Do Managers Time Securitizations to Obtain Accounting Benefits?*

Patricia M. Dechow The Carleton H. Griffin Deloitte & Touche LLP Collegiate Professor of Accounting

Stephen M. Ross School of Business University of Michigan Ann Arbor, MI 48109

Email: dechow@umich.eduPhone: (734) 764 3191

and

Catherine Shakespeare Assistant Professor of Accounting

Stephen M. Ross School of Business University of Michigan Ann Arbor, MI 48109

Email: shakespe@umich.eduPhone: (734) 647 6984

First version: February 2005

* We thank the Harry Jones Earnings Quality Research Center at University of Michigan for its support. We thank Russell Lundholm for comments.

mailto:dechow@umich.edumailto:dechow@umich.edu

Do Managers Time Securitizations to Obtain Accounting Benefits?

Abstract

Relative to recording securitizations as collateralized borrowing, the sales treatment required by SFAS 140 provides several accounting benefits: it improves efficiency ratios; lowers leverage; increases reported operating cash flows; and can be used to manage earnings. Consistent with balance sheet window dressing, we document that securitization transactions occur with greater frequency in the third month of the quarter and that the transactions are clustered in the last few days of the quarter. This result is robust across different asset classes and is documented for both high and low volume securitizers. We next investigate whether the flexibility offered by the fair-value focus of SFAS 140 is used to manage earnings. We find that whether firms report a gain or loss from securitization appears to be influenced by incentives to beat common heuristics (reporting a profit, beating last year’s earnings, or beating analysts’ forecasts). We also document that selected discount rates are clustered around common heuristics (10% and 12%) suggesting their selection is somewhat arbitrary. Taken together, our evidence suggests that the sales treatment offered by SFAS 140 is likely to encourage (i) balance sheet window dressing and (ii) earnings management.

Keywords: securitizations, earnings management, window dressing, timing, SFAS 140

1. Introduction

The objective of accounting standards is to enable financial statement users to better

understand the economic performance of the firm. However, in reality, accounting standards are

often the result of compromises between various competing self-interested parties, with rules

making little economic sense. For example, the corridor rules required in pension accounting

allow arbitrary smoothing; the bright-line lease accounting rules allow firms to engage in off-

balance sheet financing; and special purpose entities rules allow firms to arbitrarily remove debt

from their balance sheets.

The focus of our paper is on SFAS 140 “Accounting for Transfers and Servicing of

Financial Assets and Extinguishments of Liabilities.” This standard provides lucrative business

opportunities to investment bankers (in structuring deals to conform to the standard’s

requirement while meeting the firm’s financing needs); accounting firms (providing consulting

services and auditing the application of the rules); lawyers (in ensuring legal requirements for a

“bankruptcy remote” special purpose entity); and managers (increasing accounting flexibility).

The only parties whose interest does not appear to be well served are users - investors and

creditors. The lack of disclosure concerning assumptions and the lack of transparency

concerning financial statement impact, makes it almost impossible to completely back-out the

SFAS 140 accounting treatment.1 There is however, demand for such information. Moodys and

the Federal Reserve Board who can demand private information from firms, both state that they

1 Landsman, Peasnell and Shakespeare (2006) use an approximation to reverse the effects of gain on sale accounting in order to consolidate the securitization transactions. The method relies on an estimate of the liability as the true liability is unknown and is based on the method used by Moodys Investor Sevices.

1

back-out the “gain on sale” treatment of SFAS 140 when assessing the economic risk of the

firm.2

We begin our paper by briefly explaining the securitization process. We then discuss the

“gain on sale” accounting prescribed in SFAS 140. We use a simple example to compare

financial statement under “gain on sale” accounting to the alternative of treating securitizations

as collateralized borrowings. Our example highlights that the “sales treatment” offers substantial

improvements to the balance sheet ratios relative to treating the transaction as a loan. Since

firms are required to produce balance sheets on four days of the year (the last day of the fiscal

quarter), we argue that it is relatively simple for managers to maximize the accounting benefits

from SFAS 140. All firms need to do is to “time” their securitization transactions toward the end

of the quarter. At the end of the quarter, managers know best what numbers they want in the

balance sheet. By timing the securitization in the last few days of the quarter, a manager can

ensure that the desired results are obtained.

Using a sample of over 11,000 securitization transactions we investigate the dates of

securitization transactions. We show that securitizations transactions are clustered in March,

June, September, and to a lesser extent December. We find that over 41 percent of transactions

occur in these months, whereas just 27% of transactions occur in January, April, July, and

October. In addition, we find that a large proportion of transactions occur within the last five

days of the month. Only 8 percent of securitizations occur in the first five days of the month,

where as 45 percent occur in the last five days of the month.

We examine competing explanations for our results. For example, it is possible that the

clustering we observed could reflect clustering in the creation of the underlying assets to which

2 Based on presentations by representatives of the Federal Reserve Board and Moodys at the AAA/FASB Financial Reporting Issue Conference, December, 2005. Note “gain on sale” is the Wall Street name given to the SFAS 140 accounting treatment and we use this term throughout the paper.

2

the receivables relate. We compare automobile securitizations to sales of new automobiles,

credit card securitizations to retail sales, and mortgage securitizations to new home sales. In

each case, we do not find that the underlying assets exhibit the same type of clustering as

observed in the securitization markets.

We next link the securitizations transactions to public companies that report

securitization activities in their notes to their annual financial statements (10K). For ease of

exposition we call this sample our 10K sample. We use annual information because we find that

65 percent of our 10K sample provide no disclosures about the gains from securitizations in their

quarterly financial statements. For the 10K sample we can identify firms that are high volume

securitizers versus those that only perform securitizations periodically. We find that both

groups cluster their securitization transactions in the last month of the quarter, with such

clustering being stronger in the high volume group. We also find that over 75% of the

transactions for the high volume securitizers occur in the last five days of the month.

Our next tests investigate whether managers use the flexibility offered by the fair-value

accounting rules in SFAS 140 to manage their income. SFAS 140 gives managers little direct

guidance on exactly what discount rate should be used to determine the retained interest. In

addition, managers must forecast the future cash flows taking into account credit risk and

prepayment risk, which generally resides in the retained interest. Since there are no market

values for the retained interest, it is difficult to verify whether management’s assumptions are

reasonable. Our discussions with investment bankers suggest that the accounting may sometime

be backward engineered. Managers decide on the desired income effect and then assumptions

are adjusted to achieve that goal. Therefore, under SFAS 140 firms have flexibility to report a

“gain,” a “loss,” or no income effect (exactly zero). We use the fact that firms can report a gain

3

or a loss to our advantage. Rather than developing a model of the discretionary component of

the gain, our earnings management test involve examining whether firms are more likely to

report gains (or losses) in situations where they most benefit from reporting a gain (or are least

hurt by reporting a loss). By contrasting gain reporting behavior to loss reporting behavior, we

can mitigate concerns that underlying economics drive our earnings management findings.

For our earnings management test we examine three heuristics commonly considered in

the literature: the desire to report a profit rather than a loss (Burgstahler and Dichev 1997), the

desire to report positive increases in earnings (Burgstahler and Dichev 1997, Schrand and

Walther 2000), and the desire to meet or beat analysts’ expectations (Degeorge et al. 1999). For

a sample of 212 firm-years in which 161 firms report a “gain on sale”, 30 firms report a “loss on

sale,” and 21 firms report “zero” we find the following:

• 19% of firms reporting a “gain from sale” move from reporting an accounting loss to

reporting a profit. In contrast, we find that no firm reporting a “loss from sale” move from

reporting a profit to an accounting loss.

