Quant Research - Getting the Most from Point-in-Time Data

Authors

Bernd Scherer, PhD* Professor of Finance at EDHEC Business School Ruben Falk 212-438-0648 [email protected] Bala Balachander 857-383-5880 [email protected] Brian Yen, PhD 617-530-8107 [email protected]

Author

John Krowas 857-383-5601 [email protected] John Krowas is a Vice President, Product Management for Capital IQ’s quantitative applications, including ClariFI. A former Charter Oak and ITG employee, John joined Capital IQ in 2001 and also oversees ClariFI’s data integration groups. .

Point-in-Time (PIT) data has become the de facto standard for careful quantitative research. Quants who want the most accurate reflection of history feel that they need to use this data in constructing their models. Preventing lookahead bias, data snooping, and accounting errors: these are all reasons given for using PIT data. Can a quant just apply the “PIT Ointment” to their models, and make all these go away? In this paper, we will examine PIT data’s origins, structure, variations, and proper use in implementations from Compustat and Capital IQ. Misusing PIT data, or applying it haphazardly, can discard valuable information and obscure otherwise clear signals.

October 2010 / Getting the Most from PIT Data

Capital IQ Quantitative Reseach 2

The need for PIT data arose from technology and methodology choices made long before the current practices of backtesting were widely used. These choices created the need, in some cases, to construct PIT data ex post from the previously published non-PIT data. In this section, we will review these technology and methodology choices, and the development of PIT data.

1.1 Compustat and Restatements

Compustat is one of the most widely used databases in quantitative equity management, providing fundamental data on nearly every institutionally investable North American company. Balance sheet, income statement, and cash flow statement items are presented on a standardized basis. The standard North American Compustat dataset contains 20 years of annual items and up to 12 years of quarterly items. Certain additional datasets contain a longer history of data, as far back as 1950 for certain annual items and 1962 for quarterly items, but with a much smaller set of companies.

1.1.1 Restatements

Companies restate their financials for a number of reasons: to correct errors, to provide year-over-year comparables after an accounting change, and to reflect a merger or acquisition. Companies are generally required to restate their financials in the above circumstances.

Due to acquisitions or divestitures, a company’s accounting basis may change quarter to quarter or year to year. To provide a common accounting basis on which to compare figures from disparate periods, the SEC requires that companies restate prior periods when filing 10-Q or 10-K statements. To be exact, the requirement is to provide data from the current period, and the same period a year ago, on the same basis. For a fundamental analyst, performing a year-over-year analysis of a company’s financials, this is ideal.

For example, after a major acquisition, the revenue figures for the new combined entity will be compared against the prior year’s same-quarter revenue. The SEC requires that this year-ago quarter’s revenue figures represent what revenue would have been had the companies’ operations been combined at that time. This is quite useful to the analyst performing revenue growth analysis, determining if the acquisition has led to a true increase in sales. Note that in the event of a restatement due to accounting changes, typically only the prior year’s statements are restated. Figures before that year are not changed. However, in the event of a restatement due to errors, many years’ statements can change.

1.1.2 Compustat Methodology

When the Compustat database was designed, the decision was made to capture these restatements by overwriting the data previously inserted into the database. The fixed format of the database as implemented at that time did not allow for extra fields to store these (possibly multiple) restatements, nor was there market demand to capture them. In fact, quite the opposite was true; being able to easily view year-over-year comparables, as stated in the latest 10-Q, was a significant use case for the majority of Compustat subscribers. This methodology served Compustat’s clients well until the popularity of quantitative investing, and its attendant backtesting, exposed a limitation in it. However, the reader will recall that when this methodology was developed, there was no such thing as a quantitative researcher.

