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At the risk of stating the obvious, your trading/investment success can be viewed as nothing more than a financial scorecard of the buy and sell decisions you make in your portfolio, as measured across all asset classes and as weighted by such factors as investment size and market volatility. It follows that whosoever achieves a systematic mastery of the science of trading stands a great chance of achieving even the most ambitious of investment objectives, while those who fail to do so will find themselves at a most bothersome disadvantage in this regard. In turn, if you wish to max- imize your performance in the markets, it’s important to develop a solid and consistent quantitative handle on your performance at the level of the individual transaction. In this chapter, we discuss ways you can accomplish this task with relative ease.
YOUR TRANSACTION PERFORMANCE
The basic methodology for portfolio review at the trade level involves col- lecting and analyzing components of your individual transactions, based on aggregation of such pertinent information as time, date, underlying instrument, price, quantity, and counterparty. If you are able to gather this data in a manner that is efficient to the subsequent analysis, as will be described later in this chapter, you can then evaluate it for insights as to the drivers of your success over any given period. It is my experience that this can have important implications for effective portfolio management, enabling those responsible for this process, among other things, to gear their resources toward those components and conditions that are most likely to produce the desired outcomes and away from those tied to their areas of greatest frustration.
The collection of transactions-level information is a more difficult undertaking than the data-gathering processes we’ve covered so far. Princi- pally, this is due to the size of the task. Many portfolio managers trade very actively, and the practical difficulties associated with collecting multiple data elements for each transaction can be manifold. Also, the information- gathering process can be subjective, even down to the definition of what constitutes a trade. For example, if you buy 100 lots of a position, executing it across 10 trades over several hours, is this 1 trade or 10? Further, if this transaction is viewed as a single trade but has multiple counterparties and prices involved, how can you classify these attributes with consistency?
Transactions-level analysis is rife with bothersome details such as these, but the benefits you can derive from resolving these difficulties ren- der the effort well worth the hassle. To further set your mind at ease on this score, I will begin this chapter by describing a methodology designed
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to rid you of many of the ambiguities inherent in the exercise. With the appropriate set of assumptions in hand and with an associated commit- ment to the process, I believe it is possible to analyze your trading decisions and modes of operation in ways that can spotlight correctable inefficiencies and facilitate the scaling-up of unusually effective compo- nents, often with significant positive implications for your bottom line.
As has been the case with the rest of our data gathering and analysis, we will begin by establishing the appropriate analytical framework, then attempt to create tools that characterize and measure performance effi- ciency, and finally point out alternatives for modifying trading/investment behavior when opportunities identified in your transactions-level analytics indicate the need to do so. The first step is one of identifying and main- taining the appropriate data from your trading records.
Key Components of a Transactions-Level Database
In order to perform effective analysis on the transactions that comprise your portfolio, it is plainly necessary to gather and to store all discernable and relevant aspects of the individual trade. The recommended attributes that should be included in the data set are:
• Instrument name.
• Date and time of transaction.
• Buy/sell indicator.
• Price.
• Quantity.
• Executing broker/counterparty.
• Commission.
• Order type (e.g., market, limit, stop, etc.).
• Native currency denomination.
In addition, depending on the type of instrument traded, it may be neces- sary to record:
• Maturity (for fixed income) or expiration (for futures and options) date.
• Coupon rate and frequency (fixed income).
• Put/call indicator (for options).
• Strike price (for options).
While these informational elements will more or less completely define a given transaction (at least for most types of trading), there are other indirect pieces of information that you need not record for each
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trade but that will be implicit in the information already gathered and that you should by all means include in your subsequent analysis. These include:
• Asset class.
• Sector designation.
• Security-specific data (volume, correlation, volatility, shares out- standing, etc.).
Again, while it is not necessary to gather and include this information in your database, it is important to be aware of its existence, to identify sources for these statistics, and to set up a framework for subsequent cap- ture of this data. For example, let’s assume that for reasons of simplicity or targeted-market advantage, a portfolio manager restricts her universe of securities traded to, say, the Standard & Poor’s (S&P) 500. In order to ana- lyze transactions-level performance for this portfolio, it would be useful to know something about the volume of these securities, not to mention their volatilities, betas, sectors, and other market characteristics. However, it would be neither necessary nor indeed efficient to record these statistics every time the portfolio manager executed a trade in one of these names.
Rather, this information is best obtained from other sources, including electronic-pricing mechanisms such as Reuters and Bloomberg, as well as financial print publications, such as Barron’s.
Defining a Transaction
As a final consideration in setting up your trade-level analysis tool kit, you must decide what among your transactions activity specifically constitutes a “trade.” This is particularly important for active traders (including pro- fessionals) who very often maintain a core position in a given security while scaling up and down the exact quantities they are holding at any time on the basis of market conditions. For example, an experienced trader, anticipating a sustained market rally, might put on a core position of 100 S&P 500 futures with the expectation of holding it for several weeks.
However, within this time interval, he might reduce or expand this posi- tion as the relative prospects for such a rally become more or less apparent over time. For the purposes of subsequently analyzing this trade, it will be necessary to determine which elements of this sequence fully comprise the transaction. Is it each individual execution? Is it the position as a whole, with each execution folded into it? Perhaps some combination of the two?
