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Rating changes and portfolio flows to emerging markets: Evidence from active and passive funds
Economics Letters 178 (2019) 37–45
Contents lists available at ScienceDirect
Economics Letters journal homepage: www.elsevier.com/locate/ecolet
Rating changes and portfolio flows to emerging markets: Evidence from active and passive funds✩ ∗
Christina E. Bannier a , Thomas Heyden a , , Peter Tillmann b a b
Chair of Banking & Finance, University of Giessen, Licher Str. 62, 35394 Giessen, Germany Chair of Monetary Economics, University of Giessen, Licher Str. 66, 35394 Giessen, Germany
highlights • We study the impact of rating changes on portfolio flows to emerging markets. • Portfolio flow reactions differ for active and passive flows. • Flow reactions strongest for extremely weak or strong credit quality.
article
info
Article history: Received 3 September 2018 Received in revised form 4 February 2019 Accepted 7 February 2019 Available online 21 February 2019 JEL classification: E44 F32 G15
a b s t r a c t We study the short-term impact of sovereign rating and outlook changes on daily portfolio flows of active and passive mutual funds to emerging market economies. Our results indicate that active bond fund flows react only to negative rating actions, whereas passive bond fund flows are sensitive to both positive and negative rating actions. Active and passive country flows hence do not always follow the same determinants. © 2019 Elsevier B.V. All rights reserved.
Keywords: Emerging markets Event study Portfolio flows Passive funds Sovereign ratings
1. Introduction In the course of the recent global financial crisis, the central banks of the world’s largest economies started lowering interest rates until they were either close to or virtually zero. As a result of key interest rates being at the zero lower bound, central banks began to employ unconventional measures of monetary policy in order to boost economic growth. These measures involved largescale asset purchase programs, so-called Quantitative Easing (QE). Although there is mixed evidence concerning the cause, it has been extensively documented that QE was accompanied by strong ✩ We thank an anonymous referee for helpful comments. Deutsche Bundesbank, Regional Office in Hesse, provided financial support. ∗ Corresponding author. E-mail addresses: [email protected] (C.E. Bannier), [email protected] (T. Heyden), [email protected] (P. Tillmann). https://doi.org/10.1016/j.econlet.2019.02.009 0165-1765/© 2019 Elsevier B.V. All rights reserved.
and volatile private capital flows from advanced economies (AEs) to emerging market economies (EMEs) over the last decade (see, e.g., Fratzscher, 2012; Lo Duca, 2012; Bluedorn et al., 2013; Chen et al., 2016; Tillmann, 2016; Anaya et al., 2017; Fratzscher et al., 2018). Over the same time period, the relevance of passive investment funds, i.e. exchange traded funds and passive index funds, has grown remarkably. In a recent report, Moody’s (2017) even predicts the ‘‘passive market share to overtake active in the U.S. no later than 2024’’. According to the same report, outflows of actively managed equity funds are growing even faster than inflows to passive equity funds. Given the tremendous and increasing popularity of passive funds, the question arises whether the flows originating from them react in the same way to allocative factors as active flows. Answering this question with regard to capital flows to EMEs seems particularly fruitful not only because passive portfolio flows to EMEs have been strongly increasing in recent years due to
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C.E. Bannier, T. Heyden and P. Tillmann / Economics Letters 178 (2019) 37–45
Table 1 Distributional properties of daily net flows to all countries. Bonds
Mean Median S.D. Minimum Maximum
Equity
Active
Passive
Active
Passive
10.6 12.8 79.7 −1602.1 1584.5
20.0 7.5 71.5 −786.1 532.9
−29.4 −4.1
79.9 71.0 339.7 −2553.2 9397.6
299.3 −2294.8 1120.1
Remark: Net flows are denoted in million USD and calculated from daily flows over all target-countries.
QE. Rather, investors located in developed markets are subject to particularly profound informational asymmetries regarding these investments (Brennan and Cao, 1997). As a country’s fundamental setup has been shown to be a crucial ‘‘pull’’ factor for the allocation process of portfolio flows to EMEs (Milesi-Ferretti and Tille, 2011; Ahmed and Zlate, 2014), investors may reasonably be expected to resort to proxies for economic fundamentals in their investment decisions. Changes in these proxies should hence show particularly strong effects on fund flows. We address the question whether passive fund flows to EMEs react differently to changes in economic proxies than active flows and examine sovereign credit rating actions in a modified event study approach. Whereas the traditional event study is typically applied to returns data, we adapt it to daily portfolio flow data. This allows us to draw inferences on the informational content of changes in sovereign ratings and rating outlooks for the portfolio allocation process. Our results imply that rating and outlook changes indeed play a role for portfolio flows to EMEs, with partly different effects for active as compared to passive funds. More precisely, we find that if a country receives a positive rating action, there is no significant reaction of active bond fund flows. Passive flows, in contrast, show a positive reaction if the target country is rated below BBB+ and a negative reaction for ratings above. For a negative rating action, both active and passive bond fund flows react negatively if the target country is either very lowly or very highly rated. EMEs with intermediate credit quality see no reaction in fund flows to a downgrade. Overall, active portfolio flows react only to negative rating actions, whereas passive flows are sensitive to both positive and negative rating changes. 2. Data and methodology We obtain daily data on country flows from January 2012 to February 2017 from EPFR Global.1 The dataset contains daily bond and equity portfolio flows to 54 emerging market countries2 and allows for disaggregation into active and passive fund flows. According to Fratzscher (2012) and Jotikasthira et al. (2012), the EPFR data is fairly representative, covering approximately 20% of the whole market. Table 1 shows the main distributional properties of our flow data. On average, net flows from passive funds are larger than those from active funds. A particularly strong divergence can be seen for equity funds, where average and median net flows from active funds are negative over the time period considered but positive from passive funds. For our event study, we collect foreign currency sovereign ratings from Thomson Reuters. We consider only ratings that have 1 Other studies that use data from EPFR are for example Fratzscher (2012), Jotikasthira et al. (2012), Lo Duca (2012), Ahmed et al. (2015), Raddatz et al. (2017), and Fratzscher et al. (2018). 2 A list of the countries can be found in Appendix A.
