CLO Credit Ratings Gone Awry

Co-authors Gene Phillips and Mark Adelson wrote the following article, which was published in the Fall 2020 edition of the Journal of Structured Finance (JSF); it is available in its entirety on the JSF's website at this link.

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The COVID-19 pandemic has had a broad reach, spanning most sectors and industries. This distinguishes it sharply from the mortgage meltdown and the 2008 financial crisis, which were mostly confined to the housing sector and financial institutions, respectively. Collateralized loan obligations (CLOs), which were largely immune to the perils of the mortgage meltdown and the financial crisis due to their diversity among corporate issuers, find themselves exposed by the COVID-19 pandemic. 

Rating agencies have been downgrading the speculative-grade loans that support these CLOs en masse: during the four-month period ending June 30, 2020, Moody’s downgraded the ratings of 755 speculative-grade borrowers, a full 31% of the speculative-graderated universe, across a range of industries (Moody’s Investors Service, n.d.). The industry sectors most affected are shown in Exhibit 1. 

While the loan-level downgrades continue, the rating agencies are also downgrading the CLOs backed by the loans. Meanwhile, corporate defaults are already on the high end. As of the end of July 2020, S&P reported 98 year-to-date defaults, already surpassing the full-year corporate default tally for 2008, which reached 95 (Serino, Kesh, and Pranshu 2020). 

This note focuses primarily on CLO ratings — but there have been oddities in the rating of residential mortgage-backed securities (“MBS”) during the pandemic as well. 

While the credit rating agencies are actively downgrading speculative-grade corporate loans and outstanding CLOs backed by these loans, they continue to rate new CLOs at the same time. Their approaches to this tricky proposition, and their communications describing their approaches, give us pause. We find that they are: 1) not being transparent about how they apply their methodologies, 2) either not applying their methodologies or not applying them consistently, and 3) not being consistent in their deviations when they deviate from their official methodologies. 

RATINGS DOWNGRADES AND NEW RATINGS 

In an inauspicious report of May 2020, titled “How COVID-19 Changed the European CLO Market in 60 Days” (Ryan and Tamburrano 2020), S&P explained that the changes have come “in a sudden and marked way” and that the “wave of negative [corporate loan] rating actions has affected several sectors, geographies, and products.” Strikingly, S&P noted that “[m]arket challenges that existed before COVID-19, including high leverage ratios, EBITDA add-backs, and cov-lite loans, are causing speculation that this may be the perfect storm for CLOs.” 

As of early June, Moody’s had placed 1,100 CLO notes on watch for possible downgrade. That amounted to 24% of all CLO notes by count and 7% by balance (Deshpande, Mogunov, and Chatterjee 2020). The rating agency stated, “Moody’s actions today follow the CLO actions Moody’s took on 17 April 2020, and are primarily prompted by a continuing decline in the credit quality of CLO portfolios as a result of economic shocks stemming from the coronavirus pandemic. Since April, the decline in corporate credit has resulted in a significant number of downgrades among the assets underlying some CLOs.” 

The downgrading continues, but some of it has been tepid. When downgrading, Moody’s has often opted for only a single notch downgrade. For example, on July 1, Moody’s noted significant collateral deterioration in a CLO called Nassau 2017-II Ltd. The rating agency stated: 

Based on Moody’s calculation, the weighted average rating factor (WARF) was 3764 as of June 2020, or 25% worse compared to 3006 reported in the March 2020 trustee report. Moody’s calculation also showed the WARF was failing the test level of 3022 reported in the June 2020 trustee report by 742 points. Moody’s noted that approximately 40% of the CLO’s par was from obligors assigned a negative outlook and 7% from obligors whose ratings are on review for possible downgrade. Additionally, based on Moody’s calculation, the proportion of obligors in the portfolio with Moody’s corporate family or other equivalent ratings of Caa1 or lower (after any adjustments for negative outlook and watchlist for possible downgrade) is approximately 30% as of June 2020 (Aeron and Ham 2020). 

Nevertheless, despite significant deterioration, and in the face of a 30% exposure to Caa1 or lowerrated assets, Moody’s downgraded the Class C, Class D, and Class E notes by only a single notch, from A2, Baa3, and Ba3 to A3, Ba1, and B1 respectively. Moody’s affirmed the rating of the Class B at Aa2. 

In late July, S&P downgraded 63 CLO tranches by an average of 1.2 rating notches. But 496 tranches across 287 CLOs remained on CreditWatch negative. As shown in Exhibit 2, data on the “S&P CLO Insights 2020 Index” reflected the weakened condition of the deals. 

Meanwhile, the performance of CLOs is to a degree based on the vigor with which the rating agencies downgrade the corporate loans. In addition to default events, downgrades themselves can impact a CLO managers’ ability to trade assets, especially once they start to fail collateral-quality tests. With CLOs being so heavily laden with B-rated collateral, even minor downgrades tend to quickly make an impression on their bucket for CCC-rated assets. 

LACK OF TRANSPARENCY 

The rating agencies’ communications around their ratings actions are confounding. This riddle is no easier to disentangle when visiting the rating agencies’ remarks. They provide only limited clarity about the specifics of how (if at all) they are considering the impact of the COVID-19 pandemic in their rating actions. 

When downgrading CLOs, Moody’s mentions that its “analysis has considered the effect of the coronavirus outbreak on the US economy as well as the effects that the announced government measures, put in place to contain the virus, will have on the performance of corporate assets” (Deshpande, Mogunov, and Chatterjee, 2020). But there are no specifics: Moody’s does not explain how. In what ways is Moody’s changing its approach to reflect the analysis it purports to be making? Did the prepayment rate assumptions change? Did the default rate assumptions change? Did the correlation assumptions change? Did the recovery rate assumptions change? Given that Moody’s identifies a largely quantitative methodology article (Kim, et al. 2019) as the “principal methodology” used in the downgrades, it is odd that Moody’s did not express its approach in any way that enables users of ratings to apply the purported considerations within a quantitative framework. 

S&P is similarly opaque. When rating new deals and reviewing existing deals, S&P sometimes mentions the pandemic and sometimes does not. For example, in April, S&P never mentioned the effect of COVID-19 on its rating of Deerpath Capital CLO 2020-1 (Kalinauskas, et al. 2020). Moreover, when S&P does discuss the impact of the pandemic, it does so in a nebulous way, which leaves the reader guessing about the particulars of how the rating agency accounts for the pandemic when assigning ratings. When S&P assigned new ratings in May 2020 to notes issued by Guggenheim CLO 2020-1 Ltd, its sole mention of the pandemic was the following boilerplate language: 

S&P Global Ratings acknowledges a high degree of uncertainty about the rate of spread and peak of the coronavirus outbreak. Some government authorities estimate the pandemic will peak about midyear, and we are using this assumption in assessing the economic and credit implications. We believe the measures adopted to contain COVID-19 have pushed the global economy into recession (see our macroeconomic and credit updates here: www.spglobal.com/ratings). As the situation evolves, we will update our assumptions and estimates accordingly. (Kalinauskas and Davis 2020). 

