nep-fmk New Economics Papers
on Financial Markets
Issue of 2020‒01‒20
eight papers chosen by
Kwang Soo Cheong
Johns Hopkins University

  1. What’s in A(AA) Credit Rating? By Nina Boyarchenko; Or Shachar
  2. Cross-Asset Information Synergy in Mutual Fund Families By Jun Kyung Auh; Jennie Bai
  3. Recovering Investor Expectations from Demand for Index Funds By Mark L. Egan; Alexander MacKay; Hanbin Yang
  4. Avoiding Momentum Crashes: Dynamic Momentum and Contrarian Trading By Victoria Dobrynskaya
  5. Collateral Constraints, Tranching, and Price Bases By Feixue Gong; Gregory Phelan
  6. The Evolving Market for U.S. Sovereign Credit Risk By Nina Boyarchenko; Or Shachar
  7. Sovereign exposures of European banks: it is not all doom By Martien Lamers; Thomas Present; Rudi Vander Vennet
  8. Completing the Market: Generating Shadow CDS Spreads by Machine Learning By Nan Hu; Jian Li; Alexis Meyer-Cirkel

  1. By: Nina Boyarchenko; Or Shachar
    Abstract: Rising nonfinancial corporate business leverage, especially for riskier “high-yield” firms, has recently received increased public and supervisory scrutiny. For example, the Federal Reserve’s May 2019 Financial Stability Report notes that “growth in business debt has outpaced GDP for the past 10 years, with the most rapid growth in debt over recent years concentrated among the riskiest firms.” At the upper end of the credit spectrum, “investment-grade” firms have also increased their borrowing, while the number of higher-rated firms has decreased. In fact, there are currently only two U.S. companies rated AAA: Johnson & Johnson and Microsoft. In this post, we examine recent trends in the issuance of investment-grade corporate bonds and argue that the combination of increased BAA issuance and virtually nonexistent AAA issuance both reduces the usefulness of the BAA–AAA spread as a credit risk indicator and poses a financial stability concern.
    Keywords: BAA-AAA spread; bond issuance; corporate credit risk
    JEL: G3
    Date: 2020–01–08
    URL: http://d.repec.org/n?u=RePEc:fip:fednls:86706&r=all
  2. By: Jun Kyung Auh; Jennie Bai
    Abstract: Despite common wisdom that equities and bonds are segmented, the organization structure of fund families can offset frictions regarding cross-asset segmentation. We find that actively-managed equity funds and corporate bond funds linked within a mutual fund family exhibit a significant co-movement in holdings of commonly-held firms' equities and bonds. Such cross-holdings facilitate information spillover, manifesting itself in the co-movement. Synthesizing cross-asset information can predict future equity returns and create profits for fund families as well as general investors. Our findings accentuate the importance of collaboration between equity funds and bond funds within fund families.
    JEL: G11 G23 G31
    Date: 2020–01
    URL: http://d.repec.org/n?u=RePEc:nbr:nberwo:26626&r=all
  3. By: Mark L. Egan; Alexander MacKay; Hanbin Yang
    Abstract: We use a revealed-preference approach to estimate investor expectations of stock market returns. Using data on demand for index funds that follow the S&P 500, we develop and estimate a model of investor choice to flexibly recover the time-varying distribution of expected returns. Despite the fact that they are generated from a different method (realized choices) and a different population, our quarterly estimates of investor expectations are positively and significantly correlated with the leading surveys used to measure stock market expectations. Our estimates suggest that investor expectations are heterogeneous, extrapolative, and persistent. Following a downturn, investors become more pessimistic on average, but there is also an increase in disagreement among participating investors. Our analysis is facilitated by the prevalence of “leveraged” funds, i.e., funds that provide the investor with a menu over leverage. The menu of choices allows us to separately estimate expectations and risk aversion. We estimate that the availability of these funds provides investors with significant (ex ante) consumer surplus.
    JEL: D12 D81 D84 G11 L0
    Date: 2020–01
    URL: http://d.repec.org/n?u=RePEc:nbr:nberwo:26608&r=all
  4. By: Victoria Dobrynskaya (National Research University Higher School of Economics)
    Abstract: High momentum returns cannot be explained by risk factors, but they are negatively skewed and subject to occasional severe crashes. I explore the timing of momentum crashes and show that momentum strategies tend to crash in 1-3 months after the local stock market plunge. Next, I propose a simple dynamic trading strategy which coincides with the standard momentum strategy in calm times, but switches to the opposite contrarian strategy after a market crash and keeps the contrarian position for three months, after which it reverts back to the momentum position. The dynamic momentum strategy turns all major momentum crashes into gains and yields an average return, which is about 1.5 times as high as the standard momentum return. The dynamic momentum returns are positively skewed and not exposed to risk factors, have high Sharpe ratio and alpha, persist in different time periods and geographical markets around the globe.
