PrefBlog

Forward interest rates have emerged as a bone of contention in the analysis of the proposed TransAlta preferred share exchange offer, so as part of the preparation for my promised weekend post, I’ll post a few links to some papers that illustrate why the Expectations Hypothesis cannot be used as a predictor.

Joseph R. Dziwura and Eric M. Green wrote a paper in 1996 for the New York Fed titled Interest Rate Expectations and the Shape of the Yield Curve:

According to the rational expectations hypothesis of the term structure (REHTS) long term rates should reflect market expectations for the average level of future short-term rates. The purpose of this paper is to examine whether REHTS assumptions conform to the term structure of outstanding U. S. Treasury securities from 1973 to 1995, and to examine the behavior of term premiums and to what extent they influence the shape of the forward curve. REHTS assumptions are re-examined using familiar regression tests to determine the forecast power of forward rates for subsequent spot rates, and we use excess holding period returns, the extra return earned on a security sold prior to maturity, as the ex poste measurement of the term premium. We find that forward rates explain only some of the variance in future spot rates, the forecast power of forward rates varies with maturity, and the term premia is time-varying. We decompose the forward rate into the current spot rate, a term premium, and an expected interest rate change, where the term premium is the sum of a risk premium and a convexity premium. We find that on average term premiums have contributed more to the shape of the forward curve than have expected rate changes, and find that expected and past interest rate volatility, as well as the slope of the yield curve, may provide information on the size of expected term premiums.

Another paper was by Massimo Guidolin and Daniel L. Thornton of the St. Louis Fed, titled Predictions of Short-Term Rates and the Expectations
Hypothesis:

Despite its role in monetary policy and finance, the expectations hypothesis (EH) of the term structure of interest rates has received virtually no empirical support. The empirical failure of the EH has been attributed to a variety of econometric biases associated with the single-equation models most often used to test it; however, none of these explanations appears to account for the massives [sic] failure reported in the literature. We note that traditional tests of the EH are based on two assumptions—the EH per se and an assumption about the expectations generating process (EGP) for the short-term rate. Arguing that convential [sic] tests of the EH could reject it because the EGP embedded in these tests is significantly at odds with the true EGP, we investigate this possibility by analyzing the out-of-sample predictive prefromance [sic] of several models for predicting interest rates and a model that assumes the EH holds. Using standard methods that take into account parameter uncertainty, the null hypothesis of equal predictive accuracy of each models relative to the random walk alternative is never rejected.

One may hope their work is more reliable than their proof-reading!

Intuitive Analytics is a financial software firm which has published a blog-post by Peter Orr titled 50 Years of UST Yields – How Well do Forwards Predict? that was exactly what I was looking for:

As we’ve written on these pages before, forecasting is a necessary evil in finance. It’s uncertain by nature and of course the longer the horizon, the more difficult the job. The theory that forward rates are good predictors of future realized rates is called the expectations hypothesis and as one MIT professor put it, “If the attractiveness of an economic hypothesis is measured by the number of papers which statistically reject it, the expectations theory of the term structure is a knockout.”

For fun (and to dust off my fast fading coding skills) I went back and looked at how US Treasury implied forward 10Y rates have done in forecasting realized 10Y UST yields from July, 1959 to the present. We used first of month data for 3, 6 and 12 month Tbills as zero rates (making the appropriate daycount adjustments of course) and then 2, 3, 5, 7, 10, 20, and 30-year UST coupon instruments for our implied 10Y forward calculations. And this is what we get…

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The red line is the actual 10Y yield over the period and the “hair” is the implied 10Y par yield 1, 2, 3, and 5 years forward. The way to read this then is to look at how often the hair tracks with the actual realization of the 10Y yields as shown by the red line. In general, during this single big rate cycle we’ve seen over the last 50 years, forward rates have badly underpredicted when rates were going up (note the implied decreasing 10Y forwards during the 70s) and then overpredicted over the last 30 or so years as rates have fallen. How badly do forwards do? Well over this 50 year span, and this holds over most subperiods as well, you’d be better off as a forecaster just assuming today’s yield curve stays constant i.e. a perfectly random walk.

A Bloomberg piece titled How to Lose $667 Million in Bond Trades Without Trying discusses why stupid and lazy portfolio managers underperform:

Bond investors can waste a lot of money and not even know it.

They lost about $667 million in the year ended March 31 by paying higher prices for corporate bonds that were available at lower prices elsewhere, according to September research by Larry Harris, a business professor at the University of Southern California.

In most of the deals the investors simply did not know that the lower prices existed because they rely on human traders to tell them the value of bonds at any given moment before they make a trade. (Not to mention the salaries they need to pay those brokers to work the phones to find out who holds what and who might want to sell.)

The author, Lisa Abramowicz, mentions regulatory efforts to destroy the corporate bond market:

So far, the Securities and Exchange Commission is only encouraging big bond firms to use electronic marketplaces more frequently so that investors have an easier way to see market prices in real time.

But if that doesn’t work, regulators may take more invasive measures to streamline the playing field and make it cheaper to do business in the $8 trillion market for U.S. company bonds.

Regardless of what the SEC might do, it makes sense for Wall Street banks to work together to find a more efficient way to trade bonds because it may be the best for their bottom lines.

All this is interesting in light of the pending crippling of the Canadian corporate bond market discussed last week. Ms. Abramowicz buttresses her views – and regulators are virtually certain to follow her – by referencing a recent paper by Lawrence Harris of the University of Southern California titled Transaction Costs, Trade Throughs, and Riskless Principal Trading in Corporate Bond Markets:

This study analyzes the costs of trading bonds using previously unexamined quotations data consolidated across several electronic bond trading venues. Much bond market trading is now electronic, but the benefits largely accrue to dealers because their customers often do not trade at the best available prices. The trade through rate is 43%; the riskless principal trade (RPT) rate is above 42%; and 41% of customer trade throughs appear to be RPTs. Average customer transaction costs are 85 bp for retail-size trades and 52 bp for larger trades. Estimated total transaction costs for the year ended March 2015 are above $26 billion, of which about $0.5 billion is due to trade-through value while markups on customer RPTs transfer $0.7M to dealers. Small changes in bond market structure could substantially improve bond market quality.

The problem, as is usual with this type of paper, lies in the assumption of the very first sentence of the introduction:

Brokers are supposed to obtain the best available prices for their clients.

In virtually all cases in the bond market, the dealer is acting as principal. It is not just his privilege, but his job to leave his counterparties naked, hungry and freezing. This fundamental misstatement of the facts of the transaction persists throughout the paper. Particularly disgusting is the claim:

Although this transaction might not strictly be a trade through (it would not be if the broker-dealer exhausts all the size at the quoted price), the broker-dealer clearly is front-running the customer order, though not necessarily illegally.

Front-running is a breach of trust and can occur only when the intermediary is an agent of the trade initiator, therefore having a fiduciary responsibility to the initiator. The concept does not apply to trades executed as principal.

Another problem is with his definition of “transaction costs”:

I estimate the cost of trading for the side that initiated the trade by first identifying that side, and then by comparing the trade price to the quote midpoint price.

This definition makes dealer markups appear worse than they actually are.

Markups and commissions both contribute to transaction costs. Markups are incorporated in the price whereas commissions are tacked onto the price. Both allow brokers to recover the costs of arranging trades, and presumably all other costs of providing trading services to their clients.
…
Markups differ from commissions because broker-dealers generally do not fully disclose markups to their clients.

They also differ from commissions in that commissions apply to agency trades while markups apply to principal trading.

Even when broker-dealers fully disclose the nature of their relationships with their clients—that they are acting as principle [sic] and not as agent—many clients may not recognize the distinction and its implications. The distinction can be difficult to recognize when the broker-dealer sometimes acts as broker and sometimes as dealer, a process commonly called dual trading.

If clients do not recognize the distinction then they should not be trading. Traders in the institutional market will almost always be professionals and will have passed numerous proficiency tests set by the regulators. If retail traders want to play with the big boys and trade individual bonds themselves, they should recognize that step one is learning the rules of the game.

I will admit to long-term confusion over this whole concept of “fairness” and “equal access” as used by the regulators and rabble-rousers. Why are these things considered important points when discussing market structure? Are hospitals required to make operating rooms fairly accessible to DIY brain surgeons?

In his literature review, he (not surprisingly) refers to a number of papers I have discussed on PrefBlog before:

Biais and Green (2007) show that exchange-listed bond trading was quite liquid in municipal bonds before the late 1920s and in corporate bonds before the mid-1940s, and that transaction costs then were lower than they are now. The proliferation of electronic bond trading systems has the potential to substantially lower bond transaction costs, presumably to levels lower than Biais and Green document given the well-known economic efficiencies associated with electronic trading. Harris (2015) provides a survey of these efficiencies.

Well, that’s an inflammatory paragraph, isn’t it? But I reviewed Biais and Green in the post Exchange Traded Bonds? (emphasis added):

The third possibility [for the collapse of the exchange market] is due to the interaction of groups with differing objectives in a heterogeneous market:

Different equilibria will vary in terms of their attractiveness for different categories of market participants. Intermediaries benefit when liquidity concentrates in venues where they earn rents, such as opaque and fragmented markets. For reasons we will show were quite evident to observers at the time, large institutional investors fare better than retail investors in a dealership market. This was especially true on the NYSE until 1975, because commissions were regulated by the Constitution of the Exchange, while intermediary compensation was fully negotiable on the OTC market. We find that liquidity migrated from the exchange to the OTC market at times when institutional investors and dealers became more important relative to retail investors. As institutions and dealers became more prevalent in bond trading, they tipped the balance in favor of the over-the-counter markets.

Unlike many writers on this topic, Biais and Green show some understanding of the competing interests that determine market microstructure:

More Biais & Green:

Furthermore, the professionalized management and relatively frequent presence in the market of institutions makes transparency less important to them than to less sophisticated small investors who trade infrequently. The repeated interaction that dealers and institutions have with each other renders them less vulnerable to the opportunities which a lack of transparency affords other participants to profit at their expense on a one-time basis. Smaller institutions and individuals, for the opposite reasons, will tend to fare better in an exchange-based trading regime. Indeed, the theoretical model of Bernhardt et al (2005) shows that, in a dealer market, large institutions will trade more frequently and in larger amounts than retail investors, and incur lower transactions costs.(footnote)

Footnote: Bernhardt et al (2005) also offer an interesting empirical illustration of these effects in the case of the London Stock Exchange.

…there was a dramatic increase in institutional ownership in corporate bonds between 1940 and 1960. In the 1940s the weight and importance of institutional investors in the bond market grew tremendously. These investors came to amount for the majority of the trading activity in the bond market. Naturally, they chose to direct their trades to the OTC market, where they could effectively exploit their bargaining power, without being hindered by reporting and price priority constraints, and where they could avoid the regulated commissions which prevailed on the Exchange. Thus, the liquidity of the corporate bond market migrated to the dealer market.

