THE PENNSYLVANIA STATE UNIVERSITY SCHREYER HONORS COLLEGE DEPARTMENT OF FINANCE

Transcription

1 THE PENNSYLVANIA STATE UNIVERSITY SCHREYER HONORS COLLEGE DEPARTMENT OF FINANCE EQUITY MUTUAL FUNDS: A RISK-ADJUSTED PERFORMANCE ANALYSIS OF LOADS VS. NO-LOADS DANIEL E. WEBER SPRING 2014 A thesis submitted in partial fulfillment of the requirements for baccalaureate degrees in Finance and Accounting with honors in Finance Reviewed and approved* by the following: Timothy Simin Associate Professor of Finance Thesis Supervisor James Miles Professor of Finance Honors Adviser/Faculty Reader * Signatures are on file in the Schreyer Honors College.

2 i ABSTRACT This thesis applies Sharpe Ratios (1966) and alphas from the Carhart four-factor model (1997) to determine whether no-load mutual funds outperformed load mutual funds over a ten year period, December 2002 through December 2012, using U.S. equity mutual fund data. In total, 36 mutual funds were chosen for the sample, 18 no-load funds and 18 load funds. The load funds, which consist of 12 front-end loads and 6 back-end loads, were adjusted for their respective load fees throughout the study based on the investment time period under consideration. After computing 10-year, 5-year, 2-year, and recession period Sharpe Ratios and four-factor alphas, it was determined that no-load mutual funds consistently outperformed load mutual funds in every time period considered. Based on these results, it can be concluded that managers of load funds did not reward their investors with superior returns relative to no-load funds. On average, investors would have been better off investing in no-load funds rather than load funds from December 2002 through December 2012.

3 ii TABLE OF CONTENTS List of Figures... iv List of Tables... v Acknowledgements... vi Introduction... 1 No-load vs. s... 1 Equity Mutual s... 4 Sharpe Ratio... 5 Carhart Four-Factor Model... 7 Literature Review A Comprehensive Long-Term Performance Analysis of vs. No- Mutual s (2000) Should you carry the load? (2000) On Persistence in Mutual Performance (1997) vs. No- Mutual Performance in Extreme Market States (2010) Methodology Results Cumulative Returns Sharpe Ratios Carhart Four-Factor Alphas Conclusion Appendix A: Mutual s Sample Appendix B: Sharpe Ratios Year Sharpe Ratios Year Sharpe Ratios Year Sharpe Ratios Recession Period Sharpe Ratios Appendix C: Monthly Carhart Factors Appendix D: Carhart Four-Factor Alpha Year Alphas Year Alphas Year Alphas... 45

4 iii Recession Period Alphas BIBLIOGRAPHY... 50

5 iv LIST OF FIGURES Figure 1: No- vs. Total Net Assets Over Time... 3 Figure 2: Linear Sharpe Ratio... 6 Figure 3: 10-Year Cumulative Return Figure 4: 5-Year Cumulative Return Figure 5: 2-Year Cumulative Return Figure 6: Recession Period Cumulative Monthly Return... 19

6 v LIST OF TABLES Table 1: 18 Schedules Table 2: 10-Year Average Sharpe Ratios Table 3: 5-Year Average Sharpe Ratios Table 4: 2-Year Average Sharpe Ratios Table 5: Recession Period Average Sharpe Ratios Table 6: 10-Year Average Four-Factor Alphas Table 7: 5-Year Average Four-Factor Alphas Table 8: 2-Year Average Four-Factor Alphas Table 9: Recession Period Average Four-Factor Alphas... 25

7 vi ACKNOWLEDGEMENTS I would like to thank to my thesis supervisor, Tim Simin, for all of your guidance and time, as it was instrumental in completing my thesis. A special thanks to my family for your love and support. You have always encouraged and inspired me to achieve my goals and I cannot thank you enough for instilling that determination in me.

