Superannuation Funds in Australia
The difficulty in investment post retirement is a well identified grey area of any share market. Several pension funds around the world with active management serve to solve the problem. The superannuation pension funds are an important part of the share market in Australia. The funds are designed to satisfy basic retirements of the pensioners by simplifying the economic process. Moreover, financial gain from pension has redoubled considerably in each country, with obligatory pension contributions. This benchmark system has additionally managed to supervise massive amounts of savings within the economy. But the problem of proper stock selection in the superannuation funds still exists, and the present article proposes to investigate the return pattern of the different superannuation funds with expense ratio incurred due to fund management.
The dilemma in investing money in superannuation funds is primarily due to the volatility in market returns and expense ratio for investment. The current scenario of Australian social security ensures pensioners of the retirement benefits, thus generating a huge amount of idle money. The doubt about investing the financial gains from employer’s contribution and government’s benefit schemes always makes the pensioners apprehensive. Hence, performance of the pension funds depends especially on the type of stocks where the money is invested and the expense ratio incurred. The issue has also become a matter of national importance, as country’s financial market benefits from the investment of this huge money. However, the empirical proof of pension fund performance remains restricted. The literatures on the continuity of the superannuation funds’ performance have indicated that the concern entirely depends on fund managers. Price behavior, inflation rate, certain external factors have the benchmarks for adoption of asset allocation approach. A comparable study was conducted within the Australian context by Edmonds, Holle, & Hartanti in 2015, where the tax system of the country was found to be a hindrance in offshore investment. The economic importance and perpetually ever-changing dynamics of retirement need a stronger understanding of the fund’s performance (Hallahan, Faff, & Benson, 2008). This is often why it’s doable to revise the newer literatures (Cummings, 2016; Donald, & Le Mire, 2016). The aim of this text was to look at the enquiry on the performance of the Australian superannuation funds from 2013 to 2017.
The analysis focus was totally on the investigation of market returns in 2017, 2015-2017, and 5 yearly returns in the time period of 2013-2017. The breakdown of the analysis was targeted on four necessary aspects. The impacts of licensee possession standing of the funds, profit motive of the licensee, restrictive classification of superannuation funds were evaluated. Most significantly, expense ratios of the funds were compared for the two licensee profit standing teams.
Issues Related to Post-Retirement Investment
The present study obtained a historic dataset on superannuation funds with market returns of 168 funds for three time periods (Beckford, 2016). Thirteen variables, both categorical and numerical were present to describe the market returns of the funds. Specifically, the information set consisted of six categorical, and seven continuous variables. The dependent variable was considered as market return with three levels, return for the financial year of 2017, 3 years in the time period of 2015-2017, and 5 years return in a time period of 2013-2017. Licensee possession kind (LICOWNER), restrictive classification by APRA (REGULA), variety of funds (Type), and licensee profit standing (OBJECTIVE) were the four categorical independent variables (Berenson, Levine, Szabat, & Krehbiel, 2012).
A comparative analysis was designed to scrutinize the internal and important impact factors for the market returns of the funds (Hillier, 2012). The study was based on investigation of the effects of the categorical factors such as licensee ownership status, type of funds, objective of the licensee, and operating expense ratio were noted for the market returns in three different time period.
The statistical problem was framed as a set of hypotheses in the present article. The statistical significance of the effect of independent and categorical effects on the market return of the retirement funds was assessed in the inferential analysis. The researcher framed the following hypotheses based on the available information.
- Profit motive of the licensee yielded more market returns (for three levels of returns) compared to no profit motive licensee.
- Funds with no profit motives has lower investment expense ratio compared to that of the funds with profit motives of the licensee.
- Market returns (for three levels of returns) were significantly dependent on the different type of fund.
- Funds with classification of public offer yield low market returns compared to the funds with no classification of public offer, by APRA.
- Average market returns of the funds for three time periods were investigated. Market returns in the financial year 2017 for public sector funds was the worst (M = 1.69%, SD = 24.49%) with huge volatility. The returns for licensee type of nominating funds (M = 9.77%, SD = 1.65%) and employer funds (M = 9.34%, SD = 1.40%) were the two top performers with low market risk. Returns from public sector funds also noted to have high negative skewness indicating presence of few low return funds in 2017 (Levine, Stephen, Krehbiel, and Berenson, 2005).