Having cited Biais and Green, we may assume that Dr. Harris is familiar with these details, but he has chosen to ignore them in his efforts to increase market regulation.

One important point that goes against the thrust of the paper is the fact that:

The quotes used in this study are not generally available to the public, though they are available to IB’s customers in real-time.
…
Zitzewitz (2010) identifies RPTs, which he calls “trade pairing,” in the TRACE data using similar methods to those presented in this study. He finds that RPTs are very common (46% of trades under $100,000) and that they are mostly small trades. These results are similar to those obtained in this study.
…
Interactive Brokers serves as an agency-only broker for its clients. To facilitate their bond trades, IB collects pre-trade quotes and indications from several electronic trading platforms that offer automated execution services. These bond market centers include BondDesk, BONDLARGE, Knight BondPoint, NYSE Arca Bonds, and Tradeweb, and a few other centers that specialize only in municipal bonds or treasuries.13 None of these platforms provides universal coverage of all bonds that trade in the U.S. corporate bond markets. IB presents the quoted prices and sizes to its customers in real-time just as it and other brokers do for stocks, options, and futures.
…
IB reported to me that during the week ended September 10, 2015, they obtained complete fills for about 83% of its customers’ marketable orders and that they did not receive any cancellations after filling. This statistic indicates that a substantial fraction of the quoted and indicated prices that IB records are actionable.

The fact that all these quotes are available to anybody who signs up with Interactive Brokers shows that no regulatory changes are necessary. Anybody who wants to access these electronic quotes can do so. I see no problem here.

Dr. Harris does acknowledge the differing sizes of the retail and institutional trades:

Practitioners and academics often label trades with par values of $100,000 or less as retail-size trades, and larger trades as institutional-size trades. Many trades are relatively small retail-size trades. During the Primary Period, 67.3% of the trades in the full sample are retail-size trades (Table 9). Retail-size trades represent a slightly larger fraction (69.7%) in the subset sample. The median par value size of the retail-size trades is $18,000 in both samples.

The median trade size for institutional-size trades is $500,000 in both samples. The percentages of trades reported with indicators for par value sizes of $1,000,000 (speculative grade bonds) and $5,000,000 (investment grade bonds) or more are 4.6% and 1.3% in the full sample and about the same in the subset sample. Assuming that the actual size of these trades is equal to their minimum possible sizes of $1,000,000 and $5,000,000, the truncated mean par value trade size for all institutional-size trades is $908K and $953K in the two samples.
…
Among trades of a given size class, interdealer trades represent the smallest percentage of the largest class—those trades marked 5MM+ (13.1%). Many of these large trades probably are agency trades in which broker-dealers, acting as brokers, intermediate trades between customer buyers and sellers. In contrast, interdealer trades account for 40.8% of retail-size trades. The results in Section 7 show that many of these trades are riskless principal trades.

Of particular interest is the discussion of Table 19:

Most (82.3%) of the customer trade throughs are retail-size trades (Table 19). The mean price improvement for these trades is -93 bp, nearly a 1% markup. These markups seem quite large for relatively easy-to-arrange trades that can be arranged electronically. The total trade-through value for the retail trades is $74M. The mean price dis-improvement is smaller for institutional trades that traded through. Although these institutional trades are much larger, the total trade-through value is relatively small because these trades outsize the quotes. The average ratio of quote size to trade size is only 1.2% for institutional size trades in comparison to 28% for retail-size trades.
….
Standing quote to trade size ratio is the ratio of the opposing side quote size to the trade size.

So if I’m reading this correctly, the average size of a “trade-through” trade is four times the size of the quote, even when we restrict the sampling to retail sized trades (which average $18M, remember!). So these are itsy-bitsy little quotes and the “markups” calculated with respect to trade-through value would seem to be more of a market-impact cost than an extortionate dealer mark-up.

The number reported in the Bloomberg article comes from the introduction:

I find that average transaction costs that customers incur when trading range between 84.5 bp for retail size trades (under $100,000 in par value) and 52.1 bp for larger trades. These costs are several times larger than costs for similar size trades in equity markets. Trades occurring in markets with two-sided quotes that have stood at least two seconds trade through 46.8% of those markets; 40.8% of these trade throughs appear to be riskless principal transactions—trades for which the dealer has no inventory risk exposure usually because the dealer simultaneously offsets a trade with a customer with an interdealer trade. RPT transactions account for more than 41.7% of all trades. Total transaction costs borne by customers in U.S. corporate bond markets for the year ended March 31, 2015 are at least $26B, of which about $0.5B is due to trade-through value. During this period, markups on customer RPTs transferred $667M to dealers.

OK, so now we get to the good part, which is Dr. Harris’ Section 10.1, Public Policy Recommendations:

Many reasons explain why transaction costs are higher in bond markets than in stock markets. The most common explanation is that so many different bond issues make matching buyers to sellers difficult. This explanation certainly is true for the inactively traded bonds, but many bonds trade as actively as do small- and some mid-cap stocks, and they would undoubtedly trade much more actively if transaction costs were lower. Customers would benefit if the 850 bonds that are quoted nearly continuously were traded in market structures more similar to equity markets than the current OTC markets.

The problem with this paragraph is that much of it has not been supported by prior argument. Which stocks trade about as actively as which bonds, and what is the bid-offer spread on these stocks? How much size is there in these markets? Let us turn briefly to a speech by SEC Commissioner Luis A. Aguilar titled The Need for Greater Secondary Market Liquidity for Small Businesses:

In addition, it’s been reported that venture exchanges—both here and abroad—have suffered from low liquidity and, at times, high volatility.[19] This means investors could lose a lot of money quickly, and could have trouble selling their shares in a downturn. The Commission should attempt to determine the underlying causes of these problems and how best to address them. In this regard, we may need to ask some difficult questions. For example, should venture exchanges be structured as dealer markets, rather than auction markets? Also, could venture exchanges enhance liquidity through batch auctions, rather than continuous trading? How can the Commission, consistent with the Exchange Act, encourage traders to execute transactions on venture exchanges, rather than in off-exchange venues?[20] And, finally, could larger ticker sizes enhance liquidity by encouraging market maker activity and fostering research coverage? In this regard, the Commission’s proposed tick size pilot program[21] may offer valuable insights on the role of tick sizes in ensuring an active secondary market for smaller companies.

So for at least some of these smaller issues there are musings about possibly moving the other way – from exchange trading to a dealer market! We can also look at the fascinating Table 2 from the SEC’s report A characterization of market quality for small capitalization US equities:

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Given that most bond issues will be smaller than the largest equities on this table and that bonds trade less intensively than equities, it is clear that that a Dr. Harris needs to support his recommendation in considerably more detail than he has in this paper. As an aside, I think he needs to explain his tables a little better! I can’t figure out what 850 bonds he’s talking about in his Table 11! And when he’s talking about “size”, I don’t know if he’s talking about dollar value, multiples of 100, or multiples of 1000 … I suspect he means multiples of 100, but it’s certainly not very clear!

Another recommendation is:

The SEC also should consider enacting a trade through rule for bonds similar to that in Reg NMS (for equities) that would require that broker-dealers access electronically available orders when filling orders for their clients before trading through. The SEC may want to do so before a class-action lawsuit based on common law agency principles effectively imposes a Manning Rule for bonds similar to FINRA Rule 5320 (Prohibition Against Trading Ahead of Customer Orders) for equities

Well, I’m not going to pretend to know anything about the Manning Rule, or the chances that a class-action lawsuit might have! However, I will point out that while this might well apply to dealers acting as brokers, it does not apply to dealers acting as principal.

Dr. Harris suggests:

At the minimum, FINRA or the SEC should require that brokers disclose their markup rates on RPTs on a pre-trade basis as they do with their commission rates.34 Since the two rates are perfect substitutes for each other, investors would be less confused if one rate were simply set to zero. This brokerage pricing standard would ensure that brokers would compete on the same basis for order flow. Since customers understand commissions much better than they understand markups, simply banning markups on RPTs would be best. Such a ban would have no effect on competition because dealers could always raise their commissions to compensate for their lost markups. Their customers then would know the full cost of the intermediation services that they obtain from their brokers.

Readers who have gotten this far will know that I am going to object to the assertion that commissions and markups are equivalent – the former applies to brokerage and the latter to principals. I will also note that while full-service commissions are highly variable and considered top-secret, 1% for equity trades is a good place to start. The comparisons here appear to be with equity transactions via a discount brokerage, which is a different kettle of fish.

Update, 2015-9-27: I note from Rob Carrick’s fee project:

Two ways of paying for investing advice aren’t covered in depth by our calculator. One is the transactional model, where you pay commissions to trade securities. The investment industry consulting firm PriceMetrix says the average commission last year was 0.99 per cent of the cost of the trade.

Now back to Dr. Harris:

Finally, a rule that would require brokers to post limit orders of willing customers to venues (order display facilities) that widely disseminate these prices would help prevent many trade throughs. Many trade throughs undoubtedly happen simply because traders are unaware of better prices. Such a rule likely would substantially increase such offers of liquidity, especially if implemented in conjunction with a trade-through rule. These order display facilities could be existing exchanges and ATSs, or new ones formed for this purpose.

This follows from the idea that bond dealers act as brokers and the marketplace is an exchange.

If the SEC fails to take these actions, and if no class-action suit is successful, the markets will continue to improve as innovators such as IB continue to capture order flow by creating their own NBBOs. But it may be many years before most customers become sophisticated enough to demand these facilities from their brokers, if they ever do, and some brokers may never offer these facilities, either because their customers are not well enough informed or because their customers suffer various agency problems, including the problems associated with payments for order flow.

It’s the profitability of ‘innovators such as IB’ that makes the debate unnecessary. Let competition reign – particularly since for small investors the real competition is ETFs and funds.

With respect to trading, bonds are securities just like equities, only less risky. U.S. corporate and municipal bonds presently trade differently for historic reasons. They need not trade differently in the future. U.S. Treasury bonds and corporate bonds in several well developed countries trade in substantially more transparent markets that do corporate and municipal bonds in the U.S. presently do. The quality of these markets shows that opaque markets are not necessary for fixed income securities.

This paragraph is not supported by the text and ignores the work that has been done on market microstructure as it relates to market-depth and transparency.

Finally, note that the creation of more liquid markets will benefit issuers as well as customers. Investors are more willing to buy securities in the primary markets when they expect that they can sell them easily at low cost in the secondary markets. Low secondary trading costs thus imply higher bond IPO values, and lower corporate funding costs.

