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2 Christie-Schultz Study of “Implicit Collusion” By Market Makers

2 Christie-Schultz Study of “Implicit Collusion” By Market Makers

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122



S. Rathore



and others were now empowered with the opportunity to quote prices

alongside of traditional market makers. Individuals or institutions with

this capability are characterized as trading with Direct Access. In most

cases, Direct Access is conducted via ECNs, which also have the

important benefit of trading anonymity. Trades through ECNs appear to

the observer to be coming from a brokerage firm and not an individual or

particular hedge fund.

Some of the more active ECNs today include Archipelago, Island,

BATS and Knight Trading’s Edge ECN. The market share gain of

ECNs proved to be so rapid that traditional exchanges, such as The

New York Stock Exchange and NASDAQ, purchased Archipelago

and Island, respectively. ECNs account for the majority of volume on

U.S. exchanges today due to their speed, anonymity features, and low

transaction costs.



2.3 Decimalization of U.S. Markets

The U.S. Securities and Exchange Commission (SEC) ordered in 1997,

partially due to the findings of the Christie-Schultz study, that all stock

markets convert their quotation systems to decimals by April 2001.

Moving from a system that quotes prices in fractions to one that quotes

in dollars and cents is known as decimalization. The idea behind

decimalization was that it would bring several benefits to small investors,

most notably a shrinking of the bid-ask spread. Then NYSE Chairman,

Richard Grasso, testified to a Congressional Committee in June 2000

that such a move could save investors a billion dollars or more.

The decimalization of the price quotes created a massive increase in

exchange volume since orders in many cases were now executed with a

penny difference. For example, a trade size of 1,000 shares would result

in a bid-ask spread cost of $125 using eighths or $10 using pennies.

Decimalization in concert with increased trading via ECNs resulted in a

massive increase in stock market volume. The decimalization process

also enabled investors to better compare prices with exchanges globally,

where trading was already conducted in decimals.



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2.4 Regulation National Market System (Reg NMS)

The traditional market maker model fell out of favor with the advent of

decimalization, the narrowing of spreads, and the growth of ECNs. The

profitability at market maker / specialists firms declined precipitously.

Previously, the trading function of many hedge funds was outsourced

due to high costs. Specifically, it was costly for hedge funds to maintain

their own trading desks (beyond the simple clerk function of sending

trades) since discovering trading interest and employing sophisticated

trading algorithms required a significant capital commitment and access

to trading order flow. Well-capitalized wholesale execution trading

firms, which benefited from a massive amount of variegated order flow,

previously added significant value when executing trades. The

emergence of ECNs resulted in more hedge funds favoring the Direct

Access model over the outsourced trading function through traditional

market makers and institutional trading desks.

The popularity of ECNs resulted in another obstacle to best

execution — the “fragmentation” of trading interest. In short, it was

difficult for investors to get a complete picture of liquidity in the market

unless they could simultaneously view prices on all relevant exchanges

and ECNs. A trader may have missed the best possible price by not being

able to “see” the trading interest throughout various venues on the

marketplace. The SEC became concerned with the market fragmentation

problem and enacted Regulation National Market System, or Reg NMS.

Under Reg NMS, market makers and execution agents were now

obligated to sweep through the entire fragmented marketplace to provide

the best execution. The process of finding and executing at the best

prices among several exchanges is called “smart order routing.”



3. Best Execution of Hedge Fund Trades

The increased use of electronic trading and mathematical algorithms has

resulted in additional layers of complexity to the best execution process.

This complexity is an alpha opportunity for astute hedge funds. Modern

financial theories, such as those pioneered by Markowitz, Sharpe, Black,



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S. Rathore



and Scholes are largely silent on the process of best execution. The

process of best execution takes on increasing importance in “crowded

trades” or those where many hedge fund managers are following the

same core strategy. For example, a common trade for many Long / Short

hedge funds over the 2007 to mid-2008 time period was to be short

Financial stocks and long Energy stocks. Akin to a game of musical

chairs, the Darwinian process of weeding out weaker hedge funds in

crowded trades may ultimately come down to the execution prowess of a

particular hedge fund.



4. Transaction Costs

Ronald Coase was awarded the 1991 Nobel Memorial Prize in

Economics for his groundbreaking research on the nature of transaction

costs. Although the bulk of his research was conducted several decades

ago, its framework still applies well to the process of best execution of

hedge fund trades. Coase (1937) states that cost of obtaining goods or

services through the market is actually more than just the price of the

good. There are several related costs such as discovery, bargaining,

searching, trade policing, and enforcement that all might add to the total

cost of the good or the asset being purchased. Coase’s ideas apply to the

modern day traders who not only consider price, but also venue,

negotiation costs and the market impact of their trades.

Wagner (2003), Chairman of the trading firm Plexus Group,

estimated that the transaction costs faced by institutional traders were as

much as 1.5% per transaction. It is readily apparent that active trading

may entail substantial transaction costs and overwhelm much of the

expected alpha of a trade. In order to better understand the components

of trading, we will break total costs into explicit and implicit pieces.



