a small profit (arguably at my expense) in return for committing some capital, incurring some cost of sales, and taking a risk on his ability to re-sell the car.  Even in the age of the internet, intermediaries add value.

• While it might seem as if the definition of a good (or “best”) execution should be a trade done at the best publicly displayed price (i.e. the National Best Bid/Offer), this may not be the case.  As a seller of a used car, I might be happy to take a price just under “blue book value” in return for selling the car right now with no hassle.  Similarly, a trader trying to unload a large position in a stock may be happy to accept a lower price in return for certainty and speed, or simply because a trade now eliminates the market risk of getting a trade done over a period of days or weeks. RegNMS (which enshrined the NBBO as the criteria for best execution) ignores the institutional investor whose primary objectives may have more to do with getting a large volume of shares traded with minimal market impact.  And it is very difficult to move large blocks of stock with minimal impact in a market where the available quantity at the NBBO is 100 shares!   So different traders will naturally have different criteria for “best execution.” While the US was implementing RegNMS, the European markets were phasing in MiFID, the Markets in Financial Instruments Directive.  Both have obligations for “best execution”.  However, where the US regulation defines “best execution” as a trade occurring at or inside the NBBO, MiFID simply requires institutions to have a policy to achieve best execution and a mechanism to enforce that policy. The US definition is too narrow, but at the same time, the MiFID requirement for best execution is loose enough to drive a truck (or lorry) through.  What is required is a regulatory framework that recognizes that different investors have different objectives, and allows for a market structure that enables those different objectives to be achieved.

• Because different investors and traders have different objectives and strategies, a single one-size-fits-all marketplace can’t accommodate them all.  In the world of physical goods we have multiple markets: retail and wholesale markets, discounts stores and high end boutiques, etc.. In a recent article in Waters, Al Berkeley, chairman of Pipeline argues for a competitive market structure that provides 3 “distinct types of liquidity pools with three sets of rules of engagement” to cater to different trading objectives.  I don’t know if 3 is the magic number, but it is clearly preferable to look at discrete market segments and offer trading venues optimized for those market segments than it is to force everyone into a one-size-fits-all definition of how to trade.

• While it may play well to the crowds, the notion of a perfectly level playing field for individual investors and institutions is flawed.  This is partly because individuals and institutions have different objectives and are playing with different quantities of money.  It is why in the physical world we have wholesale markets (that cater to volume buyers and sellers) and retail markets.  But it is also because no amount of regulation will change the fact that professionals will always have more resources available to them to trade, whether that is market data, faster computers and networks, teams of PhDs to analyze the markets, or the information advantage that comes from having teams of professionals trading in many markets and sharing their insights and information with each other.  We can regulate a perfectly level playing field, but if we populate that field with players from the NFL and some high school kids, the high school kids are going to get hurt!

• This leads to one of the most fundamental observations of all:  Over my career I have seen a number of regulations adopted that are claimed to benefit the individual investor (RegATS and RegNMS being the most prominent.)  When carefully analyzed, however, they are designed to benefit the individual day trader, i.e. the individual who actively trades stocks and cares about short term swings in the market.  Every study that I have seen shows that such traders as a group under-perform the market, and significantly under-perform the market on a risk adjusted basis (and yes, they tend to be normally distributed so that some will randomly appear to be smarter than the average bear.)  The individual is better served by taking a truly long-term buy and hold strategy, or by investing in index funds, indexed ETFs, or managed mutual funds.

• In practice, institutions, either through IRAs and 401Ks or through pension plans, already manage most individual’s money.  Even hedge funds, which are widely perceived to be the province of a small number of secretive, Ferrari driving young hotshot traders (granted, there are some of those), manage a significant amount of money on behalf of institutions (including pension funds.)  In short, most individuals have more money in the market through mutual funds and hedge funds than through individual stocks that are actively traded.

• Therefore regulation that is really intended to benefit the individual should recognize that the individual is best served (and is already served) by professionals acting on his behalf.  And so a desirable market structure is one in which institutions (pension plans, mutual funds, and even hedge funds) are able to trade effectively.  It may play well to the crowds to talk about secretive hedge funds and dark pools, until you realize that these vehicles were developed to cater to the needs of professional investors who, for the most part, are ultimately acting on behalf of individuals.