been issued by S&P, Moody’s and Fitch, as their main determinants have been shown to be quite similar (Cantor and Packer, 1996; Afonso, 2003). If these determinants change for a particular country, all three agencies should review the country’s rating accordingly. We follow Kaminsky and Schmukler (2002) and consider not only rating changes but also rating outlook entries. In the following, we denote as a positive (negative) rating action a rating upgrade (downgrade) or a positive (negative) outlook entry. Our event study analysis follows a two-step procedure. In a first step, we standardize the flows and calculate excessive flows (EF ) according to EFi,t = Fi,t − E(Fi,t ), where Fi,t are the actual flows and E(Fi,t ) are the expected flows into country i on day t. Inspection of the flows shows that they are autocorrelated and exhibit volatility clustering. We therefore compute E(F ) with a GARCH(1,1) model (Bollerslev, 1986), adding lagged values of the flows3 to the list of independent variables. Further independent variables are chosen based on Koepke’s (2015) meta-analysis and are listed in Appendix B. We estimate the parameters for forecasting E(F ) using the 150 days preceding the event window, which is defined as t ∈ [−10; 10], with the event day being t = 0. Appendix C presents a graphical illustration of the excessive flows in reaction to positive and negative rating actions for active and passive bond and equity fund flows, differentiating between four geographical regions. In the second step, we perform cross-sectional analyses of cumulative excessive flows (CEFs) around positive and negative rating actions using different time windows. We distinguish between active and passive flows and control for rating levels via dummy variables that each combine four adjacent rating classes into a rating group, according to the scheme given in Appendix D. This allows us to control for a country’s credit quality with a limited set of independent variables. 3. Results Table 2 shows the main results from an OLS regression model of cumulative excessive fund flows to EMEs in reaction to a positive (Panel 1) or negative (Panel 2) rating action. Given the control variables employed, the constant represents the average CEF from active bond funds to an EME in the first rating group (CCC+ and below; the omitted category) due to a rating action that did not lead to a crossing of the investment/speculative-grade boundary. Interestingly, there is no significant reaction of these active bond fund flows to a positive rating action (Panel 1). Given the insignificant coefficients of the rating group dummies, active bond flows for countries with better credit qualities do not seem to react either. This observation is consistent with active fund managers monitoring closely the economic situation of a country and adjusting their portfolio allocation before the actual rating or outlook change. The dummy for passive funds, D.Passive, shows a significantly positive coefficient over all time windows, in contrast. This indicates that passive bond fund flows to EMEs rated as extremely speculative increase by approximately $1 m cumulatively, i.e. by about one standard deviation, in the three days surrounding the positive rating event. The amount of net inflows more than doubles over the two weeks after the positive rating action. The insignificant interaction terms of D.Passive with the second and third rating group dummy reveal that passive flows show the same positive reaction also for better rating classes. However, the reaction changes 3 We determine the lag length for each country with the Schwarz information criterion (SIC).
C.E. Bannier, T. Heyden and P. Tillmann / Economics Letters 178 (2019) 37–45 Table 2 (continued).
Table 2 Cross-sectional analysis of cumulative excessive flows.