DEPARTURES AND DEVIATIONS FROM METHODOLOGIES, AND INCONSISTENT APPLICATIONS 

Beyond the disappointing lack of transparency, another challenge for investors is that rating agencies appear to be deviating from their published methodologies for assigning and maintaining ratings. Moreover, they deviate in inconsistent ways from one deal to the next. 

Example 1. In one telling example (Jiang and Vasudevan 2020), Moody’s downgraded 48 MBS on April 15, 2020. The rating agency identified a mostly-quantitative methodology as the “principle methodology” for the rating actions (Vasudevan, Hannoun-Costa, and Muni 2019). Of note, the principle methodology predates the start of the pandemic. 

What was particularly striking about the April 15 rating actions was that Moody’s downgraded all of 48 MBS to the same rating level (Baa3) even though they previously carried ratings at a variety of levels (A3, Baa1 and Baa2). Moody’s did not describe a concrete basis for the Baa3 outcome. Although it explained the need for taking action, it provided no details about why Baa3 was the right rating level for the 48 tranches. The rating agency stated: “Our analysis has considered the increased uncertainty relating to the effect of the coronavirus outbreak on the US economy.” But later in the press release it revealed that it “did not use any models, or loss or cash flow analysis, in its analysis” and that it “did not use any stress scenario simulations in its analysis” (Jiang and Vasudevan 2020). It is difficult to reconcile that statement with others to the effect that 1) a quantitative methodology was used and 2) the analysis considered the increased uncertainty relating to the onset of the pandemic. 

In June, Moody’s took action on 415 US MBS, confirming its ratings on 35 of them, while downgrading the other 380 (Rossetti and Vasudevan 2020). The announcement, however, contained no language about Moody’s departing from the application of any models. Instead, the boilerplate verbiage in the announcement stated: 

Moody’s estimates expected collateral losses or cash flows using a quantitative tool that takes into account credit enhancement, loss allocation and other structural features, to derive the expected loss for each rated instrument. Moody’s quantitative analysis entails an evaluation of scenarios that stress factors contributing to sensitivity of ratings and take into account the likelihood of severe collateral losses or impaired cash flows. Moody’s weights the impact on the rated instruments based on its assumptions of the likelihood of the events in such scenarios occurring (Rossetti and Vasudevan 2020, emphasis added). 

Most interestingly, 47 of the 48 MBS, which had been downgraded to Baa3 in April (without the use of a model), were addressed again in June (Rossetti and Vasudevan 2020), this time ostensibly using a quantitative tool. The results were that the securities received different ratings: 

• 10 MBS maintained their Baa3 ratings upon review with a model.
• 15 were downgraded to Ba2 (i.e., a further two notch downgrade). 
• 22 were downgraded to B1 (i.e., a further four notch downgrade). 

Example 2. In recent surveillance updates on CLO ratings, Fitch appears to be applying new scenarios that are not included in its official methodology. For example, in the updates for Jubilee CLO 2014-XII and Penta CLO 5, the agency explained: 

Coronavirus Baseline Scenario Impact: Fitch carried out a sensitivity analysis on the current portfolio to envisage the coronavirus baseline scenario. The agency notched down the ratings for all assets with corporate issuers on Negative Outlook regardless of sector. 

∗ ∗ ∗ 

In addition to the base scenario, Fitch has defined a downside scenario for the coronavirus crisis, whereby all ratings in the ‘B’ category would be downgraded by one notch and recoveries would be lowered by 15% (Kelmer and Brewer 2020a; Segato and Brewer 2020a). 

More pointedly, Fitch has been regularly deviating from its model-implied ratings (MIRs) in downgrading CLOs notes. In addition, the deviations have not been consistent. 

For example, in reviewing certain European CLOs, Fitch refrained from downgrading tranches for which the MIR indicated a one-notch drop. Where the MIR indicated a two-notch drop, the rating agency either refrained from downgrading[1], or did so by just one notch[2]. In some cases, Fitch explained that the deviations were because the MIR results had been “driven by the back-loaded default timing scenario only” (Choraria and Brewer 2020; Ishidoya and Brewer 2020; Segato and Brewer 2020a, 2020b). In other cases, Fitch asserted that it had deviated from the MIRs because the results did not comport with its view of credit quality and also because the MIRs had been “driven by the rising interest rate scenario only, which is not our immediate expectation” (Kelmer and Brewer 2020a, 2020b). 

Fitch made several similar out-of-model adjustments when reviewing the ratings across seven CLOs in late July (Torres and Pak 2020). The rating agency stated: 

The class C notes in PSLF 2018-4, Ltd., class B notes in PSLF 2019-4, Ltd., and class B notes in PSLF 2020-1, Ltd. experienced shortfalls in some scenarios and the model-implied ratings (MIRs) of these notes were one notch below their current rating levels. However, Fitch considered the magnitude of these failures as minor and isolated to the back-loaded default timing and rising interest rate scenario that was given less weight in the analysis. 

∗ ∗ ∗ 

In addition, MIRs of the following classes were at least one notch higher than their current ratings based on current portfolio analyses, but were not upgraded in light of the ongoing economic disruption … (Torres and Pak 2020). 

In contrast to its surveillance practices, Fitch generally makes no mention of ignoring its model-based outcomes in rating new US CLOs. However, in some cases, Fitch has indicated that it is applying stress scenarios in a way similar (but not identical) to surveillance stress scenarios. 

For example, when providing ratings to two newly-issued CLO in July 2020, the rating agency explained: 

Fitch has applied two additional stress scenarios to the indicative portfolio that envisage negative rating migration as a result of business disruptions from the coronavirus. The first scenario applies a one-notch downgrade (with a CCC-floor) for all assets in the indicative portfolio with a Negative Rating Outlook.… The second scenario assumes a 5% increase in the indicative portfolio’s PCM rating default rates (RDR) for all rating levels. 

∗ ∗ ∗ 

Fitch added a sensitivity analysis that contemplates a more severe and prolonged economic stress caused by a re-emergence of infections in the major economies, before a halting recovery begins in 2Q21. (See Weiss, Joswiak, and Hughes 2020; Hunter, Lycos, and Hughes 2020, with emphasis added). 

The second stress scenario used in rating new deals is entirely absent when performing surveillance. It is unclear whether the downside scenarios are being applied equally, as Fitch has left the specifics undefined. 

It is somewhat surprising that Fitch would choose to make manual, ad-hoc, overrides to its model-driven outputs in every pandemic-era CLO surveillance action we found because it could not rely on the results produced using its official methodology. Under such a scenario, it would be easier to understand a basic adjustment to the methodology (and the associated model), so that it provides a reliable result that reflects Fitch’s actual views. 

WHY IT ALL MATTERS 

Investors and other market participants use credit ratings as signals or indicators of creditworthiness that figure, inter alia, into their processes for valuing securities and allocating capital. Securities can also be interrelated. The ratings awarded to some securities also, as we note above, directly impact the performance of other securities that reference or support them. In order for credit ratings to be useful, they must embody a measure of reliability. One of the key aspects of that reliability is that ratings are produced through a consistent, replicable process: the application of a rating agency’s official methodologies. 