- Three year annualized returns of 2015 – 2017 for the nominating funds (M = 9.35%, SD = 1.09%), public sector funds (M = 9.37%, SD = 1.13%), and employer licensee funds (M = 9.12%, SD = 0.94%) were found to be the top three performers. Whereas, financial sector fund was observed to be the average performed fund (M = 7.05%, SD = 0.94%). Average return from other sectors (M = 8.47%, SD =2.29%) was also excellent, but with high negative skewness (Nelson, 2014).
- Interestingly, average returns for five year period of 2013 – 2017 were observed to have huge market correction effects. The top performer was the public sector fund (M = 4.53%, SD = 0.75%), followed by nominating licensee fund (M = 4.27%, SD = 0.74%) and employer funds (M = 411%, SD = 0.86%). Financial funds maintained conservative returns in five year term also, and was significantly negatively skewed with few low return funds.
- Average market returns of financial funds was the best in 2015-2017 period (M= 7.05%, SD = 2.49%), followed by 2017 returns (M = 6.33%, SD = 2.7%). Five year average return was the lowest (M = 3.0%, SD = 1.4%), and importantly volatility of the fund was also comparatively lower than other two periods. Some of the financial funds were observed to provide low returns and due to which the distribution was significantly negatively skewed. The comparative analysis has been presented in Figure 4 side-by-side box plot. If opted for, only long term investment was advisable for this fund.
- Returns in employer licensee funds in last twelve months of 2017 (M = 9.34%, SD = 1.39%) was excellent, followed by the last three years (M = 9.12%, SD = 0.94%) market returns. Due to market probable market corrections, average return for employer licensee funds reduced to 4.11% with a risk of 0.86%. From the side-by-side box plot in Figure 5, it was clearly evident that five year average returns were comparatively very low.
- For public sector funds, the average return for three year period (M = 9.37%, SD = 1.13%) was outstanding, followed by five year average return (M = 4.53%, SD = 0.75%). However, the twelve month return for 2017 was the lowest (M = 1.69%, SD = 24.49%). The fund was not at all advisable in short time period of investment, due its high amount of risk associated with it. But, long term investment could have an option. The returns were significantly negatively skewed for twelve months period in 2017.
- Nominating licensee funds were stable throughout last five years in providing consistent returns. The twelve months returns for 2017 (M = 9.77%, SD = 1.65%), followed by last three years (M = 9.35%, SD = 1.09%) return were excellent. Due to probable market correction in 2013-2015, five year average return was 4.27% with a volatility of 0.74%. Investment in this particular fund was advisable to the retired employees for a good long term returns with low risk.
- Other sector funds yield very good return for 2017 (M = 8.76%, SD = 3.02%), and 2015-2017 period (M = 8.47%, SD= 2.29%). Five year average return (M = 4.04%, SD = 0.74%) was also excellent, considering other returns from other funds. From Figure 8, the side-by-side box plot reflected the low returns for five years tenure.
- Market return from corporate funds in 2015-2017 (M = 9.43%, SD = 0.02%), and 2013-2017 (M = 8.87%, SD = 1.67%) were very high compared to other type of funds. Low market volatility was also special attribute of this fund. For market corrections, average five year return (M = 4.56%, SD =0.79%) was low amongst three year time periods. The fund was notes as the most dependable for superannuation benefits with highest five year average return compared to other type of funds.
- Average returns for retail funds for three year (2015-2017) was the best (M = 6.71%, SD= 2.46%) with high market risk. Return in last twelve months in 2017 also had 2.71% volatility with an average return of 6%. Risk factor associated with the fund was very high throughout the last five years, and investment in this fund was not advisable. Distribution of five year returns was highly negative skewed.
- Public sector funds yield good returns in 2015-2017 period (M = 8.87%, SD = 0.98%). The five year average return (M = 4.15%, SD= 0.64%) was also comparable with returns from other funds. Last year market return of 2017 was comparatively low (M = 4.23%, SD = 19.09%) with very high market volatility. Hence, long term investment was only the advisable option.
