Average Annual Return (AAR) Definition
Average annual return (AAR) refers to a percentage utilized when reporting the historical return, like the three-, five-, and ten-year average returns of a mutual fund. The average yearly return is stated net of a fund’s operating expense ratio, which doesn’t include sales charges, if applicable, or even portfolio transaction brokerage commissions.
Understanding Average Annual Return (AAR)
When choosing a mutual fund, the AAR is a useful guide for calculating the long-term performance of a fund. However, investors should also check a fund’s annual performance to fully appreciate the consistency of its yearly total returns. For instance, a five-year average yearly return of 10% seems attractive; however, supposing the annual returns (those who produced the average annual return) were +40%, +30%, -10%, +5%, and -15% (50/5= 10%), performance over the last 3 years warrants analysis of the fund’s investment strategy and management.
Components of Mutual Fund AAR
Three components exist which contribute to the equity mutual fund’s AAR. Share price appreciation arises from unrealized losses or gains in the underlying stocks held in a portfolio. As a stock’s share price fluctuates over a year, it proportionately detracts from or contributes to the average annual return of the fund which maintains a holding in the issue. The American Funds AMCAP Fund’s 2.74% holding of Oracle Corporation, one stock it’s owned since 2003, has added to the portfolio’s ten-year AAR of 8.15% through the 23rd of June, 2016, as the stock of the company grew from $10.88 on April 1, 2003, to about $40.83 on June 23, 2016.
Capital gains distributions paid from a mutual fund arise from income generation or selling stocks from which a manager gains profit in a growth portfolio. Shareholders can choose to collect the distributions in cash or even reinvest then in the fund. Capital gains are the realized aspect of AAR. The distribution, which lessens share price by the amount of dollar paid out, stands for a taxable gain for shareholders. It is possible for a fund to have a negative AAR and also make taxable distributions. A $2.59 capital gain was paid by the Wells Fargo Discovery Fund on Dec. 11, 2015, irrespective of the fund having an average annual return of negative 1.48%.
Quarterly dividends which are paid from company earnings add to the AAR of a mutual fund and also lessen the value of the net asset value of a portfolio. Like capital gains, dividend income gotten from the portfolio can either be taken in cash or reinvested. Large-cap stock funds having positive earnings usually pay dividends to institutional and individual shareholders. These quarterly distributions include the dividend yield component of the AAR of a mutual fund. The T. Rowe Price Dividend Fund a 1.23% trailing 12-month yield, a contributing factor to the fund’s 1-year AAR of 5.55% through the 23rd of June, 2016.
Difference From Average Annual Rate of Return
It is easier to calculate an average annual return than it is to calculate the average annual rate of return, which utilizes and geometric average as against a regular mean. [(1+r1) x (1+r2) x (1+r3) x … x (1+ri)] (1/n) – 1, is the formula, where “r” represents the annual rate of return and “n” represents the number of years in the period. The average annual return is occasionally termed less useful for picturing a fund’s performance because returns compound as against combine. It’s mandatory that investors pay attention when checking for funds to compare the same return types for each fund.
Reference for “Average Annual Return (AAR)”
Academics research on “Average Annual Return (AAR)”
Do firms knowingly sell overvalued equity?, Lee, I. (1997). Do firms knowingly sell overvalued equity?. The Journal of Finance, 52(4), 1439-1466. This article examines the relation between top executives’ trading and the long‐run stock returns of seasoned equity issuing firms. Primary issuers, who sell mostly newly‐issued primary shares, significantly underperform their benchmarks, regardless of the top executives’ prior trading pattern. However, top executives’ trading is reliably associated with the stock returns of secondary issuers, who sell mostly secondary shares previously held by existing shareholders. On average, secondary issuers do not underperform their benchmarks. The results suggest that increased free cash flow problems after issue play an important role in explaining the underperformance of issuing firms.
Application of repeat sales analysis to determine the impact of a contamination event, Kilpatrick, J. (2004). Application of repeat sales analysis to determine the impact of a contamination event. Journal of housing research, 15(2), 129-142.rior studies of environmental contamination examine the cross-sectional impacts, either through a sales-comparison-type model or hedonic pricing. Neither model is robust at analyzing the impact of an event, such as a contamination announcement. Longer term longitudinal studies may not control for exogenous impacts, such as changes in house quality. This study uses a repeat-sales index to extract value-trend changes immediately after a contamination announcement, thus isolating the impacts of the event itself and controlling for exogenous factors. While the study is focused on contamination, it is generalizable to any systemic event.
Algorithm of construction of optimum portfolio of stocks using genetic algorithm, Sinha, P., Chandwani, A., & Sinha, T. (2015). Algorithm of construction of optimum portfolio of stocks using genetic algorithm. International Journal of System Assurance Engineering and Management, 6(4), 447-465. The objective of this paper is to develop an algorithm to create an optimum portfolio from a large pool of stocks listed in a single market index SPX 500 Index: USA (for example) using genetic algorithm. The algorithm selects stocks on the basis of a priority index function designed on company fundamentals, and then genetically assigns optimum weights to the selected stocks by finding a genetically suitable combination of return and risk on the basis of historical data. The effect of genetic evolution on portfolio optimization has been demonstrated by developing a MATLAB code to implement the genetic application of reproduction, crossover and mutation operators. The effectiveness of the obtained portfolio has been successfully tested by running its performance over a 6 month holding period. It is found that genetic algorithm is successful in providing the optimum weights to stocks which were initially screened through a predetermined priority index function. The constructed portfolio beats the market for the considered holding period by a significant margin.
Risks exposure in Islamic banks: A case study of Bank Islam Malaysia Berhad (BIMB), Bhatti, I., & Misman, F. N. (2010). Risks exposure in Islamic banks: A case study of Bank Islam Malaysia Berhad (BIMB). The growth and changes in the global financial markets pose various risks to the financial pectoral over the world. Risk cannot be avoided as it is part and parcel of its operations. Banking institutions are likewise exposed to risks. As conventional banks have to face three major risks; i) credit risk, ii) market risk, iii) operational risk, similarly Islamic banks also face the same. The perception that Islamic banks are risk free is not correct and can be an understatement. This paper explores the risk involved in Islamic banks and risk management practices by the Islamic banks. The focus of this paper is on risk and return in Bank Islam Malaysia Berhad (BIMB). The study examines the risk level in BIMB by using two approach; Financial Statement Analysis and Stock Analysis. Apart from that, this study also predicts the Islamic banks amount of financing for each concept in Malaysia for year 2010. The findings of this paper will assist Islamic banks as it will give a clear understanding about various types of risk in general and more particularly credit risk and market risk.
Benefit analysis of wind turbine generators using different economic-cost methods, Yeh, T. H., & Wang, L. (2007, November). Benefit analysis of wind turbine generators using different economic-cost methods. In 2007 International Conference on Intelligent Systems Applications to Power Systems (pp. 1-6). IEEE. Since petroleum’s price increases in recent years, the employment of renewable energy has become one of the most important tasks for government’s energy policy. To fully use wind power, different wind farms should have appropriate capacity for wind turbine generators (WTG) to get the most wind power and maximum economic benefit. Currently, there are various values for capacity and hub height of commercial WTG. This paper uses three different economical methods to analyze economical outcomes for different wind farms with different parameters for WTG sets prior to the installation of WTG. According to the comparative analyzed results, the influence of Weibull parameters proposed can quickly choose proper capacity for WTG.