The ideas of applying official methodologies to produce ratings and doing so in a consistent manner are prominent features of each rating agency’s code of conduct (Moody’s Investors Service 2020, § 1.3; S&P Global Ratings 2018, § 1.2; Fitch Ratings 2017, § 2.1.3). At least one court has held that statements in a rating agency’s code of conduct constitute “specific assertions of current and ongoing policies” and cannot be dismissed as mere puffery upon which investors cannot reasonably rely, United States v. McGraw Hill (2013). Today, applying official methodologies to produce ratings is explicitly required under US [3] and European [4] law . 

Likewise, the rating agencies undertake, in their codes of conduct, to provide clear explanations of the rationale behind each rating action (Moody’s Investors Service 2020, § 3.6(b), (c); S&P Global Ratings 2018, § 4.1; Fitch Ratings 2017, § 4.1.3). That is also required under US [5] and European [6] law. 

There are compelling reasons for why rating agencies are required to produce ratings by applying their official methodologies. One reason is that it decreases the potential for an individual analyst or team of analysts to abandon criteria in an effort to win new deals by providing advantageous ratings. Rating agencies might argue that they must have some flexibility to stray from their methodologies. To the extent that such a position is valid (and does not violate a rating agency’s legal obligations), we believe that when a rating agency deviates from its official methodology, it has an obligation to explain the rationale for the deviation and to explain in detail how it arrived at the rating produced with the deviation. Moreover, when deviations become the norm, or when they are inconsistent or poorly articulated, we believe that the rating agencies have gone too far. At times, as shown herein, rating agencies have deviated from their methodologies, but failed to explain the analyses that ensued and how they determined the final ratings that they assigned. 

CONCLUSION 

In the aftermath of the 2008 financial crisis, the credit rating agencies experienced criticism, private litigation, and government enforcement actions. Enforcement actions in the US were taxing, culminating in f ines of $1.375 billion for S&P and $864 million for Moody’s (US Department of Justice 2015, 2017). The enforcement actions particularly noted that the rating agencies had violated their codes of conduct, which required them to provide objective, independent ratings. 

Although the regulatory environment has gotten tougher since the Dodd-Frank Act was signed into law, we remain concerned that rating agencies continue to deviate from their published methodologies whenever it suits them. Based on recent evidence, they appear to view the directive to determine ratings pursuant to their official methodologies and to apply methodologies in a consistent manner as mere suggestions, rather than as mandatory rules. 

FOOTNOTES

[1] Adagio VII, classes E and F (Segato and Brewer 2020b); St Paul CLO 5, class F-R (Kelmer and Brewer 2020b); St Paul CLO 6, class E-R (Kelmer and Brewer 2020b); Jubilee 2014-XII, class F-R (Kelmer and Brewer 2020a); Jubilee 2016-XVII, class F-R (Kelmer and Brewer 2020a); Penta 5, class F (Segato and Brewer 2020a). 

[2] Euro Galaxy III, class E (Choraria and Brewer 2020); Toro European 4, class E (Ishidoya and Brewer 2020). 

[3] 15 U.S.C. § 78o-7(r) (2018), https://www.govinfo.gov/content/pkg/USCODE-2018-title15/pdf/USCODE-2018-title15-chap2B-sec78o-7.pdf; 17 C.F.R. § 17g-8(a)(3)(i), (d) (2019), https://www.govinfo.gov/content/pkg/CFR-2019-title17-vol4/pdf/CFR-2019-title17-vol4-sec240-17g-8.pdf. 

[4] Regulation (EU) No 462/2013 of the European Parliament and of the Council of 21 May 2013 amending Regulation (EC) No 1060/2009 on Credit Rating Agencies, Art. 1, § 10(a) & Annex II, ¶ 1(h), 2013 O.J. (L146/1) at 16, 31 (May 31, 2013), https://eurlex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32013R0462&from=EN; Commission Delegated Regulation (EU) No 447/2012 of 21 March 2012 Supplementing Regulation (EC) No 1060/2009 of the European Parliament and of the Council on Credit Rating Agencies by Laying Down Regulatory Technical Standards for the Assessment of Compliance of Credit Rating Methodologies, Art. 5, § 1, 2012 O.J. (L140/14) at 15 (May 30, 2012), https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32012R0447&from=EN; Regulation (EC) No 1060/2009 of the European Parliament and of the Council of 16 September 2009 on Credit Rating Agencies, Art. 8, § 2, 2009 O.J. (L302/1) at 13 (November 17, 2009), https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32009R1060&from=EN. 

[5] 15 U.S.C. § 78o-7(s) (2018), https://www.govinfo.gov/content/pkg/USCODE-2018-title15/pdf/USCODE-2018-title15-chap2B-sec78o-7.pdf; 17 C.F.R. § 17g-7(a)(1)(ii)(B), (C) (2019), https://www.govinfo.gov/content/pkg/CFR-2019-title17-vol4/pdf/CFR-2019-title17-vol4-sec240-17g-7.pdf. 

[6] Regulation (EU) No 462/2013 of the European Parliament and of the Council of 21 May 2013 amending Regulation (EC) No 1060/2009 on Credit Rating Agencies, Annex II, § 4(f), 2013 O.J. (L146/1) at 27 (May 31, 2013), https://eur-lex.europa.eu/legalcontent/EN/TXT/PDF/?uri=CELEX:32013R0462&from=EN; Regulation (EC) No 1060/2009 of the European Parliament and of the Council of 16 September 2009 on Credit Rating Agencies, Annex II, Section D, ¶ 5, 2009 O.J. (L302/1) at 28 (November 17, 2009), https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32009R1060&from=EN. 

REFERENCES 

Aeron, N., and D. Ham. 2020. “Moody’s Downgrades Ratings on $72 Million of CLO Notes Issued by Nassau 2017-II Ltd.; Actions Conclude Review.” Moody’s press release. July 1. 

https://www.moodys.com/research/Moodys-downgrades-ratings-on-72-million-of-CLO-notes-issued--PR_ 427681. 

Choraria, P., and A. Brewer. 2020. “Fitch Downgrades One Tranche of Euro Galaxy III CLO B.V., Maintains RWN on One and Affirms the Rest.” Fitch press release. May 7. 

https://www.fitchratings.com/research/structured-finance/fitch-downgrades-one-tranche-of-euro-galaxy-iii-clo-bv-maintains-rwn-on-one-affirms-rest-07-05-2020. 

Deshpande, A., L. Mogunov, and D. Chatterjee. 2020. “Moody’s Places Ratings on 241 Securities From 115 US CLOs on Review for Possible Downgrade; Also Places Ratings on 2 Linked Securities on Review for Possible Downgrade.” Moody’s press release. June 3. 

https://www.moodys.com/research/Moodys-places-ratings-on-241-securities-from-115-US-CLOs--PR_425620. 

Dodd-Frank Wall Street Reform and Consumer Protection Act, Pub. Law No. 111-203, 124 Stat. 1376 (2010)

https://www.govinfo.gov/content/pkg/PLAW-111publ203/pdf/PLAW-111publ203.pdf. 

Fitch Ratings. 2017. “Code of Conduct and Ethics.” July. 

https://assets.ctfassets.net/03fbs7oah13w/25SiZhnpbDYTLd1S8Elrc7/c75f55b3ac3ee5c95c9bdbfeda95488b/Bulletin_01_Code_of_Conduct_and_Ethics.pdf. 