While it’s nice to see a nod to the interests of issuers, the evidence actually goes the other way. In the Bessembinder paper I reviewed in the post TRACE and Corporate Bond Market Transparency, it is shown that increased transparency caused a migration to less transparent “144a” structures, which are private placements:

One way to circumvent TRACE, which applies to publicly-issued bonds, is for a firm to issue privately placed bonds (sometimes referred to as Rule 144a securities, for the section of the Securities Act of 1933 that provides exemption from registration requirements). … In 2001, before TRACE, “144a for life” bonds were 7.3 percent of dollar volume and 9.6 percent of issues. The percentage of dollar volume in “144a for life” bonds jumped to 27.8 percent in 2003, the first full year after TRACE initiation, and grew to 39.8 percent in 2004, before declining to 16.9 percent in 2006.…
Also consistent with a shift towards alternative asset classes, the credit default swap market experienced phenomenal growth in recent years relative to bonds. Table 6 reports on outstanding notional principal in these credit default swaps, which grew from $919 billion in 2001 to $34.4 trillion in 2006. One dealer suggested to us that, prior to TRACE introduction, ten times as much capital was allocated to corporate bond trading than to credit default swaps, but that the ratio has now been reversed.
…
To the extent that the shift to privately placed bonds and bank loans was initiated by corporate borrowers, and in response to TRACE, it suggests that the net costs of TRACE may exceed the benefits….

All in all, it’s an interesting paper and a good reminder that corporate bond trades should ensure they have independent access to electronic marketplaces … but note that if a dealer is sitting on a stack of inventory he’s willing to sell at 102.00, then sees all the offers below 102.00 disappear, he’s probably going to raise his price! But the data needs to be presented with more explanation in the tables and the advocacy should be taken out and used elsewhere; in addition, more account needs to be taken of previous work on market microstructure and the interests of issuers which, I assert, must be paramount when contemplating changes to the system.

Assiduous Readers will remember that I was recently quoted as saying:

“It is simply not sustainable for a five-year Canada to trade below inflation forever,” he said. “That simply cannot go on.”

I have been challenged to substantiate this assertion and my immediate response was:

You are correct – it’s because the real yield is not just negative, but significantly negative.

Some bond investors have to put up with this kind of thing; banks, for instance, are required to hold a large quantity of government bonds. Central banks will, in general, place a very high premium on liquidity, since when they want to trade they want do it in size and they also place a high premium on the ability to transact at the height of a crisis.

But the marginal investor will eventually get tired of losing money on a real basis while tying up their money for five years. In addition, the marginal borrower will step up borrowing because he’s getting paid – in real terms – to do so. We are already seeing significant economic distortions resulting from these marginal borrowers because instead of buying productive assets, they’re buying houses in Toronto and Vancouver … eventually, all the stresses will be relieved, and there will be a positive real yield on five year Canadas … either because government yields go up or because we enter a period of deflation.

These will be familiar themes to Assiduous Readers; the requirement for banks to own an increasing number of sovereign bonds for highly politicized (and economically illiterate and fiscally expedient) reasons was discussed on September 4, for instance.

While poking around for more authoritative and crushing retorts, I came across Remarks by Hervé Hannoun, Deputy General Manager, Bank for International Settlements, at the Eurofi High-Level Seminar, Riga, 22 April 2015, titled Ultra-low or negative interest rates: what they mean for financial stability and growth. He was mainly concerned with the European situation:

When policy interest rates came down to almost zero and central bank balance sheets expanded due to large-scale market interventions in the wake of the Global Financial Crisis, the consensus was that this unconventional monetary policy (UMP) would be temporary. More than six years later, the prospect of normalisation seems remote in most advanced economies. Indeed, most of continental Europe (the euro zone, Denmark, Sweden and Switzerland) have moved towards a much more extreme form of UMP by introducing negative policy interest rates, and/or negative central bank deposit rates. Together with forward guidance and large scale asset purchases, such measures have created an unprecedented situation where nominal interest rates in a number of European countries are negative across a range of maturities in the benchmark yield curve, from overnight out to five years.
…
There is no precedent in economic history for negative nominal interest rates, even during the Great Depression in the United States.[Footnote] Not even Keynes, who coined the terrifying metaphor of the “euthanasia of the rentiers”, ever contemplated negative nominal interest rates. An experiment is under way in continental Europe to test the “boundaries of the unthinkable” in monetary policy.

[Footnote reads] In the wake of the Great Depression, US short-term nominal interest rates fell to near-zero levels in 1932 but they never turned negative.

He shouldn’t be quite so absolute in his footnotes! There were brief episodes of negative US bill rates in 2013:

Treasury bills that mature as soon as November traded below zero today [2013-9-26], with the bill maturing on Nov. 29 having a negative 0.005 percent rate at 2:02 p.m. New York time. The three-month bill rate was negative 0.0051 percent, compared to 0.0152 percent yesterday. Treasury bills that mature on Oct. 24 were at a rate of 0.038 percent, up from 0.018 percent yesterday.

and in 2014:

A scramble for safe, short-term debt left some investors on Tuesday paying for the privilege of lending to the U.S. government.

The demand, which intensified following the Federal Reserve’s decision this month to curb a popular overnight-lending program, pushed up bond prices and drove down yields. The yield on the U.S. Treasury bill maturing on Oct. 2 traded at negative-0.01%, according to Tradeweb, the first negative yield in eight months. Yields on other Treasury bills due in three months or less hovered around zero.

Short-term debt trading at negative yields was essentially unheard of before the 2008 financial crisis. But since then, the condition has cropped up at times of market stress, reflecting extraordinarily expansive central-bank policy and anemic growth in much of the world. Yields on some U.S. bills traded below zero at the end of each of the past three years amid strong demand for liquid assets, according to analysts.

… and shortly after the speech:

For all the anxiety over the global selloff in bonds, the big worry in money markets is the havoc being created by a dearth of U.S. Treasury bills.

The magnitude of the problem was on display last week, when not even the Treasury Department’s surprise announcement to boost sales could do much to lift bill rates. Over the past two weeks, some of those rates have turned negative, reaching levels last seen during the financial crisis.

With supply at multi-decade lows, investors are signaling alarm as regulations intended to shore up banks and prevent a run on money-market funds exacerbate the bill shortfall. JPMorgan Chase & Co. expects an extra $900 billion of demand for government securities during the next 18 months, putting pressure on a sizable chunk of the $1.4 trillion bill market.
…
The mismatch between supply and demand has been so acute that four-week bill rates fell to minus 0.0304 percent on April 29, the lowest on a closing basis since December 2008. Yields on three-month bills also turned negative. The Treasury responded by saying at its quarterly refunding announcement on May 6 it would increase issuance to meet growing demand.

… and in the US in the Great Depression:

1The interest rate on Treasury bills would tend to not fall below zero if currency incurs no taxes, storage costs, or insurance costs. Absent such costs, if the Treasury bill rate were to be negative the holders of Treasury bills would prefer to hold currency because currency has the advantage of being a more liquid asset and its implicit interest rate of zero would be greater than the negative rate on Treasury bills. Holders of Treasury bills would sell them, drive down their price, and increase their interest rate until the interest rate reaches at least zero. For simplicity, we assume the lower bound on short-term interest rates is zero, even though nominal yields dropped slightly below zero in the United States in the Great Depression (as discussed in footnote 15) and in Japan recently (as discussed in footnote 22).
…
15See Federal Reserve Board (1943, p. 462). Two reasons for this phenomenon, perhaps responsible for a few basis points of the negative yield on Treasury bills, are as follows: First, Treasury bills were exempt from personal property taxes in some states, while cash was not. Thus, the after-tax rate of return on cash holdings was negative in some states. Second, Treasury securities were required as collateral for a bank to hold U.S. government deposits, so the total return, net of the collateral benefits, could have been zero or positive for banks. During this period, negative yields were also reported on Treasury bonds with up to two years maturity, owing to a valuable exchange privilege implicit in holding the securities. Cecchetti (1988) provides a detailed explanation of this phenomenon and shows that once the value of this exchange privilege is accounted for, yield estimates on those securities become positive. These factors allowing for negative pecuniary yields emphasize that institutional considerations such as these make it dicult to be precise about the actual lower bound for nominal interest rates.

But let’s be fair … perhaps Mr. Hannoun meant “short-term bonds” when he said “short-term” and perhaps he is not so much of a pedant as to specify “after accounting for special privileges”. Still, he could quite easily have said “rare and transient”.

At any rate, Mr. Hannoun first reviewed the effects of low interest rates on growth:

In essence, the monetary stimulus aims to lift short-term growth via five main channels: by boosting credit to the real economy (the credit channel), by lifting asset prices (the asset valuation channel), by forcing investors away from safe assets towards riskier ones (the portfolio balance and risktaking channels), by lowering the exchange rate (the exchange rate channel) and by attempting to nudge inflation up towards objectives with a view to warding off a so-called deflationary spiral (the reflation channel).

It’s all good stuff, but one thing worth highlighting is his discussion of the portfolio balance and risk-taking channel:

Advocates of UMP argue that these policies will encourage investors to shift out of government bonds and into riskier assets. This is the portfolio balance channel. Indeed, the search for yield engineered by zero or negative nominal policy interest rates has fuelled more risk-taking, leading to a convergence between the returns of risky assets and those of low-risk assets, as currently seen in the euro zone’s sovereign credit spreads. These appear to be re-enacting the extreme compression of sovereign spreads, invariant to differences in credit quality, that occurred before the crisis (Graph 2). If, as many would agree, the euro zone’s sovereign risks were mispriced then, we now seem to be heading back to that situation. The European Commission’s prudential policy of applying a uniform zero risk weight to all sovereigns in EU bank regulation strengthens this effect. The problem here is that risk weights are not differentiated according to credit quality, contrary to the Basel II requirements.
…
In reaction to negative yields in the short- and medium-term segment of the euro zone sovereign yield curves, investors are piling up interest rate risk by investing in long-dated securities at very low yields. And, in fact, the effective duration of euro-denominated debt has risen significantly since the second half of 2014 (Graph 3). As a result, an eventual normalisation of long-term yields would inflict significant and widespread losses on investors, with potentially serious consequences for financial and economic stability.

This makes it a matter of urgency to address the gap in global regulation on interest rate risk in the banking book. Pillar 1 currently does not provide for any capital charge against this risk, an anomaly that will, we hope, soon be corrected by the Basel Committee on Banking Supervision (BCBS). As monetary policymakers, central banks in Europe have contributed heavily to the build-up of duration risk by bringing nominal yields in the two- to five-year part of the yield curve down to near zero and even negative levels. As supervisors or systemic risk managers, they should ensure that commercial banks are allocating enough capital to cover the interest rate risk they are accumulating. All this puts a premium on introducing a Pillar 1 charge on interest rate risk in the banking book as soon as possible.

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However, I’m more interested in the section titled Longer-term unintended consequences of ultra-low or negative
interest rates (emphasis added):

From a longer-term perspective, there are five main risks that may make the prolongation of ultra-low or negative nominal interest rates counterproductive. These can be summarised as: disincentive, distraction, distortion, disruption and disillusion.
…
Bond market prices in the euro zone may no longer adequately reflect the risk inherent in record high debt levels. At the same time, equity prices are artificially inflated as investors are forced into increasingly risky assets. All this involves the risk of a major correction when confidence in inflated valuations is lost. The question is not whether this will happen again, but when. Of course, nobody can say when the next “Minsky moment”, a generalised loss of confidence in artificially inflated valuations, will occur. Yet there is no doubt that the probability and severity of another financial crisis is increased by the prolongation of ultra-low or negative rates.