8 1 Introduction No-load vs. s All U.S. equity mutual funds incur expenses that lower investment returns, which can be broken down into two categories: operating fees and transaction fees. The former is incurred every year by all mutual funds and is represented by the expense ratio, which consists of management fees, 12b-1 fees, and administrative costs. On average, management fees range from.5% to 1% of the total net assets of the fund and are paid to the fund s investment advisor for managing the fund s assets. Named after the Securities and Exchange Commission (SEC) ruling that permits funds to pay them, 12b-1 fees are paid by the fund out of its assets for distribution expenses and shareholder service expenses. Mutual funds are only able to incur 12b-1 fees if they have adopted a plan permitting them to do so, but may pay shareholder service expenses without adopting such a plan. If a fund chooses the latter, those shareholder service expenses will be included in administrative costs rather than 12b-1 fees. Distribution expenses encompass fees paid to brokers for marketing and selling fund shares, advertising, and the printing and mailing of prospectuses, which are documents containing all of the pertinent attributes of mutual funds. Shareholder service expenses are paid to fund employees to respond to investor inquiries and to provide shareholders with information regarding their investments. Although the SEC does not impose a limit on aggregate 12b-1 fees, the Financial Industry Regulatory Authority (FINRA) limits distribution expenses and shareholder service expenses to.75% and.25% of the fund s average net assets per year, respectively. Administrative costs include all costs that are not considered management fees or 12b-1 fees, such as custodial, legal, and accounting expenses. In the aggregate, the aforementioned expenses represent the fund s operating expense, which is then divided by the

9 2 fund s average net assets for the year, resulting in the expense ratio. Expense ratios typically range anywhere from.2% to 2% of net assets. While all mutual funds have operating expenses, some additional transaction fees, called loads, to compensate the expert investment decisions of managers attempting to earn superior returns for investors. s can either be classified as front-end loads or back-end loads. A frontend load is a commission paid when shares of a fund are purchased and cannot exceed 8.5%. These loads reduce the amount of capital invested and, effectively, an investors return on the investment. For example, if an investor invests $100 in a fund with a 5% front-end load, a $5 commission fee is paid up front, reducing the actual amount of the investment to $95. A back-end load is incurred by the investor when selling shares of a mutual fund, assessed as a percentage of the NAV of the investor s shares at the time of sale. Back-end loads are often high upon initial purchase, but gradually decline over time to encourage investors to hold their investments for several years. Although it seems logical to pay a premium for professional management that will ultimately yield higher returns for investors, the Efficient Market Hypothesis (EMH) implies that no manager will legally have information that will lead to superior security allocation within a fund and, effectively, higher returns. EMH states that security prices reflect all available information at any given time. Any security that is mispriced will almost instantaneously adjust to its proper price through the supply and demand pressures of consumers attempting to exploit the price mismatch. Assuming EMH holds it is virtually impossible to beat the market. Applying the EMH to mutual funds, it would be pointless to invest in a load fund that carries high operating expenses to pay the manager for superior stock-picking abilities because it is impossible to generate such superior returns. As suggested by the name, no-load funds do not charge investors an additional fee to compensate managers. Instead, they only charge standard operating expenses and, effectively, have lower costs for investors. Contrasting no-load and load funds coupled with the implications of EMH inspired the purpose of this study: Do load funds outperform no-load funds? One

10 3 preliminary method of addressing this question is to consider the volume of no-load investments compared to that of loads. If loads historically outperform no-load funds, investors would likely flock to the load funds to earn superior returns over time, and vice versa. Figure 1: No- vs. Total Net Assets Over Time Source: Investment Company Institute and Lipper Figure 1 demonstrates the total of all investor dollars invested in U.S. no-load and load funds from 2003 through 2012, the data period for this study. All of the no-load mutual funds had greater total net assets under management than load mutual funds, indicating that investors flocked toward no-load funds. Such high no-load total net asset amounts relative to load total net asset amounts could mean that no-loads generally outperform loads on a historical basis because rational investors are more likely to invest their capital in an asset that will yield a higher return when deciding between two highly comparable assets, such as no-load and load mutual funds. Over this ten year period, total net assets of both fund types grew steadily except for 2008, which is attributable to the 2008 Financial Crisis. Not only were investors withdrawing their capital from funds, but some funds simply did not survive the crisis. According to the 2013 Investment Company Fact Book, there were 8,026 mutual funds available at the end of 2007 and 7,663