Hu, D., S. Anderberg, R. E. Schulz, S. Wilkinson, and R. Muthukrishnan. 2020. “CLO Insights: 63 CLO Tranches Downgraded by 1.2 Notches on Average in July.” S&P newsletter. July 31. 

Hunter, M., K. Lycos, and A. Hughes. 2020. “Fitch Rates Ballyrock CLO 2020-1 Ltd.” Fitch press release. July 8. https://www.fitchratings.com/research/structured-finance/fitch-rates-ballyrock-clo-2020-1-ltd-08-07-2020. 

Ishidoya, K. and A. Brewer. 2020. “Fitch Downgrades One Tranche of Toro European CLO 4 DAC and Affirms Rest; Two Tranches on RWN.” Fitch press release. May 18. 

https://www.fitchratings.com/research/structured-finance/fitch-downgrades-one-tranche-of-toro-european-clo-4-dac-affirms-rest-two-tranches-on-rwn-18-05-2020. 

Jiang, Z., and S. Vasudevan. 2020. “Moody’s Places 404 Classes of Legacy US RMBS on Review for Downgrade.” Moody’s press release. April 15. 

https://www.moodys.com/research/Moodys-places-404-classes-of-legacy-US-RMBS-on-review--PR_422633. 

Kalinauskas, P., and C. Davis. 2020. “Guggenheim CLO 2020-1 Ltd. Notes Assigned Ratings.” S&P press release. May 4. 

https://www.standardandpoors.com/en_US/web/guest/article/-/view/type/HTML/id/2424307. 

Kalinauskas, P., T. Walsh, W. Sweatt, and D. Haynes. 2020. “Deerpath Capital CLO 2020-1 Ltd. Notes Assigned Ratings.” S&P press release. April 7. 

https://www.standardandpoors.com/en_US/web/guest/article/-/view/type/HTML/id/2408885. 

Kelmer, S., and A. Brewer. 2020a. “Fitch Assigns Negative Outlook to 1 Tranche and Downgrades Another of Jubilee CLO 2014-XII B.V.” Fitch press release. July 3. 

https://www.fitchratings.com/research/structured-finance/fitch-assigns-negative-outlook-to-1-tranche-downgrades-another-of-jubilee-clo-2014-xii-bv-03-07-2020. 

——. 2020b. “Fitch Downgrades 2 St Paul’s CLOs with 3 Tranches of Each CLO on RWN or Negative Outlook.” Fitch press release. June 29. 

https://www.fitchratings.com/research/structured-finance/fitch-downgrades-2-st-paul-clos-with-3-tranches-of-each-clo-on-rwn-negative-outlook-29-06-2020. 

Kim, J., R.O. Torres, I. Perrin, T. Klotz, A. Remeza, and J. Hu. 2019. “Moody’s Global Approach to Rating Collateralized Loan Obligations.” Moody’s methodology report. March 8. 

https://www.moodys.com/researchdocumentcontentpage.aspx?docid=PBS_1111156. 

Moody’s Investors Service. n.d. “Non-Financial Corporates: Rating Activity During COVID-19.” Moody’s infographic. 

https://www.moodys.com/sites/products/ProductAttachments/Infographics/non-finanancial-corporates-rating-activity-06July.pdf. 

Moody’s Investors Service. 2020. “Code of Professional Conduct.” March. 

https://www.moodys.com/uploadpage/Mco%20Documents/Documents_professional_conduct.pdf

Rossetti, N., and S. Vasudevan. 2020. “Moody’s Takes Action on 415 US RMBS Bonds from 237 Deals Issued Prior to 2009.” Moody’s press release. June 9. 

https://www.moodys.com/research/Moodys-takes-action-on-415-US-RMBS-bonds-from-237--PR_425884. 

Ryan, S., and E. Tamburrano. 2020. “How COVID-19 Changed the European CLO Market in 60 Days.” S&P comment. May 6. 

https://www.spglobal.com/ratings/en/research/articles/200506-how-covid-19-changed-the-european-clo-market-in-60-days-11444644. 

Segato, G., and A. Brewer. 2020a. “Fitch Ratings Revises One Tranche of Penta CLO 5 DAC to Negative Outlook; Affirms Ratings.” Fitch press release. July 6. 

https://www.fitchratings.com/research/structured-finance/fitch-ratings-revises-one-tranche-of-penta-clo-5-dac-to-negative-outlook-affirms-ratings-06-07-2020. 

——. 2020b. “Fitch Revises One Tranche of Adagio VII CLO DAC to Negative Outlook; Affirms Ratings.” Fitch press release. June 26. 

https://www.fitchratings.com/research/structured-finance/fitch-revises-one-tranche-of-adagio-vii-clo-dac-to-negative-outlook-affirms-ratings-26-06-2020. 

Serino, N., S. Kesh, and S. Pranshu. 2020. “Default, Transition, and Recovery: Consumer and Service Sector Defaults Help Push The 2020 Corporate Tally To 147,” S&P comment. July 31. https://www.spglobal.com/ratings/en/research/articles/200731-default-transition-and-recovery-consumer-and-service-sector-defaults-help-push-the-2020-corporate-tally-to-11596242. 

S&P Global Ratings. 2018. “S&P Global Ratings Code of Conduct.” March 1. 

https://www.standardandpoors.com/en_US/delegate/getPDF?articleId=2194115&type=COMMENTS&subType=REGULATORY. 

Torres, C., and A. Pak. 2020. “Fitch Affirms 38 Tranches from Seven Static CLOs; Removes Rating Watch Negative,” Fitch rating action commentary. July 29. 

https://www.fitchratings.com/research/structured-finance/fitch-affirms-38-tranches-from-seven-static-clos-removes-rating-watch-negative-29-07-2020. 

US Department of Justice. 2015. “Justice Department and State Partners Secure $1.375 Billion Settlement with S&P for Defrauding Investors in the Lead Up to the Financial Crisis.” Press release. February 3. 

https://www.justice.gov/opa/pr/justice-department-and-state-partners-secure-1375-billion-settlement-sp-defrauding-investors. 

US Department of Justice. 2017. “Justice Department and State Partners Secure Nearly $864 Million Settlement with Moody’s Arising From Conduct in the Lead up to the Financial Crisis.” Press release. January 13. https://www.justice.gov/opa/pr/justice-department-and-state-partners-secure-nearly-864-million-settlement-moody-s-arising. 

US v. McGraw Hill, No. CV-13-0779 (C.D.Ca., July 16, 2013) (order denying defendants’ motion to dismiss). https://online.wsj.com/public/resources/documents/sandpdismiss0717.pdf. 

Vasudevan, S., O. Hannoun-Costa, and K. Muni. 2019. “US RMBS Surveillance Methodology.” Moody’s rating methodology. February 22. 

https://www.moodys.com/research-documentcontentpage.aspx?&docid=PBS_1127300. 

Weiss, C., A. Joswiak, and A. Hughes. 2020. “Fitch Rates HalseyPoint CLO II, Ltd.” Fitch press release. July 1. 

https://www.fitchratings.com/research/structured-finance/fitch-rates-halseypoint-clo-ii-ltd-01-07-2020. 