Unfortunately, there is no universally correct answer to this question, and there are trade-offs no matter which method you adopt. As a rule, I lean toward an approach under which a single transactionis defined as the set
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of executions that establishes and liquidates a given position without cross- ing over either from long to short or from short to long. In the preceding example, the unit of the trade would include every execution that comprised the initial position size of 100 lots along with any trades that either increased or reduced the position down until it reached or transcended the zero threshold. If the portfolio manager actually reversed his position during this sequence, the execution that reversed the position would be split into two components: one that took the account down to zero (which would be included as part of the original, initiating trade), and the other that estab- lished a new short, with the latter being defined as the beginning of a new trade that would be in place until the account was then flat the position.
This approach has the advantage of grouping into a single transaction all individual executions that were tied to a common theme (in the exam- ple provided, a belief that a benchmark index will rally); however, this arguably comes at the expense of some precision. This is particularly true if the portfolio manager believes that the scaling up and down of a trade held for longer durations is strategic enough to merit separate analysis.
If you choose to group your executions in the manner just described, you will need to assign a single price to the transaction in order to perform the types of analyses contemplated in this chapter. You needn’t bother being particularly creative here, and the best approach is probably one that is called “weighted average” in the nomenclature of statistics or “volume weighted average price” (VWAP) in the nomenclature of the markets. This number is derived by multiplying the number of shares or contracts by the price, across each execution, summing these figures, and dividing by the overall number of shares or contracts grouped into the transaction. For example, if you buy 30 shares of IBM at 120 and 70 shares at 121, you would multiply 30 by 120 and 70 by 121, add the two products, and divide the total by 100. The result of 120.7 is a good proxy for the average execu- tion price of the transaction.
The alternative approach involves defining each individual execution as a distinct trade. This is the most granular level at which to conduct analysis and arguably offers the highest level of precision. However, this methodology also carries some hazard—particularly for traders putting on larger position sizes who, due to liquidity constraints and other factors, may require several executions in order to establish and liquidate their fully invested position sizes. For instance, in our S&P example, due to price sensitivity, a portfolio manager wishing to establish a long position of 100 contracts might choose to do so across several individual executions, across several hours. In the trader’s mind, this may very well be a single transaction; however, when he subsequently attempts to determine its statistical characteristics, it will be impossible to reconstruct it into a unit that is conducive to analysis. Moreover, statistics (which we will describe
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in greater detail later) such as win/loss ratio, average trade size, and a num- ber of others will be obscured by viewing each execution separately.
For these reasons, it is my view that the former method of grouping all executions tied to the initiation and liquidation of a position in a single name, on a single side of the market, is the preferred one. However, it may very well be worthwhile to engage in some experimentation with both approaches in order to obtain the broadest possible understanding of the statistical characteristics of your trading. Ultimately, you are by far the best judge of what constitutes a transaction within your trading universe.
As always, the discovery process itself justifies the effort.
Position Snapshot Statistics
In addition to tracking what happens at the individual transaction level, it is important to apply statistical measures to snapshots of your portfolio as they exist over time. Think of your individual transactions as being brushstrokes on the dynamic canvas that represents your portfolio. From one perspective, its content is best described by the brushstrokes themselves; but from another, the best way to look at it is in terms of what is visible on the canvas at any time. The logical way to review the content of your portfolio is to cap- ture and analyze the individual positions that comprise it on a periodic basis.
The ideal frequency for subsequent statistical analysis is daily, as this offers the most precise set of meaningful datapoints; however, even if you do noth- ing more than analyze your month-end statements, you will find yourself gaining unique and useful insights into the drivers of your success.
The key statistics you will want to retain from routine snapshots of your portfolio are relatively few and entirely intuitive (comprising what you will recognize as a standard position blotter); yet from them, as you will see, emerge myriad interesting analytical opportunities. They include:
• Ticker or instrument name.
• Entry price.
• Current price.
• Entry cost (can be derived).
• Current market value (can be derived).
• Unrealized P/L .
The retention of this information in a database will allow you to create the following time series for subsequent analysis:
• Dollar Investment in Individual Securities. Most of us trade the same instruments on a recurring basis. Every day the dollar amount of these investments will change by virtue of price fluctuation alone. The
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process of trading, of course, adds to the fluctuations. It will be useful to maintain a running total of these position sizes in order to deter- mine subsequently their impact on your return profile.
• Total Long/Short Capital Utilized. This figure is simply the amount of dollars invested on the long side of the market across all positions and the total dollars invested on the short side on the same basis. This will also be a useful statistic to maintain in time series form for subse- quent analytical purposes.
• Gross Market Value. This is defined as the sum of the dollar values of your longs and shorts. Remember to reverse the sign of the short positions so that they are added to your longs, as opposed to subtracted.