- Last one year return for 2017 (M = 9.98%, SD = 1.44%), and 2015-2017 return (M = 9.43%, SD = 1.11%) of industry sector funds noted to have low market risk with high returns. Five year returns (M = 4.22%, SD =0.88%) was also the best option after corporate type of funds. Industry type funds were the best along with corporate funds for superannuation investments.
- Market returns for objective of licensee profit was the highest in time period of 2015-2017 (M = 6.75%, SD = 2.47%), followed by 2015-2017 return (M = 6.04%, SD = 2.72%). Five year return for 2013-2017 (M = 2.88%, 1.32%) was very low compared to other funds. High market volatility associated with this fund was a matter of great concern. The side-by-side box plots revealed high negatively skewed return during 2013-2017.
- Licensee no profit objective was noted to yield very high returns in 2015-2017 (M = 9.29%, SD = 1.14%), followed by last one year return of 2017 (M = 8.36%, SD = 9.48%). Presently market risk in policy suggests that it would be unwise to invest in the policy. Five year market adjusted annual return (M = 4.28%, 0.82%) was in line with other superannuation funds. The side-by-side box plots reveal the negatively skewed one year return.
- Market return on no profit funds was hypothesized to be equal to profit to profit motive funds. Difference between returns from these two objectives of licensee was tested against the alternate hypothesis that market return with no profit motives was less than return on funds with profit motives. Independent sample t-test with 5% level of significance was the choice of test.
- From the t-test, market returns in case of no profit objective for 2017 was found to have a statistically significant difference in returns compared to that of the profit motive returns (t = 2.06, p < 0.05). Consequently, the null hypothesis was rejected at 5% level of significance, and it was concluded that market returns for no profit were higher than profit motive funds.
- Similarly, market returns of no profit objective for 2015- 2017 was found to have a statistically significant difference with that of the returns from profit objective of the licensee (t = 8.76, p < 0.05). Consequently, the null hypothesis was rejected at 5% level of significance concluding that for return non-profit objective was significantly greater than profit objective funds in 2015-2017.
- Similarly, market returns of no profit objective for 2013- 2017 was found to have a statistically significant difference with that of the returns from profit objective of the licensee (t = 8.41, p < 0.05). Consequently, the null hypothesis was rejected at 5% level of significance concluding that for return non-profit objective was significantly greater than profit objective funds in 2013-2017.
- Expense ratios for no profit motive funds were hypothesized to be equal to expense ratio of non-profit motive funds, and tested against the alternate hypothesis that expense ratio in non profit motive funds were less than that of the funds with profit motives at 5% level of significance by independent sample t-test.
Average expense ratio for non-profit licensee objective was (M = 0.25%, SD =0.0004%) was greater than that of the profit objective funds (M = 0.14%, SD= 0.0006%). From the test statistics of t-statistic = 3.02 at 5% level of significance with p-value of 0.99. Hence, at 5% level of significance the null hypothesis failed to get rejected in favor of the alternate hypothesis. Therefore, it was not possible to conclude that return from no profit objective funds were less that the return from profit motive licensee funds.
- Superannuation funds were categorized in four types of funds. They were corporate, industry, retail, and public retail sector funds. The null hypothesis of equal impact of the four types of funds was tested using a one-way ANOVA in Excel to test the claim at 5% level of significance.
- In 2017, industry oriented funds (M = 9.98%, SD = 0.02%) performed well compared to other type of funds. The initial assessment was found to be statistically significant at 5% level (F = 5.1, p < 0.05). Consequently, it was concluded that industry funds were the best performers in 2017.
- In 2015-2017, industry oriented funds (M = 9.98%, SD = 0.02%), as well as corporate funds (M = 9.98%, SD =0.02%) were the two better performed funds compared to other funds. The initial assessment was found to be statistically significant at 5% level (F = 24.18, p < 0.05). Consequently, it was concluded that the two above mentioned funds were the best performers in 2015-2017.