Panel 2: Negative rating action
Panel 1: Positive rating action
Constant D.Passive 2.Rating group 3.Rating group 4.Rating group 5.Rating group D.Passive ×2.Rating group D.Passive ×3.Rating group D.Passive ×4.Rating group D.Passive ×5.Rating group D.Rating D.Equity D.InvGrade L.DebtInd L.EquityInd Continent FE Quarter FE N R2
[−1, 1]
[−1, 5]
[−1, 10]
−0.074
−0.490
−0.139
(0.872) 0.992*** (0.000) −0.478 (0.367) −0.281 (0.540) 0.155 (0.729) −0.728 (0.355) −0.481 (0.171) −0.561 (0.126) −1.384*** (0.000) −2.105*** (0.000) −0.084 (0.861) 0.200 (0.602) −0.347 (0.475) 304.966*** (0.007) −46.209* (0.083) Yes Yes 244 0.163
(0.625) 1.449*** (0.000) −0.794 (0.407) −0.897 (0.305) −0.580 (0.551) −1.269 (0.329) −0.730 (0.369) −0.913 (0.154) −1.731** (0.015) −0.899*** (0.005) −0.894 (0.229) −0.065 (0.934) −0.260 (0.767) 681.177*** (0.001) −108.654** (0.036) Yes Yes 244 0.209
(0.928) 2.198*** (0.000) −1.222 (0.199) −1.218 (0.220) −0.597 (0.670) −1.394 (0.250) −1.456 (0.215) −1.498 (0.103) −2.775** (0.040) −2.640*** (0.000) −0.788 (0.458) −0.021 (0.984) −0.537 (0.732) 767.242*** (0.005) −149.489** (0.015) Yes Yes 244 0.203
[−1, 1]
[−1, 5]
[−1, 10]
−0.904**
−2.297***
(0.048) 0.301 (0.717) 0.642 (0.132) 0.720** (0.033) 0.387 (0.392) 0.373 (0.576) −0.097 (0.909) −0.149 (0.867) 0.414 (0.691) 0.449 (0.601) −0.160 (0.397) 0.422* (0.069) −0.787** (0.012) 53.564 (0.268) −3.630 (0.772) Yes Yes
(0.010) 3.912 (0.122) 2.210*** (0.010) 2.433*** (0.001) 2.211*** (0.004) 2.342 (0.111) −3.711 (0.143) −3.586 (0.166) −3.528 (0.199) −3.652 (0.154) −0.238 (0.481) 0.364 (0.457) −0.208 (0.735) 161.028* (0.075) −18.191 (0.412) Yes Yes
−2.805 (0.110) 3.845* (0.070) 2.534 (0.136) 1.759 (0.243) 2.389 (0.112) 2.396 (0.329) −3.344 (0.124) −3.071 (0.176) −3.314 (0.177) −2.962 (0.166) −0.112 (0.795) −0.108 (0.874) 0.051 (0.954) 127.827 (0.236) −4.240 (0.895) Yes Yes
Panel 2: Negative rating action
Constant D.Passive 2.Rating Group 3.Rating Group 4.Rating Group 5.Rating Group D.Passive ×2.Rating Group D.Passive ×3.Rating Group D.Passive ×4.Rating Group D.Passive ×5.Rating Group D.Rating D.Equity D.InvGrade L.DebtInd L.EquityInd Continent FE Quarter FE
39
(continued on next page)
N R2
[−1, 1]
[−1, 5]
[−1, 10]
342 0.199
342 0.190
342 0.226
Remark: This table reports OLS estimates of the effects of being either active or passive on CEFs to EMEs. Panel 1 and Panel 2 report the reactions on a positive and a negative rating action, respectively. The dependent variables are CEFs of different time windows relative to the event date. p-values are in parentheses. Standard errors are clustered at the country level. *Denote statistical significance at the 10% level. **Denote statistical significance at the 5% level. ***Denote statistical significance at the 1% level.
to negative for EMEs in rating group 4, i.e. BBB+ and above. This effect becomes even stronger in the fifth rating group, indicating an approximate retraction of $1.1 m in the three days surrounding the positive rating action. This may be taken as an indication that passive funds seek exposure to certain risks, so that a risk reduction via a rating upgrade or positive outlook induces a withdrawal of their investment. With respect to negative rating actions (Panel 2), the highly significant negative coefficient of the constant shows that active bond flows react negatively for EMEs in the weakest rating category over the short and medium-term period. This effect is also economically significant. Interestingly, this negative reaction persists for countries with extremely strong ratings (rating group 5) as well. The highly significant coefficients of the intermediate rating group dummies that show a similar size but opposite sign, in contrast, indicate no such reaction from active flows for EMEs with intermediate credit quality. Negative rating actions hence affect the allocation decision of active bond fund managers after all, provided they concern countries with sufficiently extreme levels of credit quality. With regard to passive bond flows, we find that neither the D.Passive dummy nor its interaction terms with the rating group dummies are significant (with only one exception). This implies that negative rating actions trigger very similar reactions from active and passive bond fund flows that are centered on EMEs with either extremely high or low credit quality. 4. Conclusion Our results suggest that sovereign rating actions are indeed a relevant driver of portfolio flows to EMEs. More precisely, we show that active funds are sensitive only to negative rating actions, which is in line with the findings of, e.g., Gande and Parsley (2014). Passive funds, in contrast, react to both positive and negative rating actions. Overall, fund flow reactions tend to be strongest for countries with extremely weak or extremely strong credit quality. This may be taken as indication that particularly investors who employ passive and, hence, less expensive funds follow trading strategies that try to seek exposure to or shy away from particular risks. Appendix A. List of sample countries and their region assignment
