The Effect of Monetary and Fiscal Policy on Bond Mutual Funds and Stock Market: An International Comparison

ABSTRACT

This study examines the relationship between bond fund flows, stock market returns and financial policies in developed and developing economies. The findings suggest a bidirectional (negative) relationship between bond flows and market returns in the presence of fiscal and monetary policy for developed countries. However, in the case of developing countries, bond flows follow the previous performance of market returns. Moreover, an expansionary monetary stance has a negative impact on bond flows while an expansionary fiscal policy exerts a positive influence on them. In addition, bond funds flourish in times of low economic activity in both developed and developing countries.

KEY WORDS:

• monetary policy
• mutual fund flows
• stock market returns

JEL CLASSIFICATION:

• G11
• G12
• G14
• E44
• E52
• G18

Supplementary Material

Supplemental data for this article can be accessed here.

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Notes

¹ Although bond, as a financial security, has been subject to interest by many researchers in the past; see for example, Fama and French (1993); Fridson and Jónsson (1995); Jones, Lamont, and Lumsdaine (1998); Beber, Brandt, and Kavajecz (2008); Marfatia (2015), the research on bond mutual funds is scant.

² The choice of bond flows is based on the fact that being a major avenue of investment after equity, balanced and money market mutual funds, there appears to have been hardly any research on the relationship of bond mutual funds with the stock market at macro level. Moreover, statistics show that the majority of US mutual fund investments are in long-term securities. Equity funds comprised 52 % of US mutual fund assets at the end of 2015, and bond funds consisted of 22 % of total US mutual fund assets, whereas money market funds and hybrid funds comprised 18 and 8 %, respectively. (Source: Investment Company Institute, Mutual funds Worldwide Market, Statistics, 2015).

³ Khorana, Servaes, and Tufano (2005) argue that developed countries have been able to establish a sound mutual fund industry due to orderly laws, rules and regulations, stringent bank secrecy laws and favorable tax systems. However, the growth of the mutual fund industry has accelerated following the Asian financial crises in the developing markets (Qureshi, Ismail, and Gee Chan 2017).

⁴ The data from the Investment Company Institute Fact Book 2015 shows the growth of the mutual fund industry. Table S1 depicts the growth of mutual funds in terms of NAVs at a worldwide level. See Table S1 available online.

⁵ The details of the total number of mutual funds taken in a sample of each country is given in Table S2, available online. Some countries were dropped as the data on the main variables was missing for a period of 15 consecutive years. The data unavailability issue was particularly prevalent in the developing countries panel (India, Bangladesh, Vietnam, Egypt, Indonesia, etc.) For a smoother comparative analysis, it was decided to choose the top 5 economies from both the developed and developing regions.

⁶ See Sun et al. (2017) for detailed discussion on four periods of quantitative easing method (QE1, QE2, QE3 and QE4) adopted in USA.

⁷ The coefficient on the monetary policy measure can be used to have an overall idea of the relationship between monetary policy and bond fund flows. However, it is difficult to measure/calculate the marginal/incremental effect of changes in monetary policy on bond funds owing to the nature of monetary policy measures, we have used in this study.

⁸ See Abrigo and Love (2016) for lag selection criteria under PVAR methodology. The results are reported in Table S7, available online.

⁹ We used quarterly data on variables – derived from International Financial Statistics (IFS) – for VAR specification. Optimal lags were selected on the basis of the Akaike Information Criteria (AIC), Schwartz’s Bayesian Information Criteria (SBIC), the Hannan Quinn Information Criteria (HQIC), and the Final Prediction Error (FPE).

¹⁰ Klapper, Sulla, and Vittas (2004) found that developing economies have poor information mechanisms and high information asymmetries. Because of this, it is possible that mutual funds may be not able to conduct contemporaneous decision-making, due to high volatility in the stock markets.