Like many pension funds around the world, the superannuation pension funds in Australia are designed to meet basic retirement needs, simplifying investment opportunities and promote economic growth. Since the introduction of the compulsory pension schemes, pension income has increased substantially in every country, with compulsory pension contributions having an increasing share of assets under management (Ebbinghaus, 2011). This meticulous system has also managed to supervise large amounts of savings in the economy, allowing investors to increase the capital they need.
The performance of the Pension Funds is a matter of national importance, as has been shown by various government surveys (Nielson, and Harris, 2010). However, the empirical evidence of pension fund performance remains limited. Although, the scientific literatures on the continuity of the superannuation funds’ performance in the United Kingdom and in the United States are present (Juravle, and Lewis, 2009; Gruen, and Soding, 2011). And similar study was conducted in the Australian Context by Cummings, in 2016. The economic importance and constantly changing dynamics of retirement require a better understanding of the fund’s performance. This is why it is possible to revise the more recent literatures. The purpose of this article was to examine the empirical research on the performance of the Australian superannuation funds from 2013 to 2017. The study provides a timely, factual and informative overview of the portfolio securities and can help researchers assess the effectiveness of recent legislative changes and recommended changes. In order to understand pension benefits, the costs of pension funds have been analyzed, and the regulating policies of APRA have been assessed (Antolín, and Stewart, 2009).
The research focus was primarily on the investigation of yearly, three yearly, and five yearly returns from the funds (Phalippou, and Gottschalg, 2008). The breakdown of the research was centered on four important aspects. Firstly, market returns were evaluated for licensee ownership status of the funds. Secondly, rate of returns were scrutinized for profit motive of the licensee. Thirdly, impact of regulatory classification of superannuation funds was tested. Finally, and most importantly, expense ratios of the funds were compared within the two licensee profit status groups.
The present research obtained a secondary data set, consisting of supper funds of 2017. A total of 168 superannuation funds were present in the data set with 13 variables. No primary data was collected for this study. The data set consisted of six categorical, and seven continuous (ratio) variables. Amongst the seven continuous variables, market returns for the period of one year, three years, and five years were considered as the primary dependent variables. The fourth dependent variable was taken as the operating expense ratio of the funds. Regulatory classification by APRA (REGULA), type of funds (Type), licensee ownership type (LICOWNER), and licensee profit status (OBJECTIVE) were the four categorical impact variables selected for this study.
The cross-sectional analyses were performed to investigate the impact of the categorical and independent factors on the three different market returns (Damanpour, Walker, and Avellaneda, 2009). Operating expense ratio for the funds was compared for the two licensee ownership type of the funds.
The present article studied the retirement funds, and for the purpose of practically and statistically significant conclusion, four hypotheses were outlined.
Twelve month return in 2017 was explored for all the licensee ownership funds. It was noted that the average return for nominating (M = 9.77%, SD = 1.65%) and employer (M = 9.34%, SD = 1.40%) licensee status were better compared to other status funds. Public sector fund was the worst fund to invest because of its highly volatile nature (M = 1.69%, SD = 24.49%). Distribution of the returns was significantly negatively skewed, with leptokurtic kurtosis.
Three year annualized returns of 2015 – 2017 were comparatively symmetric among the funds. The financial sector fund was found to be the most under-performed fund (M = 7.05%, SD = 0.94%). Public sector funds (M = 9.37%, SD = 1.13%), and nominating funds (M = 9.35%, SD = 1.09%) were the top performers. But, public sector fund was observed to possess high positive skewness for three year return. However, employer licensee fund was the less volatile funds with a standard deviation of 0.94%.
Average of five year annualized return of 2013 – 2017 was calculated. Unexpectedly, public sector fund was the leading return providers (M = 4.53%, SD = 0.75%), and nominating licensee fund was the second best with 4.27% average return (SD = 0.74%) and least market risk. Financial funds did not performed well in five year term, and the distribution of the returns was significantly negatively skewed with leptokurtic kurtosis.
Financial funds, due to their varied average returns for three different time periods, were studied separately. This particular fund returned an average three year return of 7.05% (SD = 2.49%) with slightly negatively skewness (S = -1.085), but five year average return was on the lower side (M = 3.0%, SD = 1.4%), but the risk associated was also low. Hence, a stable but low return was expected from the curve. Most of the financial funds were observed to provide return within a short interval (Figure 6), and the distribution was significantly negatively skewed with leptokurtic kurtosis. The comparative analysis was prominent from Figure 7 side-by-side box plot. The high negative skewness was also observed from the box plot of five year average return. Long term investment was not advisable for this fund.
