Bonds were very weak today, as economic news filtered through:
On Thursday, the March producer price index declined by 0.5% month-over-month, while economists had expected the measure to remain flat. That followed Wednesday’s lower-than-expected reading on consumer prices.
Weekly jobless claims, meanwhile, came in at 239,00 for the week that ended April 8, above views for 235,000.
Today’s news was similar:
But a slew of mixed economic data including retail sales, industrial production and consumer sentiment cemented expectations that the Fed will hike rates another 25 basis points at next month’s policy meeting.
“Industrial production and capacity utilization came in stronger than expected,” Bruno added. “Both point to an economy that still has some vibrancy, which gives Fed cover to continue its rate hike policy in May possibly into June.”
Those expectations were underscored by Atlanta Fed President Raphael Bostic, who said another 25 basis point hike could allow the Fed to end its tightening cycle, even as Chicago Fed President Austan Goolsbee called for the central bank to be prudent.
At last glance, financial markets have priced in a 74% likelihood of that happening, according to CME’s FedWatch tool.
BIS published a paper by Biliana Alexandrova Kabadjova, Anton Badev, Saulo Benchimol Bastos, Evangelos Benos, Freddy Cepeda- Lopéz, James Chapman, Martin Diehl, Ioana Duca-Radu, Rodney Garratt, Ronald Heijmans, Anneke Kosse, Antoine Martin, Thomas Nellen, Thomas Nilsson, Jan Paulick, Andrei Pustelnikov, Francisco Rivadeneyra, Mario Rubem do Coutto Bastos and Sara Testi titled Intraday liquidity around the world:
Focus
Banks typically make large payments to each other through large-value payment systems (LVPS). Most LVPS settle payments on a gross basis, which means that banks must fund each payment one by one. While this helps to reduce any credit risk that arises if payments are accumulated and settled on a net basis, it is liquidity-intensive, because banks need to cover any mismatches between incoming and outgoing payments by drawing on their reserves or central bank credit lines. This gives rise to strategic behaviour in how banks manage their intraday liquidity. In this paper, we use a unique cross-country data set to assess intraday liquidity usage by banks around the world.Contribution
This paper is the first to assemble a data set of payments activity, intraday liquidity usage and institutional characteristics covering LVPS in nine major economies over a long period of time, including the 2007–09 financial crisis. The data let us analyse the effects of the institutional characteristics of an LVPS on intraday liquidity usage, including the effect of so-called liquidity-saving mechanisms (LSM) that many LVPS introduced in the last two decades. As such, this study is valuable for payment system policy makers and operators seeking to update or develop new LVPS and for payment system overseers and bank supervisors that assess intraday liquidity usage in LVPS.Findings
How banks manage their intraday liquidity depends on the availability and cost of intraday liquidity and LVPS design features. Banks coordinate and recycle their payments less when reserves are higher and the opportunity cost of holding reserves increases. Payment timing, coordination and the resulting level of liquidity efficiency also vary with incentives for early payment submission and specific LSM design features. Such features include the criteria and algorithms used to prioritise/deprioritise or offset payments in a payment system queue. Another key insight is that banks appear to condition their payment behaviour on specific design features, which may weaken some of the features’ intended liquidity-saving effects.Abstract
We study intraday liquidity usage and its determinants using a unique cross-country data set on large-value payments. We document that the amount of intraday liquidity that financial institutions around the world use each day equals, on average, 15% of their total daily payment values or 2.8% of their countries’ GDP. We then define and calculate system-level measures of liquidity efficiency and inequality in liquidity provision. We show that these measures vary systematically with the degree of payment coordination among payment system participants, the quantity and opportunity cost of central bank reserves and institutional characteristics, such as incentives for early payment submission and liquidity saving mechanism (LSM) design. Our results are consistent with the notion that payment system participants behave strategically and manage intraday liquidity actively. Participants also appear to condition their payment behaviour on specific LSM characteristics, which may weaken some of the LSMs’ intended effects.
