PrefBlog

Bill C-501 is a Liberal-sponsored bill to amend the Bankruptcy and Insolvency Act and other Acts – although its chief proponent found it impossible to find any support for its provisions in a major speech.

John Manley explains in Bill C-501: Myths & Reality:

The proposed legislation would force bankrupt companies to give so-called “super-priority” status to unfunded pension plan liabilities, on the grounds that this will help to ensure benefits are paid if a company goes out of business.

DBRS Comments on Pension Fund Bill C-501:

Based on present information, the enactment of Bill C-501 would have potentially negative consequences for the ratings of certain Canadian-based companies and would likely add volatility to bond markets on a whole as market participants reacted to the meaningful change. The primary impact would relate to the reality that bondholders of entities with DBs would now rank behind the new senior obligation. Secondary effects would likely include the reality that the overall credit strength of some entities may be weakened by changes that would result in higher funding costs and accentuated liquidity challenges, noting that this would likely be concentrated within weaker credits, particularly during periods of stress.

Douglas Rienzo, a Pension & Benefits partner at Osler, comments:

Extending super-priority status to the entire solvency deficit could place significant additional burdens on the financial capacity of DB plan sponsors, impede their ability to cost-effectively raise capital, adversely affect their ability to invest in Canada’s economy and remain competitive, and, ultimately, impair their ability to fund their pension obligations.

For example, the proposed amendments to the BIA and CCAA could have the following implications:

• •the elevation of billions of dollars of potential pension claims ahead of lenders in the priority ladder;
• •the revaluation by credit markets of assets available for security and the deduction of higher-priority claims, thus resulting in a significant reduction of available credit; and
• •the creation of immediate default situations, based on covenants in existing trust indentures restricting the existence of claims that would have priority over the existing lender.

While the protection of members’ accrued benefits in a restructuring or bankruptcy situation may well be a public policy goal worth pursuing, the unintended consequences of Bill C-501 could include not only the weakening of the financial viability of DB plan sponsors, but possibly the wholesale abandonment of DB plans by corporate Canada.

As far as preferred shares go, this is pretty much a non-issue: the default assumption is that if an operating company defaults at all on its preferreds, that implies a total loss on the preferred shares. The senior bondholders and pension retirees can fight amongst themselves for whatever’s left over.

Update, 2011-6-23: Died on the order paper.

The Securities and Exchange Commission spent 2010 busily entrenching the practice of selective disclosure with respect to the credit quality of investible intruments.

In 17 CFR Parts 240 and 243 “Amendments to Rules for Nationally Recognized Statistical Rating Organizations”:

Under the re-proposed amendments: (1) NRSROs that are hired by arrangers to perform credit ratings for structured finance products would have been required to disclose on a password-protected Internet Web site the deals for which they have been hired and provide access to that site to non-hired NRSROs that have furnished the Commission with the certification described below; (2) NRSROs that are hired by arrangers to perform credit ratings for structured finance products would have been required to obtain representations from those arrangers that the arranger would provide information given to the hired NRSRO to non-hired NRSROs that have furnished the Commission with the certification described below as well; and (3) NRSROs seeking to access information maintained by the NRSROs and the arrangers pursuant to the new rule would have been required to furnish the Commission an annual certification that they are accessing the information solely to determine credit ratings and would determine a minimum number of credit ratings using the information.

So the SEC acknowledges – and, in fact, emphasizes – that it is difficult, if not impossible, to asset the credit quality of a structured-finance instrument without acess to material non-public information.

Currently, when an NRSRO is hired to rate a structured finance product, some of the information it relies on to determine the rating is generally not made public. As a result, structured finance products frequently are issued with ratings from only one or two NRSROs that have been hired by the arranger, with the attendant conflict of interest that creates. The amendments to Rule 17g-5 are designed to increase the number of credit ratings extant for a given structured finance product and, in particular, to promote the issuance of credit ratings by NRSROs that are not hired by the arranger. This will provide users of credit ratings with more views on the creditworthiness of the structured finance product. In addition, the amendments are designed to reduce the ability of arrangers to obtain better than warranted ratings by exerting influence over NRSROs hired to determine credit ratings for structured finance products. Specifically, opening up the rating process to more NRSROs will make it easier for the hired NRSRO to resist such pressure by increasing the likelihood that any steps taken to inappropriately favor the arranger could be exposed to the market through the credit ratings issued by other NRSROs.

… and only NRSROs are granted access to that information. Investors (or “Investor Scum”, as I believe they are generally known to regulators) must be made dependent upon NRSROs for assessments of credit quality – this will, of course, make it easier to blame them for that dependence when – as will inevitably happen in a competitive economy – things go pear-shaped.

Later, in an exemption to address extra-territoriality the SEC repeated:

Rule 17g-5(a)(3), among other things, requires that the NRSRO must:

• Maintain on a password-protected Internet Web site a list of each structured finance product for which it currently is in the process of determining an initial credit rating in chronological order and identifying the type of structured finance product, the name of the issuer, the date the rating process was initiated, and the Internet Web site address where the arranger represents the information provided to the hired NRSRO can be accessed by other NRSROs;
• Provide free and unlimited access to such password-protected Internet Web site during the applicable calendar year to any NRSRO that provides it with a copy of the certification described in paragraph (e) of Rule 17g-5 that covers that calendar year;12 and
• Obtain from the arranger a written representation that can reasonably be relied upon that the arranger will, among other things, disclose on a password-protected Internet web site the information it provides to the hired NRSRO to determine the initial credit rating (and monitor that credit rating) and provide access to the web site to an NRSRO that provides it with a copy of the certification described in paragraph (e) Rule 17g-5.13

… and DBRS confirms that every single particle of information that they use to rate structured finance wil be on these semi-seqret websites:

To ensure compliance with the Representation Agreement, DBRS requests the Arranger not provide new information orally to DBRS. Rather, the Arranger should post all new information on its website at the same time as it provides it to DBRS. Discussions between the Arranger and DBRS about the application of DBRS methodologies that do not relate to a transaction or a potential transaction would not need to be posted.