Advocates of ultra-low or negative interest rates argue that macroprudential tools can be used to offset/mitigate the financial risks and distortions resulting from ultra-easy monetary policy.
…
The risk of disillusion also looms large for households in the new world of low returns. Ultra-low or negative interest rates will add to their worries by making it difficult for them to build up enough retirement savings. Thus households are more likely to increase their savings rate than to reduce it.

Negative rates on deposited savings – effectively a form of taxation – will feed the debate on the
“financial repression” of savers.

True, some households stand to gain from low mortgage rates, but this benefit will accrue only to those who can afford to buy a house. Moreover, the positive effect of low mortgage loan rates is largely offset by the increase in property prices fuelled by ultra-low interest rates.

There is also the question of inequality. Most households will lose from the dwindling returns on their savings without gaining anything from asset price inflation. They are not sophisticated asset managers who can realise capital gains in financial markets when long-term yields fall, and they will be affected by the low returns on their savings.

It will be noted that the word “macroprudential” is basically the new cool way to say “credit rationing”:

In the UK the Bank of England has been flashing an amber light for months about the complacency shown by low market volatility, but in house-price obsessed Britain, mortgage excess is the focus of its worry. Last month it became the first of the major central banks to set out to try to control credit using non-monetary tools: in the jargon, “macroprudential measures”. Ms Yellen has been highlighting macropru as the first line of defence against bubbles for a while.

The problem is simple. Central bankers want money to lubricate the real economy, not to flow into pointless leverage of existing assets. Higher rates could reduce the incentives to leverage, but at the cost of damage to the real economy. Their solution is to set up barriers inside the banks to direct the flow.
…
As financial historian and CLSA consultant Russell Napier pointed out recently, credit rationing was a disaster in the 1970s. The theory relies on markets being so bad at allocating resources that the job is better done by a handful of men and women at central banks and regulators.

We’ve seen some of this credit rationing in Canada, of course, in the tightening of mortgage requirements by the CMHC and bank regulators; wildly cheered on by established Canadians who inherited their house from Mommy and Daddy and don’t like the idea that immigrants and the working class might get a shot at ownership.

Mr. Hannoun concludes:

The policy of prolonged ultra-low, or negative, interest rates relies on transmission channels with uncertain effectiveness and potentially serious unintended consequences. For central banks, such policies raise the risk of financial dominance, exchange rate dominance and fiscal dominance – that is, the danger that monetary policy becomes subordinated to the demands of propping up financial markets, massaging the exchange rate downwards, and keeping public refinancing costs low in the face of unprecedented public debt burdens. These risks have been present before,13 but never so acutely as today.

Meanwhile, financial markets continue to set the stage for policy deliberations by fuelling expectations for continued, and additional, monetary accommodation. Behind the enthusiasm of market participants for extreme monetary policy, of course, lurks the fear that asset prices might collapse when the music of monetary easing stops.

So, yeah, this speech belongs in a list of justifications for my statement!

On April 23, the OSC announced:

Today the Ontario Securities Commission (OSC) published The Canadian Fixed Income Market Report and OSC Staff Notice 21-708 – OSC Staff Report on the Canadian Fixed Income Market and Next Steps to Enhance Regulation and Transparency of Fixed Income Markets. Together, these materials summarize the OSC’s study of the fixed income markets and set out the steps the OSC will take to enhance the transparency and regulation of fixed income markets.
…
“With this report, we have compiled research that confirms our focus on enhanced post-trade transparency and regulation of the fixed income markets in Canada,” said Howard Wetston, Q.C., Chair and CEO of the OSC. “Our priority now is to develop regulation that will promote more informed decision-making for market participants regardless of size, improve market integrity and ensure that the market is fair and equitable to all investors.”

The Staff Notice stated:

In light of the observations in the Report, Staff considered steps that could be taken at this time to enhance fixed income regulation to achieve the following objectives:

• 1. Facilitate more informed decision making among all market participants, irrespective of their size;
• 2. Improve market integrity; and
• 3. Ensure that the market is fair and equitable for all investors

…
In the coming year, we will take additional steps to facilitate more informed decision making by market participants for all fixed income securities, specifically:

• a. Monitoring the implementation of new CSA cost and performance reporting rules in National Instrument 31-103 Registration Requirements, Exemptions and Ongoing Registrant Obligations, which will help retail investors better understand the cost of their fixed income transactions, and which will be fully implemented by July 2016; and
• b. Working with the CSA to make it easier for investors to find relevant documents for fixed income offerings, especially trust indentures and credit agreements, in the SEDAR system.

Subject to determining exactly what they mean, I don’t have a huge amount of problems with their second intention, to make more information available on SEDAR (although I suspect that they will not address my perennial complaint regarding their prohibition on linking directly to these public documents). As a caveat to that, however, I’ll say that I suspect it won’t make an atom’s worth of difference: it became apparent to me about ten years ago that nobody other than the lawyers ever read bond prospectuses; if there was any information anywhere other than on Bloomberg, it didn’t really exist. And Bloomberg’s information was grossly circumscribed; the summary omitted a lot of interesting stuff, like call schedules.

I will note that my direct experience of the institutional bond market is getting pretty rusty, but I was managing a small – but still institutionally sized – corporate bond portfolio in 2007/8; things were not much different than they were in the ’90’s; the big difference was that all the dealer crap came by eMail rather than fax and snail-mail. They could improve access to information quite easily by clarifying the rules about offering memoranda; on at least one occasion, I refused to consider buying an offered bond that fit the portfolio needs quite admirably, according to the basic information available, because the only documentation the dealer had (and this dealer was the original underwriter of the deal) was the offering memorandum and they refused to send me a copy on the grounds that OSC rules forbid the dissemination of the document.

All that being said, it is a pity that the report itself was written with the purpose of providing a veneer of respectability for the next OSC implementation of mission creep. For all that the report, titled The Canadian Fixed Income Market: 2014, is quite a good collection of references.

Furthermore, a significant number of bonds, ranging from 23-47%, are privately placed and only available to accredited investors.[Footnote]

Footnote reads: Based on an analysis of FP Infomart data from 2010-2013. These securities can only be traded and held by accredited investors.

Many of these private placements will be small issues, issued to institutions like pension funds and insurance companies on a bespoke basis, but it is a great pity that this characteristic was not followed up in an effort to determine why issuers choose to issue bonds privately. For example, on September 23, 2014 I noted:

However, as has been pointed out by Ron Mendel of Hartford Investment Management in his admirable essay Private Placement Debt: Diversification, yield potential in a complementary IG asset:

Private placement investors require additional yield relative to comparable public bond issues, as lenders demand greater yield to compensate for increased liquidity risk as well as the underwriting and monitoring costs. This premium is variable over time and is a function of technical, supply and demand characteristics, credit fundamentals and insurance liability requirements. The typical liquidity premium historically ranges between 25 – 45 basis points.

For those wanting some more opinion regarding private placements, the nomenclature in the states is “144a bonds” (or 144(a)), after the rule by which our wise masters graciously permit money to be borrowed and lent privately.

So why are there private placements? One answer is the price of underwriting a public issue, as a (now rather dated) paper by Oya Altınkılıç  and Robert S. Hansen, titled Are There Economies of Scale in Underwriting Fees? Evidence of Rising External Financing Costs reports:

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That’s not the end of it, as they show with some data for Chilean issues listed in the US market, taken from a paper by Sara Zervos titled The Transactions Costs of Primary Market Issuance: The Case of Brazil, Chile, and Mexico:

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Wow! Issuance costs of nearly 5% on a small issue is something fierce! While Canadian issuers will not have to deal with such an obscene amount of taxes on a bond issue, I suspect that the legal cost quoted …:

While legal fees can range according to any complications that arise, most parties quoted an approximate $50,000 for completing a straightforward deal.

… is absurdly low in today’s environment, particularly for smaller issuers that do not have a shelf prospectus in place. If we look at the recent Split Share offering by Brompton (a cookie-cutter SplitShare Corporation), found on SEDAR at “Brompton Oil Split Corp. Jan 30 2015 15:09:40 ET Final long form prospectus – English PDF 991 K” (not allowed to link!) we find:

The Company will pay the expenses incurred in connection with the offering of Preferred Shares and Class A Shares by the Company, estimated to be $725,000.

That was before selling fees of 3% (preferred) and 6% (Capital Units). An old buddy of mine blew his brains out with an ETF … spent about $750,000 and couldn’t sell the issue. So basically I think this report suffers by not examining issuance costs more – and yes, I know that legal fees aren’t public information and are therefore beyond the scope of this report. I don’t care. If they’re going to talk about costs of issue, they should have at least highlighted the fact that the cost information presented is both non-Canadian and dated.

I will also note that the information presented in the report does not differentiate by term; if we look at the information presented on SEDAR as “TELUS Corporation Mar 24 2015 17:34:26 ET Underwriting or agency agreements (or amendment thereto) PDF 275 K ” (not allowed to link!) we find:

(i) up to Cdn.$250,000,000 principal amount of Series CS Notes at a price of Cdn.$999.62 per Cdn.$1,000 principal amount of 1.50% Notes, Series CS due March 27, 2018 (the “Series CS Notes”) plus accrued interest, if any, from March 27, 2015 to the date of delivery, (ii) up to Cdn.$1,000,000,000 principal amount of Series CT Notes at a price of Cdn.$997.31 per Cdn.$1,000 principal amount of 2.35% Notes, Series CT due March 28, 2022 (the “Series CT Notes”) plus accrued interest, if any, from March 27, 2015 to the date of delivery, and (iii) up to Cdn.$500,000,000 principal amount of Series CU Notes at a price of Cdn.$999.72 per Cdn.$1,000 principal amount of 4.40% Notes, Series CU due January 29, 2046 (the “Series CU Notes”)
…
the Company agrees to pay to the Agents, at the Closing Date a fee of (i) Cdn. $2.50 per Cdn. $1,000 principal amount of Series CS Notes actually sold, (ii) Cdn. $3.70 per Cdn. $1,000 principal amount of Series CT Notes actually sold, and (iii) Cdn. $5.00 per Cdn. $1,000 principal amount of Series CU Notes actually sold, in each case exclusive of any applicable goods and services tax or any similar applicable tax.

So the underwriting costs in this case were 25bp for the Short-Term notes, 37bp for the Medium-Term notes and 50bp for the Long-Term notes.