• We currently operate in a world in which there are many different places to trade a stock, including stock exchanges, ATSs, ECNs, and brokers who match orders internally.  These are all interconnected by intermarket linkages, smart order routers, and data aggregators, and any pricing discrepancies are monitored and instantly arbitraged away by a group of high speed traders.  This competitive market has driven innovation and reduced the cost of trading.  Equally important, because of the competition for order flow, if any exchange or trading venue introduces a practice that is bad for its clients, those clients will quickly shift their order flow to other more attractive venues.  The most effective tool to improve quality of trading therefore is transparency, not of clients orders, but of the business practices (e.g. order matching process) employed by these venues.

The structure and regulation of our financial markets can certainly benefit from a comprehensive review, which in any event will be healthier than a knee-jerk reaction to poorly understood trading practices.  That review should consider that

• Different participants in the marketplace have different objectives and that all trades shouldn’t be measured by the same yardstick,
• A “fair” market may not actually be one in which there is a single level playing field, but one in which different venues serve different segments of the market,
• The best interests of the individual investor are likely to be different than those of the individual day trader and in fact may be best served by a market structure that enables institutions (mutual funds, hedge funds, pension funds, etc.) to efficiently invest on behalf of those individuals.
• Competition for trade execution has substantially reduced the cost of trading over the last 20 years.  We should encourage continued innovation and competition in the markets.

In a future post I will share my thoughts on some specific market practices, including dark pools, algorithms, high frequency trading, flash orders, etc..

First, the team at Marketcetera has come up with a platform that will revolutionize trading.  Early in my career I left IBM to join Instinet when I saw how it was changing the way the world trades by introducing the first ECN…  Automating the process of matching orders.  Radianz was a revolution in connectivity, providing a standard (outsourced) platform to connect to liquidity centers around the world.  What I see in Marketcetera is similarly game-changing.

Specifically, Marketcetera is doing two fundamental things that are significant:

1 – Marketcetera has built a Trading PLATFORM.  Until now, strategy driven traders had two choices:  They could either build an entire system from scratch, which means that they would have to recreate a lot of the “plumbing” that was not value-add.  Or they could buy a system from a vendor, spend a lot of time and money customizing and implementing that system, risk vendor lock-in, and share their proprietary trading strategies with the vendor.  Of course they would have to do it all again when they needed to update their strategy.

With the Marketcetera platform, all of the necessary infrastructure is built into the platform: real-time low latency market data processing, automated parsing of market data and FIX messages, complex event processing to support correlation of factors that lead to a trading decision, automated generation of FIX messages, order routing to deliver orders and receive execution notices, and a toolkit to build strategies in various scripting languages (java, python, ruby).  The trader can leverage this foundation and quickly build and deploy his strategy.

2 – By embracing open source, Marketcetera has made it much easier for traders to modify the platform to optimize it for their needs.  This can range from small changes that make a particular strategy easier to implement, to larger changes like addition of asset classes or other sources of data.  The general media focuses on the cost saving benefits of open source, and the advantages of a community that contributes (the “bazaar” model versus the “cathedral”.)  While these certainly apply, a much more subtle but profound benefit from the open source model is that it allows traders to be agile in response to changing markets, to protect their own intellectual property, and to retain a competitive advantage in their alpha generation.  Paradoxically, by building an open source platform, Marketcetera has enabled its users to protect their intellectual property better than any closed system would!

Fundamentally, Marketcetera has smashed the build versus buy conundrum.  Most funds don’t want to employ legions of programmers to build infrastructure, and the most talented programmers would prefer to focus their energy on implementing modern trading strategies, not re-writing the underlying plumbing needed to enable those strategies.  Marketcetera has made it possible to buy the building blocks, with the freedom to build precisely what you need to make your strategy sing.

That’s very cool.

That leads to my second reason for working with Marketcetera:  Thomas Edison once said “Genius is one percent inspiration and ninety-nine percent perspiration.”  Or as a friend of mine at a venture capital firm said:  “It’s all about execution, and more than anything we look for teams that can execute.”

When I met Graham Miller, Toli Kuznets, and Roy Agostino, I was impressed.  Not only have these guys come up with some great ideas, but they have the background (serious technical skills from Stanford, real-world experience in the trenches at Susquehanna and Jane Street) and discipline to execute.  Despite the media perception of entrepreneurs as wild-eyed visionaries with stars in their eyes, most successful entrepreneurs that I know have combined a real vision with the passion, focus, and ability to sweat the details.  “Three yards and a cloud dust.”  The Marketcetera team has a real professionalism, discipline, and work ethic that is unusual in an early stage company.  Their communications style is fact based, open, and direct, which is essential to having an effective relationship between a board and a management team.  Plus they are a real pleasure to work with!