Africa
Asia
Europe
Latin America
Eastern Africa:
Central Asia:
Eastern Europe:
Latin America:
Kenya
Kazakhstan
Hungary
Argentina
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C.E. Bannier, T. Heyden and P. Tillmann / Economics Letters 178 (2019) 37–45
Africa Mozambique Rwanda Uganda Zambia
Asia
Central Asia: China Korea (South) Mongolia
Middle Africa: Angola Gabon
Southern Asia: Sri Lanka
Europe
Latin America
Poland Russia Ukraine
Bolivia Brazil Chile Colombia
Southern Europe: Croatia
Uruguay
Macedonia Serbia Slovenia
Northern Africa:
Egypt Tunisia
SouthEastern Asia: Indonesia Malaysia
Step 2 D.Passive
D.Rating
Dummy = 1 if the event is a rating instead of an outlook change
D.Equity
Dummy = 1 if the flows are into equity instead of bonds
D.InvGrade Central America:
Dummy = 1 if rating switches from investment to non-investmentgrade and vice versa
L.DebtInd
Costa Rica El Salvador
Lagged daily return of JP Morgan GBI-EM
L.EquityInd
Lagged daily return of MSCI EM stock index
Venezuela
Guatemala Mexico Caribbean:
Botswana
Azerbaijan
Namibia South Africa
Bahrain Cyprus
Dominican Rep. Jamaica Trinidad Tobago
Georgia Iraq Israel Jordan Lebanon Oman Saudi Arabia Turkey
Appendix C. Cumulative excessive flows resulting from positive (negative) rating or outlook actions
Remark. The following graphs show the cumulative average excessive equity and bond flows into Africa, Asia, emerging Europe, and Latin America. The horizontal axis shows the days relative to the event day t = 0. The values are in million USD. The dashed red lines denote the 95% confidence intervals. On the left hand side (lhs) are reactions on positive rating actions, on the right hand side (rhs) are reactions on negative rating actions.
Appendix D. Rating groups for sovereign rating levels.
Appendix B. Independent variables list Investment grade
Step 1 MSCI world
Dummy = 1 if the flows are passive instead of active Indicator variables to account for the rating level class
Western Asia:
Variable
Definition
I.Rating Group
Southern Africa:
Western Africa: Ghana Nigeria
Variable
Definition Daily continuous stock index returns
Moody’s S&P
Fitch
Rating group
Aaa
AAA
AAA
5
Aa1
AA+
AA+
5
Aa2
AA
AA
5
Aa3
AA−
AA−
5
A1
A+
A+
4
A2
A
A
4
A3
A−
A−
4
Baa1
BBB+ BBB+ 4
Baa2
BBB
BBB− BBB− 3
BBB
3
U.S. treasury
Daily yield of ten-year U.S. bonds
Baa3
VIX
Implied daily volatility of the S&P 500
Ba1
BB+
BB+
3
Ba2
BB
BB
3
Dummy for Fed and ECB key Quantitative Easing events
Ba3
BB−
BB−
2
B1
B+
B+
2
QE Aggregated flows
The sum over all flows into EMEs
Spillover upgrade
Dummy for positive rating actions related to countries located in the same region Dummy for negative rating actions related to countries located in the same region
Spillover downgrade
Speculative grade B2
Default grade
B
B
2
B3
B−
B−
2
Caa1
CCC+ CCC+ 1
Caa2
CCC
Caa3
CCC− CCC− 1
Ca C/D
CC C/D
CCC CC C/D
1 1 0
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References Afonso, A., 2003. Understanding the determinants of sovereign debt ratings: Evidence for the two leading agencies. J. Econ. Financ. 27 (1), 56–74. Ahmed, S., Curcuru, S., Warnock, F., Zlate, A., 2015. The two components of international portfolio flows, Working Paper, Darden Graduate School of Business,
University of Virginia. Ahmed, S., Zlate, A., 2014. Capital flows to emerging market economies: A brave new world?. J. Int. Money Financ. 48, 221–248. Anaya, P., Hachula, M., Offermanns, C.J., 2017. Spillovers of us unconventional monetary policy to emerging markets: the role of capital flows. J. Int. Money Financ. 73, 275–295.
C.E. Bannier, T. Heyden and P. Tillmann / Economics Letters 178 (2019) 37–45 Bluedorn, J.C., Duttagupta, R., Guajardo, J., Topalova, P.B., 2013. Capital flows are fickle: anytime, anywhere, Working Paper No. 13-183, International Monetary Fund. Bollerslev, T., 1986. Generalized autoregressive conditional heteroskedasticity. J. Econometrics 31 (3), 307–327. Brennan, M.J., Cao, H.H., 1997. International portfolio investment flows. J. Financ. 52 (5), 1851–1880. Cantor, R., Packer, F., 1996. Determinants and impact of sovereign credit ratings. J. Fixed Income 6 (3), 76–91. Chen, Q., Filardo, A., He, D., Zhu, F., 2016. Financial crisis, US unconventional monetary policy and international spillovers. J. Int. Money Financ. 67, 62–81. Fratzscher, M., 2012. Capital flows, push versus pull factors and the global financial crisis. J. Int. Econ. 88 (2), 341–356. Fratzscher, M., Lo Duca, M., Straub, R., 2018. On the international spillovers of US quantitative easing. Econom. J. 128 (608), 330–377. Gande, A., Parsley, D.C., 2014. Sovereign credit ratings, transparency and international portfolio flows, Working Paper No. 12/2014 Hong Kong Institute for Monetary Research.