• 23% of firms reporting a “gain from sale” move from reporting a negative change in earnings

to a positive change in earnings. Only one firm reports a sufficiently large “loss from sale”

to report an earnings decline.

• 31% of firms reporting a “gain from sale” move from missing analysts consensus forecasts to

meeting or beating consensus forecasts. In contrast only 14 % or 4 firms reporting a “loss

from sale” produce a loss sufficient to miss consensus forecasts.

We also investigate the discount rates used by managers in determining the fair value of

retained interest and the size of the gain. We find that interest rates used are clustered around

4

common heuristics (10% and 12%) suggestive that discount rate choice is arbitrary and possibly

set to achieve desired accounting objectives.

Our results are consistent with SFAS 140 creating an incentive for managers to time their

securitization activity towards the end of the quarter so as to reap benefits from window-dressing

the financial statements. Our evidence also suggests that the fair-value approach to recognizing

the retained interest provides managers with flexibility to manipulate income. In addition, the

lack of disclosure in quarterly financial statements and the type of disclosures provided in annual

financial statements makes it almost impossible for users to completely back out the effects of

these transactions on the financial statements.

The next section discusses the securitization process and provides a simple example to

highlight the accounting benefits of the “gain on sale” accounting treatment offered in SFAS

140. Section 3 provides our evidence concerning window-dressing of balance sheets and the

timing of securitizations. Section 4 describes our evidence concerning earnings management

through the flexibility offered by fair-valuing the retained interest. Section 5 provides our

conclusions.

2.1 The Securitization Transaction

Exhibit 1 presents a typical securitization transaction. A firm (the securitizer) transfers

assets (the receivables) to a special purpose entity (SPE). This SPE determines with the help of a

rating agency what proportion of the receivables’ cash flows can be sold so that the sold cash

flows (tranches) get the desired credit rating. Managers (with the help of investment bankers)

also decide how to structure the payoffs. For example, some tranches could be interest only

tranches, while others could be principal only. The buyers of these securities are pension funds,

5

hedge funds, or banks. In order to obtain the desired ratings, the firm is typically required to

retain some proportion of the cash flows. This portion is transferred back to the firm from the

SPE. The tranche retained by the firm contains more credit risk and prepayment risk than the

tranches sold to investors. This is why Wall Street terms the retained interest “toxic waste.”

As the “toxic waste” is not “sold” to another party there is no market value for this

tranche. Nonetheless, managers are required to fair-value the retained interest and classify it as

an asset on the books. SFAS140 requires the interest rate to discount these cash flows to be the

“market rate.” However, there is ambiguity on what the market rate is:

• Is the market rate the rate that is charged to the end customers creating the receivables (in

which case one would expect no gains to appear in financial statements)?

• Is it the firm’s rate that it can raise money (in which case gains could represent the spread

between their cost of capital and the rate charge to customers)?

• Or is it the rate that the retained interest could be sold to a third party (in which case one

would expect mainly losses in the financial statements? 3

Consistent with the view expressed by the AAA’s Financial Accounting Standards

Committee (1996, p. 181) in their comment letter on the accounting for securitizations that

argues that: “…unless a fundamental attribute of the underlying asset has changed, the fair value

of the items exchanged should be equal to their carrying amounts, implying no gain at transfer,”

we assume that the correct rate is the rate that results in the internal rate of return being equal to

the market rate charged to customers (a rate that results in a zero effect on the income statement).

In other words, if the sold stream of cash flows sells for 4%, and represent 80% of receivables,

and the market rate charged on those receivables is 10%, then the discount rate used on

3 See, Dechow, Myers, and Shakespeare (2005) for more details on the appropriate interest rate.

6

remaining 20% of cash flows is the one that results in the internal rate of return equal to 10%.

This rate will be considerably higher than 10% and will result in no gain or loss. The assumption

here is that splitting up the receivables into different cash flow stream does not create value since

the total cash flow streams remain the same before and after the transaction.4 Therefore, any

gain or loss reported in the financial statements is the result of managerial discretion.

[Exhibit 1 here]

2.2 Comparison of Financial Statements under “Gain on Sale” Versus Collateralized Borrowing

Each securitization transaction can be structured as a sale or as a collateralized borrowing

for financial reporting purposes, but the vast majority of deals are structured to qualify for sale

accounting. Exhibit 2 provides a simple example to clarify the accounting benefits of obtaining

“gain on sale” accounting.

Assume a firm starts with $600 inventory, $100 cash, and equity of $700. It sells the

entire inventory for $1000 on credit. The firm then transfers these receivables to an SPE and

receives $900 cash from investors. As the customers make their payments to the firm, they are

used to pay back investors. We ignore the impact of servicing in this example. As a result of this

transaction the firm has $900 cash. This cash can then be used to finance their investment and

operating activities. This is the major economic benefit of the securitization process, the firm

receives cash without having to wait for customers to pay, and the predominate reason firms

engage in these transactions.

4 One could argue that splitting up cash flows could create value since different investors may desire different payoffs and so be better served with the different tranches. In competitive markets however, we believe these benefits would accrue to the buyers not to the securitizers (the securitization market is very active). In addition, information asymmetry problems would predict that the firms would mainly record losses since buyers would demand too high a rate. However, we should note that this is the main way firms justify gain recognition.

7

SFAS No. 140 requires the firm to record the transaction as a sale if the firm relinquishes

control over the assets. Therefore, even though the firm may retain an ownership stake in the

securitized assets, if the assets are first sold to an SPE (that meets the legal requirements of being

bankruptcy remote), the firm can remove them from its books. In other words, SFAS 140 does

not require consolidation of ownership interest. Rather, it permits sale accounting.5 Sale

accounting requires that managers:

a) determine the fair value of the transaction. Here the firm receives $900 from outside

investors and managers determine the fair value of the retained interest is $110;

b) remove the receivables from the books (the $1000 is removed); and

c) record a gain or loss that reflects the difference between the fair value and the book

value of the receivables (a gain of $10 is created).

The alternative accounting treatment that we consider is treating the securitization as a

collateralized borrowing. In which case

a) the firm has borrowed $900 from investors (liabilities increase to $900); and

b) cash increases by $900.6

Exhibit 2 compares the balance sheet, income statement, and statement of cash flows

under the two treatments. We also provide a comparison of common ratios used by investors

and creditors. This comparison shows the accounting benefits of securitizations. The

securitizing firm appears to be more efficient at collecting receivables since the receivables have

disappeared (even though the firm has retained the credit risk). Thus investors can no longer

examine whether collection has slowed or credit policies have changed. The securitizing firm

5 Based on our discussions with securitizations professionals, it can be difficult to structure a transaction as a collateralized borrowing as the typical structures used are designed to achieve gain on sale accounting. 6 We would recommend that the Accounts Receivable be classified under a separate heading such as “securitized accounts receivable” to alert investors to the fact that they have been securitized. SFAS140 requires disclosure of the amount of liabilities that are backed by assets under these structures.

8

appears more profitable (return on assets increase from 20% to 37%). The gain (often not

disclosed) increases profits, while total assets are smaller.