- Corporate funds turned out to be the best performer (M = 4.56%, SD = 0.01%). A one-way ANOVA was conducted to test the initial claim at 5% level of significance. The variation of returns of funds was statistically significant (F = 24.02 p < 0.05). The null hypothesis was rejected at 5% level of significance signifying that corporate fund was the best performer in 2013-2017.
- No significant difference in investment returns due to regulatory classification by APRA was assumed against the alternate hypothesis that returns on funds with public offer are lower than return on funds with no Public offer. Independent sample t-test at 5% level of significance was chosen.
- Average market return with public offer (M = 7.52%, SD = 0.06%) was observed to be low compared to no public offer returns (M = 9.06%, SD =0.02%). The claim was tested and a statistically significant difference in these two mean rates of return (t = -5.16, p < 0.05) at 5% level. Consequently, the null hypothesis was rejected concluding that returns on funds with public offer are significantly lower than that of the funds with no public offer in 2015-2017.
- Average market return with public offer (M = 3.25%, SD = 0.02%) was observed to be low compared to no public offer returns (M = 4.31%, SD =0.01%). The claim was tested and a statistically significant difference in these two mean rates of return (t = -6.06, p < 0.05) at 5% level. Consequently, the null hypothesis was rejected concluding that returns on funds with public offer are significantly lower than that of the funds with no public offer in 2013-2017.
Conclusion
Market returns of superannuation funds depended on some key impact factors. It had been currently simple for the research worker to construct a legitimate portfolio. The rapid climb of the Australian superannuation market has attracted the eye towards these classes of funds (Taylor, 2017). The hypotheses were tested and with 95% confidence it was possible to state that market returns were better for no profit objective of the licensee. Though, expense ratios were found to be independent of the profit objective of the licensee. The industrial and corporate funds were known among the highest performers for investment purpose. Restrictive classification by APRA for public supply was a big issue for performance of a fund, and also the results were found to be in line with previous literatures. The portfolio of the superannuation funds should consider these inferred results to obtain better market returns (Edmonds, Holle, & Hartanti, 2015).
The primary insinuation would be to analyse the historical returns and market volatility before investment in any superannuation funds. This analysis work would be to distribute the superannuation portfolio for the workers per the end result of the study. Special funds with important positive returns were identified and the fund managers should include these considerations in construction of portfolios of a retired person (Ellis, Tobin, & Tracey, 2008). From Australian prospective, due to continuous industrial and corporate development, the associated funds were seemed to be at low market risk and better average returns. Diversification of the portfolio should be done with proper objective, especially where the market volatility of some funds were considerably very high. The current study was the on monetary sector licensee possession fund and the Australian pension fund market looked to lack the zeal of mutual fund market (Warren, 2010).
References
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Cummings, J. R. (2016). Effect of fund size on the performance of Australian superannuation funds. Accounting & Finance, 56(3), 695-725.
Donald, M., & Le Mire, S. (2016). Independence and the governance of Australian superannuation funds.
Edmonds, M., Holle, C., & Hartanti, W. (2015). Alternative assets insights: Super funds-tax impediments to going global. Taxation in Australia, 49(7), 413.
Ellis, K., Tobin, A., & Tracey, B. (2008). Investment performance, asset allocation and expenses of large superannuation funds. APRA Insight, 3(2), 20.
Hallahan, T., Faff, R. W., & Benson, K. L. (2008). Fortune favours the bold? Exploring tournament behavior among Australian superannuation funds. Journal of Financial Services Research, 33(3), 205-220.
Hillier, F. S. (2012). Introduction to operations research. Tata McGraw-Hill Education.
Levine, D.M., Stephen, D., Krehbiel, T.C. and Berenson, B.L. (2005), Statistics for Managers: Using Microsoft Excel, Fourth Edition, Prentice Hall International (LSKB).
Nelson, S. L., (2014) Excel Data Analysis For Dummies, John Wiley & sons: New Jersey
Taylor, S. (2017). Could we nationalise the superannuation system even if we wanted to?. The Conversation, November (9).
Warren, G. J. (2010). Equity home bias in Australian superannuation funds. Australian Journal of Management, 35(1), 69-93.
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