Descriptive statistics for the three returns based on the time period has been provided in Table 5 for employer ownership funds. Average return was best in last twelve months of 2017 (M = 9.34%, SD = 1.39%), and was excellent in last three years (M = 9.12%, SD = 0.94%). But, it was observed that, alike the financial funds average return for employer licensee funds also drastically reduced to mere 4.11% (SD = 0.86%). From the side-by-side box plot in Figure 8, it was clearly evident that five year average returns were comparatively very low. Probable reason could be bullish nature of the market in 2013 and 2014.
Average return for three year period was most excellent (M = 9.37%, SD = 1.13%) for public sector funds. Historical data revealed that five year average return for this category of fund was also comparatively low (M = 4.53%, SD = 0.75%). But, the twelve month return was also abruptly low (M = 1.69%, SD = 24.49%). The fund was not at all preferable due its unpredictability and high amount of risk associated with it. The distribution of returns was significantly negatively skewed with high kurtosis for twelve months average return. Accumulation of the funds within a narrow interval of return rates was the primary reason for high negative skewness and kurtosis.
Nominating ownership funds were comparatively stable in providing consistent returns for lat twelve months (M = 9.77%, SD = 1.65%) and last three years (M = 9.35%, SD = 1.09%). The five year average return was 4.27% (SD = 0.74%), and difference of average from three year return was evident. Hence, the fund was less volatile and yielded high return for the time periods. Retired employees can invest in this fund to get good yield.
Average return from other sector funds was also consistent for one year and three year periods. Five year average return was considerably low (M = 4.04%, SD = 0.74%) compared to other two time periods. From Figure 11, the side-by-side box plot reflected the low returns for five years tenure.
Corporate funds performed the best in last three years average return (M = 9.43%, SD = 0.02%). The fund was found to have very low risk in three years return. For market corrections or some external factors, similar to other funds, average five year return was significantly low compared to three year average return. This was the most dependable fund out of all the present funds for superannuation benefits.
Retail funds were also volatile in nature, and average returns were also low. Due to the persistent risk factor associated with the fund investment in this fund would be tricky. Only for diversification purpose one can invest in this fund. The five year return was also less compared to other funds. Distribution of five year returns was highly skewed in a negative direction with leptokurtic nature (Amaya, Christoffersen, Jacobs, and Vasquez, 2011).Twelve month returns from public sector funds were highly skewed in negative direction. The distribution of one year returns was leptokurtic in nature. five year average return was in line with other funds, and three year return was found to be significantly higher (M = 8.87%, SD = 0.98%) with low volatility. But, considering the recent performance of the public sector fund and its volatility, investment seems to be unlikely in these funds in near future.
Average one year (M = 9.98%, SD = 1.44%) and three years return (M = 9.43%, SD = 1.11%) from industry sector funds were the best. Risk associated with funds were also comparatively less. Considering the five year return, the fund did well in providing 4.22% average interest rate with 0.88% volatility. This was the best fund among all for superannuation investments.
Average return for licensee profit status (Profit) was noted to be consistent in the last one year (M = 6.04%, SD = 2.72%), and in the last three years return (M = 6.75%, SD = 2.47%). But, the risk associated with policy would make the policy an unfavorable choice. Moreover, five year annual return (M = 2.88%, 1.32%) was way less compared to other funds. Investment in this fund would have been an unlikely choice. The side-by-side box plots reveal the negatively skewed five year return with high kurtosis.
Average return for licensee profit status (No-Profit) was noted to be consistent in the last one year (M = 8.36%, SD = 9.48%), and in the last three years return (M = 9.29%, SD = 1.14%). But, the risk associated with policy would make the policy an unfavorable choice. Moreover, five year annual return (M = 4.28%, 0.82%) was also comparable to other funds. Investment in this fund would have been a likely choice, provided some one is aggressively looking for returns. The side-by-side box plots reveal the negatively skewed one year return with high kurtosis.
The null hypothesis assuming, no significant difference between returns from no profit motive funds, and profit motive funds was tested against the one tail alternate hypothesis, considering that return with no profit motives were less than return on funds with profit motives. The level of significance was chosen at 5%, and the choice of test was independent sample t-test.
Returns for twelve month was found to have a statistically significant difference between the returns of two test scenarios of the test (t = 2.06, p < 0.05). The t-critical = 1.66 was less than t-calculated = 2.06, where p-value = 0.0212. Hence, the null hypothesis was rejected at 5% level of significance for return of twelve months.