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Jotikasthira, C., Lundblad, C., Ramadorai, T., 2012. Asset fire sales and purchases and the international transmission of funding shocks. J. Financ. 67 (6), 2015–2050. Kaminsky, G., Schmukler, S.L., 2002. Emerging market instability: do sovereign ratings affect country risk and stock returns?. World Bank Econ. Rev. 16 (2), 171–195. Koepke, R., 2015. What drives capital flows to emerging markets? A survey of the empirical literature, Working Paper, Institute of International Finance. Lo Duca, M., 2012. Modelling the time varying determinants of portfolio flows to emerging markets, Working Paper Series 1468, European Central Bank. Milesi-Ferretti, G.-M., Tille, C., 2011. The great retrenchment: International capital flows during the global financial crisis. Econ. Policy 26 (66), 289–346. Moody’s, 2017. Passive market share to overtake active in the U.S. no later than 2024, White Paper. Raddatz, C., Schmukler, S.L., Williams, T., 2017. International asset allocations and capital flows: the benchmark effect. J. Int. Econ. 108, 413–430. Tillmann, P., 2016. Unconventional monetary policy and the spillovers to emerging markets. J. Int. Money Financ 66, 136–156.
Contents lists available at ScienceDirect
Economics Letters journal homepage: www.elsevier.com/locate/ecolet
Rating changes and portfolio flows to emerging markets: Evidence from active and passive funds✩ ∗
Christina E. Bannier a , Thomas Heyden a , , Peter Tillmann b a b
Chair of Banking & Finance, University of Giessen, Licher Str. 62, 35394 Giessen, Germany Chair of Monetary Economics, University of Giessen, Licher Str. 66, 35394 Giessen, Germany
highlights • We study the impact of rating changes on portfolio flows to emerging markets. • Portfolio flow reactions differ for active and passive flows. • Flow reactions strongest for extremely weak or strong credit quality.
article
info
Article history: Received 3 September 2018 Received in revised form 4 February 2019 Accepted 7 February 2019 Available online 21 February 2019 JEL classification: E44 F32 G15
a b s t r a c t We study the short-term impact of sovereign rating and outlook changes on daily portfolio flows of active and passive mutual funds to emerging market economies. Our results indicate that active bond fund flows react only to negative rating actions, whereas passive bond fund flows are sensitive to both positive and negative rating actions. Active and passive country flows hence do not always follow the same determinants. © 2019 Elsevier B.V. All rights reserved.
Keywords: Emerging markets Event study Portfolio flows Passive funds Sovereign ratings
1. Introduction In the course of the recent global financial crisis, the central banks of the world’s largest economies started lowering interest rates until they were either close to or virtually zero. As a result of key interest rates being at the zero lower bound, central banks began to employ unconventional measures of monetary policy in order to boost economic growth. These measures involved largescale asset purchase programs, so-called Quantitative Easing (QE). Although there is mixed evidence concerning the cause, it has been extensively documented that QE was accompanied by strong ✩ We thank an anonymous referee for helpful comments. Deutsche Bundesbank, Regional Office in Hesse, provided financial support. ∗ Corresponding author. E-mail addresses: [email protected] (C.E. Bannier), [email protected] (T. Heyden), [email protected] (P. Tillmann). https://doi.org/10.1016/j.econlet.2019.02.009 0165-1765/© 2019 Elsevier B.V. All rights reserved.
and volatile private capital flows from advanced economies (AEs) to emerging market economies (EMEs) over the last decade (see, e.g., Fratzscher, 2012; Lo Duca, 2012; Bluedorn et al., 2013; Chen et al., 2016; Tillmann, 2016; Anaya et al., 2017; Fratzscher et al., 2018). Over the same time period, the relevance of passive investment funds, i.e. exchange traded funds and passive index funds, has grown remarkably. In a recent report, Moody’s (2017) even predicts the ‘‘passive market share to overtake active in the U.S. no later than 2024’’. According to the same report, outflows of actively managed equity funds are growing even faster than inflows to passive equity funds. Given the tremendous and increasing popularity of passive funds, the question arises whether the flows originating from them react in the same way to allocative factors as active flows. Answering this question with regard to capital flows to EMEs seems particularly fruitful not only because passive portfolio flows to EMEs have been strongly increasing in recent years due to
38
C.E. Bannier, T. Heyden and P. Tillmann / Economics Letters 178 (2019) 37–45
Table 1 Distributional properties of daily net flows to all countries. Bonds
Mean Median S.D. Minimum Maximum
Equity
Active
Passive
Active
Passive
10.6 12.8 79.7 −1602.1 1584.5
20.0 7.5 71.5 −786.1 532.9
−29.4 −4.1
79.9 71.0 339.7 −2553.2 9397.6
299.3 −2294.8 1120.1
Remark: Net flows are denoted in million USD and calculated from daily flows over all target-countries.