In a typical backtest, models are constructed for historical periods and then tested for efficacy. When using historical data, these overwritten values were all that were available. However, these values were not the values that were known at that point. Those values were only known in the future, that is, reported in subsequent filings and therefore reflect future events. This is an obvious form of lookahead bias – the model is being constructed by pulling values from the future into the simulated present. This lookahead bias was, and continues to be, pervasive throughout the Compustat restated database. Note that this is different from the lookahead bias introduced by the convention of date-stamping data with the fiscal period end date. Of course, financials are not known to the market on the period end date, but only when a press release is made, or a 10-Q or 10-K (in the United States) is filed. To avoid this lookahead bias, it is necessary to lag the data.



Quantitative investors want to replicate the data that would have been known at the time period under investigation. This includes only using the data after it has been announced, whether by press release, 10-Q filing, or insertion of the data into a database (we will examine these various availability dates later). Recreating this historical data environment, putting the model into the habitat it would have existed in, is the driving force behind the creation of PIT databases. By and large, commercially available PIT databases have accommodated this need. However, much like the dog who eventually catches the car he has been chasing, the true question is once you have recreated the past, what exactly do you do with it?



This section will address the processes through which the various PIT databases were constructed. It is important to understand the construction process to be able to use the data properly.

2.1 Compustat

Construction of a PIT database was first undertaken by Marcus Bogue III, founder of Charter Oak Investment Systems. In 1986, as an outside consultant, he developed the PC-Plus product for Compustat. This product, revolutionary for its day, used the then-new technologies of CD-ROMs and personal computers to bring Compustat data to end users directly. Previously, Compustat was delivered on open-reel tapes, to be loaded and maintained by system administrators. PC-Plus brought the data, and a powerful analytical engine, directly to users’ desktops.

The Compustat PIT database was constructed from these CD-ROMs, which were archived by Bogue from the inception of the product. In 1996, personal computer storage capacity had grown to the point where constructing a PIT database was feasible.

2.2 Unrestated Compustat

The first sub-project in creating Compustat PIT was a proto-PIT database, variously known as Unrestated, Compustat Classic, or Research/Backtest. This database was constructed using the month-end CD-ROMs which perserved for each company and for each fiscal period, the first finalized values for each item. Compustat assigns an “update code” for every fiscal period for each company. Compustat collects data from various preliminary sources, such as press releases; this data is not considered final and is given an update code of 2. Until a 10-Q or 10-K is filed, and the data has been collected and standardized, the data for that fiscal period is considered preliminary. After all data items have been inserted into the editorial database, the update code for that period is set to a value of 3, and the data is considered finalized. The Unrestated database consisted of the first values inserted into the database with an update code of 3 for each company. These values were identified by sequentially reading each CD-ROM, storing the first finalized value and ignoring any further updates to that value. The obvious form of updates is restatements, but there could also be corrections of errors in the collection or standardization process. It is important to note that these corrections are also ignored in the Unrestated database.

A good example of unmade corrections is the value of CSHOQ (Common Shares Outstanding - Quarterly) for The Coca Cola Company for the second fiscal quarter of 1996. Compustat inadvertently included treasury shares in the shares outstanding, and thus inflated CSHOQ by 274%. This error was corrected on the very next CD-ROM, but the Unrestated database maintains the erroneous figure for all time. While the incorrect value does not reflect “reality” it does represent what Compustat users saw (and hopefully caught with their data scrubbing algorithms) at the time.

It should be noted that the Compustat Unrestated database consists only of quarterly items. This is because, with a handful of exceptions, annual frequency items are not overwritten in the standard Compustat database upon restatements. Those few annual items which are overwritten are covered by corresponding quarterly items.

2.3 Compustat PIT

The next evolution in Compustat data was the Point-in-Time database. While the Unrestated database captured data that was previously lost due to restatements, there was still value information in those restatements. PIT was designed to provide an exact replica of the Compustat database as it existed at each point in time. This replica includes all restatements which existed at the observation date, but no restatements which came after the observation date, thereby avoiding lookahead bias.

The PIT database was constructed in much the same way as the Unrestated database. Each CD-ROM was read in sequence, and its contents were captured and tagged with the CD-ROM’s publication date. A data structure was devised to enable the retrieval of the data as it existed on each CD-ROM. Again, only quarterly data items were stored in the PIT database on a monthly basis.