11 4 available at the end of 2009, resulting in just over a 4.5% decrease in mutual funds over a similar time period considered as the recession period in this study. If load funds do significantly outperform no-loads, there could be justification for such high fees. Otherwise, there would be no rational incentive to invest in a load fund when it achieves a similar return to a no-load fund and charges much higher fees. In order to accurately evaluate the performance of load and no-load U.S. equity funds, this study uses widely accepted risk-adjusted performance measures. Equity Mutual s Mutual funds pool capital from many investors and invest the money in various securities, such as stocks, bonds, money market instruments, and several others, depending on the strategy of the fund. Collecting funds from so many different investors grants small, individual investors access to professionally managed, diversified portfolios, which may otherwise be unattainable for investors with limited amounts of capital. As shares are issued of funds, investors typically buy into them at the Net Asset Value (NAV), or current market price of all of the underlying securities. After buying into a fund, investors can profit from it in three possible ways, depending on the strategy and fee structure of the fund. They include: 1. As the value of an underlying security appreciates, mutual fund managers may sell off that individual investment and distribute the profit to investors as a capital gain 2. When dividends or interest is paid on the underlying stocks and bonds of the mutual fund, the profit flows through fund managers to the investors 3. With an overall increase in the NAV of the entire mutual fund, investors may choose to sell their shares for a profit, the difference between the selling price and their original purchase price

12 5 Generally, mutual funds are broken into categories based on the characteristics of the underlying securities. Although this study only considers U.S. equity funds, which invest in domestic stocks, some of the other major categories of funds include international equity funds, bond funds, commodity funds, and sector equity funds. Within the scope of U.S. equity funds, this study further breaks down that category by fund fee structure, specifically between load and no-load funds. Sharpe Ratio The Sharpe Ratio, created by William Sharpe in 1966, measures a mutual fund s excess return per unit of its risk, using a risk-free return as a benchmark. Typically, the monthly return on the U.S. Treasury bill is used as the risk-free rate of return. The ratio is expressed using the following equation: Where: Sharpe Ratio = (R p R f )/σ p R p is the rate of return for the portfolio R f is the rate of return on the risk-free asset σ p is the standard deviation of the excess portfolio returns The difference between the portfolio s rate of return and the risk-free rate of return represents the excess return. A positive excess return is the additional return an investor earns by investing in a risky asset rather than only investing in the risk-free asset, whereas a negative excess return is the additional return an investor could have earned by investing only in the riskfree asset rather than the risky asset. The standard deviation of the excess portfolio returns, in the denominator of the ratio, measures volatility, or how much the excess return strays from its average performance. By adjusting individual investments for their risk measurements with

13 6 respect to a common benchmark, the risk-free rate, investments can be directly compared to determine which investment is best. Generally, the Sharpe Ratio is a reward to volatility ratio. It measures an investor s reward through the excess return of each unit of additional risk taken on by investing in a risky asset over a risk-free asset. As a high ratio indicates a high reward for the amount of risk assumed in the investment, higher ratios indicate better investments. Graphically, the Sharpe Ratio is the slope of the capital allocation line that is tangent to the efficient frontier, depicted in Figure 2. Figure 2: Linear Sharpe Ratio Source: Forbes In Figure 2, the horizontal parabola titled the Efficient Frontier represents the most favorable portfolio combinations of varying asset weights that yield the best returns for given levels of risk. Any portfolio located on the Efficient Frontier is a better investment than any other asset within the Efficient Frontier with the same return or level of risk. In order to find the optimal portfolio along the Efficient Frontier, the capital allocation line must be drawn from the risk-free rate, located on the y-axis, to the point of tangency on the upper half of the Efficient Frontier. The risk-free rate is the y-intercept because no rational investor would invest in a risky asset with a lower return than the risk-free rate. Additionally, rational investors would only invest in portfolios on the upper half of the Efficient Frontier because higher returns can be earned with equivalent levels of risk for portfolios on opposite sides of the frontier. Since the Sharpe Ratio