QE. Rather, investors located in developed markets are subject to particularly profound informational asymmetries regarding these investments (Brennan and Cao, 1997). As a country’s fundamental setup has been shown to be a crucial ‘‘pull’’ factor for the allocation process of portfolio flows to EMEs (Milesi-Ferretti and Tille, 2011; Ahmed and Zlate, 2014), investors may reasonably be expected to resort to proxies for economic fundamentals in their investment decisions. Changes in these proxies should hence show particularly strong effects on fund flows. We address the question whether passive fund flows to EMEs react differently to changes in economic proxies than active flows and examine sovereign credit rating actions in a modified event study approach. Whereas the traditional event study is typically applied to returns data, we adapt it to daily portfolio flow data. This allows us to draw inferences on the informational content of changes in sovereign ratings and rating outlooks for the portfolio allocation process. Our results imply that rating and outlook changes indeed play a role for portfolio flows to EMEs, with partly different effects for active as compared to passive funds. More precisely, we find that if a country receives a positive rating action, there is no significant reaction of active bond fund flows. Passive flows, in contrast, show a positive reaction if the target country is rated below BBB+ and a negative reaction for ratings above. For a negative rating action, both active and passive bond fund flows react negatively if the target country is either very lowly or very highly rated. EMEs with intermediate credit quality see no reaction in fund flows to a downgrade. Overall, active portfolio flows react only to negative rating actions, whereas passive flows are sensitive to both positive and negative rating changes. 2. Data and methodology We obtain daily data on country flows from January 2012 to February 2017 from EPFR Global.1 The dataset contains daily bond and equity portfolio flows to 54 emerging market countries2 and allows for disaggregation into active and passive fund flows. According to Fratzscher (2012) and Jotikasthira et al. (2012), the EPFR data is fairly representative, covering approximately 20% of the whole market. Table 1 shows the main distributional properties of our flow data. On average, net flows from passive funds are larger than those from active funds. A particularly strong divergence can be seen for equity funds, where average and median net flows from active funds are negative over the time period considered but positive from passive funds. For our event study, we collect foreign currency sovereign ratings from Thomson Reuters. We consider only ratings that have 1 Other studies that use data from EPFR are for example Fratzscher (2012), Jotikasthira et al. (2012), Lo Duca (2012), Ahmed et al. (2015), Raddatz et al. (2017), and Fratzscher et al. (2018). 2 A list of the countries can be found in Appendix A.
been issued by S&P, Moody’s and Fitch, as their main determinants have been shown to be quite similar (Cantor and Packer, 1996; Afonso, 2003). If these determinants change for a particular country, all three agencies should review the country’s rating accordingly. We follow Kaminsky and Schmukler (2002) and consider not only rating changes but also rating outlook entries. In the following, we denote as a positive (negative) rating action a rating upgrade (downgrade) or a positive (negative) outlook entry. Our event study analysis follows a two-step procedure. In a first step, we standardize the flows and calculate excessive flows (EF ) according to EFi,t = Fi,t − E(Fi,t ), where Fi,t are the actual flows and E(Fi,t ) are the expected flows into country i on day t. Inspection of the flows shows that they are autocorrelated and exhibit volatility clustering. We therefore compute E(F ) with a GARCH(1,1) model (Bollerslev, 1986), adding lagged values of the flows3 to the list of independent variables. Further independent variables are chosen based on Koepke’s (2015) meta-analysis and are listed in Appendix B. We estimate the parameters for forecasting E(F ) using the 150 days preceding the event window, which is defined as t ∈ [−10; 10], with the event day being t = 0. Appendix C presents a graphical illustration of the excessive flows in reaction to positive and negative rating actions for active and passive bond and equity fund flows, differentiating between four geographical regions. In the second step, we perform cross-sectional analyses of cumulative excessive flows (CEFs) around positive and negative rating actions using different time windows. We distinguish between active and passive flows and control for rating levels via dummy variables that each combine four adjacent rating classes into a rating group, according to the scheme given in Appendix D. This allows us to control for a country’s credit quality with a limited set of independent variables. 3. Results Table 2 shows the main results from an OLS regression model of cumulative excessive fund flows to EMEs in reaction to a positive (Panel 1) or negative (Panel 2) rating action. Given the control variables employed, the constant represents the average CEF from active bond funds to an EME in the first rating group (CCC+ and below; the omitted category) due to a rating action that did not lead to a crossing of the investment/speculative-grade boundary. Interestingly, there is no significant reaction of these active bond fund flows to a positive rating action (Panel 1). Given the insignificant coefficients of the rating group dummies, active bond flows for countries with better credit qualities do not seem to react either. This observation is consistent with active fund managers monitoring closely the economic situation of a country and adjusting their portfolio allocation before the actual rating or outlook change. The dummy for passive funds, D.Passive, shows a significantly positive coefficient over all time windows, in contrast. This indicates that passive bond fund flows to EMEs rated as extremely speculative increase by approximately $1 m cumulatively, i.e. by about one standard deviation, in the three days surrounding the positive rating event. The amount of net inflows more than doubles over the two weeks after the positive rating action. The insignificant interaction terms of D.Passive with the second and third rating group dummy reveal that passive flows show the same positive reaction also for better rating classes. However, the reaction changes 3 We determine the lag length for each country with the Schwarz information criterion (SIC).
