logo

 

February 4, 2022

Dear EFA Member

As a current or recent EFA member, we are pleased to forward you the contents of the recently published first issue of Volume 26 of the Review of Finance – the EFA’s own journal – along with digests (short summaries) and abstracts.

ARTICLES


Authors: John H Cochrane

How should long-term investors form portfolios in our time-varying, multi-factor and friction-filled world? Two conceptual frameworks may help: first, look directly at the stream of payments that a portfolio and payout policy can produce. Second, include a general equilibrium view of the markets’ economic purpose, and the nature of investors’ different preferences, risk-taking ability, and function in that equilibrium. These perspectives can rationalize some of investors’ behaviors, suggest substantial revisions to standard portfolio theory, and help us to apply portfolio theory in a way that is useful in practice.

Back


Authors: James Brugler, Carole Comerton-Forde, J Spencer Martin

We examine the impact of increased secondary market transparency following the introduction of mandated post-trade transparency (TRACE) in the US corporate bond market. We answer two questions: (1) does enhanced secondary market transparency impact the cost of issuing bonds? and (2) if costs change, what is the economic mechanism through which this reform impacts costs?

We find that secondary market transparency is associated with a fall of 14 bps in the yield spread of a typical issue, from a sample mean of 144 bps. This corresponds to a 1.1% increase in price. We causally identify these reductions in costs by exploiting the staggered roll-out of TRACE, which allows us to estimate the magnitude of transparency-related changes via difference-in-differences regressions both pooled across all bonds and split by roll-out phase. Our results split by phase show that TRACE mainly affects issuing costs for smaller, lower rated bonds.

There are at least three ways secondary market transparency might influence bond issuing costs: through transactions costs in the secondary market; changes in information asymmetry in the primary market; and changes in information asymmetry in the secondary market.

While these mechanisms are not mutually exclusive – all could simultaneously affect bond issuing costs – our analysis points to reduced information asymmetry in the primary market issuance process as the most important driver of lower issuance costs in the bond market. Intuitively, prices paid in the secondary market for other, related bonds provide useful information for investors trying to price a new bond issue. Prior to TRACE, agents responsible for more trading activity, such as large dealers or fund managers with high turnover, have an information advantage when a new issue comes to market as they observe more information about the trading process of related bonds than smaller dealers or less active fund managers. After TRACE, everyone observes the complete trading history, reducing this important source of information asymmetry.

Our results provide new evidence that the economic consequences of increased transparency in secondary markets for corporate bonds extends to primary markets and the cost of raising debt capital. Reducing information asymmetry in the issuing process is an important driver of lower issuing costs. This should provide further impetus for European regulators to move ahead with the creation of a post-trade consolidated tape for the European bond markets. For regulators in other countries that wish to develop their bond markets, our results suggest that a post-trade transparent secondary market can help this process.

Back


Authors: Christopher Hrdlicka

Asset’s risks change overtime. These changes are central to understanding price dynamics. I show that these risk changes also drive trade.

Trade arises from investors rebalancing to offset these risk exposure changes to maintain their risk level targets. Maintaining their targets requires more trade when the risk exposure of an asset falls than when its risk increases. For example, a stock with a beta of one-half requires twice as much trade as does a stock with a beta of one to change portfolio risk exposure by one unit. Panel A illustrates this asymmetric relation between trade and risk changes. A similar effect makes trade in stocks with smaller initial risk exposures more sensitive to risk changes, as illustrated in Panel B.

The need to trade does not diversify away in large portfolios, because failing to trade exposes investors to unnecessary and uncompensated idiosyncratic risk. Theoretically transaction costs could outweigh the benefit of this rebalancing. However, the data confirm the predicted relationship, and that it has strengthened as transaction costs have decreased.

On average a one standard deviation fall in a stock’s market beta raises turnover by as much as 25%. Changes in market betas explain as much as 5% of the monthly cross-sectional variation in turnover. As transaction costs have fallen this relation has strengthened. Changes in SMB and HML exposures also drive trade as should changes to any risk exposure priced or unpriced that investors care about. The generality of the rebalancing need when risk exposures change, makes these estimates lower bounds on the importance of risk changes for understanding trade. Trade is generated by other sources too, such as differences in investor risk aversion, outside income or opinions. All of these are amplified by changing risk exposures.

That the same underlying shock to risk moves both prices (see Panel C) and trade produces a positive correlation between trading volume and absolute changes price. Panel D illustrates how the previously discussed asymmetry propagates to the price and volume relationship. This asymmetry matches the patterns documented in the literature.

Not all price changes are driven by risk changes. Some are driven by changes to cash flows. In contrast to what we see for risk changes, we expect little trade from shocks to cash flow levels. Why? Investors all prefer more dividends and prices must adjust to offset increased (decreased) demand for a stock after positive (negative) shock, e.g., the no trade theorem. The different trade patterns are illustrated with solid (blue) and dashed (red) lines in Panel D. This observation means that the amount of trade accompanying a price change can illuminate its cause. Vector auto regressions confirm this prediction: turnover is more strongly associated with discount rate news than cash flow news. Consequently, trading volume helps predict returns: more trading volume accompanying a price change increases the likelihood of its reversal out to a horizon of three year.