I have long argued that Regulation FD (and the corresponding Canadian National Policy 51-201) must be repealed; instead, it is being entrenched. One wonders when the first scandal regarding leakage of passwords will occur.

The Banking Subcommittee of the Bank for International Settlements has released a consultation paper titled Capitalisation of bank exposures to central counterparties:

Generally speaking, the Committee proposes that trade exposures to a qualifying CCP will receive a 2% risk weight. In addition, default fund exposures to a CCP will, in accordance with a risk sensitive waterfall approach (based on a CCP’s actual financial resources and hypothetical capital requirements), be capitalised according to a method that consistently and simply estimates risk arising from such default fund.

As has been discussed on PrefBlog, the purpose of Central Counterparties is to increase the vulnerability of the global financial system to single-point failure, and to provide additional extra-judicial power to bureaucrats who will be empowered to decide who gets to be a member of the Clearing Corporation.

This is considered to be far superior to a system in which the bank at risk makes a credit assessment and, when a trade is going their way, either gets collateral for the exposure or accepts an increment to Risk-Weighted Assets. Such a system would require banks such as CIBC to be as thoughtful about their counterparty exposure as Goldman Sachs; decent risk-management procedures in the industry would leave regulators with less to do.

With the new system, CIBC will be able to load up the Central Counterparty with billions in exposure to the Bank of Downtown Medicine Hat, without having to worry overmuch about its credit quality – the exposure will be covered by the other clearinghouse members.

One of the great problems with the Panic of 2007, we are told, was moral hazard. What I initially misunderstood was the regulators’ attitude: it’s not that they feel there was too much of it; they feel that there wasn’t enough, so they have designed a system to create more.

Sic transit gloria mundi.

The Basel Committee on Banking Supervision has released the Basel III: A global regulatory framework for more resilient banks and banking systems:

To address the systemic risk arising from the interconnectedness of banks and other financial institutions through the derivatives markets, the Committee is supporting the efforts of the Committee on Payments and Settlement Systems (CPSS) and the International Organization of Securities Commissions (IOSCO) to establish strong standards for financial market infrastructures, including central counterparties. The capitalisation of bank exposures to central counterparties (CCPs) will be based in part on the compliance of the CCP with such standards, and will be finalised after a consultative process in 2011. A bank’s collateral and mark-to-market exposures to CCPs meeting these enhanced principles will be subject to a low risk weight, proposed at 2%; and default fund exposures to CCPs will be subject to risk-sensitive capital requirements. These criteria, together with strengthened capital requirements for bilateral OTC derivative exposures, will create strong incentives for banks to move exposures to such CCPs. Moreover, to address systemic risk within the financial sector, the Committee also is raising the risk weights on exposures to financial institutions relative to the non-financial corporate sector, as financial exposures are more highly correlated than non-financial ones.

It is worthwhile thinking about how the CCP exposure will blow up – because it will, never fear! I have often stressed the poor design inherent in developing a system so exposed to long-term single point failure, but the desperate need of the regulators to be seen as doing something has overwhelmed such antiquated notions as common sense.

On the other hand, it is good to see that risk-weights on exposure to financial institutions is being raised, but the details need to be examined. Unfortunately, they appear to apply only to collateralized derivative transactions, not to bank paper itself.

They also tout the introduction of a leverage ratio cap:

One of the underlying features of the crisis was the build up of excessive on- and off-balance sheet leverage in the banking system. The build up of leverage also has been a feature of previous financial crises, for example leading up to September 1998. During the most severe part of the crisis, the banking sector was forced by the market to reduce its leverage in a manner that amplified downward pressure on asset prices, further exacerbating the positive feedback loop between losses, declines in bank capital, and the contraction in credit availability. The Committee therefore is introducing a leverage ratio requirement that is intended to achieve the following objectives:

• constrain leverage in the banking sector, thus helping to mitigate the risk of the destabilising deleveraging processes which can damage the financial system and the economy; and
• introduce additional safeguards against model risk and measurement error by supplementing the risk-based measure with a simple, transparent, independent measure of risk.

  17. The leverage ratio is calculated in a comparable manner across jurisdictions, adjusting for any differences in accounting standards. The Committee has designed the leverage ratio to be a credible supplementary measure to the risk-based requirement with a view to migrating to a Pillar 1 treatment based on appropriate review and calibration.

The previously agreed capital requirements are confirmed:

All elements above are net of the associated regulatory adjustments and are subject to the following restrictions (see also Annex 1):

• Common Equity Tier 1 must be at least 4.5% of risk-weighted assets at all times.
• Tier 1 Capital must be at least 6.0% of risk-weighted assets at all times.
• Total Capital (Tier 1 Capital plus Tier 2 Capital) must be at least 8.0% of risk-weighted assets at all times.