    Keywords: momentum, contrarian, downside risk, crash risk, trading strategy
    JEL: G12 G14 G15
    Date: 2019–10
    URL: http://d.repec.org/n?u=RePEc:sek:iacpro:9912063&r=all
  5. By: Feixue Gong (Massachusetts Institute of Technology); Gregory Phelan (Williams College)
    Abstract: Tranching an asset increases its basis; tranching a CDS, as occurs with the CDX index, increases the basis on the underlying asset. We consider a general equilibrium model with collateralized financial promises and multiple states of uncertainty to study how allowing an asset to back multiple financial contracts (i.e., tranching) affects price bases. A positive basis emerges when risky assets and their derivative contracts can be used as collateral for financial promises. We provide an empirical test of our theory using inclusion in the CDX and find that inclusion in the CDX increases the CDS basis.
    Keywords: Collateral, securitized markets, cash-synthetic basis, credit default swaps, asset prices, credit spreads.
    JEL: D52 D53 G11 G12
    Date: 2020–01–09
    URL: http://d.repec.org/n?u=RePEc:wil:wileco:2020-03&r=all
  6. By: Nina Boyarchenko; Or Shachar
    Abstract: How should we measure market expectations of the U.S. government failing to meet its debt obligations and thereby defaulting? A natural candidate would be to use the spreads on U.S. sovereign single-name credit default swaps (CDS): since a CDS provides insurance to the buyer for the possibility of default, an increase in the CDS spread would indicate an increase in the market-perceived probability of a credit event occurring. In this post, we argue that aggregate measures of activity in U.S. sovereign CDS mask a decrease in risk-forming transactions after 2014. That is, quoted CDS spreads in this market are based on few, if any, market transactions and thus may be a misleading indicator of market expectations.
    Keywords: US sovereign CDS
    JEL: G1
    Date: 2020–01–06
    URL: http://d.repec.org/n?u=RePEc:fip:fednls:86693&r=all
  7. By: Martien Lamers; Thomas Present; Rudi Vander Vennet (-)
    Abstract: In this paper we investigate whether or not observed changes in the composition of the sovereign bond portfolios of European banks are determined by a risk-return trade-off. Banks have been shown to disproportionally invest in bonds issued by their domestic sovereign, causing a negative bank-sovereign doom loop. Several motivations for such behavior have been demonstrated in the extant literature, such as e.g., search for yield or moral suasion, which from an investment perspective all involve some degree of irrational behavior. We depart from this approach and investigate the risk-return trade-off in the bank sovereign bond portfolios. We use data from all stress tests and transparency exercises conducted by the EBA between 2011 and 2018 for a sample of 76 European banks. Using the Sharpe ratio for the risk-return assessment, we find that over the entire period banks’ investments and divestments of sovereign bonds are characterized by rational risk-return considerations. Moreover, both bond risk (measured by the standard deviation of bond returns) as well as sovereign risk (sovereign CDS spreads) are negatively related to bond buying, implying that, on average, banks do not engage in excessive risk-taking behavior in their sovereign bond portfolios. Our main conclusion is that over the 2011-2018 period banks may have exhibited spells of excessive risk behavior in their sovereign bond buying, but over the entire period their sovereign bond investments exhibit a sound risk-return trade-off. These findings have implications for policy initiatives to tackle concentrations in sovereign bond holdings by European banks.
    Keywords: Sovereign Exposures, Risk Return, Securities portfolio, Bank balance sheet
    JEL: G11 G18 G21 G28
    Date: 2019–12
    URL: http://d.repec.org/n?u=RePEc:rug:rugwps:19/989&r=all
  8. By: Nan Hu; Jian Li; Alexis Meyer-Cirkel
    Abstract: We compared the predictive performance of a series of machine learning and traditional methods for monthly CDS spreads, using firms’ accounting-based, market-based and macroeconomics variables for a time period of 2006 to 2016. We find that ensemble machine learning methods (Bagging, Gradient Boosting and Random Forest) strongly outperform other estimators, and Bagging particularly stands out in terms of accuracy. Traditional credit risk models using OLS techniques have the lowest out-of-sample prediction accuracy. The results suggest that the non-linear machine learning methods, especially the ensemble methods, add considerable value to existent credit risk prediction accuracy and enable CDS shadow pricing for companies missing those securities.
    Date: 2019–12–27
    URL: http://d.repec.org/n?u=RePEc:imf:imfwpa:19/292&r=all

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