 

Back


Authors: Stefania D’Amico, N Aaron Pancost

Although the market for nominal US Treasury bonds is very large and liquid, previous research has identified a number of cases in which assets with almost identical payoffs carry significantly different prices, including the on-the-run premium, the TIPS-Treasury bond puzzle, and the note-bond spread. We hypothesize that there could be a commonality across these anomalies: a missing risk premium on Treasuries with special collateral (SC) value in the repurchase agreement (repo) market.

To test our hypothesis, we derive a dynamic no-arbitrage term structure model that jointly prices Treasury bonds in the cash and repo markets. We estimate our model using daily prices of all outstanding Treasury securities and corresponding SC repo rates, allowing us to construct a time-varying SC risk premium. The SC risk premium explains about 80% of the on-the-run premium, and a substantial share of other Treasury price anomalies, suggesting that unexpected fluctuations in the specialness spreads of recently-issued nominal Treasury securities are a common risk factor.

We focus on pricing SC risk and provide three main contributions. First, we develop a methodology to account for special spreads at the security level within a dynamic term structure model, which allows us to decompose special spreads of recently-issued Treasuries into expected specialness and SC risk premia. Second, we show that the SC risk premium, and not expected specialness, explains the on-the-run cash premium. Third, we show that the SC risk premium also explains a substantial share of other Treasury price anomalies, confirming that unexpected fluctuations in the SC value of recently-issued securities are a common risk factor. Models that ignore the SC risk premium might interpret a spread between actual and model-implied Treasury prices as anomalous, though most of this spread can be justified as compensation for SC repo risk.

We show that repo special spreads are equivalent to a dividend that is proportional to the asset’s price. Crucially, Treasury market participants price not only the expected repo “dividends,” but also their associated risk. Because our model explicitly accounts for observed SC risk factors, we can identify and estimate SC risk premia separately from other classical risk premia, such as those related to the level, slope, and curvature risk factors.

We find that the SC risk premium is large in 2009--2011, especially at the 10-year maturity, as those issues are the “most special” in the repo markets. It fluctuates significantly over time, exhibiting sharp variations following supply shocks, approximated by the FOMC announcements of the Federal Reserve's asset purchase programs, which reduce the net supply of safe and liquid securities.

At the 10-year maturity, the SC risk premium accounts for about 80% of the on-the-run premium, 64% of the TIPS-Treasury bond puzzle, and 63% of the note-bond spread. This suggests a common underlying economic mechanism across these Treasury price anomalies. These assets command an extra premium due to their exposure to SC risk, which drives a positive wedge between their prices and those of relatively less liquid and safe substitutes.

 

Back


Authors: Sascha O Becker, Hans K Hvide

How large is entrepreneurs’ personal importance to startups? We use the death of nearly 1,500 entrepreneurs as a source of exogenous variation, and find large and sustained negative effects on growth and profitability. For small startups, the effects go mainly via firm survival, while for larger startups the effects are mainly on firm growth. For larger startups, the mean effect on sales is about 60%. The effects appear to be driven by entrepreneur specialness rather than leadership transition; the effects of death of entrepreneur managers are economically and statistically stronger than the death of managers that are not entrepreneurs.

Back


Authors: Robert Clark, Shaoteng Li

Following the crisis, macroprudential regulations targeting mortgage-market vulnerabilities were widely adopted, their success often relying on the response of financial intermediaries. We provide evidence from Canada suggesting banks may have behaved strategically to limit the effectiveness of recently implemented mortgage stress tests. Before implementation, borrowers had to prove they could make mortgage payments based on the interest rate specified in the contract. The new tests require borrowers to show they can afford payments based on a typically higher qualifying rate, derived from the mode of 5-year rates posted by the six largest banks. The government’s objective was to cool credit markets, but, since many mortgages are government-insured, the big banks’ interests were not aligned. We find evidence of rate manipulation using a difference-in-differences approach comparing changes in spreads for 5-year mortgages with 3-year spreads, unaffected by the policy. The qualifying rates were lowered encouraging continued borrowing, muting the tests’ impact.

Back


NOTE: This and previous issues of Review of Finance [ISSN 1572-3097 | EISSN 1573-692X] are freely available to current EFA members as a benefit of annual membership.  For information on how to submit a manuscript to Review of Finance, please visit the RF Editorial Office website revfin.org.  Follow us on LinkedIn.

European Finance Association
Executive Secretary 
Rue Fossé aux Loups, 38, 1000 Brussels, Belgium 
Website: www.european-finance.org 
| Twitter: @EFA_org   LinkedIn 
You can check & update your EFA profile on the EFA or the EIASM website: http://www.eiasm.org
If you no longer wish to receive e-mailings from EFA, click on unsubscribe for an online confirmation.

EFA News