The Bank of Canada has released a Staff Working Paper by Alistair Macaulay and Wenting Song titled Narrative-Driven Fluctuations in Sentiment: Evidence Linking Traditional and Social Media:
This paper studies the role of narratives for macroeconomic fluctuations. We micro-found narratives as directed acyclic graphs and show how exposure to different narratives can affect expectations in an otherwise standard macroeconomic model. We capture such competing narratives in news media’s reports on a US yield curve inversion by using techniques in natural language processing. Linking these media narratives to social media data, we show that exposure to a recessionary narrative is associated with a more pessimistic sentiment, while exposure to a nonrecessionary narrative implies no such change in sentiment. In a model with financial frictions, narrative-driven beliefs create a trade-off for quantitative easing: extended periods of quantitative easing make narrative-driven waves of pessimism more frequent, but smaller in magnitude.
…
We collect news articles devoted to an economic event and use topic models from natural language processing to extract narratives surrounding the event. We obtain empirical estimates of both the prevailing narratives and each article’s reliance on the narratives. Using these narratives, we provide empirical evidence on the importance of narratives for sentiment fluctuations. To isolate the effects of narratives, we focus on an episode of yield curve in version in 2019–—a popular recession indicator in the US with a nebulous theoretical foundation. Two competing narratives emanate from major news outlets: a “recession” narrative that links the inverted yield curve to an imminent recession, and a “nonrecession” narrative that makes no such connection.Using these identified narratives, we provide empirical evidence on the importance of narratives for sentiment fluctuations. To isolate the effects of narratives, we focus on an episode of yield curve in version in 2019–—a popular recession indicator in the US with a nebulous theoretical foundation. Two competing narratives emanate from major news outlets: a “recession” narrative that links the inverted yield curve to an imminent recession and a “nonrecession” narrative that makes no such connection.
Our main analysis studies the effects of narratives on the readers who are exposed. The most novel part of our data is the link from narratives in newspaper coverage to rich social network data from Twitter, which allows us to measure the spread of narratives. We use retweeting activities on Twitter to trace whether a Twitter user has engaged with news articles containing certain narratives. We find that after users are exposed to the recessionary narrative, their posted tweets display a more pessimistic sentiment, while exposure to the more neutral, nonrecessionary narrative has no such effect. The drop in sentiment following engagement with a recessionary narrative is persistent, remaining significant 30 days after the retweet. In addition, we apply our empirical framework to study recent inflation narratives. We document the rising prevalence of a narrative that emphasizes the connection of inflation to the real economy. Such a narrative is associated with sentiment declines during high inflation periods.
…
Formalizing narratives as directed acyclic graphs, we show that certain groups of narratives will, in fact, have exactly the same effect on expectations. In the context of the inversion of the US yield curve in 2019, the distinguishing feature between a “recession” narrative and a “nonrecession narrative” is, therefore, whether there is a link connecting the inverted yield curve with an upcoming recession.\Standard tools from topic modeling in natural language processing are well suited to making this distinction. We do this in a large corpus of articles from traditional news media, which is a key source of macroeconomic narratives (Andre et al., 2022b). Linking these articles with rich data on Twitter activity, we find that engaging with an article advancing a “recession” narrative causes a significant and persistent decline in the sentiment of that Twitter user, as embodied in their other activity on the social media site at the time. In contrast, engaging with a “nonrecession” narrative has no such effect on sentiment. This is precisely what would be predicted by models in which viral narratives affect aggregate behavior by shifting expectations. It also suggests a powerful role for the media in influencing aggregate sentiment (highlighted, for example, in Nimark, 2014).
We confirm this aggregate implication in a quantitative model informed by our empirical results. Yield curve inversions cause declines in expected incomes among households holding narratives in which such events are linked to recessions. This implies that extended periods of quantitative easing generate two novel offsetting effects: by flattening the yield curve, they make such narrative-driven fluctuations in sentiment more frequent. However, they
also reduce the prevalence of the “recession” narrative, reducing the magnitude of those fluctuations.
Another BoC Staff Working Paper by Ajit Desai, Zhentong Lu, Hiru Rodrigo, Jacob Sharples, Phoebe Tian and Nellie Zhang was titled From LVTS to Lynx: Quantitative Assessment of Payment System Transition:
Modernizing Canada’s wholesale payments system to Lynx from the Large Value Transfer System (LVTS) brings two key changes: (1) the settlement model shifts from a hybrid system that combined components of both real-time gross settlement (RTGS) and deferred net settlement (DNS) to an RTGS system; (2) the policy regarding queue usage changes from discouraging it to encouraging the adoption of the new liquidity-saving mechanism. We utilize this unique opportunity to quantitatively assess the effects of those changes on the behaviour of participants in the high-value payments system. Our analysis reveals the following: (1) At the system level, most payments are settled in a single stream with the liquidity-savings mechanism in Lynx—facilitating liquidity pooling and leading to higher efficiency than LVTS where payments were distributed in two streams. Moreover, due to Lynx’s liquidity-saving mechanism, many payments arrive earlier than those in LVTS, providing more opportunities for liquidity saving at the cost of slightly increased payment delay. (2) At the participant level, the responses are rather heterogeneous; however, our analysis suggests that liquidity efficiency is improved for several participants, and most experience slightly longer payment delays in Lynx than in LVTS.