But anyway, the main thrust of the report is to detail the difficulties retail investors have in building a portfolio of directly held bonds, e.g.:

In a 2010 study of US corporate bond trades, researchers observed that transaction costs were ten to twenty times lower for trades of $500,000 or more than for trades up to $100,000.[Footnotes]

[Footnotes read]: Transactions under $100,000 are considered to be retail transactions. See Appendix I: Additional Background, “Table 4: Spreads by Trade Size – Corporate Bonds (November 2008-April 2010)”.

Equivalent Canadian data is not available; however, we would expect to see a large disparity in Canada as well.

They do acknowledge concerns about transparency:

Negotiated Markets

In the fixed income market, there are many differentiated securities that do not trade very frequently. This leads to high search costs for each transaction since the market for individual securities tends to be concentrated among a small number of participants (fragmented liquidity).

This is one of the reasons the fixed income market operates as a negotiated market, where buyers and sellers negotiate the price of each transaction.

To facilitate the matching of buyers and sellers, dealers (or market makers) can help facilitate a transaction by serving as the trade counterparty. The market maker then assumes inventory risk while it looks for a seller (or buyer) to net out its position.

Complete transparency can deter market makers from participating for a number of reasons. One concern is that buyers or sellers can gain bargaining power over market makers. This could allow them to determine a market maker’s position and cost information, which drastically reduces the market maker’s potential profit.

The other concern is the free-rider effect: in a negotiated market, the initial search costs are high, but the marginal cost of disseminating and using this information is (or close to) zero. Full transparency can reduce bid-ask spreads but also reduces the incentive for market makers to participate because they rely on these spreads to compensate for their search efforts. While spreads in the fixed income market appear high relative to those in the equity market, one could argue that it is more appropriate to compare the fixed income market to other negotiated markets such as those for real estate and private equity, where both search and transaction costs can be significantly higher.

They acknowledge disputes about the effects of TRACE, without actually defining what they mean by liquidity:

A consensus on lower transaction costs with a continuing debate on liquidity Empirical evidence, gathered after the rollout of the TRACE system, showed that post-trade transparency lowered transaction costs in the fixed income market without decreasing liquidity.[Footnotes] As a corollary, these findings indicate that greater price transparency, leads to less information asymmetry and lower economic rents,[Footnote] which makes the market more efficient.[Footnote] However, in a more recent study, researchers argue that while post-trade transparency has reduced transaction costs in the fixed income market, it has had a negative impact on liquidity, particularly for less frequently traded bonds.[Footnote]

Footnotes read:

See Edwards, Amy K., Lawrence E. Harris, and Michael S. Piwowar. “Corporate Bond Market Transaction Costs and Transparency.” The Journal of Finance 62.3 (2007): 1421-451. Web. 24 July 2014. <[LINK]>; Learner, Heidi. “An Examination of Transparency in European Bond Markets.” An Examination of Transparency in European Bond Markets. CFA Institute, Oct. 2011. Web. 06 Apr. 2015. <[LINK}>;and M. Goldstein, E. Hotchkiss, and E. Sirri, “Transparency and Liquidity: A Controlled Experiment on Corporate Bonds,” Babson College working paper, 2005, <[LINK]>.

See International Comparisons, “Comparing Transparency” for additional details related to TRACE.

Economic rent represents the return on an asset in excess of the amount needed to keep it productive in a competitive market. Alternatively economic rent is the return that can be eliminated by competition. Rent-seeking actors are those that enter a market to capture economic rents.

Large traders can obtain a proprietary advantage by keeping the traded prices of bonds hidden. See United States. Library of Congress. Congressional Research Service. Does Price Transparency Improve Market Efficiency? Implications of Empirical Evidence in Other Markets for the Health Sector. By D. Andrew Austin and Jane G. Gravelle. United States Congress, 24 July 2007. Web. 31 July 2014. <[LINK]>.

Asquith, Paul, Thomas R. Covert, and Parag Pathak. The Effects of Mandatory Transparency in Financial Market Design: Evidence from the Corporate Bond Market. Working paper. SSRN, 5 Sept. 2013. Web. 25 Nov. 2014. <[LINK]>

I reviewed the last of these papers in the post TRACE and the Bond Market. And I’m pretty upset that they did not include the observations of Bessembinder and Maxwell (which I reviewed in the post TRACE and Corporate Bond Market Transparency) in this section, although they’re clearly aware of this paper since they cited it twice. One observation is critical and was conveniently ignored; it was:

Market participants with whom we spoke, including both dealers and the traders at investment firms who are their customers, were nearly unanimous in the view that trading is more difficult after the introduction of TRACE. Whereas it may have previously been possible to complete a sizeable bond purchase with a single phone call to a dealer who held sufficient quantities of the bond in inventory, the post-TRACE environment may involve communications with multiple dealers, and delays as the dealers search for counterparties. A bond trader with a major insurance company told us that there is less liquidity, in that market makers carried less “product,” and it has become more difficult to locate bonds for purchase in the post-TRACE environment. A bond trader for a major investment company responded to the publication of Bessembinder, Maxwell, and Venkataraman (2006) by sending the authors an unsolicited e-mail stating: “I want to be able to execute a trade even if a bond dealer does not have a simultaneous counterparty lined up…. [T]oo much price transparency reduces dealers’ willingness to commit capital…. [T]he focus on the bid-ask spread is too narrow, and a case of being penny-wise and pound-foolish.”

However, having acknowledged (however imperfectly) a debate about liquidity, the authors of the OSC paper immediately start advocating for greater transparency:

Why is price transparency important?

Markets can operate more efficiently when pricing is transparent for both buyers and sellers. Price transparency helps to ensure the buyer can make a more informed purchase, especially in financial markets that involve an intermediary, and helps sellers by making it easier to gauge demand. Price transparency also helps to prevent price discrimination in the market, where different people pay different prices for otherwise identical goods or services.

Why are prices in some markets less transparent than others?
1. Search costs. There are opportunity, including time, and monetary costs to acquire information; and
2. Privacy. Some participants are concerned that any increase in transparency might have a negative effect on their ability to manage their positions. However, it is not clear if these privacy concerns should dominate if most participants do not intend to trade the securities.

What are some of the arguments for greater transparency in the fixed income market?
1. The internet has significantly reduced search costs for consumers across many industries ranging from consumer retail to stock markets by reducing the marginal cost of information dissemination close to zero; and
2. Given that fixed income markets are generally not liquid, many participants in the market are buy-and-hold investors, so it is not clear if the privacy concerns are valid for investors that do not intend to trade these bonds.

And then there’s the usual whining:

Transparency depends on the investor’s level of sophistication … The market is relatively transparent to institutional investors … There is limited information available to retail investors … COSTS TO INVESTORS ARE NOT TRANSPARENT

In short, as I stated at the beginning of this post, the report itself was written with the purpose of providing a veneer of respectability for the next OSC implementation of mission creep. There is very little attempt to address the issue of ‘what is the corporate bond market for’ and an overarching bias towards the idea that greater transparency is always good. Well, maybe it’s good for retail investors, but is it good for the capital markets? Is it good for issuers who seek to raise funds to invest in fixed assets? As Assiduous Readers know, I take the view that it ain’t. Retail investors are well served by ETFs and, to a lesser extent (because of the fees!), by mutual funds; I have explained in the past Why only millionaires should invest in bonds directly. I have advocated for a version of Treasury Direct to be established in Canada, but those are for Canada bonds; and it’s in the context of creating something more useful than CSBs for small retail investors.

All in all, if the OSC really wants to know how investors get abused in the bond marketplace, they would be better advised to investigate manipulation of the bond indices.

But my prediction is that increased transparency will in fact come to the Canadian corporate bond market and quote spreads will in fact tighten and all the morons will be very, very happy. As a corollary to this, the market will become thinner, therefore more volatile and less liquid (when we define liquidity along the lines of ‘the ability to perform a 1-million pv transaction in a reasonable time without significant market impact); therefore investors will want higher spreads, therefore more issuers will head to the States and build fewer factories. We might also see an increase in the bespoke market, where issuers do more financing by with tiny issues sold in their entirety to insurance companies and pension plans. But the allegedly good part is a headline and the bad parts are only statistics, so who cares?

Morningstar had an article on the paper, titled Regulators: Retail investors deserve true bond transparency, which makes it clear that facts don’t matter; increased transparency and the degradation of Canada’s tiny corporate bond market is a foregone conclusion:

”We believe there is a need for additional transparency, both to regulators and to market participants, as well as enhanced regulation,” says Susan Greenglass, director of market regulation at the OSC.

In the Bank of Canada’s December 2003 Financial System Review, Tran-Minh Vu wrote Transparency in the Canadian Fixed-Income Market: Opportunities and Constraints which included a very peculiar assertion:

In Canada, because of the decentralized nature of the fixed-income market, customers typically contact several dealers to obtain the best price.[Footnote]

Footnote reads: Because they are primarily institutional investors, customers usually have a fiduciary duty to obtain at least three quotes from different dealers.

Let’s just say I’d like to see some supporting documentation for both parts of that quotation!

Itay Goldstein, Hao Jiang and David T. Ng have released a preliminary paper titled Investor Flows and Fragility in Corporate Bond Funds:

Investment in bond mutual funds has grown rapidly in recent years. With it, there is a growing concern that they are a new source of potential fragility. While there is a vast literature on flows in equity mutual funds, relatively little research has been done on bond mutual funds. In this paper, we explore flow patterns in corporate-bond mutual funds. We show that their flows behave very differently than those of equity mutual funds. While we confirm the well-known convex shape for equity funds’ flow-to-performance over the period of our study (1992-2014), we show that during the same time, corporate bond funds exhibit a very clear concave shape: their outflows are sensitive to bad performance much more than their inflows are sensitive to good performance. Funds have more concave flow-performance relationships when they have more illiquid assets and when the overall market illiquidity is high. Overall, our empirical results suggest that corporate bond funds are prone to fragility. The illiquidity of their assets seems to create strategic complementarities that amplify the response of investors to bad performance or other bad news.

So that’s interesting, and echoes the old stockbroker adage that people hate losing money on bonds. In fact, the “concave shape” they refer to in the abstract suggests that the market might be imposing a sort of ‘negative convexity’ on bond yields.

Observing this trend [of increasing flows to bond funds], several commentators have argued that bond funds pose a new threat to financial stability. What will happen when the current trend of loose monetary policy changes? Will massive flows out of bond funds and massive sales of assets by these funds destabilize debt markets with potential adverse consequences for the real economy? Feroli, Kashyap, Schoenholtz, and Shin (2014) use evidence from the dynamics of bond funds to show that flows into and out of funds seem to aggravate and be aggravated by changes in bond prices. They conclude that this suggests the potential for instability to come out of this industry. They analyze the market “tantrum” around the announcement of the possible tightening of monetary policy in 2013, and suggest that events like this can put the bond market under stress due to amplification coming from bond mutual funds.

