Virtually all equity managers have experiences suggesting paper portfolios outperform real ones. A paper portfolio is an imaginary holding consisting of all of the security positions investors decide to hold, acquired at the midquote price that prevailed at the time they decided to hold them. Paper portfolios incur no commissions, no bid-ask spreads, no market impact, and no opportunity costs. Real portfolios incur all of these costs.
The best way to measure transaction costs is to look at the difference between real and paper portfolios. This was first suggested by Jack Treynor in 1981. More recently, Andre Perold, in a frequently cited paper, named this difference "the implementation shortfall."
A famous example of the implementation shortfall is in the striking difference between the performance of the paper Value Line portfolio and the real Value Line fund. From 1979 to 1991, the Value Line paper portfolio had an annualized return of 26.2%. The real Value Line fund lagged substantially, with an annualized return of only 16.1%. Clearly, something happened. That something was the cost of implementation.
Heat, light & transaction costs
The subject of transaction costs tends to generate quite a bit more heat than light. Many measures have been proposed, but true transaction costs are immeasurable, because they are the difference between the price you paid and the price that would have prevailed if you had not transacted. We can never observe this price, so we can never measure true cost. If this were the last word on the subject, we could all go home now, but it is not. The implementation shortfall method has been widely accepted by both academic economists and market practitioners as a good surrogate measure for true (but fundamentally unobservable) transaction costs.
Some claim it is futile to attempt to control transaction costs: You cannot measure them precisely, so why try? This argument is specious. The ability to make perfectly precise measurements of a phenomenon is not required to influence or control it. Recall that it is fundamentally impossible to precisely (and simultaneously) measure the position and velocity of a particle such as an electron. A single glance at a television or computer screen confirms that we certainly can control the little devils well enough to broadcast a Dodgers game or display the S&P 500 on a computer screen in 32,767 colors, even if we cannot measure their position and velocity with perfect precision. The analogy to transaction costs: Even if we cannot measure the costs precisely, we can influence them by the actions we take in executing trades.
Implementation of ideas
Trading is the implementation of investment ideas, and the quality of the implementation is as important as the idea itself. For small transactions in large markets, this is not a major concern. As the transaction size grows, or when smaller markets are involved, it becomes more important for investment managers and their clients to pay more attention to implementation.
This article summarizes some surprising results from an analysis of a large U.S. pension fund's equity trading, and describes how some of these lessons are being applied in the management of U.S. and international equity portfolios.
A $2 billion experiment
We examined 13,651 equity purchase transactions, totaling nearly $2 billion, made by one of the largest U.S. corporate pension plans. Trade sizes ranged from 100 shares to blocks of more than 400,000 shares. Both active and passive management styles were represented. All orders in this sample were filled, some on the first day after the decision to trade, some up to 21 trading days later.
Because all orders eventually were filled, no opportunity costs were observed in this study. This greatly simplifies the measurement and interpretation of costs. Trading cost was simply computed using the implementation shortfall method - the trade price less the decision price plus commissions. There is no "time-horizon" knob to twist in calculating opportunity costs, so this often critical assumption cannot cloud the results.
We set out to test the validity of the conventional wisdom in several areas - specifically, the relationships between transaction costs and (1) trade size relative to market capitalization, (2) trade size relative to average trading volume, (3) management style, (4) patience in trading and (5) use of crossing networks.
Some findings were surprising. In the early incarnations of this report, they were accompanied by this caveat: "These transactions all come from a single pension fund, and generalization to all funds may be premature." However, it is noteworthy that as these results have become more widely known, a number of investment managers, in both the United States and Japan, have reported very similar observations in their own independent analyses of trading data.
What should we expect regarding costs and block size? The most reasonable model is Tom Loeb's, first published in 1983 and extended in 1991. This very plausible model was based on queries to brokers and market makers for blocks valued between $5,000 and $20 million at various levels of market capitalization. These transaction cost estimates were suppositions. The trades were not actually made. The 1991 paper generalized the earlier result by fitting an equation to the data. The Loeb function predicts transaction cost (in percentage of value) as a function of trade size expressed as a percentage of shares outstanding and market capitalization. The solid red line on Figure 1 shows these predictions (for a midcap stock). Our expectation was that a scatter plot of cost vs. size for the 13,651 transactions in our sample would lie along these lines. Instead, the trumpet shaped scatter plot of dots in Figure 1 was what we actually found - clearly different from our expectations. Expressing trade size in terms of average volume instead of shares outstanding had little effect.
The largest trades have costs much lower than those predicted by the model. The highest and lowest percentage costs are associated with the smaller trades. Many trades, of all sizes, have negative costs - that is, they produce transaction profits. Overall, costs rose slightly as trade size increased, but they rose less than we expected.