4.1 Explicit Costs

Explicit costs are readily observable and quantifiable and include

commissions, ECN costs for removing liquidity, ECN rebates for adding



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liquidity, taxes, bid-ask spreads, payments for data services and so forth.

Traders have always paid special attention to explicit costs since it is one

of the easiest ways for supervisors to gauge their effectiveness. However,

the previously cited study by Wagner, and those conducted by others,

found that implicit costs generally make up the largest component of

transaction costs.



4.2 Implicit Costs

Implicit transaction costs are determined by estimating the change in

market factors such as price, information content, and liquidity, due to

the act of trading or an expression of trading interest. In other words, it is

the movement in the price of a security due to the actions of a particular

trader or investor. For example, a hedge fund seeking to buy 1,000,000

shares of IBM may end up moving the market adversely if the

marketplace becomes aware of pending block trades. A good trader will

discretely chop the block up into several smaller pieces and execute the

trade with minimal impact over time. However, if the market rapidly

moves up in the interim, the trader may incur an opportunity cost due to

delaying. IBM is a large and liquid stock, so the delay issue comes into

greater focus when trading smaller issues. Nevertheless, it would be

unwise to dismiss implicit or market impact costs when examining large

stocks, since many institutional managers tend to hold the same

securities. This herding behavior may result in a large and seemingly

liquid stock dropping sharply in value, as exhibited by many world

equities in October, 2008.

An astute trader will therefore have to make several decisions in order

to achieve best execution for his fund. He may ask the hedge fund

manager about the urgency of execution. If timeliness is of high priority,

perhaps due to an impending earnings report, then the trader may have to

pay up in transaction costs in order to get the full position established

before a particular event date. Conversely, if there is no immediate

urgency, the trader can break up the order into smaller sizes to reduce

market impact by not showing his hand. For example, the trader could

engage in a patient style of trading using not only limit orders, but also



S. Rathore



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capturing ECNs rebates for providing liquidity. One helpful way to

model the best execution process is to create a matrix of preferences with

respect to price, liquidity, time, cost, and other relevant dimensions.

Table 1 lists a simple trading preference matrix.

Table 1: Trading Preference Matrix

Security



Price



IBM

TEX

VIP



Low

High

Medium



Preferences

Liquidity

Time

High

Moderate

Low



High

Low

Medium



5. Pre, During and Post Trade Analyses

Statistical techniques provide traders with many tools to analyze costs for

analyzing the best execution process. It is helpful to break the best

execution process into three components — pre, during, and post trade

analyses. Best execution is a never-ending process since the market is

a self-learning and adjusting mechanism. In other words, today’s best

execution process, may not be optimal next month or year.

Wall Street has embraced in recent years the importance of best

execution and is putting in significant intellectual, financial and

technological resources behind providing an execution solution that best

fits a particular trader or hedge fund’s needs. Hedge fund traders of the

past primarily focused on forecasting market movements, but today’s

traders must also be familiar with proprietary and third party capability

with respect to trading algorithms. Many trading desks have algorithms

tailored to a particular hedge fund strategy, such as Statistical Arbitrage,

Distressed, and Event Driven.

5.1 Pre-Trade Analyses

Pre-trade analyses may include the historical volatility of the security,

technical analysis, money flow, correlation to fund’s benchmark,



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127



tracking error (if internal records from previous executions are kept),

investigation into news flow surrounding the company, put / call activity,

and other aspects of market sentiment. Pre-trade analyses should provide

traders with insight into the best method of executing a trade before the

“send” button is selected. Pre-trade analyses may also help traders find

out what algorithm offered by external brokerage firms is best suited for

their particular trade. Over time, a scorecard or “report card” should be

kept in order to analyze the effectiveness of the trader’s decisions and

the performance of external trading desks. Accordingly, it is important

for hedge funds to keep a data warehouse to analyze the effectiveness of

all of its trading related decisions.

The field of Pre-trade analyses is growing rapidly as firms

increasingly compete to execute their trades with minimal impact. In

many ways, Pre-trade intelligence is somewhat similar to an information

knowledge advantage the firm may develop by investing in hiring more

qualified and insightful analysts. For example, many traders will argue

that certain stocks exhibit price behavior that is idiosyncratic to the

security. Pre-trade analyses may help to identify these patterns. Some

stocks move slowly, while others are more reactive to news and earnings

announcements. A trader may be able to get a fill for 1,000,000 shares in

IBM without significantly moving the market but not be able to sell

50,000 shares of an illiquid security without creating a massive market

impact. In short, pre-trade analyses may help a trader sketch out his

strategy of best execution while being cognizant of the trading mandates

of time, size and price.



5.2 During Trade Analyses

During trade analyses focus on the segment of time when an order is

being filled. Given that explicit transaction costs have fallen sharply in

recent years, its main focus is on reducing implicit costs, such as market

impact. One commonly adopted method of minimizing market impact is

to split up a large order into smaller orders designed to leave a minimal

footprint of trading activity. In our IBM example, instead of posting a

complete 1,000,000 shares in the market, the order would be presented in



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