2.3.1 Terminology

Let us now discuss some terminology associated with PIT data. The key characteristic of PIT data is that it has two time dimensions, rather than the typical one time dimension of financial data. We will term these dimensions “observation time” and “fiscal time”. Observation time is the normal progression of chronological or “calendar” time. Fiscal time is the progression of fiscal periods (quarters or years). Due to the lag between the end of a fiscal period and the reporting of that fiscal period’s data, the fiscal and calendar timescales are not synchronous. For example, the period ending on June 30 may not be reported until August 4. The intersection of the two time dimensions represents the most recently known fiscal period (in the fiscal time dimension) as of the observation date (in the observation time dimension).

2.3.2 Compustat Preliminary

After the introduction of Compustat PIT, a database containing preliminary data only was released. As both the PIT and Unrestated Compustat databases contain only finalized data, they fail to capture data reported in press releases or other non-final documents. While the number of items captured in these documents is small, it is well-understood that markets react to the information contained in them, and quantitative models which do not make use of them may be compromised. Users of preliminary data should exercise caution when using multiple data items in factor construction to assure that the appropriate fiscal periods are properly aligned. For example, EPS is typically captured as a preliminary data item from press releases, but Total Assets is not. When constructing a Return on Assets factor, a user needs to ensure that the Preliminary EPS for the most recent quarter is not used along with the Total Assets from the prior quarter. Opportunities for this asynchronicity to occur when using the Preliminary data abound; the utility of preliminary data can be limited by this problem.

2.4 Capital IQ extends PIT to “All Instance”

Capital IQ began collecting and standardizing company fundamental data in 2000 and has history which extends to the early 1990s depending on the country. From the beginning, the data collection process preserved, rather than overwrote, subsequent revisions of the data. This is often referred to as an “All Instances” database. That is, for each financial period (quarter, year, etc.) all instances of data for that period are available. An instance could consist of data collected from a press release, regulatory filing, or filings for subsequent periods which restate the data for that period. Additionally, the filing date for each instance of each period is stored.

One can see how this “All Instances” format can be remade into a PIT format. Since each instance is timestamped with its filing date, instances can be filtered based on the desired observation date, eliminating lookahead bias. It should be noted that in the Capital IQ database, the filing date, or data availability date, is the date the document from which the data was collected was filed. This is in contrast to the Compustat PIT data, where the data availability date corresponds to the date it was published on the CD-ROM.

The filing date timestamp allows models to react to data at the same time the market as a whole does, but is slightly unrealistic. Due to collection and standardization delays, that same model going forward will not have access to that data immediately. While both methodologies are correct, each is based on separate assumptions, which users may want to account for when building models. A careful practitioner may lag the Capital IQ data by a few days to account for the processing of the data. In contrast with the Compustat PIT database, the inclusion of filing dates can be used to capture alpha which may exist in short-term signals that fade prior to insertion into the Compustat database. This short-term alpha may be leveraged in production portfolios if a sufficiently rapid data capture process is utilized. However, capturing this data without standardization can introduce difficulties in comparing across industries and regions.



Although the purpose of PIT data is to allow the user to use only, and exactly, the data known at each point in time, one needs to question whether that data is what is desired. Let us now examine, by way of example, a circumstance where using PIT data might not result in the desired outcome.

In September 2006, Sara Lee Corporation divested its Hanes line of business. In reports subsequent to the divestiture, Sara Lee restated prior periods’ data to provide comparable figures to reflect the discontinued operations. These restatements, as we will see, disrupted the time-series of data during the year following the divestiture. This disruption can have a detrimental effect on models constructed during this period.

We will begin by examining the evolution of revenue data during this time. Total revenue is typically thought of as a “safe”, simple item with few potential modifications due to standardization. Revenue is a component of common factors such as gross margin, sales/price, and needless to say, revenue growth. In the Compustat PIT database, the value for SALEQ for Sara Lee’s fourth fiscal quarter, ending June 30, 2006, is first given (on the September 30, 2006 CD-ROM) as $4100 million. By examining the EDGAR filing database, we see that the 10-Q containing this figure was filed September 14, 2006. On the September 2006 CD-ROM, the four most recent quarters were as follows (dates given in fiscal time).