Of greatest interest to preferred share investors are the rules regarding eligibility for inclusion in “Additional Tier 1 Capital”. Most of this is a simple continuation of extant rules, but:

Dividend/coupon discretion:
a. the bank must have full discretion at all times to cancel distributions/payments(footnote)
b. cancellation of discretionary payments must not be an event of default
c. banks must have full access to cancelled payments to meet obligations as they fall due
d. cancellation of distributions/payments must not impose restrictions on the bank except in relation to distributions to common stockholders.

footnote: A consequence of full discretion at all times to cancel distributions/payments is that “dividend pushers” are prohibited. An instrument with a dividend pusher obliges the issuing bank to make a dividend/coupon payment on the instrument if it has made a payment on another (typically more junior) capital instrument or share. This obligation is inconsistent with the requirement for full discretion at all times. Furthermore, the term “cancel distributions/payments” means extinguish these payments. It does not permit features that require the bank to make distributions/payments in kind.

Heretofore, it has been possible (unlikely, but possible) that preferred shares could default while Innovative Tier 1 Capital pays in full. If I’m reading this section correctly, the footnote means that, in principle if not necessarily in practice, all elements of Tier 1 Capital will be parri passu.

The footnote nearly gave me a heart attack, as in and of itself it appears to prohibit the preference of dividends; however, such an interpretation conflicts with point (d), which explicitly allows seniority to common.

Also of interest is section 12:

Neither the bank nor a related party over which the bank exercises control or significant influence can have purchased the instrument, nor can the bank directly or indirectly have funded the purchase of the instrument.

This would appear to prohibit margining in stock accounts. Come to think of it, I’m not sure if this has ever been addressed: can you buy TD common on margin in your TDW account?

The discussion of subordinated debt is interesting:

May be callable at the initiative of the issuer only after a minimum of five years:
a. To exercise a call option a bank must receive prior supervisory approval;
b. A bank must not do anything that creates an expectation that the call will be exercised;(19) and
c. Banks must not exercise a call unless:
i. They replace the called instrument with capital of the same or better quality and the replacement of this capital is done at conditions which are sustainable for the income capacity of the bank(20); or
ii. The bank demonstrates that its capital position is well above the minimum capital requirements after the call option is exercised.(21)

Footnotes:

(19) An option to call the instrument after five years but prior to the start of the amortisation period will not be viewed as an incentive to redeem as long as the bank does not do anything that creates an expectation that the call will be exercised at this point.

(20) Replacement issues can be concurrent with but not after the instrument is called.

(21) Minimum refers to the regulator’s prescribed minimum requirement, which may be higher than the Basel III Pillar 1 minimum requirement.

In Canada, one wonders whether footnote (19) will prohibit bank-owned brokerages from quoting the yields on sub-debt to a presumed maturity due to a call at the start of the amortization period. It should; but this might damage the long term career prospects of OSFI personnel, so I’m not holding my breath.

Market pressures – due to the way sub-debt is sold – are so extreme as to make the call de facto mandatory; it is very disappointing that BIS is not addressing this problem.

Given the abuses that have ocdurred in the sub-debt market over the past few years due to market expectation of such a call, I am rather disappointed to see that such calls are still allowable.

Of interest to Citigroup is:

69. Deferred tax assets (DTAs) that rely on future profitability of the bank to be realised are to be deducted in the calculation of Common Equity Tier 1.

Citigroup now has a Tier 1 Capital Common Ratio of 2.16% and a Tier 1 Capital Ratio of 11.92%; although it looks like they’re playing games in the press release:

Citi remained one of the best capitalized banks with $125.4 billion of Tier 1 Capital and a Tier 1 Common ratio of 10.3% at the end of the third quarter 2010.

When in doubt, invent a new non-GAAP, non-BIS ratio, that’s what I say! The 2009 Annual Report is more forthcoming with calculation methodologies:

Of Citi’s approximately $46 billion of net deferred tax assets at December 31, 2009, approximately $15 billion of such assets were includable without limitation in regulatory capital pursuant to risk-based capital guidelines, while approximately $26 billion of such assets exceeded the limitation imposed by these guidelines and, as “disallowed deferred tax assets,” were deducted in arriving at Tier 1 Capital. Citigroup’s other approximately $5 billion of net deferred tax assets primarily represented approximately $3 billion of deferred tax effects of unrealized gains and losses on available-for-sale debt securities and approximately $2 billion of deferred tax effects of the pension liability adjustment, which are permitted to be excluded prior to deriving the amount of net deferred tax assets subject to limitation under the guidelines. Citi had approximately $24 billion of disallowed deferred tax assets at December 31, 2008.

Back to BIS … I’m very pleased to see this:

Banks are also required to make available on their websites the full terms and conditions of all instruments included in regulatory capital. The Basel Committee will issue more detailed Pillar 3 disclosure requirements in 2011.