Further, the nature of funds can cause bad returns to accelerate:

Indeed, corporate bond funds are in many cases illiquid. Unlike equity, which typically trades many times throughout the day, corporate bonds may not trade for weeks and trading costs in them can be very large. Despite the illiquidity of their holdings, corporate bond funds quote their net asset values and prices to investors on a daily basis. As a result, there is a mismatch between the illiquidity of the fund’s holdings and the liquidity that investors holding the fund get: they are able to redeem their shares at any moment and get the quoted net asset value. This implies that investors’ outflows may lead to costly liquidation by the funds, where the costs could be borne by remaining investors. This creates a ‘run’ dynamic which amplifies the reaction of outflows to bad performance, suggesting that the potential for fragility indeed exists in bond funds.

So does this self-destructive behaviour apply to retail or institutional investors, or both?

Third, following the model and empirical results in Chen, Goldstein, and Jiang (2010), we expect that strategic complementarities will be less important in determining fund outflows if the fund ownership is mostly composed of institutional investors. This is because institutional investors are large and so are more likely to internalize the negative externalities generated by their outflows. Indeed, consistent with this hypothesis, we find that the effect of illiquidity on the sensitivity of outflow to bad performance diminishes when the fund is held mostly by institutional investors.

My first thought on reading the above was ‘so what about the run on Money Market Funds following the Lehman bankruptcy?’ They’ve thought about it too:

These ‘run’ dynamics are very familiar from the banking context, and recently were on display in the run on money market mutual funds following the collapse of Lehmann Brothers.[Footnote] Attempts to prevent such runs are at the core of long-standing government intervention and regulation in the banking sector and now also in the money-market funds industry. It is likely that the surge in activity in corporate bond funds is a response to the restrictions in these sectors, and so the run problem can shift into the corporate bond funds arena. Hence, regulators should be on the alert and consider steps to achieve the value from intermediation by corporate bond funds while minimizing the damage from fragility.

[Footnote reads:]For an empirical study of the run on money market funds, see Schmidt, Timmerman, and Wermers (2014).

The hint that regulators should ‘consider steps’ scares me, since in general investors need protection from regulators!

But, bless their hearts, they acknowledge the argument that MMFs require a capital buffer:

Indeed, many argue that imposing a floating net asset value is not a perfect fix to the problems in money market funds, but other solutions such as holding a capital buffer or putting restrictions on redemptions are likely more appropriate.[Footnote]

[Footnote reads]See, for example, Hanson, Scharfstein, and Sunderam (2014).

In discussing their hypotheses, they make an interesting claim:

As Moneta (2015) documents, the average turnover rate of corporate bond funds is much higher than that of equity funds. For instance, from 1996 to 2007 the average turnover rate of general corporate bond funds is approximately twice as large as that of equity funds, which suggests more active trading and relatively shorter investment horizons of corporate bond funds. Considering the relatively low liquidity in corporate bond markets, the high trading activities of corporate bond funds are likely to generate substantial market impact.

I’ll have to look at that Moneta paper! In a world of long-term value investors who have no particular need to tell a story to potential investors, one would expect higher turnover in a bond fund, since income receipts will generally be higher and the portfolio is directly affected by the passage of time; but I confess I would have thought that turnover would be higher with the equity cowboys.

They also make a more serious charge:

Indeed, Cici, Gibson and Merrick (2011) document substantial dispersions of month-end valuations placed on identical corporate bonds by different mutual funds. Their tests reveal that such dispersion of valuations is consistent with returns smoothing behavior by managers, which involves marking positions such that the net asset value is set above or below the true value of fund shares, resulting in wealth transfers across existing, new, and redeeming fund investors. They find that the returns smoothing is particularly serious for corporate bond funds with hard-to-mark assets and not as much for Treasury bond funds; furthermore, when a fund’s return is low, the fund is more likely to mark the bond positions higher than the true value. Under this situation, existing shareholders would have particularly high incentives to withdraw their money while the mark is good.

Naughty, naughty! This usually becomes known only when such behaviour is egregious – see the market post of June 22, 2011 for one example.

The authors are clearly not fans of behavioural economics!

Why do bond funds experience much higher outflows during negative performance compared to stock funds? Our leading explanation is the presence of strategic complementarities. Corporate bond funds invest in more illiquid assets. Investors’ outflows may lead to costly liquidation by bond funds, where the costs would be borne by the remaining investors. This creates a ‘run’ dynamic which amplifies the reaction of outflows to bad performance. Under this explanation, outflows should be much more sensitive to bad performance among bond funds that are more illiquid.

I think this hypothesis is simply too sophisticated for the market to bear. I would be more interested in an explanation based on risk aversion of the investors, where “risk” is defined as “absolute performance over the past M months”, which refers back to the ‘investors hate losing money on bonds’ adage noted above. This would apply to funds, rather than direct bond holdings by retail, since there is also a persistent belief that a portfolio of bonds held directly is somehow fundamentally different from a fund since direct holdings can be held until they mature at par.

After disaggregating their data to distinguish institutional from retail behaviour, they conclude:

From a policy perspective, it is good news that institutional-oriented funds face less runlike behavior at low performance times. Such funds tend to be larger; and weaker run tendency implies more stability during low performance periods. The retail-oriented funds can still create big problems, as retail investors engage in run-like behavior.

Sadly, their concluding paragraph is a plea for increased employment of box-tickers:

This suggests that bond funds are prone to fragility. Bad events may lead to amplified outflows and these may have adverse consequences for bond prices and ultimately for firms’ financing and real activities. These issues have to be taken into account in the broad scheme of regulation of the financial sector. While it is well understood that banks, and now money-market funds, are prone to such run dynamics, these usually are not associated with bond funds, but our empirical results show that similar forces operate for them as well.

Hat tip for bringing this paper to my attention: Lisa Abramowicz, Bloomberg, You call this a bond rout? Wait until the real selling starts.

The FINRA page titled “Independent TRACE Studies” leads me to some studies that have come out of mandatory TRACE reporting for Structured Credit. The data collected are not made public in raw form, but aggregate data is made available on a daily and monthly basis.

Structured Credit is a little far from my bailiwick, but some of the statistics cited are so entertaining I just had to highlight them here.

A 2013 paper by Hendrik Bessembinder, William F. Maxwell and Kumar Venkataraman is titled Trading Activity and Transaction Costs in Structured Credit Products:

After conducting the first study of secondary trading in structured credit products, the authors report that the majority of products did not trade even once during the 21-month sample. Execution costs averaged 24 bps when trades occurred and were considerably higher for products with a greater proportion of retail-size trades. The authors estimate that the introduction of public trade reporting would decrease trading costs in retail-oriented products by 5-7 bps.

An acknowledgement in this paper, by the way, introduced me to the Montreal Institute of Structured Finance and Derivatives, which appears to be funded by agencies of the Province of Quebec and provides modest funding to genuine research – only $300,000 p.a., but that’s not bad when compared to their 10-year total budget of $15-million (meanwhile, here in Ontari-ari-ari-owe, we fund ex-regulators and lawyers at FAIR Canada. Gag.) You learn something new every day!

Anyway, back to Bessembinder et al. and the amazing statistics:

Notably, less than twenty percent of the SCP universe trades at all during the twenty-one month sample period. One-way trade execution costs for SCPs average about 24 basis points. However, trade execution costs vary substantially across SCP categories, from 92 basis points for CBOs to just 1 basis point for TBAs. We show that trading costs depend in particular on what we term the product’s “customer profile,” which depends on issue size and the proportion of Retail- versus Institutional-size trades. Sub-products with an Institutional Profile tend to have lower costs. The highest average trading costs are observed for Agency CMOs (74 basis points) and CBOs (92 basis points), each of which has a low (22% or less) proportion of large trades. The lowest average trading cost estimates are observed for TBA securities (1 basis point), CMBS (12 basis points), and ABS secured by auto loans and equipment (7 basis points), each of which has a large (54% or greater) percentage of large trades.
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Structured Products (SCPs), including asset-backed (ABS) and mortgage-backed (MBS) securities, comprise one of the largest but least-studied segments of the financial services industry. As of the end of 2012, there was $8.6 trillion outstanding in mortgage-backed securities and $1.7 trillion outstanding in asset-backed securities, implying that the SCPs markets are comparable in size to the $11 trillion U.S. Treasury Security market
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Increased transparency has the potential to reduce the dealer mark-ups or bid-ask spreads, provide more information on the fair price of the security, and improve regulators and customers’ ability to control and evaluate trade execution costs. These ideas have been emphasized by Rick Ketchum, Chairman and CEO of the Finance Industry Regulatory Authority (FINRA):

“From the standpoint of investor protection, which is and always will be FINRA’s top priority, we simply must shed more light on the darker areas of the fixed income market.

That last quote from Rick Ketchum illustrates the big problem with securities regulation nowadays. They have swung so far over to the ‘consumer protection’ objective that they have – at least to some degree – lost sight of why capital markets exist in the first place: to get money from savers to those who want to invest in their businesses. I will certainly agree that investor protection is a worthy objective; and I will agree that it is related reasonably closely to the objective of having a well functioning capital market; but for it to be called the “top priority” shows very strange priorities.

So the first amazing statistic is:

The MBS database provided to us by FINRA contains almost 1.1 million distinct securities. The large number of securities reflects that a basic pool of assets may have more than a hundred tranches, each with a unique payoff structure, and assets can be re-securitized (By comparison, less than 5,000 companies were listed on the U.S. equity exchanges at the end of 2012). However, as Panel A of Table 1 shows, many of these issues are very small, as the 25th percentile issue size is less than $2MM. The median issue size is less than $5MM. However, the distribution is positively skewed, as the mean issue size is $22.8MM. The MBS securities are of long average maturity, as shown on Panel B, with a mean maturity close to 19 years.

Holy smokes! I knew there were lots, but I would have guessed ‘under a million’. Hundred-tranche structured products sound pretty amazing, too.

Table 2 reports on trading activity for MBS. Notably, only 17.8% of the issues traded at all during the twenty one month period studied. The mean dollar volume traded across the full sample of MBS securities is $106MM, with an average of only 4.1 trades in each security. Fannie’s issues average six trades during the sample, and the average trading volume for Fannie issues is almost three times as large as for the next most frequently traded issue (Ginnie). Freddie’s issues are traded significantly less than either of the other agencies. Non-Agency issues trade an average of only 1.8 times each, but surprisingly have the largest proportion of issues (23%) that trade at all. Non-Agency issues have an average of 3.5 dealers at issuance, compared to slightly over four dealers for each Agency issue
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Table 3 contains information regarding the ABS data, which contains slightly over 300,000 issues (compared to 1.1 million issues in the MBS universe).5 The ABS issues are larger than MBS issues, with the mean ($114MM) and median ($29MM) issue size each close to five times larger than for ABS. Still, some ABS issues are very small; the 5th percentile of the issue size distribution is only $100,000 for ABS, compared to over $1MM for MBS. ABS issues have an average maturity of 23.2 years, about 5 years longer than MBS products.