Securitizing firms are not required to separately disclose cash flows from securitizations

and they can be classified either as operating activities or investing activities depending on the

nature of the underlying asset, under either definition reported free cash flows improves. In

contrast, a firm that classifies the transaction as a collateralized borrowing must classify the same

cash flows as financing. Finally, the securitizing firm appears to have lower leverage. All

obligations are off-balance sheet. Therefore, the securitizing firm appears more liquid, less

risky, and appears to be creating more value through its operating activities.

To summarize, Exhibit 2 highlights that the SFAS 140 sales treatment improves the

financial statements considerably. Of course, investment bankers and accountants can make the

transaction appear more complicated than what we have described here and introduce more

ambiguity. However the essence of all transactions is the same. Obtaining the sales treatment

allows the firm to engage in off-balance sheet financing.

[Exhibit 2 here]

3. Balance Sheet Window Dressing and Timing of Securitizations

3.1 Results for the ABSNET Sample

Our first sample consists of transaction level data of individual securitization

transactions. We hand-collect 11,942 individual securitization transactions from www.absnet.net

9

http://www.absnet.net/

provided by Lewtan Technologies. 7 We refer to this sample as the ABSNET sample throughout

the remainder of the paper. The database provides details on individual securitizations

transactions including the date of the transaction, the amount securitized, the tranche structure,

the SPE, the asset type and the seller’s name for the period 1987 through 2006. In addition to the

transaction date and asset type, we collected the seller and the amount securitized, when

available.

Figure 1 Panel A presents the distribution of the transactions by month. Although the

ABSNET database does not disclose financial year ends (no information is provided on the

parent company), firms typically have December year ends. What is observable in Figure 1 is

that a greater proportion of transactions occur in months 3, 6, 9, and 12. These represent March,

June, September, and December. Panel B reports the distribution of transaction by day of the

month. September, April, June, and November have 30 days, while remaining months (except of

course, February) have 31 days. In this Figure we use the classification “last day” to indicate the

last day of the month (whether it is 30, or 31). Clearly, the last day of the month is very different

from the first day of the month. Panel C presents the transaction data by day of the year. The

largest securitization transaction days of the year are March 30/31, June 29/30, and September

29/30 (depending probably how weekends fall). December 30/31 does not exhibit such strong

clustering. There are several explanations for this. First, it is the holiday season (December 25

7 ABSNET require the following explanation to be disclosed when the data from the website is downloaded and used: “The information and data contained on this screen is derived from sources considered reliable, but Lewtan and its suppliers do not guarantee its correctness or completeness. The user is solely responsible for the accuracy and adequacy of any information used by it and the resultant output thereof, and Lewtan and its suppliers disclaim any and all liability therefore. Some information contained in ABSNet(tm) is also copyrighted (© 1998-2000) by Standard & Poor’s, a division of The McGraw-Hill Companies, Inc. While this information is based on sources considered reliable, neither Standard & Poor’s nor its affiliates guarantees the accuracy, adequacy, or completeness of the information and they are not responsible for errors, omissions re results obtained from use of the information. Standard & Poor’s receives compensation for ratings. Such compensation is based on the time and effort to determine the rating and is normally paid either by the issuers of securities or by the underwriters participating in the distribution thereof. The fees generally vary from $2,500 to $100,000. While Standard & Poor’s reserves the right to disseminate the rating, it receives no payment for doing so, except for subscriptions to its publications.”

10

to January 1) so many people may be on vacation and so securitizations are done earlier in the

month. December is quite different from other months in that more transaction occur between

days 16 to 24. Second, auditors may view transactions occurring on the last day of the year with

particular scrutiny so by timing the transaction slightly earlier the firm avoids this obvious red

flag.

At the bottom of Panel C we provide Chi-square tests examining the distributions. The

monthly test examines whether transactions occur evenly over the months. The quarterly test

classifies months in two three groups (first month of quarter, second month, and third month)

and tests whether the three groups are different. The daily test exclude day 31 and examines

whether transactions are distributed evenly across days. In all three cases the Chi-square tests

rejects that the distributions are uniform.

[Figure 1 here]

One possible explanation for our finding is that the underlying assets being securitized

exhibit the same clustering and that this is driving the clustering of securitizations (rather than

window dressing). We therefore, examine several other macroeconomic distributions that we

expect to be correlated with securitization activity. The coverage in the ABSNET database prior

to 1990 is sparse as securitizations were infrequent. Therefore, we collect macro level variables

for the period 1990 though 2005 when available. We obtain monthly new house sales and retail

sales from the U.S. Census Bureau, monthly automobile unit sales from the Bureau of Economic

Analysis for the period 1990 through 2005. We obtain the monthly 30-year fixed conventional

mortgage rate from Fannie Mae and monthly federal funds rate from the Federal Reserve Board

for the period 2000 through 2005.

11

Figure 2 compares monthly automobile securitizations (Panel A) to automobile sales

(Panel B). The data on automobile sales is based on sales from the auto companies to dealers

and it is possible that the auto companies could force acceptance of vehicles toward the end of

the quarter. However, the distribution of auto sales is relatively smooth peaking in June. It does

not show the saw-tooth pattern exhibited in the securitization market.

[Figure 2 here]

Figure 3 compares mortgage-backed securitizations (MBS) to new house sales. Again

this is not the perfect comparison, but it is interesting to compare the distributions. 8 The four

months with the greatest securitization activities are months 3, 6, 9, and 12. Housing sales

appear to peak in the last four months of the year. Again, the distributions appear to be quite

different.

[Figure 3 here]

Figure 4 compares credit card securitizations to retail sales. Retail sales increase in a

linear fashion through out the year, whereas credit card securitizations peak in June. We have

fewer observations for this distribution (941) but for whatever reason, November is a popular

month (rather than December) for undertaking securitizations. There are two possible

explanations. Perhaps because December is the busiest retail month of the year, managers are

involved in running the business and so delay their securitizations until March (the next reporting

month). Alternatively, we are likely to have a greater problem with our assumption of December

fiscal year ends. Many retailers have Jaunary or February fiscal year ends. This would mean

that October or November would be the last month of the third quarter. It is interesting to note

that this is the only figure in which the number of transactions in January is almost equal to the

8 Optimally, we should compare MBS transactions to mortgage originations. Mortgage origination statistics are only available on a quarterly basis and are issued by Department of Housing and Urban Development..

12

number of transactions in February. In all other figures, January has far fewer observations than

February. Not knowing the fiscal year end could be particularly problematic for investigating

window-dressing in this sample of securitizations.

[Figure 4 here]

Figure 5 reports monthly interest rates from January 2000 to December 2005. We

provide this figure to investigate the unlikely possibility that interest rates dip in March, June,

September, and December. If this occurred then securitizers could time their transactions to take

advantage of the lower cost of financing. We report both the 30-year fixed Mortgage rate as well

as the “risk free” treasury fund rate. Neither rate exhibits a consistent dip in the third months of

the quarters.

[Figure 5 here]

We also examine the total dollar value of securitizations by month (instead of total

transactions). The distribution (not reported) is almost identical to Figure 1. Table 1 provides

other break-downs by asset classes, the same type of third month of the quarter clustering is

exhibit across all asset classes.

[Table 1 here]

In summary, the results in this section suggest that firms cluster their securitization

activity in the third month of the quarter. This is consistent with incentives to window-dress the

financial statements. Perhaps firms perform securitizations at the end of the month because of

their accounting systems. If this was the case, then one might expect a greater proportion of

firms to securitize on the first day of the next month. Yet this is a very low securitization date.

13

We can think of no other explanation of why the last few days of the third month of the quarter

should be the peak time to engage in securitization activities, other than window-dressing.

