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Star rating, fund flows and performance predictability: evidence from Norway

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Abstract

This paper studies the effect of Morningstar ratings on fund flows and fund performance predictability using a proprietary data set of equity funds from Norway. Controlling for a number of variables proxying for fund and firm visibility, we find that fund flows respond asymmetrically to changes in Morningstar ratings. Specifically, 4- and 5-star rated funds get more flows, and funds upgraded to 5-star get significantly more flows not only in the next month, but also over the following 12 months after the rating change. Downgraded funds suffer outflows, but the results only become statistically significant when fund performance falls to a 2-star rating. We also find evidence of long-term performance predictability for top-rated funds. As the mutual fund industry develops worldwide, our results suggest that Morningstar has been successful in bringing its brand name to markets outside the USA, and that Morningstar ratings are a valuable tool for helping investors make strong investment decisions.

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Notes

  1. At the end of 2019, the world mutual fund industry managed financial assets of nearly $53 trillion, nine times the $6 trillion managed at the end of 1996 (Investment Company Institute 2020). The number of funds has also increased dramatically, to almost 137,000 funds. This growth has been registered worldwide, and particularly in the mutual fund industry outside the USA, where the share of assets under management grew from 38% in 1996 to 52% in 2019.

  2. The bulk of the literature on fund flows has focused on flow-performance sensitivity. In the USA, the literature shows that this relation is convex, as investors flock disproportionately to the best performers and fail to sell poorly performing funds (e.g., Ippolito 1992; Sirri and Tufano 1998; Del Guercio and Tkac 2002). Dahlquist et al. (2000) and Keswani and Stolin (2008) also find a convex flow-performance relationship in Sweden and in the UK, respectively. Ferreira et al. (2012) show that the flow-performance relationship varies across countries, depending on the level of participation costs in the fund industry and on how sophisticated investors are. Search costs and participation costs also affect the flow-performance sensitivity, as its convexity is more pronounced among funds with higher search costs (Sirri and Tufano 1998) and participation costs (Huang et al. 2007).

  3. At the end of 2019, Australia is the sixth largest mutual fund industry in the world by assets under management, and the Australian mutual fund industry represents 30% of the assets under management in the Asia-Pacific region (EFAMA 2020).

  4. Despite the fast growth of China’s financial markets observed in recent years, there are still fundamental differences between the USA and China mutual fund industries and overall capital markets. The mutual fund industry in China only started in 2001, while, in the USA, the first open-end mutual fund was created in 1924 (Khorana et al. 2005). Additionally, mutual funds in China present unique features, including high risk and a rather low performance persistence compared to that observed in U.S. funds (Jun et al. 2014), and, China’s unique regulatory system also affects investor allocation decisions.

  5. Different studies show that many of the most stylized facts observed in the US fund industry do not apply to other mutual fund industries. For example, Ferreira et al. (2019) find that differences in competition in the mutual fund industry explain considerable variation in performance persistence levels across countries, while Ferreira et al. (2012) find marked differences in the flow-performance relationship across countries, suggesting that the documented convexity in the U.S. literature does not apply universally. Additionally, Keswani et al. (2020) show that cultural influences are likely to be as important as institutional, financial and macroeconomic influences in explaining differences in mutual fund conduct across countries.

  6. It was only in 1993 the starting year of the open-end fund industry in Norway (Khorana et al. 2005).

  7. Different studies show that more sophisticated investors are less likely to be influenced by behavioral tendencies, including Gruber (1996), Bailey et al. (2011), and Huang et al. (2007).

  8. The average monthly flow across funds in our sample is 0.11%.

  9. For detailed information on the Morningstar rating methodology, see: https://www.morningstar.com/content/dam/marketing/shared/research/methodology/771945_Morningstar_Rating_for_Funds_Methodology.pdf.

  10. Morningstar changed their methodology in 2002 by introducing a new improved risk-adjusted return measure and new peer groups. Blume (1998) presents a detailed description of the rating system used from 1996 to 2002. Our sample starts in 2003 and, therefore, this change does not affect our results.

  11. In October 2006, Morningstar introduced the 5-year and 10-year rating to Europe; thus, the current European overall rating is based on the weighted average of the 3-, 5- and 10-year rating. Previously, the overall rating in Europe was only based on the 3-year rating. This change has no impact on our results.

  12. We impose all funds in our sample to have a complete rating history and also to have data on all explanatory variables. There are several reasons why some funds have an incomplete rating history, such as inception date, rating suspension, or category change.

  13. Equation (1) assumes that flows occur at the end of each month. Sirri and Tufano (1998) show that the results do not change whether flows are assumed to occur at the beginning, middle, or continuously throughout the period.