A great idea and a top-notch team are critical to success, but I have also learned from experience that it is essential to have the right investors backing the team.  Marketcetera is backed by Shasta Ventures and Clarium Capital, and is very fortunate to have Ravi Mohan and Jack Selby on the board.  When I met with Ravi and Jack, the key points I wanted to get answered were:  Did they have a shared vision with the management team?  Did they have the financial resources to support the company adequately?  Did they have the personal backgrounds, skills, and behaviors to support the management team and help steer the company through its growth?  After several meetings and calls, I was convinced that this was a set of investors that truly had an aligned vision with the company and the ability to help its achieve its vision.

The last reason I joined the board of Marketcetera was because it is the kind of business where I believe I can make a contribution.  I have seen enough boards where directors lack the relevant experiences to add value.  I have had several opportunities to sit on corporate boards, and I have focused on those where I have directly relevant experience.  In the case of Marketcetera, they are in the right stage of their growth (up and running and ready to scale to a meaningful business), and with an industry focus (automation of core trading functions) where my experience and relationships can contribute.

So that’s why I have joined the board.  Like any early stage company, especially in the current economy, Marketcetera will have its challenges.  It has the right idea, the right team, and the right backers to be a huge success, and I hope that I can contribute to that success.

Mar 23, 2009 15:15 ET
Marketcetera Appoints Financial Technology Pioneer to Board of Directors

Financial Industry Veteran Brings Decades of C-Level Experience to Open Source Automated Trading Platform Provider

Brennan Carley

NEW YORK, NY and SAN FRANCISCO, CA–(Marketwire – March 23, 2009) – Marketcetera, the leader in open source platforms for automated trading, has appointed W. Brennan Carley to its board of directors.

Carley has held C-level and board positions at financial services powerhouses such as BT Radianz, Reuters, Instinet, NYFIX, Warburg Pincus, and is currently Managing Principal at Proton Advisors, an independent consulting firm for the financial services technology and telecommunications industries. He has extensive experience building and growing ground-breaking financial technology businesses, with experience ranging from VC funded startups through private equity-backed and public companies.

“Market forces have redefined what traditional financial institutions look like and raised the bar in terms of what they expect from their service providers,” said Graham Miller, CEO, Marketcetera. “The curtain has closed on slow and expensive proprietary systems creating a perfect opportunity for companies like Marketcetera to define a new era of fast, open, and extensible products that redefine value to financial customers. With experienced and influential leaders like Brennan on our board, Marketcetera will continue to set the pace for a smarter and more efficient Wall Street.”

Carley founded Radianz (now called BT Radianz) in 2000 and was responsible for strategy, products, technology, and operations. Prior to Radianz, he was senior vice president of strategic planning at Reuters.

“The financial services industry can’t escape the new day dawning on Wall Street,” said Carley. “Marketcetera has the vision, ambition, and audacity to retool the fundamentals of how trading platforms are built and change the future of financial markets technology. I look forward to adding my experience to a stellar team so I can help Marketcetera capture the opportunities created by the current revolution in the financial markets.”

Before joining Reuters, Carley ran Information Technology for Instinet, and before that held a series of positions at IBM in consulting, systems engineering and marketing, primarily focused on distributed systems and networks in the financial services industries. He began his career with IBM as a programmer responsible for operating system and communications software development.

Carley received his bachelor’s degree in economics from New York University and studied at Harvard Business School, MIT Sloan School of Management, and Columbia Business School.

About Marketcetera

Marketcetera (www.marketcetera.com) provides open source software for automated trading systems. By providing maximum flexibility and technology choice to financial services institutions of all sizes, Marketcetera’s platform lets brokers and traders build effective automated trading systems, develop proprietary algorithms, create order management solutions and manage risk faster, easier and at much lower cost than with closed platforms.
Media Contacts:

East Coast:
Steph Johnson
Feintuch Communications
212.808.4904
marketcetera@feintuchpr.com

West Coast:
Jenna Boller
Page One PR
415.321.2344
marketcetera@pageonepr.com

While I do not intend to be an active blogger, this page will be used to periodically post news, items of interest, and commentary and the financial, financial technology, and telecommunications businesses.

Brennan