What could be responsible for this? A large part of the answer may be that these are actual trades, not suppositions. Apparently, when real orders are filled using real money, block traders can deliver better performance than the model (based on their answers to hypothetical questions) predicts. The important observations in this analysis of cost and size are:
Smaller trades were responsible for a disproportionate share of the costs.
The trades expected to be low-cost "no-brainers" were not.
Larger trades, generally handled by higher commission brokers, had lower than expected costs.
Many smaller trades produce transaction profits, partially offsetting costs.
There is a clear lesson here in that a trading strategy based on dealing with the expected problems would be misdirected with respect to the actual problems.
Any study with this much data involved presents ample opportunity to slice, dice, make charts and tables, and cross-examine the findings in many dimensions, particularly by management style and trading technique. This was done in the full report, and the detailed results are found in Using Information from Trading In Trading and Portfolio Management ("Execution Techniques, True Trading Costs, and the Microstructure of Markets", AIMR, December 1992).
Transaction cost control
Our analysis suggests several ways to reduce costs:
Value is added by skilled equity block traders. Because of constraints on their time and availability, these traders can handle only a small fraction of the total order flow. If the high-cost smaller trades, which would not normally be sent to block desks, could be given the benefit of their attention, we would expect better execution.
Limit-order or dynamic market order strategies may be appropriate to keep the transaction costs below the anticipated alpha added by the manager. New electronic trading systems such as ITG's Quantex and First Boston's Lattice allow the use of computerized trading strategies that would be impossible by other means. Price-sensitive demand schedules and substitutions also are useful here.
Identify expensive problem trades before they are made. There are two aspects to this. A general approach is to avoid trading in illiquid stocks by careful consideration of the investible universe and the sizes one is willing to trade within that universe. A more particular approach is to try to predict costs for each transaction, and use this information in portfolio decisions. These predictions are hard to do.
Guaranteed principal bid trading. That is, receive a firm bid on an entire portfolio from a broker. Typically, this transaction will be priced, in advance, in cents per share or basis points off the closing prices for the stocks. This can be regarded by the portfolio manager as a perfectly accurate prediction of transaction costs.
Brokers seek to match a large number of such portfolios, and thus reduce the risk that their cost of trading the unmatched stocks in the market will exceed their revenue from the guaranteed principal bid. One of the difficulties of GPB trading from the point of view of the manager is the difficulty of obtaining bids from a variety of brokers who all want different information describing the portfolio. A new electronic system, CompBid, greatly simplifies the process for both participants in the transaction, by defining a set of standard portfolio descriptors, which are then used in an electronic auction market for GPB transactions.
Information from current and prior trading can be used to directly affect the outcome of the trading process. Electronic trading systems provide a uniquely powerful means of capturing this information and exploiting it in ongoing trading. Electronic order working is a general term for this type of trading. The basic ideas behind electronic order working are these:
Exploit the multiple execution channels available today.
Allow short-term market volatility to work in your favor.
Apply the techniques simultaneously to a large number of orders.
Incorporate feedback from the results of trading strategies on multiple time scales to refine the performance of those strategies.
Cost prediction and GPBs
If we could predict trading costs with perfect accuracy, we would just add them to our decision prices, and make perfectly informed decisions. There is some evidence of predictability, but these predictions are far from perfect. The guaranteed principal bid is the only perfectly predictable trading technique. This is illustrated in Figure 2. As in Figure 1, the solid red line again shows the expected transaction costs. The scatter points show the actual costs. This time there are no surprises. This is particularly valuable for quantitatively managed portfolios.
At First Quadrant, our $3 billion U.S. Style Management fund and new U.K. and Japanese Style Management funds rely heavily on GPBs. This trading technique is not a magic bullet. This entails a close relationship with the brokers on defining the liquid universe and portfolio characteristics that result in acceptable costs. This is particularly true for market-neutral funds, because there is far less liquidity on the short side of the market. By careful structuring of GPB trades, transaction costs for these portfolios have been held to roughly half of the reported averages for institutional equity portfolios in their respective countries.
Are GPBs a free lunch for equity managers? No, the guarantee applies only to one portfolio at a time. While there will be no unanticipated market impact for each individual portfolio, brokers are under no obligation to maintain the same pricing for subsequent portfolio trades. The impact costs can be just as real, but delayed. New electronic trading systems increase the liquidity and efficiency of the GPB market by lowering barriers to entry and increasing the likelihood of matched transactions.
Changes in the equity markets and the remarkable advances in the level of technology that have been brought to bear on these programs have changed the nature of equity management for all participants. Sponsors have new ways to get their money's worth from equity management. Managers have new ways to transmit value, and traders are a critical link.