Also of interest are the specifics of the Capital Conservation Buffer:

131. The table below shows the minimum capital conservation ratios a bank must meet at various levels of the Common Equity Tier 1 (CET1) capital ratios. For example, a bank with a CET1 capital ratio in the range of 5.125% to 5.75% is required to conserve 80% of its earnings in the subsequent financial year (ie payout no more than 20% in terms of dividends,share buybacks and discretionary bonus payments). If the bank wants to make payments in excess of the constraints imposed by this regime, it would have the option of raising capital in the private sector equal to the amount above the constraint which it wishes to distribute. This would be discussed with the bank’s supervisor as part of the capital planning process. The Common Equity Tier 1 ratio includes amounts used to meet the 4.5% minimum Common Equity Tier 1 requirement, but excludes any additional Common Equity Tier 1 needed to meet the 6% Tier 1 and 8% Total Capital requirements. For example, a bank with 8% CET1 and no Additional Tier 1 or Tier 2 capital would meet all minimum capital requirements, but would have a zero conservation buffer and therefore by subject to the 100% constraint on capital distributions.

Unfortunately, the Countercyclical Buffer remains a matter of regulatory discretion:

Each Basel Committee member jurisdiction will identify an authority with the responsibility to make decisions on the size of the countercyclical capital buffer. If the relevant national authority judges a period of excess credit growth to be leading to the build up of system-wide risk, they will consider, together with any other macroprudential tools at their disposal, putting in place a countercyclical buffer requirement. This will vary between zero and 2.5% of risk weighted assets, depending on their judgement as to the extent of the build up of system-wide risk.

The leverage cap looks pretty loose

The Committee will test a minimum Tier 1 leverage ratio of 3% during the parallel run period from 1 January 2013 to 1 January 2017.
…
161. Banks should calculate derivatives, including where a bank sells protection using a credit derivative, for the purposes of the leverage ratio by applying:

• the accounting measure of exposure plus an add-on for potential future exposure calculated according to the Current Exposure Method as identified in paragraphs 186, 187 and 317 of the Basel II Framework. This ensures that all derivatives are converted in a consistent manner to a “loan equivalent” amount; and
• the regulatory netting rules based on the Basel II Framework.

(iii) Off-balance sheet items
162. This section relates to off-balance sheet (OBS) items in paragraphs 82-83, (including 83(i)), 84(i-iii), 85-86, and 88-89) of the Basel II Framework. These include commitments (including liquidity facilities), unconditionally cancellable commitments, direct credit substitutes, acceptances, standby letters of credit, trade letters of credit, failed transactions and unsettled securities. The treatment of the items included in 83(ii) and 84, ie repurchase agreements and securities financing transactions is addressed above.

163. The Committee recognises that OBS items are a source of potentially significant leverage. Therefore, banks should calculate the above OBS items for the purposes of the leverage ratio by applying a uniform 100% credit conversion factor (CCF).

164. For any commitments that are unconditionally cancellable at any time by the bank without prior notice, banks should apply a CCF of 10%. The Committee will conduct further review to ensure that the 10% CCF is appropriately conservative based on historical experience.

It’s going to take quite a while to digest all this!

Julie Dickson spoke against the IFRS Exposure Draft on Insurance Contracts in a speech at the 2010 Life Insurance Invitational Forum:

On the “positive side”, the new approach might better capture financial risks of companies, particularly equity and interest rate risks, and thus provide more early warnings of risks. On the “negative” side, the discount rate change could potentially lead to extreme earnings volatility especially given the large blocks of long-duration guaranteed product liabilities on the books of Canadian insurance companies. As such, we think the proposals may go too far in terms of capturing short-term interest rate movements on long-term exposures. Consequently, we are working on options to help deal with this issue.

In fact we are encouraged by recent developments in this regard. One such development is that the IASB’s Insurance Working Group is meeting later this week to discuss possible ways to minimize the effects of any inappropriate volatility. This group’s objective is to analyze accounting issues relating to insurance contracts. The group brings together a wide range of interests and includes senior financial executives who are involved in financial reporting. Other developments closer to home are discussions by the Canadian Accounting Standards Board’s Insurance Accounting Task Force and the Canadian Institute of Actuaries group to develop their comment letters to the IASB. Both these groups are discussing the volatility issue.
…
OSFI is committed to continuing to work with industry and other international stakeholders as we complete our response to the IASB, which is due November 30th. We encourage the industry to contribute to this work; the more that we work together, the better the result will be.

See also the Canada Life and Health Insurance Association comment letter, discussed briefly in the post SLF Coy on Capital Rule Changes.

Themis Trading refers me to a comment letter from R T Leuchtkafer which in turn referred me to an excellent paper by Andrei A. Kirilenko, Albert S. Kyle, Mehrdad Samadi and Tugkan Tuzun titled Flash Crash: The Impact of High Frequency Trading on an Electronic Market.

We define Intermediaries as those traders who follow a strategy of buying and selling a large number of contracts to stay around a relatively low target level of inventory. Specifically, we designate a trading account as an Intermediary if its trading activity satisfies the following two criteria. First, the account’s net holdings fluctuate within 1.5% of its end of day level. Second, the account’s end of day net position is no more than 5% of its daily trading volume. Together, these two criteria select accounts whose trading strategy is to participate in a large number of transactions, but to rarely accumulate a significant net position.

We define High Frequency Traders as a subset of Intermediaries, who individually participate in a very large number of transactions. Specifically, we order Intermediaries by the number of transactions they participated in during a day (daily trading frequency), and then designate accounts that rank in the top 3% as High Frequency Traders. Once we designate a trading account as a HFT, we remove this account from the Intermediary category to prevent double counting.

This seems like an entirely sensible division, although one might quibble about the 3% cut-off. Why not 2% or 4%? It might also be illuminating to make the division based on the technology used.