| HIMIPref™ Preferred Indices These values reflect the December 2008 revision of the HIMIPref™ Indices Values are provisional and are finalized monthly |
|||||||
| Index | Mean Current Yield (at bid) |
Median YTW |
Median Average Trading Value |
Median Mod Dur (YTW) |
Issues | Day’s Perf. | Index Value |
| Ratchet | 0.00 % | 0.00 % | 0 | 0.00 | 0 | -0.5795 % | 2,308.4 |
| FixedFloater | 0.00 % | 0.00 % | 0 | 0.00 | 0 | -0.5795 % | 4,427.5 |
| Floater | 9.76 % | 9.91 % | 40,179 | 9.62 | 2 | -0.5795 % | 2,551.6 |
| OpRet | 0.00 % | 0.00 % | 0 | 0.00 | 0 | 0.2517 % | 3,348.2 |
| SplitShare | 5.02 % | 7.25 % | 44,995 | 2.63 | 7 | 0.2517 % | 3,998.5 |
| Interest-Bearing | 0.00 % | 0.00 % | 0 | 0.00 | 0 | 0.2517 % | 3,119.8 |
| Perpetual-Premium | 0.00 % | 0.00 % | 0 | 0.00 | 0 | -0.0724 % | 2,760.5 |
| Perpetual-Discount | 6.18 % | 6.20 % | 54,910 | 13.60 | 34 | -0.0724 % | 3,010.2 |
| FixedReset Disc | 5.76 % | 7.81 % | 90,039 | 11.96 | 63 | 0.2438 % | 2,140.9 |
| Insurance Straight | 6.07 % | 6.14 % | 74,449 | 13.72 | 19 | 0.0283 % | 2,962.2 |
| FloatingReset | 10.43 % | 10.95 % | 37,448 | 8.84 | 2 | -0.6406 % | 2,392.0 |
| FixedReset Prem | 6.92 % | 6.52 % | 319,829 | 3.93 | 1 | 0.0000 % | 2,336.6 |
| FixedReset Bank Non | 0.00 % | 0.00 % | 0 | 0.00 | 0 | 0.2438 % | 2,188.4 |
| FixedReset Ins Non | 6.01 % | 7.80 % | 68,257 | 11.77 | 11 | 0.0000 % | 2,304.8 |
| Performance Highlights | |||
| Issue | Index | Change | Notes |
| MFC.PR.Q | FixedReset Ins Non | -1.56 % | YTW SCENARIO Maturity Type : Limit Maturity Maturity Date : 2053-04-14 Maturity Price : 18.91 Evaluated at bid price : 18.91 Bid-YTW : 7.80 % |
| PWF.PF.A | Perpetual-Discount | -1.56 % | YTW SCENARIO Maturity Type : Limit Maturity Maturity Date : 2053-04-14 Maturity Price : 18.28 Evaluated at bid price : 18.28 Bid-YTW : 6.18 % |
| GWO.PR.Y | Insurance Straight | -1.55 % | YTW SCENARIO Maturity Type : Limit Maturity Maturity Date : 2053-04-14 Maturity Price : 18.41 Evaluated at bid price : 18.41 Bid-YTW : 6.17 % |
| MIC.PR.A | Perpetual-Discount | -1.53 % | YTW SCENARIO Maturity Type : Limit Maturity Maturity Date : 2053-04-14 Maturity Price : 20.00 Evaluated at bid price : 20.00 Bid-YTW : 6.83 % |
| FTS.PR.G | FixedReset Disc | -1.43 % | YTW SCENARIO Maturity Type : Limit Maturity Maturity Date : 2053-04-14 Maturity Price : 17.20 Evaluated at bid price : 17.20 Bid-YTW : 7.96 % |
| SLF.PR.J | FloatingReset | -1.42 % | YTW SCENARIO Maturity Type : Limit Maturity Maturity Date : 2053-04-14 Maturity Price : 14.60 Evaluated at bid price : 14.60 Bid-YTW : 10.25 % |
| TRP.PR.B | FixedReset Disc | -1.38 % | YTW SCENARIO Maturity Type : Limit Maturity Maturity Date : 2053-04-14 Maturity Price : 10.69 Evaluated at bid price : 10.69 Bid-YTW : 9.55 % |
| TRP.PR.A | FixedReset Disc | -1.21 % | YTW SCENARIO Maturity Type : Limit Maturity Maturity Date : 2053-04-14 Maturity Price : 13.93 Evaluated at bid price : 13.93 Bid-YTW : 9.02 % |