Table 1: Observation Date: September 30, 2006 Since the divestiture closed on September 5, 2006, Sara Lee’s next fiscal quarter (the first fiscal quarter of 2007) reflected results without the discontinued operations of Hanes. In order to provide comparable year-over-year data, Sara Lee restated its prior periods beginning with the 10-Q for Q1Y07. This 10-Q was filed on November 9, 2006, and the data first appeared on the November 30, 2006 Compustat CD-ROM. The restatement overwrote the Q1Y06 value for SALEQ to $2763 million. Table 2 shows the five most recent quarters’ data, as of November 30, 2006. Table 2: Observation Date: November 30, 2006 Now we begin to see the discontinuity in the time-series of SALEQ as of the November 30, 2006 observation date. The figure for Q1Y06 has now been restated to reflect the results which would have been reported had the divestiture occurred prior to that fiscal period. The previous number of $4192 million has been restated to $2763 million. The most recent quarter, and the fifth-most-recent quarter, are on the “new” basis, reflecting the discontinuation of the Hanes operations. The intervening three quarters are on the “old” basis, including the discontinued operations. A simple year-over-year analysis shows a modest growth in sales. However, factors constructed by combining any of the two “old" quarters with any of the three “new” quarters will not reflect a consistent accounting basis. A typical sales/price factor is constructed with the sum of the trailing four quarters’ sales, dividing by the most recent market capitalization. Of course, the most recent market capitalization will reflect the divestiture (as will the most recent quarter’s sales number) but three of the four quarters of sales data

Fiscal Quarter SALEQ Q4Y06 4100 Q3Y06 3789 Q2Y06 3863 Q1Y06 4192

Fiscal Quarter SALEQ Q1Y07 2891 Q4Y06 4100 Q3Y06 3789 Q2Y06 5292 Q1Y06 2763



will reflect the revenues of a company that no longer exists! We can see that the PIT database reflects exactly the state of the Compustat database on November 30, 2006, but simply using this database does not guarantee that the data is what we wish it to be.

Subsequent restatements through the 2007 fiscal year produced the following snapshots of the database.

Table 3: Observation date: February 28, 2007

Table 4: Observation date: May 31, 2007

Table 5: Observation date: September 30, 2007 By the end of the 2007 fiscal year, the time-series becomes consistent for the prior two years, and we see a fairly steady, modest growth in sales for Sara Lee. The picture changes when we are in the midst of those restatements, however. We see in Table 3 on the February 28, 2007 observation date that Sara Lee has experienced quite a bit of “volatility” in its sales. Quantitative investors looking for stable revenues in their models would not favor Sara Lee, and any model looking at quarter-over-quarter sales growth rate would give a large negative score to Sara Lee in November 2006. However, when everything is restated (almost a year later) to reflect a consistent accounting basis, we see that Sara Lee experienced only a small decline in revenues from Q4Y06 to Q1Y07.

Now the question becomes what to make of all this. We have observed that despite the fact that the Compustat PIT database presents a faithful copy of the data as it existed on the observation date, it does not give us quite what we are looking for. While it is useful for year-over-year comparisons, as it presents the current and year-ago quarters properly, it is not so useful for factors which include the intervening periods. Limiting one’s research to just the most recent and year-ago quarters is quite

Fiscal Quarter SALEQ Q2Y07 3182 Q1Y07 2891 Q4Y06 4100 Q3Y06 3789 Q2Y06 2974

Q1Y06 2763

Fiscal Quarter SALEQ Q3Y07 3006 Q2Y07 3182 Q1Y07 2891 Q4Y06 4100 Q3Y06 2754 Q2Y06 2974 Q1Y06 2763

Fiscal Quarter SALEQ Q4Y07 3199 Q3Y07 3006 Q2Y07 3182 Q1Y07 2891 Q4Y06 2969 Q3Y06 2754 Q2Y06 2974 Q1Y06 2763



restrictive, but could assure accurate data, thereby improving the quality of what few signals could be constructed.