3.2 Results for the 10K Sample

We collect a second sample of firms that disclose securitization activity in their annual

financial statements to provide more insights into window-dressing. We refer to this sample as

the 10K sample throughout the paper. We use Edgar to search the 10-K filings of all firms filing

with the Securities and Exchange Commission (SEC) during the period September 2000 to

December 2004 inclusive. We selected this time period because this is when SFAS No. 140

became effective, and under SFAS No. 140, firms are required to disclose more details of their

securitization activities. We read each firm’s 10K and require firms to disclose gains, proceeds

from securitizations undertaken during the year, the fair value of the retained interest, and the

related adverse changes disclosures at the year-end. This yields a sample of 212 firm year

observations representing 90 firms.

Next, we link the ABSNET sample with the 10K sample. This is a non trivial exercise

because the seller in the ABSNET database, when it is disclosed, is typically a subsidiary and the

10K sample is made up of ultimate parents only.9 In addition, the seller’s name is not always

reported, nor are the full legal name of the subsidiaries always disclosed. For example, no sellers

were disclosed for Collateralized Debt Obligations (CDO) securitizations. We next searched 10-

K filings, Hoovers online and ultimately did a google search (www.google.com) for the seller’s

9 ABSNET reports significant detail about the transaction structures. However, it was not possible to link most of this detail to the financial statements. For example, though the structure of the various tranches is typically disclosed, it is not possible to identify which tranches the seller may have bought back. In addition, the database discloses the current coupon rate for each security. It is not possible to link this coupon rate to the discount rate disclosed in the financial statements. The coupon rate may not reflect the underlying market risk as the security may have been issued at a discount or a premium. Or even more perversely, for some classes of securities referred to as Inverse Rate Floaters, the coupon rate may even move in the opposite direction to interest rates.

14

name and linked the seller names to the ultimate parent reporting a 10K. We were able to link the

two databases for 1,852 transactions for the period 2000 to 2004 representing 47 firms.

Table 2 provides a distribution across industries of the 212 firm-year observations as well

as the 47 firms that we can link to the ABSNet database. Banking is both strongly represented in

both groups.

[Table 2 here]

For the sample of 47 firms we know their fiscal year end and the volume of securitization

transactions that they have been involved in. Table 3 reports the asset classes of securitizations

for the 47 firms. Automobiles (e.g., General Motors), Credit Cards (e.g., Citigroup), MBS (e.g,

Wells Fargo) and Home Equity (e.g, Countrywide Financial) are strongly represented in the

sample. For this sample, we know their fiscal year end and so 3, 6, 9, and 12 now represent the

last month of the fiscal quarter. As can be seen from the totals listed at the bottom of the table,

the four fiscal months with the greatest securitization activities are the last month of the fiscal

quarter. The Chi-square test indicates that the distribution of months is significantly different

from a uniform distribution.

[Table 3 here]

Table 4 provides the frequency distribution of securitization transactions for the 47 firms.

We classify 29 firms as low volume (small) securitizers performing less than 20 securitizations

in five years (2000 to 2005). There are 12 firms that perform between 21 and 70 transactions

over the five years (classified as frequent), and 6 firms that perform over 100 transactions over

the five years (Bank of America, Countrywide Financial, General Motors, Morgan Stanley,

Washington Mutual, Wells Fargo).

15

[Table 4 here]

Figure 6 Panel A reports the number of transactions across fiscal month time for the three

separate groups. For all three groups, the last month of the fiscal quarter is the peak time to

engage in securitization activity. The pattern is particularly strong for the Super group. Figure 6

Panel B provides the distribution by day. We combine the low volume and frequent securitizers

into a group “other” to make the Figure easier to view. There is strong clustering of transactions

in the last five days of the month, particularly for the super group.

[Figure 6 here]

Table 5 provides our statistical tests comparing the distributions of observations in the

third month of the quarter to those of the first month and second month. All Chi-square tests

reject that the distribution is uniform. Interestingly, low volume securitizers appear more likely

to engage in securitizations in the 3rd month (45% ) than in the first month (22%). Table 4 Panel

B decomposes months into five day intervals (tests exclude day 31). Only 5 percent of

transactions occur in the first five days of the month versus 63% in the last five days of the

month. For the super group 75% of transactions occur in the last five days of the month. All

Chi-squares reject a uniform distribution.

[Table 5 here]

To summarize, for our linked 10K sample we know the fiscal year end and the frequency

of transactions. This enables us to more directly test the window-dressing prediction, but at the

sacrifice of far fewer observations. The results reveal a similar story to our large sample results.

We find that both high and low volume securitizers cluster their transactions in the last month of

the fiscal quarter. In addition, super securitzers show strong clustering in the last five days of the

16

month. These results are consistent with managers timing securitization transactions to window

dress the balance sheet and possibly also, to improve reported operating cash flows.

4. Fair-value Accounting and Earnings Management in the Income Statement.

Our next set of test uses the 10K sample to examine whether firms that securitize take

advantage of the flexibility offered in SFAS 140 to use fair-value to value the retained interest.

Most assets in the balance sheet are the result of a capitalized expenditure. The main exception

being Accounts Receivable. Prior research such as Dechow, Sloan, and Sweeney (1996) show

that Accounts Receivable is the account most manipulated by firms subsequently investigated by

the SEC for committing fraud. In addition Nelson, Elliott, and Tarpley (2002) show that

receivables and revenue are the account where auditors are most likely to find material

misstatements. Securitizations added an additional layer of discretion to this already less reliable

account. When the securitization occurs, the firm sells off the “safe” cash flows, and retains the

risk. There are no disclosures to alert investors as to whether managers forecast are ever met and

so it is possible for management to be overly optimistic in one securitization and then be overly

pessimistic in the next. Firms do however, have to report the gain or loss from securitization

activities in their annual financial statement when it is material.

In Table 6 we investigate how many of the 212 firm-years that we identify as having

reported securitization activity in their 10K disclose their gains in their quarterly financial

statement (10Q). Our earlier results of the 47 firms suggest that securitization activity tends to

be strongest in the first three quarters of the year. Therefore, it is reasonable to assume that firms

are not all doing their securitizations in the fourth quarter. We review the third quarter’s

financial statements for each of the 212 firm-years and examine whether they disclose

17

securitization gains or losses in this 10Q (assuming that if the securitization occurred in either

the first or second quarter and was disclosed, it would also be disclosed in the third quarter). The

results indicate that there is little voluntary disclosure of securitization effects on the income

statement. For 66% of the sample, securitizations are not mentioned in notes nor recognized

separately in the income statement.

Table 6 Panel B investigates whether disclosure differs by whether the firm is reporting a

gain or a loss from securitization. One might suspect that firms may be more eager to disclose

securitization losses than gains. However our evidence suggests that given a firm chooses to

disclose, the proportion of firms disclosing a gain versus a loss are similar (30% and 40%).

Overall, it does not appear that losses are disclosed more than gains.

[Table 6 here]

Table 7 provides our tests of earnings management. In Panel A we investigate incentives

to report a profit. We provide some descriptive statistics on the variables used in the analysis.

We scale observations by assets and so we also report descriptive statistics on assets. For our

earnings management test, we first classify firms into whether they have reported a profit or a

loss. We then determine their presecuritization income by subtracting off the gain (or adding

back the loss). We then determine how many firms that report a gain have switched from

reporting a loss to a profit; and how many firms that reported a securitization loss have switch

from reporting a profit to a loss. If firms prefer to report profits, then we expect more gain firms

to be switching to a profit, then loss firms switching to a loss. The results indicate that 20% of

firms that report a gain are able to report a gain sufficient to result in a profit. We find that there

are no cases where a securitization loss moves the firm from reporting a profit to a loss.