C.E. Bannier, T. Heyden and P. Tillmann / Economics Letters 178 (2019) 37–45 Table 2 (continued).
Table 2 Cross-sectional analysis of cumulative excessive flows.
Panel 2: Negative rating action
Panel 1: Positive rating action
Constant D.Passive 2.Rating group 3.Rating group 4.Rating group 5.Rating group D.Passive ×2.Rating group D.Passive ×3.Rating group D.Passive ×4.Rating group D.Passive ×5.Rating group D.Rating D.Equity D.InvGrade L.DebtInd L.EquityInd Continent FE Quarter FE N R2
[−1, 1]
[−1, 5]
[−1, 10]
−0.074
−0.490
−0.139
(0.872) 0.992*** (0.000) −0.478 (0.367) −0.281 (0.540) 0.155 (0.729) −0.728 (0.355) −0.481 (0.171) −0.561 (0.126) −1.384*** (0.000) −2.105*** (0.000) −0.084 (0.861) 0.200 (0.602) −0.347 (0.475) 304.966*** (0.007) −46.209* (0.083) Yes Yes 244 0.163
(0.625) 1.449*** (0.000) −0.794 (0.407) −0.897 (0.305) −0.580 (0.551) −1.269 (0.329) −0.730 (0.369) −0.913 (0.154) −1.731** (0.015) −0.899*** (0.005) −0.894 (0.229) −0.065 (0.934) −0.260 (0.767) 681.177*** (0.001) −108.654** (0.036) Yes Yes 244 0.209
(0.928) 2.198*** (0.000) −1.222 (0.199) −1.218 (0.220) −0.597 (0.670) −1.394 (0.250) −1.456 (0.215) −1.498 (0.103) −2.775** (0.040) −2.640*** (0.000) −0.788 (0.458) −0.021 (0.984) −0.537 (0.732) 767.242*** (0.005) −149.489** (0.015) Yes Yes 244 0.203
[−1, 1]
[−1, 5]
[−1, 10]
−0.904**
−2.297***
(0.048) 0.301 (0.717) 0.642 (0.132) 0.720** (0.033) 0.387 (0.392) 0.373 (0.576) −0.097 (0.909) −0.149 (0.867) 0.414 (0.691) 0.449 (0.601) −0.160 (0.397) 0.422* (0.069) −0.787** (0.012) 53.564 (0.268) −3.630 (0.772) Yes Yes
(0.010) 3.912 (0.122) 2.210*** (0.010) 2.433*** (0.001) 2.211*** (0.004) 2.342 (0.111) −3.711 (0.143) −3.586 (0.166) −3.528 (0.199) −3.652 (0.154) −0.238 (0.481) 0.364 (0.457) −0.208 (0.735) 161.028* (0.075) −18.191 (0.412) Yes Yes
−2.805 (0.110) 3.845* (0.070) 2.534 (0.136) 1.759 (0.243) 2.389 (0.112) 2.396 (0.329) −3.344 (0.124) −3.071 (0.176) −3.314 (0.177) −2.962 (0.166) −0.112 (0.795) −0.108 (0.874) 0.051 (0.954) 127.827 (0.236) −4.240 (0.895) Yes Yes
Panel 2: Negative rating action
Constant D.Passive 2.Rating Group 3.Rating Group 4.Rating Group 5.Rating Group D.Passive ×2.Rating Group D.Passive ×3.Rating Group D.Passive ×4.Rating Group D.Passive ×5.Rating Group D.Rating D.Equity D.InvGrade L.DebtInd L.EquityInd Continent FE Quarter FE
39
(continued on next page)
N R2
[−1, 1]
[−1, 5]
[−1, 10]
342 0.199
342 0.190
342 0.226
Remark: This table reports OLS estimates of the effects of being either active or passive on CEFs to EMEs. Panel 1 and Panel 2 report the reactions on a positive and a negative rating action, respectively. The dependent variables are CEFs of different time windows relative to the event date. p-values are in parentheses. Standard errors are clustered at the country level. *Denote statistical significance at the 10% level. **Denote statistical significance at the 5% level. ***Denote statistical significance at the 1% level.