| BN.PF.I | FixedReset Disc | -1.10 % | YTW SCENARIO Maturity Type : Limit Maturity Maturity Date : 2053-04-14 Maturity Price : 19.75 Evaluated at bid price : 19.75 Bid-YTW : 8.48 % |
| IFC.PR.A | FixedReset Ins Non | -1.09 % | YTW SCENARIO Maturity Type : Limit Maturity Maturity Date : 2053-04-14 Maturity Price : 17.26 Evaluated at bid price : 17.26 Bid-YTW : 7.24 % |
| BN.PF.A | FixedReset Disc | -1.02 % | YTW SCENARIO Maturity Type : Limit Maturity Maturity Date : 2053-04-14 Maturity Price : 18.36 Evaluated at bid price : 18.36 Bid-YTW : 8.47 % |
| GWO.PR.N | FixedReset Ins Non | -1.00 % | YTW SCENARIO Maturity Type : Limit Maturity Maturity Date : 2053-04-14 Maturity Price : 11.88 Evaluated at bid price : 11.88 Bid-YTW : 8.50 % |
| NA.PR.E | FixedReset Disc | 1.01 % | YTW SCENARIO Maturity Type : Limit Maturity Maturity Date : 2053-04-14 Maturity Price : 20.00 Evaluated at bid price : 20.00 Bid-YTW : 7.31 % |
| MFC.PR.M | FixedReset Ins Non | 1.09 % | YTW SCENARIO Maturity Type : Limit Maturity Maturity Date : 2053-04-14 Maturity Price : 16.62 Evaluated at bid price : 16.62 Bid-YTW : 8.24 % |
| BMO.PR.Y | FixedReset Disc | 1.10 % | YTW SCENARIO Maturity Type : Limit Maturity Maturity Date : 2053-04-14 Maturity Price : 18.30 Evaluated at bid price : 18.30 Bid-YTW : 7.62 % |
| BIP.PR.A | FixedReset Disc | 1.12 % | YTW SCENARIO Maturity Type : Limit Maturity Maturity Date : 2053-04-14 Maturity Price : 17.10 Evaluated at bid price : 17.10 Bid-YTW : 9.37 % |
| MFC.PR.L | FixedReset Ins Non | 1.49 % | YTW SCENARIO Maturity Type : Limit Maturity Maturity Date : 2053-04-14 Maturity Price : 16.35 Evaluated at bid price : 16.35 Bid-YTW : 8.21 % |
| CM.PR.P | FixedReset Disc | 1.49 % | YTW SCENARIO Maturity Type : Limit Maturity Maturity Date : 2053-04-14 Maturity Price : 17.00 Evaluated at bid price : 17.00 Bid-YTW : 7.81 % |
| CM.PR.Q | FixedReset Disc | 1.50 % | YTW SCENARIO Maturity Type : Limit Maturity Maturity Date : 2053-04-14 Maturity Price : 18.25 Evaluated at bid price : 18.25 Bid-YTW : 7.64 % |
| BIK.PR.A | FixedReset Disc | 1.58 % | YTW SCENARIO Maturity Type : Limit Maturity Maturity Date : 2053-04-14 Maturity Price : 21.94 Evaluated at bid price : 22.50 Bid-YTW : 8.01 % |
| BMO.PR.E | FixedReset Disc | 1.64 % | YTW SCENARIO Maturity Type : Limit Maturity Maturity Date : 2053-04-14 Maturity Price : 21.02 Evaluated at bid price : 21.02 Bid-YTW : 7.15 % |
| CU.PR.I | FixedReset Disc | 2.37 % | YTW SCENARIO Maturity Type : Limit Maturity Maturity Date : 2053-04-14 Maturity Price : 22.41 Evaluated at bid price : 22.88 Bid-YTW : 7.21 % |
| TRP.PR.G | FixedReset Disc | 2.68 % | YTW SCENARIO Maturity Type : Limit Maturity Maturity Date : 2053-04-14 Maturity Price : 16.84 Evaluated at bid price : 16.84 Bid-YTW : 8.47 % |
| Volume Highlights | |||
| Issue | Index | Shares Traded |
Notes |
| FTS.PR.H | FixedReset Disc | 46,700 | YTW SCENARIO Maturity Type : Limit Maturity Maturity Date : 2053-04-14 Maturity Price : 12.45 Evaluated at bid price : 12.45 Bid-YTW : 8.66 % |