18

Table 7 Panel B provides a similar analysis except that this time the heuristic is beating

last year’s earnings. We first calculate the change in earnings and determine whether the change

is positive or negative. We then calculate the change in earnings had the firm not undertaken the

securitization in the current year (Presecuritization earnings less earnings reported last year) and

classify the firms into whether the change is positive or negative. We then examine how many

firms reporting securitization gains switch from reporting a negative change to a positive change,

and how many securitization loss firms switch from reporting a positive change to a negative

change. We find that 23% of firms reporting gains, have gains sufficiently large to switch them

to reporting a positive change. In only 1 case is a loss sufficiently large to result in a decline in

reported earnings.

[Table 7 here]

Table 8 investigates incentives to meet or beat analysts forecast. We obtained the analyst

forecast data from the unadjusted IBES files. We used the last forecast reported in the database

for each financial year. We calculate the forecast error (FE) as actual EPS as reported by IBES

minus the median analyst forecast. We calculate the presecuritization forecast error (PFE) as

actual EPS as reported by IBES minus the gain per share minus the median analyst forecast or in

other words, FE minus the gain per share (securitization losses are treated as negative gains).

Negative forecast errors are where a firm missed the consensus, positive are where they beat.

We then investigate how many firms reported a gain sufficiently large that they switch from

missing to beating. Our results indicate that 31% of firms reported a gain sufficiently large to

meet or beat analysts’ forecasts. In contrast only 14% or 4 firms reported a loss sufficiently large

to make them miss analysts’ forecast. Interestingly, we find that 32% of loss firms are already

beating analysts expectations before reporting the loss, so perhaps these firms report a loss to

19

make their forecast errors smaller (see Abarbanell and Lehavy (2003) for a discussion of

earnings management motivations in different areas of the distribution). In contrast only 13% of

gain firms have a positive forecast error without undertaking the securitization transaction.

These results are consistent with gains being used to meet expectations.

[Table 8 here]

We provide Table 8 for completeness since meeting analysts’ forecasts is a commonly

assumed incentive to manage earnings in the accounting literature. However, unlike the earnings

distribution and the earnings change distribution, the analysts forecast error distribution suffers

from some difficult problems. We assume that analysts do not forecast the gain (or the full

magnitude of the gain) when we calculate the Pre FE. However, if analysts correctly forecast the

gain, then we will obtain a switch result mechanically. For example if the firm has

presecuritization EPS of $1.00 and a gain of $0.20 then actual EPS is $1.20. If analysts correctly

forecast $1.20, then the true forecast error is zero. However, we assume the true forecast error

excludes the gain so that we would classify this firm as a miss of 20 cents ((1.20-0.20)-1.20).

The large forecast errors documented for the Pre FE distribution suggest that this could be a

problem. We randomly read analysts forecast reports to determine whether they did or did not

forecast the gains. We did not find clear cases where they did forecast the gain, however, one

would suspect that for large volume securitizers analysts would be forecasting the gain. We

should however note that the mechanical problem applies also to securitization losses. In fact,

prior research on proforma earnings (e.g., Bradshaw and Sloan, 2002 and Bhattacharya, Black,

Christensen, and Larson, 2003) suggest firms are more likely to alert investors to losses than to

gains. Yet we find far fewer loss firms switching to a “miss.” Nonetheless these results should

be interpreted with this concern taken into account.

20

In summary, we provide three sets of earnings management tests in Table 7 and 8. Each

test may suffer from problems in interpretation, but these problems differ across the three groups

and are not likely to be perfectly correlated. The results however provide a consistent pattern. A

greater proportion of firms reporting gains move in a direction that benefits the heuristic than do

securitization loss firms move in directions to be hurt by the heuristic.

Finally, Figure 7 reports the discount rates used by firms. These discount rates are an

important input into gain determination. Unfortunately firms do not disclose the proportion of

receivables securitized versus retained (i.e., in Exhibit 2, 10% of the receivables are retained).

Therefore, it is difficult to evaluate what the correct rate should be. In addition, firms report

ranges of rates and state that the rate depends on the securitization transaction (again making it

difficult to assess the validity of the rate). When a firm reported the discount rate as a range, we

took the median point. Nonetheless we believe that something can be learned by viewing the

distribution of interest rates. What is interesting about the distribution is that managers tend to

use the same rates we professors use in our examples in class (10% and 12%). What is also

interesting is that very low rates are often used. Figure 5 shows that the 30 year mortgage rate

hovers between 5 and 8%. Are the retained interests consistently less risky than 8 percent even

though the firm tends to keep all the credit risk and prepayment risk? Obviously, we cannot

obtain the data to address this question. However, at least for us, this distribution raises

questions concerning whether firms use lower rates to report larger gains.

[Figure 7 here]

21

5. Summary and Conclusion

SFAS No. 140 requires sale accounting for the vast majority of securitizations. When

receivables are transferred to an SPE, the firm removes the receivables from its books, increases

cash by the amount of cash received, and creates an asset (called “retained interest”) that reflects

the firm’s ownership stake in the future cash flows of the securitized assets. Any difference is

recorded as a gain or loss from securitization and is reported in the income statement.

We investigate whether the accounting for securitizations provides management with

opportunities to window-dress the balance sheet and manage earnings through the reported gain.

Our results suggest that a significantly greater proportion of securitization transactions occur in

the last month of the quarter. We find this result holds across various classes of securitized

assets and does not appear to be driven by underlying economics. We also find that firms that

engage more heavily in securitization activity are more likely to time securitization transactions

in the last few days of the quarter. These results are consistent with management timing

securitization transactions to improve balance sheet ratios.

Our results also suggest that managers are more likely to report a gain when it will move

them from reporting a loss to a profit, and are less likely to report a loss from securitization when

it will move them from reporting a profit to a loss. A similar pattern holds for earnings changes.

Managers are more likely to report a securitization gain when doing so results in a positive

change then they are to report a loss when doing so would result in a negative earnings change.

Similarly, we find that firms are more likely to report gains that result in the firm meeting or

beating analysts’ consensus forecasts that they are to report a loss that results in the firm missing

consensus forecasts. Taken together this evidence suggests that managers use the flexibility

offered by SFAS 140 to meet earnings objectives.

22

In summary, SFAS 140 focuses on the balance sheet and the recognition or derecognition

of assets. However, securitizations represent a source of financing and are not inherently an

operating activity. We believe it would be worthwhile for the FASB to consider whether

financial statement users would be better served with these transactions being treated as

collateralized borrowings rather than “sales” for accounting purposes. Such a change in

accounting policy would appear to reduce managers ability to window dress the balance sheet

and manage earnings.

23

REFERENCES

Abarbanell, Jeffrey S. and Reuven Lehavy, 2003, Biased forecasts or biased earnings? The role

of reported earnings in explaining apparent bias and over/underreaction in analysts’ earnings forecast, Journal of Accounting Economics, 105-146.

Bhattacharya, Nilabhra, Ervin L. Black, Theodore E. Christensen, and Chad R. Larson, 2003,

Assessing the relative informativeness and permanence of pro forma earnings and GAAP operating earnings, Journal of Accounting and Economics 36 (1-3): 285-319.

