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Investors’ Appetite for Money-Like Assets: The MMF Industry after the 2014 Regulatory Reform

Investors’ Appetite for Money-Like Assets: The MMF Industry after the 2014 Regulatory Reform. The views expressed in this presentation are our own and may not necessarily reflect the view of the Federal Reserve Bank of New York or the Federal Reserve System.

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Investors’ Appetite for Money-Like Assets: The MMF Industry after the 2014 Regulatory Reform

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  1. Investors’ Appetite for Money-Like Assets: TheMMF Industry after the 2014 Regulatory Reform The views expressed in this presentation are our own and may not necessarily reflect the view of the Federal Reserve Bank of New York or the Federal Reserve System Marco Cipriani and, Gabriele La Spada

  2. Recent Theoretical Literature on Money • Hölmostrom, 2015 • Dang et al, 2015; Lester et al. 2011 and 2012; Hanson et al 2015. • Which assets can be used as money? • (as a means of payment) • Immune from adverse selection • Information insensitive • Money-like assets: debt-like securities with short maturity and/or low risk; debt on debt • Public money-like assets: Treasuries • Private money-like assets: Money Market Funds (MMFs) before 2016

  3. The Premium for Money-like Assets • Recent empirical literature estimates the premium for money-likeness (convenience yield) • Krishnamurthy and Vissing-Jorgensen (2012); Nagel (2017); van Binsbergen et al. (2018) • Convenience yield for Treasuries ranges between 20 and 40bps • Money-likeness estimated indirectly • K-VJ: comparing yield on Treasuries with that of securities with a similar risk profile

  4. Our Work • In 2014, a regulatory change (the SEC MMF reform) affected the information sensitivity of the shares issued by a segment of the MMF industry • Quasi-natural experiment • Difference-in-differences design • Estimates of the value attached to informational insensitivity • Premium for money-likeness • First empirical evidence directly supporting information-based theories of money

  5. Outline • Introduction • Some institutional background • The impact of the 2014 reform • The premium for money likeness • In progress

  6. Money Market Funds • Economic Importance • 2017: $3 trillion AUM managed by 334 MMFs • Three Types of MMFs • Prime: Treasuries, GSE debt, repos, CD, CP, ABCP, FRNs • Muni: mainly municipal securities in the form of FRNs • In this presentation: prime={prime,muni} • Government: • Treasuries, Agency debt, and repos backed by treasuries or Agency debt • Two investor types (share classes): institutional and retail

  7. Rule 2a-7 before the 2014 reform • Before 2014 • investment in all MMFs redeemable at par (stable NAV) • mandated repricing if NAV drops below 0.995 (breaking the buck) • no gates or fees • MMF shares: debt-like securities • akin to uninsured bank deposits • Typical example of privately-issued money • Used for payroll purposes by non-financial firms

  8. The 2014 SEC Reform • New SEC regulation approved in 2014 • A reaction to the 2008 run on the industry • All prime MMFs: redemption gates and liquidity fees • Optional if weekly liquid assets fall below 30% • “Mandatory” if they fall below 10% • Institutional share classes of prime MMFs: floating NAV • Government MMFs unaffected • Regulation took effect in October 2016

  9. The 2014 Reform and Money-likeness • Prime MMFs became less “money-like” • Institutional share classes to a greater extent than retail • Why? the reform made prime MMF shares less informationally insensitive: • retail investors must now consider the possibility that fund managers gate redemptions or introduce fees • additionally, institutional MMF shares are no longer a debt-like security: institutional investors have an incentive to acquire private information on the underlying MMF portfolio at all times

  10. Outline • Introduction • Some institutional background • Investors’ response to the regulation • The premium for money likeness • If time allows: intertemporal rate of substitution

  11. Government vs. Prime: Total Net Assets (TNA) New SEC rule

  12. Within-Family Flows • Each dot is a fund family • Sample: Nov. 2015 – Oct. 2016 • OLS regression in level: slope 1, R2=0.92 • This relationship is absent before 2015

  13. Retail vs. Institutional Investors Retail Institutional +37.5 pps +48pps

  14. 2008 Run vs. SEC Reform Consistent with the idea that MMF investors wanted to preserve the money-likeness of their investment Consistent with the idea that MMF investors responded to an increase in perceived risk and sought safe assets Flight to safety as opposed to preference for money-like assets!

  15. Outline • Introduction • Some institutional background • Investors’ response to the regulation • The premium for money likeness • If time allows: work in progress

  16. Prime vs. Government Net Yields

  17. The Premium for Money-like Assets • Increase in the net yield of prime vs. government MMFs after the regulation • Spread: 8bps through Nov. 2015; jumps to 25bps in Oct. 2016; and has remained above 14 bps since. • Why? Investors are willing to pay a premium for money-like assets (government MMFs) • Spread for institutional share classes should increase even more (floating NAV) • Confounding factors: • Increase in risk taking by prime MMF managers • Difference-in-difference approach

  18. The Diff-in-Diff Regression • Monthly regression at the family level: Jan 2015-Sept. 2017 • i family; j=share class (institutional or retail); t month; k fund type • yi,j,k,t: average net yield weighted by share-class TNA • For each family in month t, there are four observations: • the weighted average net yield of its prime institutional funds • the weighted average net yield of its prime retail funds • the weighted average net yield of its government institutional funds • the weighted average net yield of its government retail funds

  19. The Diff-in-Diff Regression • Two regulatory dummies • I(t≥Nov.2015): first conversions take place • I(t≥Oct.2016): reform goes into effect • One “Prime” and one “Inst.” (institutional) dummy • Controls: • αi,j,k= family X fund type X investor type FE • μj,k= investor type X month FE • robust standard errors (heteroscedasticity, cross and autocorrelation)