Some Fundamental Traders accumulate directional positions by executing many small-size orders, while others execute a few larger-size orders. Fundamental Traders which accumulate net positions by executing just a few orders look like Noise Traders, while Fundamental Traders who trade a lot resemble Opportunistic Traders. In fact, it is quite possible that in order not to be taken advantage of by the market, some Fundamental Traders deliberately pursue execution strategies that make them appear as though they are Noise or Opportunistic Traders. In contrast, HFTs appear to play a very distinct role in the market and do not disguise their market activity.

Naturally, the better you are at disguising your activity, the better you are going to do for your clients. This point is lost upon the regulators, who generally take the view that an order cancellation is an indication of fraudulent activity and spend their time crafting rules to penalize smart traders and their clients.

It will also be noted that the ultimate disguise consists of not showing your order publicly at all – which means using a dark pool.

In order to further characterize whether categories of traders were primarily takers of liquidity, we compute the ratio of transactions in which they removed liquidity from the market as a share of their transactions.[Footnote] According to Table 2, HFTs and Intermediaries have aggressiveness ratios of 45.68% and 41.62%, respectively. In contrast, Fundamental Buyers and Sellers have aggressiveness ratios of 64.09% and 61.13%, respectively. This is consistent with a view that HFTs and Intermediaries generally provide liquidity while Fundamental Traders generally take liquidity. The aggressiveness ratio of High Frequency Traders, however, is higher than what a conventional definition of passive liquidity provision would predict. [Footnote]

In order to better characterize the liquidity provision/removal across trader categories, we compute the proportion of each order that was executed aggressively.[Footnote] Table 3 presents the cumulative distribution of ratios of order aggressiveness.

Footnote: When any two orders in this market are matched, the CME Globex platform automatically classifies an order as ‘Aggressive’ when it is executed against a ‘Passive’ order that was resting in the limit order book. From a liquidity standpoint, a passive order (either to buy or to sell) has provided visible liquidity to the market and an aggressive order has taken liquidity from the market. Aggressiveness ratio is the ratio of aggressive trade executions to total trade executions. In order to adjust for the trading activity of different categories of traders, the aggressiveness ratio is weighted either by the number of transactions or trading volume.

Footnote: One possible explanation for the order aggressiveness ratios of HFTs is that some of them may actively engage in “sniping” orders resting in the limit order book. Cvitanic and Kirilenko (2010) model this trading behavior and conclude that under some conditions this trading strategy may have impact on prices. Similarly, Hasbrouck and Saar (2009) provide empirical support for a possibility that some traders may have altered their strategies by actively searching for liquidity rather than passively posting it. Yet another explanation is that after passively buying at the bid or selling at the offer, HFTs quickly reduce their inventories by trading aggressively if necessary.

Footnote: The following example illustrates how we compute the proportion of each order that was executed aggressively. Suppose that a trader submits an executable limit order to buy 10 contracts and this order is immediately executed against a resting sell order of 8 contracts, while the remainder of the buy order rests in the order book until it is executed against a new sell order of 2 contracts. This sequence of executions yields an aggressiveness ratio of 80% for the buy order, 0% for the sell order of 8 contracts, and 100% for the sell order of 2 contracts.

This is a much better indicator of order intent than the puerile “Order Toxicity” metric, but remains flawed, as shown by the last footnote. If somebody needs to sell a large block, for instance, and places an offer well below the prevailing market price, the vast majority of it will execute as buyers take advantage of this low offer (this happens on a routine basis in the preferred share market). However, since this order is “resting”, these execution will indicate that the seller is providing liquidity and the buyers are taking it – when the actual situation is the other way around.

In fact, the first quoted section above explicitly demonstrates this fact of trading, with Fundamental Traders going to great lengths to look like Noise and Opportunistic traders.

According to Figure 4, HFTs do not accumulate a significant net position and their position tends to quickly revert to a mean of about zero. The net position of the HFTs fluctuates between approximately +/- 3000 contracts. Figure 5 presents the net position of the Intermediaries during May 3-6, 2010.

According to Figure 5, Intermediaries exhibit trading behavior similar to that of HFTs. They also do not accumulate a significant net position. Compared to the HFTs, the net position of the Intermediaries fluctuates within a more narrow band of +/- 2000 contracts, and reverts to a lower target level of net holdings at a slower rate.

We also find a notable decrease in the number of active Intermediaries on May 6. As the Figure 6 shows, the number of active Intermediaries dropped from 66 to 33, as the large price decline ensues.

In contrast, as presented in Figure 7, the number of active HFTs decreases from 13 to 10.

This demonstrates the position limits highlighted by the SEC report.

We interpret these results as follows. HFTs appear to trade in the same direction as the contemporaneous price and prices of the past five seconds. In other words, they buy, if the immediate prices are rising. However, after about ten seconds, they appear to reverse the direction of their trading – they sell, if the prices 10-20 seconds before were rising. These regression results suggest that, possibly due to their speed advantage or superior ability to predict price changes, HFTs are able to buy right as the prices are about to increase. HFTs then turn around and begin selling 10 to 20 seconds after a price increase.
…
The Intermediaries sell when the immediate prices are rising, and buy if the prices 3-9 seconds before were rising. These regression results suggest that, possibly due to their slower speed or inability to anticipate possible changes in prices, Intermediaries buy when the prices are already falling and sell when the prices are already rising.