• Net Market Value. While Gross Market Value is the sum of all posi- tions, long and short, Net Market Value is derived as the difference between longs and shorts. In time series form, this will give you an idea of your tendency to trade with a directional bias as well as of the extent to which this bias shifts over time and across market condi- tions. In addition to being a useful metric for subsequent analysis, the results you uncover may surprise you.
• Number of Positions. This statistic can be measured in terms of long positions or short positions or in aggregate across the portfolio. The number of positions on your sheets is a crude but useful measure of the level of diversification in your portfolio; and by comparing this to the profitability in your account, you stand to learn a great deal about how well this diversification program is working.
As you may very well surmise, it is in your power to combine these core data sets with other position attributes in order to obtain an even clearer and deeper understanding of portfolio dynamics. For example, you may wish to see how each of the listed metrics operates at the sector level or asset group level (e.g., grains versus meats) for commodities. Similarly, you may wish to beta adjust the totals in order to obtain some idea of how volatility and market correlation factors play into your investment deci- sion-making process. I highly encourage these types of second-order analy- ses and believe that they can only expand your knowledge base in ways that are conducive to the objective of increased portfolio efficiency.
CORE TRANSACTIONS-LEVEL STATISTICS
Now that you have set up your transactions-level database, you will find that there is a virtually unlimited set of statistical analyses that are available to you. I don’t intend to cover all such analytics in this book. I will focus
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very directly on a few key themes in order to provide the broadest possible idea of how this data can be processed and interpreted with an eye toward improving performance. We will start with some basic calculations that in and of themselves may not be particularly fascinating but that will serve as the basis for evaluating performance in a manner that cannot be simulated absent this information set. With these basics in place, we will then move toward more complex concepts that hone in on specific aspects of trading performance that might lend themselves to efficiency improvement.
Let’s begin at the beginning then, shall we? The first set of statistics you need to gather will not give you great insight on its own, but rather will serve as the basis for subsequent analysis on a more detailed level:
Trade Level P/L
Although we’ve discussed the P/L time series concept in great detail, we now want to turn to the equally valid methodology of evaluating your rela- tive profitability across individual transactions. The idea here is simply to calculate the profitability of the individual trade, which can be done by either matching up buys and sells of equal quantity or calculating average entry/liquidation prices. This will be a key unit of account for your overall transactions-level analysis.
Holding Period
Also for reasons of subsequent analysis (which will become more appar- ent later in this chapter), it is important to calculate the time duration of your transactions. This figure can be derived in several different ways, but to me the most accurate approach is one that mirrors our approach to defining an individual transaction, where the holding period is defined as the time interval between the beginning of the initiation of a trade and such time as you have fully liquidated it or reversed its direction from long to short or short to long. The ideal unit of account for this statistic is the num- ber of days; however, for day traders it may be possible to express the holding period in terms of hours or even minutes. This level of specificity is in most cases inefficient, though, as it becomes very difficult to accu- rately report the precise length of time that a given position is held intra- day in your account; the associated statistics are often skewed by such factors as the interval between the time the trade is executed and when it is reported back to you, and even the lags that may exist in terms of record- ing the transaction in your books and records. For these reasons, I suggest that you express holding periods for your intraday trading in daily units, using either 0 or 1, as appropriate.
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These “derived” statistics, along with the transactions attributes that I asked you to collect at the beginning of this chapter, place you on a path toward improved understanding of the trade-level characteristics of the portfolio. The first such bits of information that I recommend you analyze are the following:
Average P/L
This statistic is your total P/L divided by your total number of transactions.
While admittedly limited in scope, average P/L can actually have somewhat important implications for understanding overall success levels. First of all, at the risk of stating the obvious, this number can only be positive if your overall P/L is positive; so in this sense, average P/L becomes an absolute indicator of your overall performance. However, beyond this, the evalua- tion/comparison of your average profitability across different periods of analysis enables you to explore the finer points of your performance more deeply than was possible with tools previously discussed in this book.
Significantly, when comparing results across two periods, you can look independently at both the numerator (total P/L) and the denominator (number of trades) of the equation. Are they moving in a consistent fash- ion? Does it appear that you are more profitable when you execute more individual trades or fewer trades? If you are more profitable, why? If not, why not? These issues will become increasingly interesting when we add more components to the transactions-level tool kit. However, there is no reason not to look at this data in isolation because it begins to enable you to understand the power of information available to you at the level of the individual trade.
Once you are able to calculate average P/L across the entire portfolio, it may be useful to subdivide the individual observations into various cat- egory classifications in order to determine whether there are wide dis- crepancies across them. It could be extremely edifying, for example, to calculate your average P/L on trades initiated on the long side versus those on the short side in order to determine whether you have a more finely tuned edge based on a specific market direction. You may also want to undertake this exercise across market sectors (e.g., technology versus financials), specific instruments (soy beans versus live cattle), counter parties, modes of execution, and so on. Indeed, there are as many ways of comparing average P/Ls as there are of characterizing individual trades;
and later in this chapter, I will introduce the comparison I feel to be the one of single greatest importance: P/L on winning versus losing trades. In the meantime, please take some time out to think about the various ways you might want to parse out the average P/L statistic, as this alone will get you thinking about the subcomponents of your portfolio management process
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