| TD.PF.A | FixedReset Disc | 43,150 | YTW SCENARIO Maturity Type : Limit Maturity Maturity Date : 2053-04-14 Maturity Price : 17.00 Evaluated at bid price : 17.00 Bid-YTW : 7.80 % |
| TD.PF.C | FixedReset Disc | 34,800 | YTW SCENARIO Maturity Type : Limit Maturity Maturity Date : 2053-04-14 Maturity Price : 16.94 Evaluated at bid price : 16.94 Bid-YTW : 7.84 % |
| NA.PR.E | FixedReset Disc | 25,500 | YTW SCENARIO Maturity Type : Limit Maturity Maturity Date : 2053-04-14 Maturity Price : 20.00 Evaluated at bid price : 20.00 Bid-YTW : 7.31 % |
| SLF.PR.C | Insurance Straight | 21,805 | YTW SCENARIO Maturity Type : Limit Maturity Maturity Date : 2053-04-14 Maturity Price : 18.94 Evaluated at bid price : 18.94 Bid-YTW : 5.93 % |
| BN.PR.N | Perpetual-Discount | 17,600 | YTW SCENARIO Maturity Type : Limit Maturity Maturity Date : 2053-04-14 Maturity Price : 18.40 Evaluated at bid price : 18.40 Bid-YTW : 6.52 % |
| There were 9 other index-included issues trading in excess of 10,000 shares. | |||
| Wide Spread Highlights | ||
| Issue | Index | Quote Data and Yield Notes |
| CU.PR.C | FixedReset Disc | Quote: 19.00 – 22.72 Spot Rate : 3.7200 Average : 2.0030 YTW SCENARIO |
| BN.PR.Z | FixedReset Disc | Quote: 19.71 – 21.50 Spot Rate : 1.7900 Average : 1.0166 YTW SCENARIO |
| MIC.PR.A | Perpetual-Discount | Quote: 20.00 – 21.25 Spot Rate : 1.2500 Average : 0.9258 YTW SCENARIO |
| PWF.PR.Z | Perpetual-Discount | Quote: 20.85 – 21.79 Spot Rate : 0.9400 Average : 0.6167 YTW SCENARIO |
| GWO.PR.H | Insurance Straight | Quote: 19.80 – 20.58 Spot Rate : 0.7800 Average : 0.4784 YTW SCENARIO |
| BIP.PR.B | FixedReset Disc | Quote: 21.75 – 22.75 Spot Rate : 1.0000 Average : 0.7100 YTW SCENARIO |
Banned.
— James Hymas
Canadian General Investments redeeming CGI.PR.D.
https://www.mmainvestments.com/cgi/press-releases/2023/1105/canadian-general-investments-redemption-375-cumulative-redeemable-class-preference-shares-series-4-cgiprd
3 days “early” so slightly lower dividend than usual.
“I love you guys; have no doubt … but please get back to the pref market, and maybe lighten up on the academic wannabe stuff!”
Well, “you guys” is just James and I, for one, don’t want him to change one bit. I love the eclectic grouping of clips from here and there. It’s why I come back.
“but for the life of me I’m not seeing much connection to the pref market.”
I really appreciate the macro highlights. Turns out jobless claims, monetary policy, bank runs, recessionary psychology, etc can affect bond yields. Like it or not, but movement of bonds drive the pref market (and so much more).
Prefblog is the ideal location for James to store his bookmarks.
Yeah, so ratchetrick aka PrefTrader aka [lots of other handles] has been banned. I had hoped that he meant his most recent farewell – but it was not to be.
I have no doubt but that he will be back again with yet another handle. I may pretend not to notice for a while again, but will almost certainly lose patience eventually.