So in other words, part of the thing that differentiates HFT and Intermediaries is not simply the volume of trade, but also that the HFT guys can do it better. In many cases, HFT strategies attempt to predict the (short-term) future direction of the market by looking at the order book … if there’s a huge volume of offers compared to the bids, get out of the way! One method of counter-attack against this is, as mentioned above, the use of dark pools for trading.

We consider Intermediaries and HFTs to be very short term investors. They do not hold positions over long periods of time and revert to their target inventory level quickly. Observed trading activity of HFTs can be separated into three parts. First, HFTs seem to anticipate price changes (in either direction) and trade aggressively to profit from it. Second, HFTs seem to provide liquidity by putting resting orders in the direction of the anticipated the price move. Third, HFTs trade to keep their inventories within a target level. The inventory management trading objective of HFTs may interact with their price-anticipation objective. In other words, at times, inventory-management considerations of HFTs may lead them to aggressively trade in the same direction as the prices are moving, thus, taking liquidity. At other times, in order to revert to their target inventory levels, HFTs may passively trade against price movements and, thus, provide liquidity.

This is consistent with my speculation on October 25 that HFT acts as a capacitator that will discharge if a certain inventory level is breached.

We find that compared to the three days prior to May 6, there was an unusually level of HFT “hot potato” trading volume — due to repeated buying and selling of contracts accompanied a relatively small change in net position. The hot potato effect was especially pronounced between 13:45:13 and 13:45:27 CT, when HFTs traded over 27,000 contracts, which accounted for approximately 49% of the total trading volume, while their net position changed by only about 200 contracts.

We interpret this finding as follows: the lack of Opportunistic and Fundamental Trader, as well as Intermediaries, with whom HFTs typically trade, resulted in higher trading volume among HFTs, creating a hot potato effect. It is possible that during the period of high volatility, Opportunistic and Fundamental Traders were either unable or unwilling to efficiently submit orders. In the absence of their usual trading counterparties, HFTs were left to trade with other HFTs.

So in other words, it wasn’t the HFTs that left the market, it was the Opportunistic and Fundamental Traders.

Aggressiveness Imbalance is constructed as the difference between aggressive buy transactions minus aggressive sell transactions. Figure 8 shows the relationship between price and cumulative Aggressiveness Imbalance (aggressive buys – aggressive sells).

In addition, we calculate Aggressiveness Imbalance for each category of traders over one minute intervals. For illustrative purposes, the Aggressiveness Imbalance indicator for HFTs and Intermediaries are presented in Figures 9 and 10, respectively.
…
According, to Figures 9 and 10, visually, HFTs behave very differently during the Flash Crash compared to the Intermediaries. HFTs aggressively sold on the way down and aggressively bought on the way up. IN contrast, Intermediaries are about equally passive and aggressive both down and up.

As suggested above, this could simply be a result of HFT looking at the order book and taking a view, in addition to the considerations implied by their inventories.

I have added emphasis below to what I suggest is the central conclusion to be drawn from the Flash Crash.

We believe that the events on May 6 unfolded as follows. Financial markets, already tense over concerns about the European sovereign debt crisis, opened to news concerning the Greek government’s ability to service its sovereign debt. As a result, premiums rose for buying protection against default on sovereign debt securities of Greece and a number of other European countries. In addition, the S&P 500 volatility index (“VIX”) increased, and yields of ten-year Treasuries fell as investors engaged in a “flight to quality.” By midafternoon, the Dow Jones Industrial Average was down about 2.5%.

Sometime after 2:30 p.m., Fundamental Sellers began executing a large sell program. Typically, such a large sell program would not be executed at once, but rather spread out over time, perhaps over hours. The magnitude of the Fundamental Sellers’ trading program began to significantly outweigh the ability of Fundamental Buyers to absorb the selling pressure.

HFTs and Intermediaries were the likely buyers of the initial batch of sell orders from Fundamental Sellers, thus accumulating temporary long positions. Thus, during the early moments of this sell program’s execution, HFTs and Intermediaries provided liquidity to this sell order. However, just like market intermediaries in the days of floor trading, HFTs and Intermediaries had no desire to hold their positions over a long time horizon. A few minutes after they bought the first batch of contracts sold by Fundamental Sellers, HFTs aggressively sold contracts to reduce their inventories. As they sold contracts, HFTs were no longer providers of liquidity to the selling program. In fact, HFTs competed for liquidity with the selling program, further amplifying the price impact of this program.

Furthermore, total trading volume and trading volume of HFTs increased significantly minutes before and during the Flash Crash. Finally, as the price of the E-mini rapidly fell and many traders were unwilling or unable to submit orders, HFTs repeatedly bought and sold from one another, generating a “hot-potato” effect. Yet, Opportunistic Buyers, who may have realized significant profits from this large decrease in price, did not seem to be willing or able to provide ample buy-side liquidity. As a result, between 2:45:13 and 2:45:27, prices of the E-mini fell about 1.7%.

At 2:45:28, a 5 second trading pause was automatically activated in the E-mini. Opportunistic and Fundamental Buyers aggressively executed trades which led to a rapid recovery in prices. HFTs continued their strategy of rapidly buying and selling contracts, while about half of the Intermediaries closed their positions and got out of the market. In light of these events, a few fundamental questions arise. Why did it take so long for opportunistic buyers to enter the market and why did the price concessions had to be so large? It seems possible that some opportunistic buyers could not distinguish between macroeconomic fundamentals and market-specific liquidity events. It also seems possible that the opportunistic buyers have already accumulated a significant positive inventory earlier in the day as prices were steadily declining. Furthermore, it is possible that they could not quickly find opportunities to hedge additional positive inventory in other markets which also experienced significant volatility and higher latencies. An examination of these hypotheses requires data from all venues, products, and traders on the day of the Flash Crash.