Returns for five years 2013- 2017, was found to have a statistically significant difference between the returns of two test scenarios of the test (t = 8.41, p < 0.05). The t-critical = 1.65 was less than t-calculated = 8.41, where the left tail p-value = 0.0000. Hence, the null hypothesis was rejected at 5% level of significance for return of three years.
Therefore, it was concluded that the investors’ belief about the return on funds with objective of the licensee was significantly true.
The null hypothesis assuming, no significant difference in investment expense ratio for objective of the licensee was tested against the alternate hypothesis that expense ratio in non profit motive funds were less than that of the funds with profit motives. The level of significance was chosen at 5%, and the choice of test was independent sample t-test.
Average expense ratio for non-profit situation was 0.0025, whereas for profit situation it was 0.0014. The detail results have been provided Table 16 of the Appendix. The left tail t-test was conducted in Excel. The calculated t-statistic = 3.017 was greater than t-critical = -1.645 at 5% level of significance. Hence, the p-value was for the left tail test. Hence, at 5% level of significance the null hypothesis failed to get rejected. Therefore, it was concluded that return from funds with no profit were not less that the return from profit motive scenario.
Average three years return for corporate and industry type funds were both 0.0943. Both the funds were less volatile and claimed to be the better fund compared two other two funds. A one-way ANOVA was conducted to test the claim at 5% level of significance. The difference in variance of returns amongst the four types of funds was cross checked in the ANOVA. The F-value was = 24.17 with p-value < 0.05. Hence, variation in average three yearly returns between the four types of group was found to be statistically significant. Hence, it was concluded that corporate and industry funds were the two better performers in three year average return.
A one-way ANOVA was conducted to test the claim that a five year average return for corporate fund was the highest at 5% level of significance. The difference in variance of returns amongst the four types of funds was cross checked in the ANOVA. The F-value was = 24.02 with p-value < 0.05. As, F-calculated = 24.02 was greater than F-critical value = 2.66, and the p-value < 0.05 at 5% level of significance, the null hypothesis was rejected. Hence, variation in average five yearly returns between the four types of group was found to be statistically significant. Consequently, it was concluded that corporate fund was the better performer in five year average return.
Three year average return with public offer was 0.0752, and with no public offer it was 0.0906. There was a statistically significant difference in these two mean rates of return (t = -5.16, p < 0.05) at 5% level. Hence, based on the evidenced, the null hypothesis was rejected. It was concluded that returns on funds with public offer are lower than return on funds with no Public offer.
Five year average return with public offer was 0.0325, and with no public offer it was 0.0431. There was a statistically significant difference in these two mean rates of return (t = -6.06, p < 0.05) at 5% level. Hence, based on the evidenced, the null hypothesis was rejected. It was concluded that returns on funds for five years with public offer are lower than return on funds with no Public offer.
Conclusion
Considering the market returns of superannuation funds and the key independent impact factors, it was now straightforward for the researcher to construct a valid portfolio (Basu, and Andrews, 2014). The rapid growth of the Australian superannuation market has attracted the attention towards these categories of funds, especially when the total worth of the funds is becoming hefty (Marshall, Anderson, and Ramsay, 2009). The corporate and industrial funds were identified among the top performers for investment purpose. In view of the licensee ownership, funds with ownership for employer and nominating were identified as better performers with less volatility. Regulatory classification by APRA for public offer was a significant factor for performance of a fund, and the results were found to be in line with previous literatures (Authority, 2009). Expense ratio for the operational purpose was found to be less for profit motive objectives compared to non profit objectives.
As an alternate analysis, dependency on membership of the fund and license board structure would have revealed the interdependencies between the return figures and these two categorical variables.
The primary inference of the current research work would be to distribute the superannuation portfolio for the employees according to the outcome of the study. Special funds with significant positive return figures would be included in the portfolio (Sy, 2007). Funds with low risk and higher average returns would be selected for obtaining better profits (Cooper, and de Valores Mobiliários, 2006; Barrett, and Tseng, 2008). Diversification would be done with the funds with low volatility as well as low average return, such a fund in the current study was the financial sector licensee ownership fund. Australian superannuation fund market seems to lack the zeal of mutual fund market (Phalippou, and Gottschalg, 2008). But, once the volume of funds starts mobilizing in superannuation funds, managers would start looking into the profitability scenario with proper portfolio management (Roca, and Wong, 2008; Worthington, 2008).
References
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