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Gene Phillips is the CEO of PF2 Securities Evaluations, Inc. in Los Angeles, CA. gene.phillips@pf2se.com 

Mark Adelson is the editor of The Journal of Structured Finance, in New York, NY. m.adelson@pageantmedia.com

Leveraged Loan CLOs and Rating Agencies - Policy Solutions

Over the last couple of years, financial market commentators have become concerned that leveraged loans and Collateralized Loan Obligations (CLOs) are becoming the newest “financial weapons of mass destruction”.  The fear is that mispricing and over-production of these assets could lead to a bubble that would ultimately take down our financial system – just as subprime mortgage backed securities did a dozen year ago.

Further, critics worry that rating agencies – still following the traditional issuer-pays model – lack the incentive to protect us from a leveraged lending meltdown. Instead, agencies are thought to be engaged in a "race-to-the-bottom," lowering their rating standards to enable (or keep) even the less credible corporate borrowers in the Investment Grade category.

If SEC-licensed Nationally Recognized Statistical Rating Organizations (NRSROs) – or so-called credit rating agencies -- are not up to the task, investors could turn to non-licensed analytics firms to more objectively evaluate leveraged loans and the securitization vehicles that house them.  Outside of the market for debt and credit-based financial products, we see many types of ratings published by non-licensed providers. For example, Consumer Reports assigns ratings to a wide array of products, US News ranks colleges and Yelp assigns ratings to service establishments. These systems are imperfect and sometimes deservedly attract criticism, but no rating system is perfect and the widespread use of these assessments suggests that users find them valuable.

The main barrier to entry for non-NRSROs that would want to assess leveraged loans and CLOs specifically is lack of access to data, and this is an issue that the SEC could rectify. The leveraged loans at the center of CLOs are often borrowings made by privately held companies – including holdings of private equity firms – that are not required to make their financial statements public. Only current investors and the rating agencies hired to rate these entities can see these financial statements.

The SEC could simply require all such companies that borrow on the leveraged loan market, subject to a minimum borrowing size, to file their 10-Q and 10-K statements on the EDGAR system. That way independent firms could assess their financial status and estimate default probabilities and expected losses on their loan facilities.

Second, CLO issuers should be required to post both their loan portfolios and details of their capital structures on EDGAR as well. In such a scenario, CLOs could no longer be exempted under Section 4(a)(2) of the Securities Act and sold as Rule 144A private securities. Instead, they would be regulated as public securities.

Finally, many CLOs have complex rules governing how proceeds from the collateral pool should be distributed among the various classes of noteholders and the firms – like the asset manager – that provides services to the CLO deal. These “priority of payment” provisions are outlined in dense legalese included in the CLO's offering documents. Rather than compelling investors and analysts to decipher these legal provisions, issuers should be required to code them as computer algorithms which would also be published as part of the deal’s disclosure. CLOs could then operate like any other “smart contract,” easing the work of deal participants and third parties who need to analyze the many “what-ifs” that can occur over the life of a transaction.

Leveraged loans and CLOs may or may not be the ticking time bomb that will blow up our economy. One way to limit the potential for bubble-creation is to remove dependence on parties (like the incumbent credit rating agencies) that are financially motivated to provide high ratings, thus prompting issuers and other market participants to seek out their services. Thus, our solution is, in short, to make these transactions more transparent, so that other third parties can access information on the securities and analyze them in a cost-effective manner.

The Art of (Illiquid) Securities Pricing

As you all know, the financial meltdown was caused by some part faulty-product (mortgages, RMBS, CDOs) and some part market-panic itself and its influence on certain other products (auction rate securities, SIVs) and market mechanics (pricing, rating).

Faulty products are not new: the world is awash with faulty products.  But we need buyers for them, and to encourage buyers we need forums (e.g. securitization) and mechanisms (e.g. ratings) that would induce buyers and give them comfort that the faulty products weren't, err, all that bad.

Well that's a long and old story.  But where we're going today is that many of the mechanics that went awry, and needn't have, have not been fixed.

Back in 2008, we had majestic moments of illiquidity, which spurred quotes like this one, from a conversation among AIG employees:

“we can’t mark any of our positions [to market price], and obviously that’s what saves us having this enormous mark to market. If we start buying the physical bonds back then any accountant is going to turn around and say, well, John, you know you traded at 90, you must be able to mark your bonds then.”

Since the crisis, the SEC has ramped up its investigations into pricing issues, and in 2013 set in motion three initiatives (the Financial Reporting and Audit Task Force; the Microcap Fraud Task Force; and the Center for Risk and Quantitative Analytics).  There has been a steady and growing stream of findings of asset valuation mismanagement.  Some hedge funds have been shut down. (A list of issues here.)

But there is much to be done, if the recent dispute between a Canadian pension fund and a US hedge fund is anything to go by.

We have written about that dispute in detail here and here, but a transcript was released as part of discovery in the matter that depicts just how tricky and error-prone our pricing systems are as soon as there is any level of illiquidity.  Before we get to the transcript, here's a brief picture of the issue at play, per the pension fund's (original) allegations.

• Pension fund requested a full redemption of its investment in Saba’s hedge fund 
• Prior to redemption, Saba marked down its valuation of one issuer’s corporate bonds (a relatively illiquid issuer), lowering the fund’s NAV and the amount to be returned to redeeming investors
• Saba altered its valuation methodology to mark down the bonds issued by The McClatchy Company (“MNI”): it applied a bids-wanted-in-competition (BWIC) approach instead of relying, as usual, on its external pricing sources 

• Saba had made sales of MNI bonds in March 2015 at prices from 58% to 60% (of par)
• 3/31/15 mark used for redemption, based on all-or-none $50 mm BWIC: 31% 
•  Saba later made sales of MNI bonds in April 2015 at prices from 53.75% to 55.75%
• After the redemption was completed, Saba resumed its prior valuation methodology for MNI bonds, marking them back up

With that background, here is a concise depiction (provided here by Bloomberg columnist Matt Levine) of the hedge fund's chats with its pricing providers in 2015, demonstrating just how much the pricing process is based on art, rather than science.

• Saba Capital's Weinstein: Z, where would you bid a few mm of the 29s with or without 5yr cds? ... 
• Trader: most likely below where you care. 50- 2mm 
• Weinstein: Yes, that is low I think. 
• Trader: where would u bid? 
• Weinstein: Who knows. See it quoted much higher. Actually you should change your 65/66 quote I guess. 
• Trader: im happy to reflect any market you would like me to make 
• Trader: i have no position 
• Trader: and quote it only 
• Trader: but thats the discount i would bid to go at risk 
• Weinstein: Yeah, the quote seems wrong I guess. 
• Trader: given how illiquid it is 
• Trader: sure do u have a two sided market? 
• Trader: or what is an appropriate quote? 
• Weinstein: I guess if you only care at 50 on 2mm then probably 65/ for any size is wrong. 

So we have reliance, for pricing purposes, on information produced by traders who are not really willing to meet their quotes, and whose quotes may differ depending on the size of the investment, and are therefore not well-tailored to depict the hedge fund's specific investment size. The quotes are just that, quotes.  Nothing more.