  14. To ensure that extreme values do not drive our results, we winsorize fund flows at the bottom and top 1% level of the distribution.

  15. To examine whether the effect of upgrades and downgrades persists, we also compute cumulative flows for the next 3, 6, and 12 months (Flows(t,t+x), where x = 3,6 and 12) after the rating change.

  16. In untabulated results, we also use benchmark-adjusted returns and that does not change the tenor of our main results.

  17. Factors are from AQR Capital Management: https://www.aqr.com/Insights/Datasets.

  18. See Ferreira et al. (2013) and Demirci et al. (2019) for further details on the calculation of the four-factor alpha.

  19. We winsorize fund performance at the bottom and top 1% level of the distribution.

  20. This minimizes the concerns about age bias in Morningstar overall ratings (Morey 2002; Adkisson and Fraser 2003). Age bias originates fundamentally from the weighting system. The overall rating assigns differential weights to the last 36 months of return history based on fund age.

  21. The TNA managed by fund management companies affiliated with a top brand Scandinavian bank represents 28% of the TNA in our sample at the end of 2019.

  22. Keller (1993) presents different financial benefits of creating a strong brand, including greater customer loyalty, less vulnerability to marketing actions from competitors and marketing crises, more inelastic consumer response to price increases, greater trade support and cooperation, and increased opportunities for marketing communication effectiveness, licensing, and brand extensions.

  23. In untabulated results we obtain similar results when computing ranks based on fund’s performance in the prior 36 months.

  24. We include dummy variables indicating Morning star rating grades from 2- to 5-star. This means that the intercept provides the impact on flows for 1-star funds; thus, the coefficients of the dummy variables measure the marginal flows with respect to 1-star rated funds.

  25. In untabulated results, we use Morningstar Style Box as investment style fixed effects instead of Morningstar Category classifications and this change does not affect our main results.

  26. Del Guercio and Tkac (2008) use an event-study methodology on Morningstar rating changes to study the impact of Morningstar on mutual fund flows. They argue that this approach circumvents the problem of correlation between performance variables, which makes it difficult to obtain an accurate estimate of the marginal effect of any given measure on fund flows. However, one problem that arises from doing event studies with ratings already identified by Faff et al. (2007) is that ratings are highly transitory in nature, making it difficult to apply the full Del Guercio and Tkac (2008) methodology. For example, in our sample, in 2019, 36 funds changed their rating, of which 23 changed their rating more than once over the year. This would make the inference from an event study very noisy.

  27. This means that if, for example, a fund was upgraded to 5-star in the last month, Upgraded to 5-star takes the value of 1.

  28. Ferreira et al. (2012) argue that investor sophistication is explained by the level of development of the financial industry and the mutual fund industry in a country, and that there is less sensitivity to poor performance and high sensitivity to top performance in countries with less sophisticated investors.

  29. This is consistent with the findings in Huang et al. (2007) that show that the convexity of the flow-performance relationship has declined over time for U.S. funds due to investors becoming better informed.

  30. The 1-star rated fund group is the reference group, as it provides a bottom limit from which it is possible to evaluate the performance of higher-rated funds. If the Morningstar ratings accurately forecast abnormal performance, then we would observe increasingly positive and significant coefficients moving from 1 to 4. For example, if 4 is significant, then the difference in performance between 5-star and 1-star funds is statistically significant.

  31. In robustness tests we also control for past long-term performance by adding to our baseline model prior period 5-year raw returns.

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Acknowledgements

We thank Morningstar Norway, particularly Ketil Myhrvld and Afsheen Azam. They made this paper possible through providing essential data. We also thank two anonymous referees for comments suggestions and the Professor Markus Schmid Editor. This research is supported by a Grant from the Fundação para a Ciência e Tecnologia (Grant UIDB/00315/2020).

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Authors and Affiliations

  1. ISCTE–Instituto Universitário de Lisboa (ISCTE–IUL), Lisbon, Portugal

    Linn K. Aasheim & António F. Miguel

  2. Business Research Unit (BRU–IUL), Lisbon, Portugal

    António F. Miguel

  3. ESSEC Business School, Paris, France

    Sofia B. Ramos

Authors
  1. Linn K. Aasheim
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  2. António F. Miguel
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  3. Sofia B. Ramos
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Corresponding author

Correspondence to Sofia B. Ramos.

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Appendix 1

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Table 6 Variable definitions
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Aasheim, L.K., Miguel, A.F. & Ramos, S.B. Star rating, fund flows and performance predictability: evidence from Norway. Financ Mark Portf Manag 36, 29–56 (2022). https://doi.org/10.1007/s11408-021-00390-8

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