  20. The Diff-in-Diff Regression • γ1+γ2 = premium for money-likeness paid by retail investors • γ3+γ4 = additional premium for money likeness paid by institutional investors

  21. The Value of Fixed NAV: Net Yield • γ1+γ2= premium for money likeness paid by retail investors=20 bps • γ3+γ4 = additional premium for money likeness paid by institutional investors=8 bps • estimates are comparable to Krishnamurthy and Vissing-Jorgensen (2012) and Nagel (2017) for Treasuries

  22. Robustness checks Balanced panel Longer sample (from November 2010) Saturated regression with MMF-type X time FE Pre-existing trends Time-varying risk aversion (VIX) Monetary policy (EFFR) Risk taking by MMF managers

  23. Robustness Check: Risk Taking • The reform may have impacted the relative risk taking of prime vs. government MMFs • Remaining prime MMF investors more risk tolerant • Prime fund managers invest in riskier asset classes • Four proxies for fund-risk taking: • Weighted average maturity (WAM) • Weighted average CDS spread • Portfolio share of risky minus safe asset classes • Risky asset classes: bank obligations • Safe asset: Treasuries, Agency debt and repos • Portfolio share of safe asset classes

  24. Relative Risk-taking has not Increased • Relative risk taking by prime MMFs has not increased • We re-estimate premium adding risk proxies as controls; results doe not change.

  25. Outline • Introduction • Some institutional background • Investors’ response to the regulation • The premium for money likeness • If time allows: understanding the premium

  26. Understanding the Premium Net Yield = Gross Yield - Fee • Therefore, the premium for money likeness observed after the reform must come either: • From a change in relative fees • From an change in relative gross yield unrelated to risk • Why unrelated to risk? because we have learned that the increase in the spread is not due to risk • We re-run our baseline regression using fees as the dependent variable

  27. The Role of Fees • γ1+γ2= the relative reduction of fees for retail investors is 10 bps • γ3+γ4 = the additional relative reduction in fees for institutional is 2 bps • overall, slightly less than 50% of the premium for money likeness is paid through a reduction in fees • what about the remainder?

  28. Supply Effects • Half of the premium for money-likeness stems from a change in relative fees • The remainder? (Possibly) higher relative rate paid by (institutional) prime MMF borrowers because of a reduction of a supply of funds from prime MMFs • Hypothesis: because of supply effects, rates on assets typically part of prime MMF portfolios have increased more than rates on assets typically part of government MMF portfolios

  29. Relative Gross Yield and Market Rates

  30. Outline • Introduction • Some institutional background • Investors’ response to the regulation • The premium for money likeness • If time allows: the value of information insensitivity

  31. Information Sensitivity: Gates and Fees • Gates and fees are triggered when weekly liquid assets (WLA) follow below a threshold: • the money-like premium should decrease with the level of WLA

  32. Information Sensitivity: Gates and Fees • If the reform changed prime MMF information sensitivity, prime MMF investors should be trying to acquire more information relative to government MMF investors • SEC platform (EDGAR) allows us to extract information on the number of queries

  33. Conclusion • The SEC reform of the MMF industry offers a quasi-natural experimental allowing the estimate of the premium for money-likeness • The premium is linked to a change in the informational sensitivity of prime MMFs • The premium is estimated on private money • The premium is estimated to be around 20 bps for retail investors and 8 bps for institutional investors • The estimate is robust to controlling for changes in portfolio risk

  34. The End Thanks!

  35. DATA • Form N-MFP: monthly regulatory filing with the SEC • November 2010-present • For each fund: type, TNA, gross yield, weighted average maturity (WAM), securities-level information • For each share class: redemptions, subscriptions, net yield • iMoneyNet: • November 2010-Septmeber 2017: 90% TNA coverage • For each share class: institutional, retail • Net yield

  36. Within-Family Flows --- Levels • Each dot is a funds’ family. • Sample: Nov. 2015 – Oct. 2016.

  37. Within-Family Flows --- Regressions

  38. Within-Family Flows --- Excluding Conversion

  39. Conversions • Sample: Nov. 2015 – Oct. 2016

  40. Fund Families • Sample: Nov. 2015 – Oct. 2016

  41. Within-Family Flows --- Control • November 2014-October 2015

  42. Within-Family Flows: Treasuries vs. Agency β=0.90 β=0.10 • Two dots per fund family: agency and treasury MMFs • Sample: Nov. 2015 – Oct. 2016

  43. Investors’ Risk Appetite: Agency vs. Treasury Funds New SEC rule +35.3 pps +5.7pps

  44. 2008 Run: Agency vs Treasury Funds

  45. 2008 Run: Within-Family Flows • Each dot is a funds’ family • Sample: Aug. 2008 – Oct. 2008 • OLS regression in level: slope 0.51, R2=0.20

  46. 2008 Run: Agency vs Treasury Funds • Two dots per fund family: agency and treasury MMFs; • Sample: Aug. 2008 – Oct. 2008.

  47. Risk Regressions

  48. Adding Risk-taking as a Control • We re-estimate premium adding risk proxies as controls; results doe not change.

  49. Funds with Stable Investor Base • Restrict sample to share-classes with stable investors base • Share classes whose TNA did not change by more than 5% in absolute value between Nov 2015 and Oct 2016 • Regressions run at the share-class level • Premium estimates are similar

  50. Robustness Check: Time-Varying Trends

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