The concern, then, is that the next softening in the market will also be magnified by our pre-existing market's structural deficiencies: the issues of illiquidity, and our ability to cater appropriately for them in our pricing procedures, could again magnify the uncertainties at play and exacerbate the downturn.

Right now, the price is just not right for illiquid assets, and prices are not consistently applied across firms.  We are ill-advised to think that the prices presented are in any way reliable, given the clumsy (antiquated?) nature with which those prices are derived.

More on this topic soon.
~PF2

Can Deregulation and Open Data Solve the Credit Ratings Problem?

This blog is provided by guest contributor Marc Joffe.  The following views are his own, and do not necessarily reflect those of PF2.

~~~   

Credit rating agency scandals, widely blamed for the 2008 financial crisis, now seem to be a distant memory. We have gone several years without another major ratings failure, so casual observers may be forgiven for thinking that the underlying problem has been solved. But as a new Brookings study shows, the defective rating agency market structure that triggered the crisis remains in place. Further reforms would seem unlikely under unified Republican government, but bipartisan support for open financial data may offer a way forward.

Since 2008, the government has taken several steps to address credit rating agency problems. In the waning days of the Obama Administration, the Department of Justice and State Attorneys General settled complaints against Moody’s for $864 million. This followed a larger settlement with S&P and a series of regulatory changes spurred by the 2010 passage of Dodd Frank. That law mandated the removal of credit ratings from regulations, tighter control of SEC-licensed Nationally Recognized Statistical Ratings Organizations (NRSROs) and an SEC study of possible changes to the way investment banks choose rating agencies to rate newly-issued structured finance securities.  

Writing for Brookings, former CBO Director Alice Rivlin and researcher John Soroushian criticize the SEC for failing to execute its Dodd Frank mandate to change the rating agency business model. By not acting, the SEC has left in place a regime under which rating agencies have an incentive to competitively dumb down their standards so that they can sell more ratings to bond issuers. Rivlin and Soroushian recommend that the SEC implement a process under which new structured finance issues are randomly assigned to rating agencies.

Joe Pimbley, a risk analyst who  - like me - used to work at a credit rating agency, goes even further, calling for an outright prohibition of issuer payments for credit ratings. This change would oblige rating agencies to serve investors first, as they did in the years before the transition to the “issuer pays model” around 1970. (Pimbley and I both have consulted for PF2 Securities, which publishes this blog).

Such interventions would seem unlikely under a Republican-led government. If anything, President Trump and Congressional Republicans have expressed the desire to roll back many aspects of Dodd Frank. But, led by Congressman Darrell Issa, Congressional Republicans have shown an interest in more open financial data – and this could be a way forward toward further reform.

To understand the relevance of open data, we must first realize that the credit rating business is not a standalone industry. Instead, the rating agencies are part of a larger industry: the business of credit risk assessment.  This field includes in-house credit analysts at banks, independent credit advisors, and analytics firms, as well as the NRSROs.

By mandating the elimination of credit ratings from federal regulation, Dodd Frank has helped to level the playing field between rating agencies and alternate credit assessment providers. (But, as Rivlin and Soroushian remind us, this process of removing credit ratings from regulations is incomplete.)

Deregulators can go further by pursuing Pimbley’s suggestion of removing credit rating agencies from the list of entities that can receive non-public disclosures from securities issuers, as provided under Regulation FD. Such a reform would allow credit analysts not employed by rating agencies to receive all of the same data at the same time as their agency counterparts.

Indeed, Republicans could completely eliminate the special status of credit rating agencies by scrapping the NRSRO certification entirely, as recommended by NYU’s Lawrence J. White. If ratings are not required by regulation and all credit consultants and analytics firms have equal data access, there would seem to be little benefit to NRSRO status anyway.

But even without regulatory-conferred privileges, rating agencies would still have an advantage over upstart providers of credit analysis. The incumbents’ size allows them to invest in systems and manual procedures to assimilate the large volume of issuer and security data needed to rate and review a large number of debt issues.

Reforms that lower the costs of collecting this data would enable new analytic firms to compete against incumbent rating agencies despite their relatively small size. Representative Issa’s new bill, the Financial Transparency Act of 2017 (HR 1530), would make all financial regulatory data available in machine readable form. Right now, much of this data is only available in PDFs which are costly to process. By instead providing financial filings in the form of structured text, new credit data sets will become more readily available at little or no cost.

One regulator affected by the Act is the Municipal Securities Rulemaking Board (MSRB) which oversees the municipal bond market. Right now, offering materials and continuing disclosures such as annual financial reports are published as PDFs. Anyone hoping to analyze them must either mine the PDFs for relevant data or buy data sets from third parties, usually at high costs and with tight restrictions on redistribution (effectively preventing smaller firms from showing how their opinions are driven by issuer fundamentals). If the MSRB switches to structured text, the cost of analyzing municipal securities would drop, making it easier for municipal analytics startups such as MuniTrend to provide insight across a broad range of instruments.

The MSRB is one of ten regulators affected by the proposed act.  If passed and implemented, the bill would trigger a wave of free and low cost data sets that could help analysts outside of the credit rating agencies keep up with these powerful incumbents. When combined with reforms that remove the special privileges now enjoyed by licensed NRSROs, open financial data could usher in a new era of competition and innovation in the field of credit assessment.

Shining a Light onto Municipal Bond Issuance Costs

In a recent study of 800 municipal bond issues for UC Berkeley, I found that issuance costs varied widely – from less than 0.2% of face value to over 10%. Issuance costs are to local governments like points are to a consumer taking out a home mortgage. In both cases, the goal should normally be to minimize them. While consumers have many forums to compare against and thus reduce financing costs, local government officials have been less fortunate – but that situation is starting to change.

Aside from publishing the study, I also released a data set showing each bond’s total issuance costs – as shown on Official Statements – and itemized details for a sub-sample of the bonds. My group obtained these details by sending Public Records Act or Freedom of Information Act requests to local government bond issuers. We found that the largest components of issuance costs were underwriting expenses, legal fees, financial adviser expenses, rating agency fees and bond insurance premiums.

While my study provides data for a nationwide sample of bonds, the California State Treasurer’s Office has now posted issuance cost details for all municipal bonds issued in the largest state. This impressive data set can be found here. The data were collected by the California Debt and Investment Advisory Commission (CDIAC), a unit of the State Treasurer’s Office. Under state law, California local governments must report their debt data to CDIAC. The commission had been publishing some of this data, but Treasurer John Chiang, an advocate for transparency, recently decided to publish everything, including details on issuance costs.

Issuance Costs often > 10%

A review of the California data shows numerous issuance cost ratios in excess of 10% of the issued amount - and even some exceeding 20%. Just like a consumer would never pay 20 points on a home mortgage, it is hard to understand why a bond issuer would do the same.

Many of the higher issuance cost levels were associated with small bond issues from rural school districts and special districts. Since some of the issuance costs don’t vary with issuance size, they can hit small issuers relatively hard. Further, small issuers often receive lower bond ratings, creating the necessity to purchase municipal bond insurance.