Panel B of Table 3 reports on trading activity in the ABS market. Like MBS, ABS trade infrequently, but the percentage of issues that trade at all is almost 30%, considerably higher than MBS at 18%. The average number of trades per security is 4.97, but the trades are on average smaller for ABS; the mean cumulative trading volume for ABS is $16.3MM, compared to the $106MM for MBS issues. The likelihood of trading and mean number of trades is surprisingly homogenous across issue size terciles. However, average trade size and cumulative dollar volume is larger for ABS of greater issue size.

And what are these trades?

Table 5 reports on the distribution of trade sizes in SCPs. We consider a trade to be small if it is for less than $100,000 and large if it is for more than $1MM.
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For comparison purposes, we examine the distribution of trade sizes for corporate bonds during the six months before and after the introduction of public transaction dissemination. Our analysis includes 1.9 Million trades in 10,108 corporate bonds phased into TRACE dissemination between January 2003 and March 2011.8 We find that 72% of corporate bond trades are small (less than $100,000), both before and after trades were publicly disseminated. We conclude that, on average, the market for corporate bonds is more similar to the retail-oriented markets for SCPs, including CMOs and MBSs, and is more distinct from the institutionally-oriented markets for CMBS and TBA securities.

And the cost?

The resulting estimates of customer trade execution costs are reported on Table 6. For the full
sample, the estimated average one-way trade execution cost is 24 basis points. Consistent with results previously reported for corporate and municipal bonds, trade execution costs for SCPs decline with trade size, averaging 83 basis points for small trades, 24 basis points for medium-sized trades, and only five basis points for large trades. Trade execution costs also vary depending on trading frequencies. Average costs for the least-heavily-traded tercile of securities are 31 basis points, compared to 28 basis points for the second tercile and 24 basis points for the most frequently traded tercile. The finding that trade execution costs for SCPs decline with trade size mirrors the findings reported for corporate bonds by Edwards, Harris and Piwowar (2007) and Goldstein, Hotchkiss and Sirri (2007) and for municipal bonds by Harris and Piwowar (2006) and Green, Hollifield, and Schurhoff (2007). The overall level of estimated trading costs for SCP is in line with estimates for corporate bonds.

Bessembinder, Maxwell and Venkataraman (2006) study institutional trades in corporate bonds, and report average one-way trade execution costs (prior to transaction dissemination) that average 10 to 20 basis points. Schultz (2001) also studies institutional trades in corporate bonds and estimates that trading costs average 27 basis points. Edwards, Harris and Piwowar (2007) study a broader cross-section that includes retail trades, and estimate that one-way trade execution costs for corporate bonds range from 75 basis points for very small trades to 4 basis points for very large trades.

And the effect of TRACE?

We first implement expression (2) for the full set of corporate bonds that became TRACE-eligible in March of 2003, including in the analysis trades executed six months before to six months after the initiation of public trade dissemination. We find that trading costs for corporate bonds were reduced after the introduction of price dissemination by 9 basis points for small trades, 6 basis points for medium trades, and 3 basis points for large trades. These results are quite similar to those reported by Edwards, Harris, and Piwowar (2006), who study the same sample but rely on more complex estimation techniques.

I take issue with the authors when they claim:

These estimates of lower trading costs for SCPs have important implications for security issuers, investors in these products and broker-dealers who supply liquidity. Improved liquidity that is attributable to post-trade price transparency has the potential to affect the valuation of the bonds themselves and lower yield spreads (see Chen, Lesmond and Wei (2007) for evidence from corporate bonds) for SCP issues. Additionally, the cumulative dollar impact of these trading cost reductions is potentially large. In the case of the transparency experiment for corporate bonds, Bessembinder, Maxwell and Venkataraman (2006) estimate annual trading cost reductions of about $1 billion for the full corporate bond market. In addition, they document the existence of “liquidity externalities”, by which improved transparency for some products can lead to improved valuation and lower trade execution costs for related securities.

As I pointed out in an earlier post, a tighter spread between the dealer buy price and dealer sell price does not necessarily indicate “fairer” prices, since the dealer may well quote only stink bids on customer sales so that a profitable re-sale can be executed quickly. This mechanism, if correct, would actually mean that the liquidity-seeker in the chain of trades is paying more for liquidity under TRACE and that both the interim and ultimate liquidity providers are making excess profits (I refer to this as the Shitty Price Hypothesis). The authors do not examine how the execution prices in the secondary market compare with new-issue prices, which renders their conclusion regarding the “improvement” in liquidity dubious.

In addition to this, the putative benefits of TRACE, estimated as “annual trading cost reductions of about $1 billion for the full corporate bond market”, does not make any attempt to compare this with the cost of the programme. And I don’t mean direct costs, either. If the Shitty Price Hypothesis is correct – and it is consistent with the finding of lower trading levels in the Asquith, Covert and Pathak paper, then actual liquidity has decreased, which means issuers will have to pay more for funds, which means that some bricks-and-mortar projects will be abandoned (this link in the chain is the entire basis for central banking policy rates) … and how much does that cost? Huh?

Anyway, the authors told us to “see Chen, Lesmond and Wei (2007) for evidence from corporate bonds”, so let’s look at Chen, Lesmond and Wei (2007) and see what they have to say.

The paper by Long Chen, David A. Lesmond & Jason Wei is titled Corporate Yield Spreads and Bond Liquidity and it turns out that the last named author is from our very own Rotman School of Management at UofT:

We examine whether liquidity is priced in corporate yield spreads. Using a battery of liquidity measures covering over 4000 corporate bonds and spanning investment grade and speculative categories, we find that more illiquid bonds earn higher yield spreads; and that an improvement of liquidity causes a significant reduction in yield spreads. These results hold after controlling for common bond-specific, firm-specific, and macroeconomic variables, and are robust to issuers’ fixed effect and potential endogeneity bias. Our finding mitigates the concern in the default risk literature that neither the level nor the dynamic of yield spreads can be fully explained by default risk determinants, and suggests that liquidity plays an important role in corporate bond valuation.
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The notion that investors demand a liquidity premium for illiquid securities dates back to Amihud and Mendelson (1986). Lo, Mamaysky, and Wang (2004) further argue that liquidity costs inhibit the frequency of trading. Because investors cannot continuously hedge their risk, they demand an ex-ante risk premium by lowering security prices. Therefore, for the same promised cash flows, less liquid bonds will be traded less frequently, have lower prices, and exhibit higher yield spreads. Thus, the theoretical prior is that liquidity is expected to be priced in yield spreads. We investigate bond-specific liquidity effects on the yield spread using three separate liquidity measures. These include the bid-ask spread, the liquidity proxy of zero returns, and a liquidity estimator based on a model variant of Lesmond, Ogden, and Trzcinka (1999). We find that liquidity is indeed priced in both levels and changes of the yield spread.

Contemporaneous studies by Longstaff et al. (2004) and Ericsson and Renault (2002) also relate corporate bond liquidity to yield spreads.
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Historically, the lack of credible information on spread prices or bond quotes has been a major impediment in the analysis of liquidity (Goodhart and O’Hara, 1997) and liquidity’s impact on yield spreads. We employ Bloomberg and Datastream to provide our three liquidity estimates. Among them, the bid-ask spread is arguably the most demonstrable measure of liquidity costs, while the percentage of zero returns is increasingly used as a liquidity proxy in a host of empirical studies.2 Despite the clear intuition surrounding the zero return proxy, it is a noisy measure of liquidity, since it is the combination of a zero return and the simultaneous movement of bond price determinants that more properly estimates liquidity costs, not the lack of price changes per se.
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We find a significant association between corporate bond liquidity and the yield spread with each of the three liquidity measures. Depending on the liquidity measure, liquidity alone can explain as much as 7% of the cross-sectional variation in bond yields for investment grade bonds, and 22% for speculative grade bonds. Using the bid-ask spread as the measure, we find that one basis point increase in bid-ask spread is related to 0.42 basis point increase in the yield spread for investment grade bonds, and 2.30 basis point increase for speculative grade bonds.

So I don’t find anything objectionable in the conclusion; I’ve argued in this blog for a long time that liquidity is a major factor in corporate bond yields, far outweighing credit quality considerations. I will, however, point out that their primary liquidity estimator is at least a little suspect:

Data on the quarterly bid-ask quotes are hand-collected from the Bloomberg Terminals. Most quotes are available only from 2000 to 2003. For each quarter, we calculate the proportional spread as the ask minus the bid divided by the average bid and ask price. The bond-year’s proportional bid-ask spread is then calculated as the average of the quarterly proportional spreads. To include as many bonds as possible, we compute the annual proportional spread as long as there is at least one quarterly quote for the year. The bid-ask quotes recorded are the Bloomberg Generic Quote which reflects the consensus quotes among market participants.

I have to point out that Bloomberg quotes are suspect according to the Jankowitsch, Nashikkar and Subrahmanyam paper referenced in an earlier post, with almost half of actual trades executed outside the quote. This doesn’t necessarily mean that the Bloomberg quotation spreads are useless as a liquidity estimator, but it does mean that somebody has to do some work to show that Bloomberg spreads do in fact have a solid relationship to real life (e.g., that if the bid on bond A is less than the bid on bond B, then you can in fact sell B at a higher price than A).

So what it comes down to is that I agree with Bessembinder, Maxwell and Venkataraman that if TRACE does improve liquidity, then this is a good thing, but I will claim that you cannot measure liquidity in a practical way by comparing dealer sell prices with dealer buy prices if the Shitty Price Hypothesis holds.

As it happens, there is a paper by Nils Friewald, Rainer Jankowitschy and Marti G. Subrahmanyamz which seeks to validate the round-trip trading cost as a measure of liquidity, titled Transparency and Liquidity in the Structured Product Market:

We use a unique data set from the Trade Reporting and Compliance Engine (TRACE) to study liquidity effects in the US structured product market. Our main contribution is the analysis of the relation between the accuracy in measuring liquidity and the potential degree of disclosure. We provide evidence that transaction cost measures that use dealer-specic information can be eciently proxied by measures that use less detailed information. In addition, we analyze liquidity, in general, and show that securities that are mainly institutionally traded, guaranteed by a federal authority, or have low credit risk, tend to be more liquid.
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For example, measuring liquidity based on the round-trip cost uses the most detailed information, i.e., each transaction needs to be linked to a particular dealer, on each side of the trade. Other liquidity metrics, such as the effective bid-ask spread, do not need such detailed trade information for their computation; but, transactions need to be flagged as buy or sell trades. Many alternative liquidity measures rely on trading data as well: However, they use only information regarding the price and/or volume of each transaction. On the other hand, product characteristics or trading activity variables represent simpler proxies, using either static or aggregated data.
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Exploring the various liquidity metrics and focusing on the predictive power of transaction data, we show that simple product characteristics and trading activity variables, by themselves, may not be sufficient statistics for measuring market liquidity. In particular, when regressing state-of-the-art liquidity measures on product characteristics and trading activity variables, we find that the various liquidity measures over significant idiosyncratic information. Thus, dissemination of detailed transaction data, necessary for the estimation of liquidity measures, is of importance in the fixed-income structured product market. However, there is evidence that liquidity measures based on price and volume information alone (e.g., the imputed round-trip cost measure) can explain most of the variation observed in the benchmark measure, which uses significantly more information and certainly runs the risk of compromising the confidentiality of trader identity. In a second set of regressions, we explain the observed yield spreads using various combinations of liquidity variables and nd similar results: Liquidity measures provide higher explanatory power than product characteristics and trading activity variables alone. However, this result is mostly driven by price and volume information. Thus, details regarding the identities of the specific dealers involved with a particular trade or the direction of the trade are not an absolute necessity in terms of their informational value to market participants: Reasonable estimates of liquidity can be calculated based on prices and volumes of individual trades, without divulging dealer-specific information. This is an important result for all market participants, as it provides valuable insights concerning the information content of reported transaction data.