to negative for EMEs in rating group 4, i.e. BBB+ and above. This effect becomes even stronger in the fifth rating group, indicating an approximate retraction of $1.1 m in the three days surrounding the positive rating action. This may be taken as an indication that passive funds seek exposure to certain risks, so that a risk reduction via a rating upgrade or positive outlook induces a withdrawal of their investment. With respect to negative rating actions (Panel 2), the highly significant negative coefficient of the constant shows that active bond flows react negatively for EMEs in the weakest rating category over the short and medium-term period. This effect is also economically significant. Interestingly, this negative reaction persists for countries with extremely strong ratings (rating group 5) as well. The highly significant coefficients of the intermediate rating group dummies that show a similar size but opposite sign, in contrast, indicate no such reaction from active flows for EMEs with intermediate credit quality. Negative rating actions hence affect the allocation decision of active bond fund managers after all, provided they concern countries with sufficiently extreme levels of credit quality. With regard to passive bond flows, we find that neither the D.Passive dummy nor its interaction terms with the rating group dummies are significant (with only one exception). This implies that negative rating actions trigger very similar reactions from active and passive bond fund flows that are centered on EMEs with either extremely high or low credit quality. 4. Conclusion Our results suggest that sovereign rating actions are indeed a relevant driver of portfolio flows to EMEs. More precisely, we show that active funds are sensitive only to negative rating actions, which is in line with the findings of, e.g., Gande and Parsley (2014). Passive funds, in contrast, react to both positive and negative rating actions. Overall, fund flow reactions tend to be strongest for countries with extremely weak or extremely strong credit quality. This may be taken as indication that particularly investors who employ passive and, hence, less expensive funds follow trading strategies that try to seek exposure to or shy away from particular risks. Appendix A. List of sample countries and their region assignment
Africa
Asia
Europe
Latin America
Eastern Africa:
Central Asia:
Eastern Europe:
Latin America:
Kenya
Kazakhstan
Hungary
Argentina
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Africa Mozambique Rwanda Uganda Zambia
Asia
Central Asia: China Korea (South) Mongolia
Middle Africa: Angola Gabon
Southern Asia: Sri Lanka
Europe
Latin America
Poland Russia Ukraine
Bolivia Brazil Chile Colombia
Southern Europe: Croatia
Uruguay
Macedonia Serbia Slovenia
Northern Africa:
Egypt Tunisia
SouthEastern Asia: Indonesia Malaysia
Step 2 D.Passive
D.Rating
Dummy = 1 if the event is a rating instead of an outlook change
D.Equity
Dummy = 1 if the flows are into equity instead of bonds
D.InvGrade Central America:
Dummy = 1 if rating switches from investment to non-investmentgrade and vice versa
L.DebtInd
Costa Rica El Salvador
Lagged daily return of JP Morgan GBI-EM
L.EquityInd
Lagged daily return of MSCI EM stock index
Venezuela
Guatemala Mexico Caribbean:
Botswana
Azerbaijan
Namibia South Africa
Bahrain Cyprus
Dominican Rep. Jamaica Trinidad Tobago
Georgia Iraq Israel Jordan Lebanon Oman Saudi Arabia Turkey
Appendix C. Cumulative excessive flows resulting from positive (negative) rating or outlook actions
Remark. The following graphs show the cumulative average excessive equity and bond flows into Africa, Asia, emerging Europe, and Latin America. The horizontal axis shows the days relative to the event day t = 0. The values are in million USD. The dashed red lines denote the 95% confidence intervals. On the left hand side (lhs) are reactions on positive rating actions, on the right hand side (rhs) are reactions on negative rating actions.
Appendix D. Rating groups for sovereign rating levels.
Appendix B. Independent variables list Investment grade
Step 1 MSCI world
Dummy = 1 if the flows are passive instead of active Indicator variables to account for the rating level class
Western Asia:
Variable
Definition
I.Rating Group
Southern Africa:
Western Africa: Ghana Nigeria
Variable
Definition Daily continuous stock index returns
Moody’s S&P
Fitch
Rating group
Aaa
AAA
AAA
5
Aa1
AA+
AA+
5
Aa2
AA
AA
5
Aa3
AA−
AA−
5
A1
A+
A+
4
A2
A
A
4
A3
A−
A−
4
Baa1
BBB+ BBB+ 4
Baa2
BBB
BBB− BBB− 3
BBB
3
U.S. treasury
Daily yield of ten-year U.S. bonds
Baa3
VIX
Implied daily volatility of the S&P 500
Ba1
BB+
BB+
3
Ba2
BB
BB
3
Dummy for Fed and ECB key Quantitative Easing events
Ba3
BB−
BB−
2
B1
B+
B+
2
QE Aggregated flows
The sum over all flows into EMEs
Spillover upgrade
Dummy for positive rating actions related to countries located in the same region Dummy for negative rating actions related to countries located in the same region
Spillover downgrade
Speculative grade B2
Default grade
B
B
2
B3
B−
B−
2
Caa1
CCC+ CCC+ 1
Caa2
CCC
Caa3
CCC− CCC− 1
Ca C/D
CC C/D
CCC CC C/D
1 1 0
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