14 7 equals the reward of an investment, the y-axis, over the volatility of an investment, the x-axis, it is considered the slope of the capital allocation line that connects the risk-free rate to the optimal portfolio on the Efficient Frontier. As the Sharpe Ratio increases, the slope of the capital allocation line gets steeper and becomes tangent with the Efficient Frontier at an optimal portfolio which will yield a higher return for less risk, a more desirable investment. Carhart Four-Factor Model The Carhart four-factor model was created in 1997 by Mark Carhart as an extension of the Fama-French three-factor model. Carhart s model, similar to Fama and French s, is a riskadjusted performance measure that computes abnormal risky portfolio returns earned over the return predicted by the combination of four style-adjusted factors: the market, size, value, and momentum. The four style-adjusted benchmarks control for the wide range of mutual fund-style investment choices available to fund managers and adjust for their respective risk levels with betas. For example, a fund manager may choose to allocate different amounts of the fund s assets to low value or high market capitalization stocks in any equity mutual fund. The betas of each factor are computed as the covariance of the factor and the portfolio return divided by the variance of the factor. The Carhart four-factor model is expressed by the following formula: α a = R p [R f + β mkt (R mkt R f ) + β size (SMB) + β val (HML) + β mom (MOM)] 1 Where: α a measures the fund s abnormal return R p is the return of the fund R f is the rate of return on the risk-free asset 1 The four factors, their descriptions, and the risk-free rate were retrieved from Ken French s website, as cited in the Bibliography.

15 8 β mkt is the beta of the market factor R mkt is the rate of return on the overall market R mkt R f is the market risk premium β size is the beta of the size factor SMB is the size factor (Small Minus Big) β val is the beta of the value factor HML is the value factor (High Minus Low) β mom is the beta of the momentum factor MOM is the momentum factor The return on the risk-free asset, as in the Sharpe Ratio, is the monthly return on U.S. Treasury bills. Theoretically, the rate of return on the overall market is the rate of return an investor would earn if it was possible to invest in every risky asset available. Therefore, the market risk premium is the level of compensation an investor requires for taking on the additional risk of the market over the risk-free asset. β mkt adjusts for the market risk and represents the volatility of the risky asset relative to the market. Any risky asset with a beta value of 1 will follow the market exactly as it changes and any risky asset with a beta value greater than 1 will increase or decrease by greater amounts than the market. For example, if a risky asset has a beta value of.9 and the market increases by 1%, the risky asset will rise by.9%. The size, value, and momentum factors are all constructed using six value-weighted portfolios, but differ in their formation styles. The size factor is based on the underlying market capitalizations of all stocks traded on the NYSE, AMEX, and NASDAQ. It is represented by the variable SMB, Small Minus Big, and is the average monthly return on the three small portfolios minus the average monthly return on the three big portfolios. The monthly market capitalization breakpoints are the bottom 30%, middle 40%, and top 30% of all stocks traded on the NYSE. Mathematically, it is expressed by the following equation:

16 9 SMB = 1/3(Small Value + Small Neutral + Small Growth) 1/3(Big Value + Big Neutral + Big Growth) The value factor is based on the book-to-market ratio of all stocks traded on the NYSE, AMEX, and NASDAQ. It is represented by the variable HML, High Minus Low, and is the average monthly return on the two value portfolios minus the average monthly return on the two growth portfolios. The monthly book-to-market ratio breakpoints are the bottom 30%, middle 40%, and top 30% of all stocks traded on the NYSE. It is expressed with the following equation: HML = 1/2(Small Value + Big Value) 1/2(Small Growth + Big Growth) Momentum is the idea that a stock that has recently performed well will continue to do so and a stock that has recently performed poorly will continue to do so in the short-term, as determined by supply and demand pressures. As the demand increases for a particular stock, its price will continue to rise until investors begin selling the stock for a profit on the price appreciation. Those sales will result in a corresponding decrease in demand and increase in supply, meaning that the price will decrease until the security is seen as a desirable investment again. Equity mutual funds have momentum because they are portfolios of stocks. managers choose which securities to include in the fund based on their expected performance. Stocks expected to experience upward momentum are more likely to be chosen for the fund over alternatives. If the underlying securities collectively have upward momentum, the entire fund will also experience upward momentum. The momentum factor is based on two portfolios formed on size (market capitalization) and three portfolios formed on prior returns with a one-year lag, all of which incorporating NYSE, AMEX, and NASDAQ stocks with prior return data. The monthly size breakpoint is the median NYSE market capitalization and the monthly prior return breakpoints are the 30 th and 70 th NYSE percentiles. It is represented by the variable MOM, momentum, and is the average monthly return on the two high prior return portfolios minus the average monthly return on the two low prior return portfolios. It is expressed with the following equation:

17 10 MOM = 1/2(Small High + Big High) 1/2(Small Low + Big Low) The difference between the return of the risky asset and its four-factor predicted return results in the alpha. A positive alpha over some period of time indicates that the fund manager successfully allocated the assets of the mutual fund so that it outperformed its four-factor expected return. Contrarily, a negative alpha over some period of time indicates the opposite. Hence, higher alphas mean better investment performance. As load mutual funds carry higher expenses than no-load funds, investors choosing load funds should be compensated with higher returns than they would have otherwise received from a no-load fund. The four-factor alpha will not only determine how well a manager allocates the assets of a fund compared to the predicted four-factor return, but it will also provide a platform to compare the alphas earned by managers of no-load and load mutual funds. If the average no-load alphas outperform the average load alphas, load fund investors would essentially be paying more money for a lower return than could be achieved with a no-load fund. In that case, no-load mutual funds would be preferable to load mutual funds.