I suggest that the reason this happened is because Opportunistic traders are simply not very smart people. They’re prep-school smiley-boys who got their jobs through Daddy’s connections and can make a fat living without the necessity of labour or thought. This will not change until performance genuinely becomes a desirable metric in the marketplace (as opposed to consumer-goods style branding) and regulators dispose of their fixation on turnover, which is simply a hangover from legitimate concern regarding commission-driven churning.

But a lot of it is simply ultimate investors’ desire for a good story. In general, investors want to hear “I bought it because Bernanke this and Buffet that and in-depth macro-economic analysis the other thing”, not “I bought it because somebody really, really wanted to sell it and it was outside its fair-value range compared to what I sold. I think. Maybe. This type of trade works about 60% of the time.”

That being said, however, I will also suggest that it is possible that the Opportunistic Buyers were dissuaded from entering the market through the quote-stuffing identified by Nanex, which has yet to be explained in a satisfactory manner.

And, I think, one piece of information we need is a look at the order book at the time – such as it was! It is possible that the selling by HFT was not due merely to a desire to square their books, but there was also the motivation supplied by a huge volume of resting sells relative to resting buys. Appendix IV.2 of the SEC Flash Crash Report gave order-book data for seven securites, but not the eMini contract. I republished two of the depth-charts (for Accenture) in my post regarding the report.

Based on our analysis, we believe that High Frequency Traders exhibit trading patterns consistent with market making. In doing so, they provide very short term liquidity to traders who demand it. This activity comprises a large percentage of total trading volume, but does not result in a significant accumulation of inventory. As a result, whether under normal market conditions or during periods of high volatility, High Frequency Traders are not willing to accumulate large positions or absorb large losses. Moreover, their contribution to higher trading volumes may be mistaken for liquidity by Fundamental Traders. Finally, when rebalancing their positions, High Frequency Traders may compete for liquidity and amplify price volatility. Consequently, we believe, that irrespective of technology, markets can become fragile when imbalances arise as a result of large traders seeking to buy or sell quantities larger than intermediaries are willing to temporarily hold, and simultaneously long-term suppliers of liquidity are not forthcoming even if significant price concessions are offered. We believe that technological innovation is critical for market development. However, as markets change, appropriate safeguards must be implemented to keep pace with trading practices enabled by advances in technology.

Update: This is probably as good a place as any to pass on some information about Stop-Loss orders, from Mary Schapiro’s September 7 speech to the Economic Club of New York, titled Strengthening Our Equity Market Structure:

To understand where individual investors are coming from, we must truly recognize the impact of severe price volatility on their interests: one example is the use and impact of stop loss orders on May 6. Stop loss orders are designed to help limit losses by selling a stock when it drops below a specified price, and are a safety tool used by many individual investors to limit losses.

The fundamental premise of these orders is to rely on the integrity of market prices to signal when the investor should sell a holding. On May 6, this reliance proved misplaced and the use of this tool backfired.

A staggering total of more than $2 billion in individual investor stop loss orders is estimated to have been triggered during the half hour between 2:30 and 3 p.m. on May 6. As a hypothetical illustration, if each of those orders were executed at a very conservative estimate of 10 percent less than the closing price, then those individual investors suffered losses of more than $200 million compared to the closing price on that day.

I disagree with her view of the fundamental premise of a stop-loss order. The purpose of a stop-loss order is to demonstrate that you’re an ignorant little turd who deserves to go bankrupt. If we consider an earlier section of Ms. Schapiro’s speech …:

Those who purchase stock in an initial public offering, for example, can have confidence that they will be able to sell that stock at a fair and efficient price in the secondary market when they need or want to. And of course, the values assigned to stocks in the secondary market play an important role in the ability of companies to raise additional funding.

Markets are powerful and they are the most efficient and effective tools for turning savings into capital and growth.

But, if the equity market structure breaks down — if it fails to provide the necessary and expected fairness, stability, and efficiency — investors and companies pull back, raising costs and reducing growth.

… we see that the fundamental premise of a market is to indicate a fair value of a listed company. I have no arguments with that. A stop-loss order says “I don’t want to sell this stock at $50. But if it goes down to $40, that’s the time I want to sell it.” – a sentiment completely divorced from the objective of fairly valuing a listed company.

Update: Despite all this – despite the complete lack of evidence that either HFT or algorithmic trading was in any way the root cause of the debacle – there are some who don’t want to be confused with facts:

“While I do not believe that the Flash Crash was the direct result of reckless misconduct in the futures market, I question what the CFTC could have done if the opposite were true. When does high frequency or algorithmic trading cross the line into being disruptive to our markets? And, along those same lines, who is responsible when technology goes awry? Do we treat rogue algorithms like rogue traders? These are the issues I hope to explore at our October 12th meeting,” stated Commissioner O’Malia.

Nothing wrong with the world that a few extra rules wouldn’t cure, eh Commisioner?