Bradshaw, Mark T. and Richard G. Sloan, 2002, GAAP versus the street: An empirical assessment of two alternative definitions of earnings, Journal of Accounting Research 40 (1): 41-65.

Burgstahler, David C. and Ilia Dichev, 1997, Earnings management to avoid earnings decreases and losses, Journal of Accounting and Economics 24 (1): 99-126.

Dechow, P. M., R. G. Sloan, and A. P. Sweeney. Causes and Consequences of Earnings Manipulations: An Analysis of Firms Subject to Enforcement Actions by the SEC. Contemporary Accounting Research 13 (1996): 1–36.

Dechow, P. M., L Myers, and C Shakespeare, 2005, Reporting a Rosy Future Today: The Role

of Corporate Governance in Asset Securitizations. Working Paper, University of Michigan.

Degeorge, F., J. Patel, and R. Zeckhauser. 1999. Earnings management to exceed thresholds.

Journal of Business, 72,1-33. FASB, Financial Accounting Standards Board. Accounting for Transfers and Servicing of

Financial Assets and Extinguishments of Liabilities. Stamford, Conn.: FASB., 2000. Landsman, Wayne, Kenneth Peasnell and Catherine Shakespeare, 2006, Are Securitizations

Sales or Loans? Working paper, University of North Carolina.

Nelson, Mark, John Elliott, and Robin Tarpley, 2002, Evidence from auditors about managers’ and auditors’ earnings-management decisions, Accounting Horizons (Supplement): 17-35.

Schrand, C. M., and B. R. Walther, Strategic benchmarks in earnings announcements: The selective disclosure of prior-period earnings components. The Accounting Review 75 (2000) 151-178.

24

http://www.aimrpubs.org/cfa/issues/v33n3/abs/c0330054a.html#authors#authorshttp://www.aimrpubs.org/cfa/issues/v33n3/abs/c0330054a.html#authors#authors

Exhibit 1 A “Typical” Asset-Backed Securities Issue

Class C: Transferred

to Securitzer

Class A: Sold to Investor

Class B: Sold to Investor

SPE Firm - Securitizer

Asset Pool

Cash – Payment for Pool

Security

Security

Transferred to Firm

Payment for security

Payment for Security

25

Exhibit 2 Comparison of Two Alternate Accounting Treatments for Securitization Transactions: Collateralized Borrowing Versus Gain on Sale Accounting Opening Balance Sheet $ Cash 100Accounts Receivable 0Inventory 600Total Assets 700 Equity 700Total Liability and Equity 700 Firm sells all inventory on credit for $1,000 and securitizes the accounts receivable receiving $900 in cash from the SPE. The retained interest has a fair value of $110. Closing Balance Sheet Collateralized Borrowing Gain on Sale $ $ Cash 1,000 Cash 1,000Accounts Receivable 1,000 Accounts Receivable 0Inventory 0 Inventory 0 Retained Interest 110Total Assets 2,000 Total Assets 1,110 Liability 900 Liability 0Equity 1,100 Equity 1,110Total Liability and Equity

2,000 Total Liability and Equity

1,110

26

Income Statement Collateralized Borrowing Gain on Sale $ $ Revenue 1,000 Revenue 1,000Cost of Goods Sold 600 Cost of Goods Sold 600 Gain on Sale 10 Net Income 400 Net Income 410 Statement of Cash Flows Collateralized Borrowing Gain on Sale $ $ Cash from Operations 0 Cash from Operations 900Cash from Investing 0 Cash from Investing 0Cash from Financing 900 Cash from Financing 0 Change in Cash 900 Change in Cash 900 Ratio Analysis Collateralized

Borrowing Gain on

Sale Improvement

in Ratio Efficiency Days Receivable Outstanding 365 days 0 days √ Profitability Return on Assets 20% 37% √ Profit Margin 40% 41% √ Liquidity Free Cash Flows (CFO+CFI) to Assets

0% 81% √

Leverage Debt to Assets 45% 0% √

27

Figure 1 Transactions by Calendar Time for ABSNET Sample Panel A Month

0

200

400

600

800

1000

1200

1400

1600

1 2 3 4 5 6 7 8 9 10 11 12

Month

Num

ber o

f Tra

nsac

tions

Panel B Day

0

200

400

600

800

1000

1200

1400

1600

1800

2000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29

Last

Day

Day of Month

Num

ber o

f Tra

nsac

tions

28

Panel C Day of Calendar Year

Distribution of Transaction by Day of Year

0

20

40

60

80

100

120

140

160

180

200

1.01

1.11

1.21

1.31

2.10

2.20

3.01

3.11

3.21

3.31

4.10

4.20

4.30

5.10

5.20

5.30

6.09

6.19

6.29

7.09

7.19

7.29

8.08

8.18

8.28

9.07

9.17

9.27

10.0

7

10.1

7

10.2

7

11.0

6

11.1

6

11.2

6

12.0

6

12.1

6

12.2

6

Day of Year

Num

ber o

f Tra

nsac

tions

Chi square Tests: Chi- Square P-Value Month test 506.19

Figure 2 Automobiles – ABSNET Sample Panel A Automobile Securitization Transactions by Month

-

20

40

60

80

100

120

1 2 3 4 5 6 7 8 9 10 11 12

Month

Num

ber o

f Tra

nsac

tions

Panel B Automobile Unit Sales by Month

0

5,000

10,000

15,000

20,000

25,000

1 2 3 4 5 6 7 8 9 10 11 12

Month

Tota

l Uni

ts S

old

('000

)

Source: Bureau of Economic Analysis

30

Figure 3 Mortgages – ABSNET Sample Panel A Residential MBS Transactions by Month

-

50

100

150

200

250

300

350

400

450

1 2 3 4 5 6 7 8 9 10 11 12

Month

Num

ber o

f Tra

nsac

tions

Panel B New House Sales by Month

12,200

12,400

12,600

12,800

13,000

13,200

13,400

13,600

13,800

14,000

1 2 3 4 5 6 7 8 9 10 11 12

Month

Tota

l Num

ber o

f Sal

es

Source U.S. Census Bureau

31

Figure 4 Credit Card – ABSNET Sample Panel A Credit Card Securitization Transaction by Month

-

20

40

60

80

100

120

140

1 2 3 4 5 6 7 8 9 10 11 12

Month

Num

ber o

f Tra

nsac

tions

Panel B Retail Sales by Month

3,600,000

3,650,000

3,700,000

3,750,000

3,800,000

3,850,000

3,900,000

3,950,000

1 2 3 4 5 6 7 8 9 10 11 12

Month

$ (m

iilio

ns)

Source: US Census Bureau

32

Figure 5 Monthly Interest Rates over period 2000 to 2004

0

1

2

3

4

5

6

7

8

9

10

Dec-9

9

Mar-0

0

Jun-0

0

Sep-0

0

Dec-0

0

Mar-0

1

Jun-0

1

Sep-0

1

Dec-0

1

Mar-0

2

Jun-0

2

Sep-0

2

Dec-0

2

Mar-0

3

Jun-0

3

Sep-0

3

Dec-0

3

Mar-0

4

Jun-0

4

Sep-0

4

Dec-0

4

Mar-0

5

Jun-0

5

Sep-0

5

Dec-0

5

Month

Perc

enta

ge

30 FixedFed Fund

Source: Fannie Mae and Federal Reserve Board

33

Figure 6 Transactions by Fiscal Time – 10-K Sample Panel A: Distribution of Transactions by fiscal month for sample of firms for period 2000-2004

0

20

40

60

80

100

120

140

1 2 3 4 5 6 7 8 9 10 11 12

Fiscal Month

Num

ber o

f Tra

nsac

tions

SuperFrequentSmall

Panel B: Distribution of Transactions by day of month for sample of firms for period 2000-2004

0

50

100

150

200

250

300

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29

Last

Day

Day of the Month

Num

ber o

f Tra

nsac

tion

SuperOther

34

Notes: These figures are based on 1,852 securitization transactions from the10-K sample. The 10-K sample is the subsample of transactions where the ultimate seller could be identified and the transaction is during the period 2000 to 2004. SUPER refers to firms that had more than 100 securitization in the period. FREQUENT refers to firms that had less than 101 transactions but more than 20 transactions in the period (i.e., more than one transaction per quarter). SMALL refers to firms that had less than 21 transactions in the period. OTHER is the sum of FREQUENT and SMALL groups. See Table 5 for Chi square tests.