18 11 Literature Review A Comprehensive Long-Term Performance Analysis of vs. No- Mutual s (2000) In a study published in the Journal of Financial and Strategic Decisions in the summer of 2000, the authors, James Kuhle and Ralph Pope, conducted a comprehensive analysis of the longterm performance of no-load and load mutual funds from 1983 to The sample size consisted of 8,100 mutual funds and the return data was gathered from Morningstar. For each year in the study, 1-year total returns, 5-year average annualized returns, and 10-year average annualized returns were used for comparison. In addition, data for the year 1997 was further analyzed and broken down by investment objective category (Balanced, Growth, Income, International, Specialty, and Small Company) and by the fund s beta and alpha values. Using descriptive statistics and Z tests, this study found that in almost every year and return time period there was either no statistically significant difference between load and no-load fund returns or no-load funds outperformed load funds. With respect to beta values, only two of the years, 1996 and 1997, had statistically different results. In these two years, the beta values for no-load funds were lower than that of load funds, indicating that no-load funds carried less market risk. With respect to investment objective category, there was either no significant statistical difference between no-loads and loads or no-loads outperformed loads in the Balanced, Growth, International, and Small Company categories. For Income and Specialty funds, loads outperformed no-loads or there was no significant statistical difference in returns. Overall, on a risk-adjusted basis, no-load funds performed better than load funds in almost every investment category.

19 12 Should you carry the load? (2000) Matthew Morey took a different approach to comparing load and no-load performances in his study, titled Should you carry the load? A comprehensive analysis of load and no-load mutual fund out-of sample performance. This paper used return data for domestic equity mutual funds from 1993 through 1997 to determine whether no-loads outperformed loads and whether there is a significant difference in the returns between high and low load funds. Instead of just using risk-adjusted returns like most other studies, this one incorporates loads into the return data and compares performance using load-adjusted returns. In addition, the funds are analyzed using four different performance measures, which include mean monthly returns, Sharpe Ratios, a version of Jensen s Alpha, and a 4-index alpha. After analyzing the data, Morey found that before adjusting for the loads in load funds, no-loads generally outperformed loads, although not significantly. Considering this result, it is no surprise that after adjusting for loads, no-load funds significantly outperformed load funds. Among load funds, the data suggested that there was no significant difference between the risk-adjusted and load-adjusted returns of high-load and lowload funds. On Persistence in Mutual Performance (1997) Mark Carhart conducted a study on the persistence of mutual fund performance in 1997, arguing that persistent fund performance is not the result of superior stock-picking skill by fund managers, but is explained more through fund expenses. His sample included return data on domestic equity funds from 1962 through 1993 and was free of survivorship bias. To analyze returns, he used the CAPM and the Carhart 4-factor model. His 4 risk factors included high vs. low beta stocks, large vs. small capitalization stocks, value vs. growth stocks, and one-year return momentum vs. contrarian stocks. According to Carhart, his 4-factor model improves on the

20 13 average pricing errors that result from CAPM and the Fama and French 3-factor model. After analyzing the data using the aforementioned models, Carhart came away with three main conclusions. First, investors should avoid purchasing consistently poor performing funds. In his study, buying the previous year s top performing mutual funds and selling the worst performing funds resulted in a return of 8% per year. Second, consistent with the momentum theory, mutual funds with high returns in the previous year have higher than average returns in the following year, but not in the subsequent years. Third, investment costs, transaction costs, and load fees all have an adverse effect on performance. Specifically, no-load funds significantly outperformed load funds. vs. No- Mutual Performance in Extreme Market States (2010) To test mutual fund performance during extreme market states, Steve Nenninger conducted a study in 2010 that compared risk-adjusted returns of index funds and actively managed funds. His data, taken from both CRSP and Morningstar, ranges from the years 2003 through In the study, mutual fund performance was examined in both good and bad market states. Index funds and actively managed funds were compared during each market state and the performance of index funds was directly compared with the performance of diversified 4-fund portfolios, created by the author. By using Jensen s Model and the Carhart 4-Factor Model, the study found that actively managed funds outperformed index funds by several percentage points in both good and bad market conditions. The diversified 4-fund portfolios rendered similar results. Despite Nenninger s clear findings, his results are contradicted by earlier studies. Specifically, earlier studies found that actively managed funds only outperform index funds during recessionary periods.