The Office of the Superintendent of Financial Institutions has released Revised Guidance for Companies that Determine Segregated Fund Guarantee Capital Requirements Using an Approved Model. This is the change we were told to expect in August.

The guts of the change appears to be distribution and correlation requirements for equity indices:

New minimum quantitative calibration criteria are mandated for the scenarios used to model the returns of the following total return equity indexes (henceforth referred to as “listed indexes”):

• TSX
• Canadian small cap equity, mid cap equity and specialty equity
• S&P 500
• US small cap equity, mid cap equity and specialty equity
• MSCI World Equity and MSCI EAFE

The actual investment return scenarios for each of the listed indexes used in the determination of total requirements must meet the criteria specified in the following table.
…
Furthermore, the arithmetic average of the actual investment return scenarios for each listed index over any one-year period (including the one-year period starting on the valuation date) cannot be greater than 10%. All of these criteria must be met for the scenarios of a listed index to be in accordance with the new minimum calibration criteria.

Modeled scenarios of TSX total return indexes must continue to satisfy the CIA calibration criteria at all percentiles over the five- and ten-year time horizons as published in the CIA’s March 2002 report, in addition to the criteria above. Modeled scenarios of S&P 500 total return indexes must satisfy the American Academy of Actuaries’ calibration criteria for equities [footnote] at all percentiles over the five-, ten- and twenty-year time horizons, in addition to the criteria above.

The scenarios used to model returns of an equity index that is not one of the listed indexes need not meet the same calibration criteria, but must still be consistent with the calibrated scenarios used to model the returns of the listed indexes.

Correlation: The scenarios used to model returns for different equity indexes should be positively correlated with one another. Unless it can be justified otherwise, the correlation between the returns generated for any two equity indexes (whether or not they are listed) should be at least 70%. If scenarios are generated using a model that distinguishes between positive and negative trend market phases (e.g. the regime-switching lognormal model with two regimes) then, unless it can be justified otherwise, the scenarios should be such that there is a very high probability that different equity indexes will be in the same market phase at the same time, and a very low probability that different equity indexes will be in different phases at the same time.

Footnote: For example, as published in the June 2005 document entitled “Recommended Approach for Setting Regulatory Risk-Based Capital Requirements for Variable Annuities and Similar Products”.

The table excised from the quotation above is:

These criteria equate, very approximately, to a mean expected return of 8% and a standard deviation of 20.5%. Interested readers can fiddle with the variables and log-normal distributions in the comments.

The American Academy of Actuaries’ Recommended Approach for Setting Regulatory Risk-Based Capital Requirements for Variable Annuities and Similar Products notes:

Short period distributions of historic equity returns typically show negative skewness, positive kurtosis (fat tails) with time varying volatility and increased volatility in bear markets. The measure of kurtosis declines when looking at returns over longer time horizons and successive application of a short-term model with finite higher moments will result in longer horizon returns that converge towards normality. Ideally the distribution of returns for a given model should reflect these characteristics. Of course, due to random sampling, not every scenario would show such attributes.

Unfortunately, at longer time horizons the small sample sizes of the historic data make it much more difficult to make credible inferences about the characteristics of the return distribution, especially in the tails. As such, the calibration criteria are derived from a model (fitted to historic S&P500 monthly returns) and not based solely on empirical observations. However, the calibration points are not strictly taken from one specific model for market returns; instead, they have been adjusted slightly to permit several well known and reasonable models (appropriately parameterized) to pass the criteria. Statistics for the observed data are offered as support for the recommendations.

… and they also provide a table:

where:

The ‘wealth factors’ are defined as gross accumulated values (i.e., before the deduction of fees and charges) with complete reinvestment of income and maturities, starting with a unit investment. These can be less than 1, with “1” meaning a zero return over the holding period.

To interpret the above values, consider the 5-year point of 0.72 at the α = 2.5th percentile. This value implies that there is a 2.5 percent probability of the accumulated value of a unit investment being less than 0.72 in 5-years time, ignoring fees and expenses and without knowing the initial state of the process (i.e., this is an unconditional probability). For left-tail calibration points (i.e., those quantiles less than 50%), lower factors after model calibration are required. For right-tail calibration points (quantiles above 50%), the model must produce higher factors.

To my astonishment, I was able to find a copy of CIA Document 202012 (sounds like an analysis of the Mayan calendar) via the World Bank. I will refer to it as Final Report of the CIA Task Force on Segregated Fund Investment Guarantees. Ths calibration is:

The new standard has a significantly nastier left-tail than the prior standards:

However, in the absence of information regarding the insurers’ models together with detailed data, it is impossible to determine how capital requirements will be affected by the change. This could be a welcome first step towards rationalizing seg fund capital requirements; it could also be window-dressing that OSFI knows will have no effect but makes them look tough. As suggested by Desjardins, we will simply have to wait for commentary in the coming batch of quarterly reports. It will be noted that GWO, SLF and MFC have all warned about the potential for adverse change.

For myself, I am disappointed that while OSFI is addressing intricacies of model calibration, it is not mandating additional disclosures or reviewing their highly politicized cover-up from the Fall of 2008. It became quite apparent during the Panic of 2007 that the currently mandated disclosure of the effect of a 10% decline in equity prices is nowhere near good enough to allow investors to take an informed view on the adequacy of capitalization. Would it really be so difficult and so invasive to mandate a table showing the effects on capital and comprehensive income of the effects of 10%, 20% and 30% declines?