Monoline insurance was not a factor in a couple of the 20%+ cost of issuance situations I found in the CDIAC data. 

In 2013, San Jacinto special districts (called Community Facilities Districts) issued two special tax bonds totaling $985,000 and $925,000 respectively. In each case, cost of issuance exceed 20%.

Focusing on the $925,000 bond, we find that the district received a mere $532,066 of the bond proceeds (see the Official Statement).   The Estimated Sources and Uses of Funds on page 6 of the document, show $90,428 being deposited into a reserve fund and a total of $295,890 going to the underwriter and other service providers. The remaining $6,616 reflected an original issue discount, arising from the bonds being sold below face value.

The debt service schedule on page 10 of the Official Statement shows that the district will spend $1,240,252 of interest on the $925,000 of bonds through 2043.  Total debt service of $2,165,252 over the life of the bond issue is four times the net proceeds received by the district. All in all, not a great deal for San Jacinto's taxpayers.

In an influential 2011 paper, Andrew Ang and Richard Green found that state and local governments lose billions of dollars due to the opacity and illiquidity of the municipal bond market. They proposed the creation of a municipal bond issuer consortium (they called it CommonMuni) to share information and best practices in order to lower these costs. A cost of issuance data set that allows us to identify disparities across issuers seems like a good opportunity to begin realizing the CommonMuni vision.

EMMA: Time to Grow Up and Be Like Your Big Brother, EDGAR

In 2009, the Municipal Securities Rulemaking Board (MSRB) launched its Electronic Municipal Market Access (EMMA) system: the place to go for all things muni. EMMA contains information about all publicly traded municipal bonds and their issuers including offering documents, trade activity, ratings, issuer financial statements and event notices (such as those required when an issuer misses a payment or calls its bonds).

As a frequent user, I’m impressed not only by the wealth of information available on EMMA, but also with the system’s usability, reliability and ongoing feature improvements. That said, EMMA has very serious limitations that are inconsistent with both open government and a liquid municipal bond market.

In a 2014 open letter to the MSRB, the Sunlight Foundation pointed out that restrictions on downloading and the fact that much of EMMA’s data is still in PDF form greatly limit the system’s transparency. In these respects, it is worth comparing EMMA with the SEC’s system for collecting and presenting company financial filings, which is known as EDGAR (Electronic Data Gathering, Analysis and Retrieval).

Unlike EMMA, EDGAR provides free FTP and RSS access, allowing users to consume as much content as they wish. EMMA only offers bulk downloads as a high cost subscription option and specifically forbids using automated techniques to quickly capture (or “scrape”) site content. It also limits the number of records that can be returned in “Advanced Searches”, hampering the ability of market participants and academic researchers to gather and analyze the big data EMMA contains.

EDGAR further facilitates analysis by providing key company disclosures – most notably quarterly (10-Q) and annual financial statements (10-k) – in machine readable format. Municipal financial statements on EMMA typically appear only in PDF form, requiring laborious parsing or re-keying to obtain usable data.

Recent legislation proposed by Congressman Darrell Issa (R-CA) and co-sponsored by 26 other representatives from both parties would require MSRB to implement machine-readable disclosures on EMMA. The Financial Transparency Act of 2015 (HR 2477) mandates the use of standards based, machine readable disclosures by all financial regulatory agencies and self-regulatory bodies deriving their powers from federal regulators. This includes the MSRB whose power to oversee the municipal securities market is delegated by the Securities and Exchange Commission (SEC).

The SEC also operates EDGAR, which – as we have seen – is far more open than EMMA. But the SEC has not always been an exemplar of open data. It took a combination of outside pressure and bureaucratic innovation to make corporate financial disclosure fully open.

As late as the early-1990s, the primary method of reporting corporate financial results to the public was through printed annual reports and paper regulatory filings. Even after the SEC received company filings electronically, it proved unable to share this machine readable data with the general public.

This situation changed by virtue of work done by Carl Malamud, a northern California open government advocate. Malamud obtained SEC disclosures and began posting them on a web site he built with a National Science Foundation grant. Seeing the success of Malamud’s efforts, the SEC was shamed into providing this service itself. More recently, Malamud, through this work at Public.Resource.Org, has made a similar breakthrough with not-for-profit organization disclosures submitted to the IRS –Form 990. Malamud’s group began putting these forms on line at no charge a few years ago, and recently won a court judgment against the IRS requiring the agency to provide the Form 990 disclosures in machine readable format.

Meanwhile, the SEC has continued to improve EDGAR data. When it began publishing corporate disclosures in the late 1990s, the data appeared in SGML format (SGML is a close relative of HTML). SGML is more easily parsed than PDFs, so the SEC was way ahead of the MSRB and the IRS from the start. But the SGML disclosures were not self-describing: the data files were not tagged in such a way as to provide consistency across files. In the mid-2000s, the SEC began to embrace eXtensible Business Reporting Language (XBRL) which is self-describing. Beginning in 2009, the SEC began to mandate that corporate filers use XBRL – starting with the largest companies and working down to smaller ones. Now EDGAR users can click an “Interactive Data” button next to each disclosure to see the XBRL rendered as an interactive web page.

To this author, it seems odd that private companies and now private, not-for-profit entities have more accessible financial filings than do state and local governments. Many private organizations affect relatively small number of stakeholders – perhaps just a few hundred customers, employees and shareholders. But governments large enough to issue bonds touch the lives of thousands of taxpayers, service users, beneficiaries and other parties: their financial affairs are much more a matter of public interest.

EMMA could serve that public interest if its content were more open – but a number of factors prevent this. For example, MSRB’s board contains members employed by firms in the municipal bond industry whose revenue might be reduced by greater industry transparency.

Some of the content on EMMA is proprietary. This restricted data includes CUSIP numbers that identify each bond, as well as credit ratings. CUSIPs are owned by the American Bankers Association and administered by McGraw Hill Financial; they normally cannot be displayed on a web page without a costly CUSIP license. Although individual bond ratings may be freely reproduced, rating agencies take measures to prevent the bulk redistribution of credit ratings, because they sell ratings feeds to large financial industry customers. Finally, the MSRB also realizes revenue from selling EMMA content in bulk:  users are offered subscriptions to EMMA data feeds that includes various portions of the primary market and continuing disclosures available on the system.  If this material could be bulk downloaded at no charge, MSRB would lose subscription revenue.

While these institutional factors may preclude free bulk access to EMMA content, it is less clear why MSRB has not mandated filings in XBRL or some other open, standardized format – rather than PDFs. This idea appeared on an MSRB road map in 2012, but there does not seem to be momentum toward implementing it.

That situation would change if the Financial Transparency Act of 2015 (HR 2477) becomes law. Once PDFs are replaced by structured data, the cost of creating municipal finance data sets will greatly decline and their availability will greatly increase. The ultimate results should be better value for municipal bond investors and substantial cost savings for cities, counties, school districts and other issuers.

High Frequency (Non) Trading

This week's release of Michael Lewis' new book, Flash Boys, has renewed focus on a little understood area of the market, an area that has garnered the recent attentions of market regulators, New York's Attorney General, and more recently the FBI -- but never as much attention as it garnered from Michael Lewis' interview on 60 Minutes on Sunday, with his book pending release the following day.

Without going into too many specifics, one of the central themes that Lewis discusses is the potential for high frequency traders (or HFTs) to take advantage of certain market information -- like bids and offers -- that are unknown to many other market players.