They acknowledge the Bessembinder paper and discuss the differences:

However, our paper is different from Bessembinder et al. (2013) for at least five important reasons, relating to various aspects of liquidity effects in the structure product market: First, while their analysis is based only on one single estimate of liquidity, we, in contrast, rely on a much broader set of liquidity proxies, which allows us to discuss the information contained in measures employing reported data at different levels of detail. Second, while Bessembinder et al. (2013) use a regression based estimate of liquidity, our round-trip cost measure (which serves as our benchmark) reflects the cost of trading more accurately, since it is based on detailed dealer-specific transaction costs, which are straightforward to compute, and does not depend, in any way, on modeling assumptions. Third, in their analysis, they focus solely on customer-to-dealer trades which constitute only a rather small fraction of all trades in the structured product market, whereas our analysis is based on all customer-to-dealer and dealer-to-dealer transactions. Fourth, unlike their study, we analyze different sub-segments (e.g., tranche seniority, issuing authority, credit rating) of the overall market in much more detail. These sub-segments have either turned out to be important in other fixed income markets, or are unique to the structured product market. Finally, a novel contribution of our paper is that we also analyze which of the liquidity measures best serves to explain yield spreads in the securitized product market.

So more particularly:

Thus, we ask how much information should be disseminated to allow for the accurate measurement of liquidity, compared to our benchmark measure using the most detailed information, in particular trader identity and trade direction, which certainly runs the risk of compromising the identities of individual traders or their trading strategies. Therefore, we measure the efficacy of liquidity metrics that require different levels of detail in terms of the information used to compute them. We analyze two aspects of this question, using different sets of regressions: First, we explore to what extent product characteristics, trading activity variables and liquidity measures using less information can proxy for the benchmark measure which is based on all available information. Second, we study which liquidity measures can best explain the cross-sectional differences in yield spreads for our sample.
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Product characteristics are rather crude proxies of liquidity that rely on the lowest level of informational detail of all the categories.13 Thus, product characteristics are typically used as liquidity metrics when there is a limitation on the level of detail in the transaction data. In particular, we use the amount issued of a security measured in millions of US dollars. We presume securities with a larger amount issued to be more liquid, in general. Another important product characteristic is the time-to-maturity, which corresponds to the time, in years, between the trading date and the maturity date of the security. We expect securities with longer maturities (over ten years) to be generally less liquid, since they are often bought by “buy-and-hold” investors, who trade infrequently. We also consider the instrument’s average coupon as a relevant proxy. Despite the ambiguity of the relationship between the coupon and both liquidity and credit risk, we expect that instruments with larger coupons are generally less liquid.

Trading activity variables such as the number of trades observed for a product on a given day represent the aggregate market activity.15 Other similar variables that we calculate on a daily basis, for each product, are the number of dealers involved in trading a specific product, and the trading volume measured in millions of US dollars. We expect these variables to be larger, the more liquid the product. On the contrary, the longer the trading interval, which refers to the time elapsed between two consecutive trades in a particular product (measured in days), the less liquid we would expect the product to be.

Note that the Shitty Price Hypothesis negates this last assumption: dealers will set prices so they can exit their positions quickly.

Liquidity measures are conceptually based, and hence, more direct proxies for measuring liquidity, and require transaction information for their computation. However, the level of detail concerning the required information set varies considerably across measures. The liquidity measure that uses the most detailed information and, thus, serves as our benchmark measure, is the round-trip cost measure, which can be computed only if the traded prices and volumes can be linked to the individual dealer; see, e.g., Goldstein et al. (2007). It is defined as the price difference, for a given dealer, between buying (selling) a certain amount of a security and selling (buying) the same amount of this security, within a particular time period, e.g., one day. Thus, it is assumed that in a “round-trip” trade, the price is not affected by changes in the fundamentals during this period. Following the literature, the round-trip trade may either consist of a single trade or a sequence of trades, which are of equal size in aggregate, on each side. The effective bid-ask spread, proposed by Hong and Warga (2000), can be computed when there is information about trade direction available. The effective bid-ask spread is then defined as the difference between the daily average sell and buy prices (relative to the mid-price).

Many other liquidity measures use only the price and/or volume of each transaction, without relying on dealer-specific or buy/sell-side information. A well-known metric proposed by Amihud (2002), and conceptually based on Kyle (1985), is the Amihud measure. It was originally designed for exchange-traded equity markets, but has also become popular for measuring liquidity in OTC markets. It measures the price impact of trades on a particular day, i.e., it is the ratio of the absolute
price change measured as a return, to the trade volume given in US dollars. A larger Amihud measure implies that trading a financial instrument causes its price to move more in response to a given volume of trading and, in turn, reflects lower liquidity. An alternative method for measuring the bid-ask spread is the imputed round-trip cost, introduced by Feldhutter (2012). The idea here is to identify round-trip trades, which are assumed to consist of two or three trades on a given day with exactly the same traded volume. This likely represents the sale and purchase of an asset via one or more dealers to others in smaller trades. Thus, the dealer identity is not employed in this matching procedure; rather, differences between the prices paid for small trades, and those paid for large trades, based on overall identical volumes, are used as the measure. The price dispersion measure is a new liquidity metric recently introduced for the OTC market by Jankowitsch et al. (2011). This measure is based on the dispersion of traded prices around the market-wide consensus valuation, and is derived from a market microstructure model with inventory and search costs. A low dispersion around this valuation indicates that the nancial instrument can be bought for a price close to its fair value and, therefore, represents low trading costs and high liquidity, whereas a high dispersion implies high transaction costs and hence low liquidity. The price dispersion measure is defined as the root mean squared difference between the traded prices and the average price, the latter being a proxy for the respective market valuation.

The Roll measure, developed by Roll (1984) and applied by Bao et al. (2011) and Friewald et al. (2012), for example, in the context of OTC markets, is a transaction cost measure that is simply based on observed prices. Under certain assumptions, adjacent price movements can be interpreted as a “bid-ask bounce”, resulting in transitory price movements that are serially negatively correlated. The strength of this covariation is a proxy for the round-trip transaction costs for a particular nancial instrument, and hence, a measure of its liquidity. This measure requires the lowest level of detail as only traded prices, and not trading volume or dealer-specific information, are used in the computation.

Whoosh! That’s a lot of liquidity measures! And I thought I was obsessive!

The descriptive statistics and correlations presented in Section 5.1 provide initial indications of the informational value of the various liquidity measures. When analyzing the liquidity of the different markets and their sub-segments, the liquidity measures offer additional insights compared to the product characteristics and trading activity variables. For example, when comparing the different market segments, higher trading activity is not always associated with lower transaction costs. The correlation analysis hints in the same direction: There is low correlation between the product characteristics and the liquidity measures (the highest correlation coefficient is 0.26 in absolute terms) and between trading activity variables and liquidity measures (less than 0.20 in absolute terms). Thus, it seems that liquidity measures that rely on more detailed transaction data can provide important additional information, based on this perspective.
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Table 10 shows the results for this analysis, presenting the six specifications. In regressions
(1) to (5), we use each of the liquidity measures in turn, plus all trading activity variables and product characteristics, to explain the round-trip costs. When we add just one individual proxy to the regression analysis, we find that the imputed round-trip cost, the effective bid-ask spread and the price dispersion measure are the best proxies, with R2 values of around 50% to 60%, whereas the Amihud and Roll measures slightly increase the R2 to around 40% compared to regressions without liquidity measures. When adding all the liquidity measures to the regression equation, in regression (6), we obtain an R2 of 67%, i.e., the explanatory power increases considerably when we include all these proxies. We consider this level of explanatory power quite high, given the rather diverse instruments with potentially different liquidity characteristics and the low number of trades per security and day, in general. We get similar results (not reported here) when explaining the effective bid-ask spread with liquidity measures using less information. Thus, we find evidence that liquidity measures using more detailed data can be proxied reasonably well by similar measures using less data. We further discuss this issue in the next section and analyze the importance of the disclosure in the context of pricing.

And correlation with yields?

Analyzing the effect of the trading activity variables in the full model, we find economically significant results only for the trading interval: An increase in the trading interval by one standard deviation is associated with an increase in the yield spread of 15 bp. The information contained in the other trading activity variables, e.g., traded volume, seems to be adequately represented by the liquidity measures. However, more important are the results for the product characteristics. The most relevant variable in the full model turns out to be the coupon. A one-standard-deviation higher coupon results in an increase of 137 bp in the yield spread. Thus, the coupon rate has the highest explanatory power of all the variables, indicating that a higher coupon is also associated with higher credit risk for certain products, in particular when there is no credit rating available. The amount issued shows important effects as well, where a one-standard-deviation increase leads to an 19 bp decrease in the yield spread: Larger issues have lower yield spreads. The maturity of a structured product is related to the yield spread as well, indicating that longer maturities are associated with somewhat lower spreads. However, compared with the other product characteristics, the maturity is of minor importance. Overall, the full model has an R2 of 69.9% with significant incremental explanatory power shown by the liquidity measures. Thus, liquidity is an important driver of yield spreads in the structured product market; therefore, the dissemination of trading activity information is important, given the size and complexity of this market.

And they conclude:

Exploring the relation between the various liquidity proxies and the depth of disseminated information, we find that product characteristics or variables based on aggregated trading activity, by themselves, are not sucient proxies for market liquidity. The dissemination of the price and volume of each individual trade is important for the quantification of liquidity effects, particularly for explaining yield spreads. However, we also provide evidence that liquidity measures that use additional dealer-specific information (i.e., trader identity and sell/buy-side categorization) can be efficiently proxied by measures using less information. In our regression analysis, we find that liquidity effects cover around 10% of the explained variation in yield spreads. Thus, the dissemination of trading activity is essential, given the trade volume and complexity of this market. These results are important for all market participants in the context of OTC markets, as it allows establishing an understanding of the information content contained in the disclosure of trading data.