3.1 Comparison of Compsutat and Capital IQ

The main differences between the Compustat and Capital IQ PIT data are in the filing date/insertion date as mentioned above, standardization methodologies (which are beyond the scope of this paper), the number of data items available for each reporting template, and the coverage, both in terms of history and number of companies. The intent and outcome of the databases are materially similar and can be used fairly interchangeably when the user’s needs are aligned with the area of overlap. The same cautions as discussed above when using a fiscal time-series of quarterly data apply to the Capital IQ PIT data. However, one unique feature of the Capital IQ data, which mitigates this problem to some degree, is the presence of the “last twelve months” data items.

Figure 1: Compustat vs. Capital IQ PIT data Observation Date Fourth Quarter Sum CIQ LTM Total of SALEQh Revenue 09/30/06 15944 15944 11/30/06 16072 16072 02/28/07 13962 16280 05/31/07 13179 16532 09/30/07 12278 12278 Table 6: Compustat PIT vs. Capital IQ LTM



3.1.1 Capital IQ Last Twelve Months Data

Capital IQ Last Twelve Months (LTM) data has quarterly fiscal frequency, and attempts to calculate a trailing sum of data on smoother basis than a basic sum of the last four known quarter might be. LTM data for fiscal quarter q of fiscal year y is calculated as

LTM(q, y) = YTD(q) + Annual(y – 1) – YTD(q, y – 1).

In this way, a user is more likely to capture all the restatements reflected in the annual report for y – 1, rather than mixing and matching accounting bases as in the above example with Compustat PIT. This methodology reflects what the typical fundamental analyst might do if given the task of constructing LTM figures for a company. In Figure 1, this smoothing effect is observed as Sara Lee’s annual report is used to present revenue on a basis most nearly like that which existed as of the annual report for the period ending June 2006, seen in Table 6.

Figure 2 highlights the seasonality of the differences between the simple trailing four quarter sum and the calculated LTM values. Capital IQ data is used exclusively in this chart to eliminate any methodology or standardization differences. At each month end in the time range, each S&P 1500 constituent company’s most recently available LTM Total Revenue was compared with the sum of the four most recently available quarters’ Total Revenue. The absolute percent difference was then averaged across the universe. As most companies end their fiscal year in December, one can observe the maximal differences towards the end of each calendar year. It is at this point in the fiscal year where the maximum potential inaccuracies exist in the trailing four quarter sum, and the benefit of using LTM data becomes most clear.

Figure 2: Absolute Difference: Capital IQ Total Revenue, LTM vs. Trailing Four Quarter Sum, Cross-Sectional Average over the S&P 1500



The key difficulty in working with PIT data is the second time dimension. Handling time-series data is difficult enough for many analytics systems without the additional difficulty of two time dimensions. While the second time dimension adds complexity, we can reduce it to a large degree by examining some typical use cases. For example, users often want to know the (fiscal) time-series average, sum, variance, or change in a value over the n most recent fiscal periods, as of an observation date. Subsequently, they may be interested in knowing the (calendar) time-series (that is, over a series of observation dates) average, sum, etc., of that (fiscal) time-series aggregate. They may also want to know the cross-sectional average, etc., of the most recent fiscal period’s value as of some observation date or the cross-sectional average of a fiscal-time aggregate of values.

An analytics system for PIT data should be able to address these use cases with ease. One important thing to note about the above use cases is that they do not depend on knowing the absolute fiscal period. That is, they are only concerned with the most recent fiscal period as of an observation date, or the n most recent fiscal periods. For the purposes of the calculations above, it is irrelevant whether the fiscal period ended on May 31, 2006 or June 30, 2006. The only information needed is “what is the most recent fiscal period’s value known as of the observation date?” All manipulations of the n most recent periods can be done on this relative basis, without knowledge of the absolute fiscal periods. This greatly simplifies the collapse of the two time dimensions into one, as it is no longer necessary to keep track of the dates associated with one of the dimensions, only the increment away from the most recent fiscal period (which we will term 0 periods back).