Defenders of HFTs have come out aggressively, with claims that HFTs increase market activity and liquidity, and have lowered trading costs.  The WSJ published an extensive opinion editorial by hedge fund guru Cliff Asness and his colleague Michael Mendelson of AQR, which energetically claims that much of what HFTs do is "make markets" and that they do it best because "their computers are much cheaper than expensive Wall Street traders, and competition forces them to pass most of the savings on to us investors."

Of course this sounds altogether too convincing.  Unfortunately, Asness and Mendelson provide little or no evidence (although their business as long term traders relies heavily on evidence, and they claim in the article to spend considerable energies looking into their trading costs) and they admit that they actually don't have too much conviction in the premise of their exposition:

"We think it helps us. It seems to have reduced our costs and may enable us to manage more investment dollars. We can't be 100% sure. Maybe something other than HFT is responsible for the reduction in costs we've seen since HFT has risen to prominence, like maybe even our own efforts to improve." (emphasis ours)

But this aside, no doubt all forms of HFTs bring liquidity.  They're a good thing.  Let's focus our attention elsewhere.  

Or not?

Might there be another type of HFT, that doesn't always bring liquidity for the greater good of the market ...  perhaps a type that uses obscure mechanisms to change the look and feel of the market -- to make people think there is a bid, think there is an offer, without there being one?  

This is what Flash Boys, and the interest it has invigorated in HFTs, really concerns itself with -- understanding market maneuvers like spoofing or pinging: the submission of phantom orders, immediately cancellable, that have the potential to create a false impression of market levels.

Are we creating a whole lot of (potentially fictitious) orders, but not a whole lot of activity?  Are there high-frequency non-traders?  Are we mis-marking our portfolios as a result? We continue to investigate.  But we couldn't help but bring you back to a 2013 chart from Mother Jones, which highlights the growing contrast between actual trades (in orange) and quotes/orders (in red).

Good Intentions are Not Enough: The Problem of SEC Mandated XBRL Reporting

Public companies have been required to supply financial reports since the Depression, but gathering and analyzing this disclosure has had its challenges. In the 1990s, the SEC began uploading 10-K’s and 10-Q’s to the internet, greatly simplifying the data collection task. These electronic reports were not standardized, creating the need for downstream users to write complex parsing algorithms and/or use manual processes to harvest the financial statements.

In the late 1990s, accounting and technology firms devised a standard called XBRL – eXtensible Business Reporting Language – to streamline the data acquisition process. XBRL disclosures rely on a common system of tags that consistently identify financial statement elements. The universe of elements differ amongst accounting standards, such as US Generally Accepted Accounting Principles (US-GAAP) and International Financial Reporting Standard (IFRS). An XBRL taxonomy lists all the acceptable financial statement elements for a given accounting standards.

Beginning in 2009, the SEC started requiring public companies to file 10-K and 10-Q disclosures in XBRL using a US-GAAP taxonomy – maintained by the accounting community and approved each year by the SEC.

Recently, I worked with UK-based OpenCorporates to gather SEC XBRL disclosures and harvest data from them. The goal was fairly simple: walk through all the XBRL documents and gather some basic parent company data points (like total assets, total liabilities, total revenue and net income) for the latest fiscal year from these disclosures.

This task proved surprisingly difficult because of a lack of standardization between XBRL documents from different companies. For example, many companies did not report a value for Total Liabilities. One might “back into” this value by subtracting Shareholders’ Equity from Total Assets, but this doesn’t always work. A small percentage of XBRL reports even lacked a Total Assets field. On the income statement side, the dispersion was even greater, with Total Revenue, Operating Income and Net Income often unavailable.

Finding data for the latest period also proved challenging. XBRL files can contain numerous contexts. Each context refers to a reporting period (e.g., a particular quarter or year) and a scope – which may be the parent company or a particular segment of the corporation (e.g., a subsidiary). Contexts contain period elements and an optional segment element indicating which timeframe and what scope the context covers. To find the latest year’s parent company data, it is necessary to develop a program to walk through each context.

These examples suggest that processing SEC mandated XBRL disclosures is less than straightforward. Indeed, the industry group XBRL.US reports finding 1.4 million errors in the universe of XBRL documents filed thus far.

A recent letter from Darrel Issa (R-CA) to the SEC notes that the agency itself is not using the XBRL files it requires corporations to file. Instead, it continues to rely on commercial data aggregators. Electronic disclosure won’t improve unless numerous eyes are scrutinizing it and reporting issues. Data sets need to be exercised; otherwise they remain unfit.

The lack of XBRL utilization represents a major threat for transparency advocates. If we ask for more accessible disclosures and then don’t use them, filers can be expected to push back. In the case of SEC XBRL, the filings are sufficiently complex to require the use of third party XBRL submission firms. In other words, it is too difficult for most companies to prepare XBRL submissions themselves – they need to use an independent preparer, just as individuals often need to hire professionals to file their annual tax returns. Corporations would undoubtedly like to economize on this cost, and can be expected to resist the XBRL reporting requirement if the filings are not used.

From my perspective, a big problem with the XBRL rollout is that it started with large public companies. By 2009, many data aggregators already had mature processes for assimilating the traditional SEC disclosure. As a result, fielded public company financial data has become a commodity; individuals can access these data for free at Yahoo Finance and many other portals. The incentive for aggregators to use XBRL is thus limited because the problem has already been solved at some level, and because the data are widely available, there is little benefit to potential new entrants.

XBRL can provide much greater benefits for data sets that have not received as much attention. I became interested in XBRL back in 2001 because I was hoping to get a standard source of private company data at my bank. The idea was to provide unlisted corporate borrowers with an XBRL template to provide quarterly disclosure.

Another high impact application for XBRL is state and local government financial reporting. When XBRL was growing up in the 1990s and 2000s, US municipal bonds were generally perceived to be safe. That perception started to change in 2008 with Vallejo’s bankruptcy filing and the collapse of the municipal bond insurance industry. Subsequent municipal bankruptcies culminating with that of Detroit in 2013, have reinforced the perception that municipal securities are risky. Government financial statements, which may have been ignored previously, now have significance as investors search for the next bankruptcy candidates.

However, the rollout of XBRL to other areas – such as local government – may now depend on its successful implementation in existing areas: especially the high profile SEC US public company application. If the SEC is unwilling or unable to engage, the community would be well served by collaborating to implement its own improvements. Although XBRL filing companies compete with one another, they all have an interest in the success of the XBRL standard. Thus, as an industry group, these companies can propose and implement improvements to SEC XBRL filings that will make them easier to use. For example, they can develop an enhanced XML Schema Definition which provides additional checks over and above those legally mandated. Such a schema should ensure that filers always include common financial statements such as Total Assets and Total Liabilities. It should also ensure that, within any given XBRL file, the latest period’s parent company filing is easily identified.

XBRL was and remains a good idea. Transparency advocates need to ensure that it does not become an idea whose time has come and gone. To keep XBRL on track, its public company instance needs to be refined so that implementation costs are reduced. Further, it needs to be applied to other areas – such as US local governments – which stand to gain greater benefits from its adoption.