35

Figure 7 Distribution of Disclosed Discount Rate

0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

20%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40

Discount Rate

Perc

ent o

f Sam

ple

36

Table 1 Number of Transaction by Calendar Month for Each Major Asset Class for ABSNET Sample Asset Class Jan Feb Mar April May June July Aug Sept Oct Nov Dec Total Chi

Square Manufactured Housing 19 18 33 12 20 32 13 18 33 12 21 25 256 31.53

Equipment Leases 4 6 31 12 30 35 16 16 39 19 28 35 271 69.30

CDO 27 24 67 56 56 70 59 57 44 56 67 117 700 104.79 Commercial MBS 20 78 98 74 71 90 93 72 52 68 82 129 927 98.64

Automobile Loans 36 50 87 50 61 108 65 67 71 65 70 79 809 56.82

Credit Card 49 51 95 67 73 116 67 97 76 77 105 68 941 60.74 MBS 223 290 381 298 323 364 317 275 349 291 329 339 3,535 56.82 Home Equity 174 249 424 254 269 432 211 290 410 234 253 335 3,779 278.06 Total 552 766 1,216 823 903 1,247 841 892 1,074 822 955 1,127 11,218 476.19 Note: Major Asset class is defined as an asset class with more than 250 transactions during the period. The chi-square tests test the null hypothesis that the underlying distribution of the transactions follows a uniform distribution. Chi square test is significant at 0.01 level when it exceeds 24.725.

37

Table 2 Frequency Distribution of Firm-Years by Industry SIC Code SIC Name N 10K Sample

(Firms=90) N Linked 10K

Sample (Firms=47)

6021 National Commercial Banks 51 24.06% 31 29.52% 6141 Personal Credit Institutions 21 9.91% 16 15.24% 6162 Mortgage Bankers and Loan

Correspondents 11 5.19%

9 8.57% 6199 Finance – Services 11 5.19% 11 10.48% 6035 Savings Institutions, Federally Chartered 9 4.25% 5 4.76% 3711 Motor Vehicles and Passenger Car

Bodies 7 3.30%

6 5.71% 5311 Department Stores 7 3.30% 1 0.95% 6153 Short-Term Business Credit Institutions,

Except Agricultural 7 3.30%

6036 Savings Institutions, Not Federally

Chartered 6 2.83%

2 1.90% 6022 State Commercial Banks 5 2.36% 3 2.86% 6172 Finance Lessors 5 2.36% 2 2.86% 9997 Conglomerates 5 2.36% 4 3.81% 2086 Bottled and Canned Soft Drinks and

Carbonated Waters 4 1.89%

2631 Paperboard Mills 4 1.89% 3523 Farm Machinery and Equipment 4 1.89% 3531 Construction Machinery and Equipment 4 1.89% 4 3.81% 3714 Motor Vehicle Parts and Accessories 4 1.89% 3751 Motorcycles, Bicycles, and Parts 4 1.89% 2451 Mobile Homes 3 1.42% 3842 Orthopedic, Prosthetic, and Surgical

Appliances and Supplies 3 1.42%

5621 Women’s Clothing Stores 3 1.42% 3 2.86% 5731 Radio, Television, and Consumer

Electronic Stores 3 1.42%

6500 Real Estate Agents And Managers 3 1.42% 3 2.86% 7011 Hotels and Motels 3 1.42% 5065 Electronics Parts and Equipment, Not

Elsewhere Classified 2 0.94%

5500 Auto Dealers, Gas Stations 2 0.94% 2 1.90% 5961 Catalog and Mail-Order Houses 2 0.94% 6111 Federal and Federally-Sponsored Credit

Agencies 2 0.94%

6211 Security Brokers, Dealers, and Flotation

Companies 2 0.94%

2 1.90% 6311 Life Insurance 2 0.94% 6531 Real Estate Agents and Managers 2 0.94% 6799 Investors, NEC 2 0.94% 7200 Personal Services 2 0.94% 3021 Rubber and Plastics Footwear 1 0.47% 1 0.95% 3823 Industrial Instruments for Measurement,

Display, and Control of Process Variables; and Related Products 1 0.47%

4911 Electric Services 1 0.47% 5063 Electrical Apparatus and Equipment,

Wiring Supplies 1 0.47%

5651 Family Clothing Stores 1 0.47% 6726 Unit Investment Trusts 1 0.47% 6798 Real Estate Investment Trusts 1 0.47% 212 100.00% 105 100.00%

38

Table 3 Number of Transaction by Fiscal Month for Each Major Asset Class for Sample Period 2000 through 2004 for Linked 10-K Sample

Asset Class 1 2 3 4 5 6 7 8 9 10 11 12 Total Chi

Suqare Manufactured Housing 0 1 2 2 1 3 0 1 1 0 1 2 14 8.29

Equipment Leases 0 0 1 1 5 0 2 1 0 0 4 1 15 24.20

Commercial MBS 1 0 1 1 0 0 1 0 2 2 2 2 12 8.00

Automobile Loans 10 12 17 7 18 13 10 20 7 16 14 9 153 16.18

Credit Card 7 15 14 15 16 25 11 16 11 12 16 9 167 16.59 MBS 53 54 63 53 64 73 60 71 76 60 72 76 775 13.21 Home Equity 36 46 86 48 41 85 44 47 97 57 49 80 716 82.31 Total 107 128 184 127 145 199 128 156 194 147 158 179 1,852 62.13 Note: Major Asset class is defined as an asset class with more than 250 transactions in the ABSNET sample. The chi-square tests test the null hypothesis that the underlying distribution of the transactions follows a uniform distribution. Ultimate seller was not available for any CDO transactions. Chi square test is significant at 0.01 level when it exceeds 24.725.

39

Table 4 Frequency of Securitization by Linked 10-K Sample Firms Number of Firms Number of

Transactions Small 20 1-10 9 11-20 Total for Small 29 Frequent 4 21-30 4 31-40 0 41-50 3 51-60 1 61-70 0 71-100 Total for Frequent 12 Super 4 101-150 2 151+ Total for Super 6 Overall Total 47 Notes SUPER refers to firms that had more than 100 securitization in the period. Frequent refers to firms that had less than 101 transactions but more than 20 transactions in the period (i.e., more than one transaction per quarter). Small refers to firms that had less than 21 transactions in the period.