An important aspect of working in relative time across various items, even for the same company, is that the most recent period for each item should be the same absolute period for all other items. For example, in a company’s press release, a few “headline” numbers are typically presented. The fiscal period reported in the press release could be considered the most recent fiscal period as of the date of the press release. However, the most recent values for items not reported in the press release is the prior fiscal period. When constructing a factor using disparate data items, any analytic system must take care to properly align relative periods so they correspond to the same absolute fiscal periods.

4.1 Cross-sectional Alignment

Even if all the items for a given company as referenced by a relative fiscal period are aligned to the same absolute fiscal period, that does not guarantee alignment across companies.

For example, if Company A has filed a 10-Q for its quarter ending September 30, its 0th period back will be this quarter. However, if Company B has not filed its September quarter-end data, its 0th period back will be the quarter ending June 30. When comparing 0th quarter back data across these two companies, the data will not be referring to the same absolute time period. This fact could be important if a significant event happened sometime during the third calendar quarter. The effects of this event would be reflected in Company A’s data, but not in Company B’s. For factors which depend on this absolute alignment of fiscal periods, one can introduce a check on the 0th period’s end date for each company. Unaligned data can then be adjusted or discarded. Of course, it should be noted that due to different companies’ fiscal year end dates, it is impossible to perfectly align all data at all times, so some tolerance of misalignment must usually be in place.



In this paper we have examined two major datasets’ development and presentation of PIT data. Although PIT data faithfully represents the world as it was known at any historical time, it must be acknowledged that any database’s view of the world is not necessarily what we wish it to be. The pattern of restatement of data can introduce unwitting inaccuracies into factor construction. Some of these inaccuracies can be mitigated by using Capital IQ LTM data to construct factors which rely on multiple historical fiscal periods. Analytics systems for PIT data need to be developed with consideration given to this second time dimension, and accommodate the common use cases in quantitative backtesting.

PIT data is a powerful tool for constructing quantitative models, but like all tools, it must be used judiciously to produce the desired results.



This document was prepared by the Capital IQ Quantitative Research group. Capital IQ is a division of Standard & Poor’s. The information contained in this document is subject to change without notice. Capital IQ cannot guarantee the accuracy, adequacy or completeness of the information and is not responsible for any errors or omissions or for results obtained from use of such information. Capital IQ makes no warranties of merchantability or fitness for a particular purpose. In no event shall Standard & Poor’s be liable for direct, indirect or incidental, special or consequential damages resulting from the information here regardless or whether such damages were foreseen or unforeseen. This material is not intended as an offer or solicitation for the purchase or sale of any security or other financial instrument. Securities, financial instruments or strategies mentioned herein may not be suitable for all investors. Any opinions expressed herein are given in good faith, are subject to change without notice, and are only correct as of the stated date of their issue. Prices, values, or income from any securities or investments mentioned in this report may fall against the interests of the investor and the investor may get back less than the amount invested. The information contained in this report does not constitute advice on the tax consequences of making any particular investment decision. This material does not take into account your particular investment objectives, financial situations or needs and is not intended as a recommendation of particular securities, financial instruments, strategies to you nor is it considered to be investment advice. Before acting on any recommendation in this material, you should consider whether it is suitable for your particular circumstances and, if necessary, seek professional advice. Capital IQ Quantitative Research is analytically and editorially independent from any other analytical group at Standard & Poor’s, including Standard & Poor’s Ratings. ©2010 Capital IQ, a division of Standard & Poor’s. All rights reserved. Redistribution, reproduction and/or photocopying in whole or in part is prohibited without written permission. STANDARD & POOR’S, Capital IQ and S&P are registered trademarks of The McGraw-Hill Companies, Inc.

Quant Research - Getting the Most from Point-in-Time Data

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