40

Table 5 Panel A Distribution by Month of the Quarter for Linked 10-K Sample 1st Month 2nd Month 3rd Month N Chi Square Super 28% 31% 41% 1,156 27.55 Frequent 28% 32% 40% 473 11.09 Small 22% 33% 45% 223 16.15 Total 27% 32% 41% 1,852 51.65 Panel B: Distribution by Day of the Month for Linked 10-K Sample Day

1- 5 Day 6-10

Day 11-15

Day 16-20

Day 21 - 25

Day 26-30

N Chi Square

Super 3% 2% 3% 4% 15% 73% 1,133 2,668 Frequent 7% 7% 7% 14% 17% 47% 454 331 Small 9% 9% 9% 11% 16% 46% 216 137 Total 5% 4% 5% 7% 16% 63% 1,803 2,918 Notes Super refers to firms that had more than 101 securitization in the period. Frequent refers to firms that had less than 101 transactions but more than 20 transactions in the period (i.e., more than one transaction per quarter). Small refers to firms that had less than 20 transactions in the period. The chi-square tests test the null hypothesis that the underlying distribution of the transactions follows a uniform distribution. The N in Panel B is less than the N in Panel A as day 31 is not included in Panel B.

41

Table 6 Disclosure of Securitization Gains and Losses in Interim Reports for 10K Sample Firms Panel A: Frequency of Disclosure in Third Quarter Financial Statements Income Statement -

Yes Income Statement

– No Total

Footnotes - Yes 8 (3.77%) 17 (8.02%) 25 Footnotes – No 48 (22.64%) 139 (65.57%) 187 Total 56 156 212 Chi-Square Test = 202.68 P-Value =

43

Table 7 A comparison of the proportion of firms reporting “gains” from securitizations sufficient to beat common heuristics (reporting a profit in Panel A and reporting an increase in earnings in Panel B) versus reporting “losses” from securitization sufficient to miss common heuristics. Panel A Impact on Reported Earnings from “Gain on Sale” Accounting N Mean Median Std.Dev. 25 % 75 % Gain/Assets 212 0.015 0.0009 0.040 0.000 0.014 Presecuritization Earnings (Pre_Inc)

212 -0.003 0.010 0.098 -0.003 0.020

Total Assets 212 87,373 10,728 197,000 2,295 50,833 Stay reporting

a Loss Switch Stay reporting a

Profit Total

Securitization Effect Report Loss 7 (30%) 0 (0%) 23 (70%) 30 Report Zero 3 (14%) 0 (0%) 18 (86%) 21 Report Gain 21 (13%) 30 (19%) 110 (68%) 161 Total 31 30 151 212 Chi-squared Value 12.27 Likelihood Ratio Chi-Squared 19.009 P-value 0.0155 P-value 0.0008 Panel B Impact on Changes in Reported Earnings from “Gain on Sale” Accounting N Mean Median Std.Dev. 25 % 75 % Change in Pre_Inc 211 -0.020 -0.0025 0.077 -0.023 0.003 Earnings Change 211 -0.005 0.0008 0.066 -0.006 0.006 Stay Negative

Change Switch Stay Positive

Change Total

Report Loss 12 (40%) 1 (3%) 17 (57%) 30 Report Zero 9 (43%) 0 (0%) 12 (57%) 21 Report Gain 71 (44%) 37 (23%) 52 (33%) 160 Total 92 38 81 211 Chi-squared Value 15.789 Likelihood Ratio Chi-Squared: 20.173 P-value: 0.0033 P-value: 0.004 Notes: Gain/Assets is defined as gains from securitizations (from the 10-K filings) divided by Assets. Negative gains are firms that report a loss. Pre_Inc is income minus gain from securitization deflated by assets; Stay Reporting a Loss are observations that have negative Pre_Inc and negative earnings. Stay Reporting a Profit are observations that have positive Pre_Inc and positive earnings. For securitization loss firms, Switch are observations with positive Pre_Inc and negative earnings. For securitization gain firms, Switch are observations with negative Pre_Inc and positive earnings. In Panel B, Earnings Change is equal to the change in reported earnings deflated by assets. Change in Pre_Inc is equal to Pre_inc less earnings reported in the prior year deflated by assets. Stay Negative Change are observations that have a negative change in earnings and a negative change in Pre_Inc. Stay Positive Change are observations that have positive change in earnings and a positive change in Pre_Inc. For securitization loss firms, Switch are observations that have a positive change in Pre_Inc and a negative change in earnings. For securitization gain firms, Switch are observations that have a negative change in Pre_Inc and a positive change in earnings. For one firm year a prior year 10-K was not available.

44

Table 8 A comparison of the proportion of firms reporting “gains” from securitizations sufficient to meet or beat analysts’ consensus forecasts versus reporting “losses” from securitization sufficient to miss analysts’ consensus forecasts. N Mean Median Std.Dev. 25 % 75 % Gain/Shares 173 0.67 0.08 2.91 0.00 0.34 Pre FE 173 -1.02 -0.34 2.60 -1.23 -0.03 FE 173 -0.34 -0.07 0.99 -0.60 0.06 Stay Miss

(Negative FE) Switch Stay Meet or Beat

(Positive FE) Total

Report Loss 15 (54%) 4 (14%) 9 (32%) 28 Report Zero 11 (69%) 0 (0%) 5 (31%) 16 Report Gain 72 (56%) 40 (31%) 17 (13%) 129 Total 98 44 31 173 Chi-squared Value: 13.856 Likelihood Ratio Chi-Squared 17.321 P-value 0.0078 P-value 0.0017 Notes: Forecast Error (FE) is calculated as actual earnings per share as reported by IBES less the median consensus forecast. Median consensus forecast and the actual earnings per share are from the unadjusted file. Pre securitization forecast error (Pre_FE) is calculated as the forecast error minus gain per share. Shares are as reported in IBES. Stay Negative FE are observations that have negative forecast error both for FE and Pre_FE. Stay Positive FE are observations that have positive forecast error both for FE and Pre_FE. For securitization loss firms, Switch are observations that switch from meeting or beating analysts’ consensus forecast to missing the analyst forecast after reporting the loss (Pre_FE is positive and FE is negative). For gain firms, Switch are observations that switch from missing analysts’ consensus forecast to meeting or beating analysts’ forecast after reporting the gain (Pre_FE is negative and FE is positive). Chi-square test is based on the differences between the observed and expected frequencies. Likelihood chi-square test is based on the ratios between the observed and expected frequencies.

Abstract1. Introduction2.1 The Securitization Transaction [Exhibit 1 here] 2.2 Comparison of Financial Statements under “Gain on Sale” Versus Collateralized Borrowing[Exhibit 2 here] 3. Balance Sheet Window Dressing and Timing of Securitizations

3.1 Results for the ABSNET Sample [Figure 1 here] [Figure 2 here] [Figure 3 here] [Figure 4 here] [Figure 5 here] [Table 1 here] 3.2 Results for the 10K Sample [Table 2 here] [Table 3 here] [Table 4 here] [Figure 6 here] [Table 5 here] 4. Fair-value Accounting and Earnings Management in the Income Statement.[Table 6 here] [Table 7 here] [Table 8 here] [Figure 7 here] 5. Summary and Conclusion REFERENCES Exhibit 1A “Typical” Asset-Backed Securities IssueFigure 1 Transactions by Calendar Time for ABSNET SampleFigure 2 Automobiles – ABSNET Sample Figure 3 Mortgages – ABSNET SampleFigure 4 Credit Card – ABSNET SampleFigure 6 Transactions by Fiscal Time – 10-K SampleFigure 7 Distribution of Disclosed Discount RateTable 4Table 5Table 6