21 14 Methodology For the purpose of this study, I randomly chose 36 U.S. equity mutual funds, 18 no-loads and 18 loads, from the Wharton Research Data Services (WRDS) CRSP database. During the selection process, I made sure that I didn t have multiple funds from the same investment firm to avoid any bias resulting from managers of the same firm investing in the same assets. Of the 18 load funds, 12 are front-end loads and 6 are back-end loads. Every fund chosen has at least a ten year return history as this study analyzes returns from December 2002 through December After obtaining the monthly returns, monthly net asset values (NAVs), front-end and back-end load percentages, and applicable timelines for the effectiveness of back-end loads from CRSP, I calculated 10-year, 5-year, 2-year, and recession period holding period returns, cumulative returns, Sharpe Ratios, and Carhart four-factor alphas for all 36 mutual funds. The recession period is defined as December 2007 through June 2009, based on the 2008 Financial Crisis data provided by the National Bureau of Economic Research. All expenses and fees, except for loads, were incorporated into the monthly NAVs of every fund retrieved from CRSP. In every instance when loads needed to be imposed on the returns, whether front-end or back-end, load-adjusted returns were calculated following Morningstar s load-adjusted return equation: R LA = R p (1-L) Where: R LA is the load-adjusted return R p is the return of the asset over some time period L is the load percentage For example, if a fund has a 5% front-end load, an investor will ultimately only see.95, or 1-.05, of the return, regardless of the time period held as front-end loads do not diminish over time. If a fund has a 5% back-end load for one year, an investor will again receive.95. Over time,

22 15 that back-end load may decrease to 2% if the investment is held for five years. In that case, an investor will receive.98 of the five-year return. With a no-load, L will be 0% and the investor will receive the entire return. All 18 load funds adhere to the following load schedules: Table 1: 18 Schedules Type Applicable Time Period % 3926 Front Constant Back 12 Months Months Months Months Months Front Constant Back 12 Months Front Constant Back 12 Months Months Months Months Months Front Constant Front Constant Front Constant Front Constant Back 12 Months Months Months Months Months Front Constant Front Constant Front Constant Back 12 Months Back 12 Months Front Constant Front Constant 5.50 After appropriately adjusting the load funds for their respective front-end or back-end loads, I computed holding period returns for all 36 mutual funds using the percentage change in

23 16 the beginning and end date NAVs. Then, using the load-adjusted returns, I calculated each fund s compounded returns, using the equation R c = (1+R) 1 (1+R) 2 (1+R) 3 (1+R) n. Returns were compounded based on the number of periods the investment was held over the 10-year time horizon. For example, a 2-year cumulative return was compounded five times as there are five 2- year periods within ten years. To calculate Sharpe Ratios for all 36 mutual funds, I retrieved monthly risk-free rates from Ken French s website and subtracted the risk-free rate from each load-adjusted monthly return. Then I divided the average excess return in each return period by the standard deviation of the monthly excess returns to compute 10-year, 5-year, 2-year, and recession period Sharpe Ratios. The market, size, value, and momentum factors necessary to compute the Carhart fourfactor alphas were obtained from Ken French s website. After subtracting monthly risk-free rates from monthly load-adjusted returns, I calculated monthly betas for each factor as the covariance of the asset and the factor divided by the variance of the monthly factor. Each return period beta was then multiplied by the average of the factor over the return period. Adding the factor and beta combinations created the four-factor predicted return for the fund. Subtracting the predicted return from the actual return resulted in the abnormal return. As with every other performance measure used in this study, I calculated 10-year, 5-year, 2-year, and recession period Carhart four-factor alphas.

24 17 Results Cumulative Returns Over the ten year data period, all 36 funds experienced significant changes in their NAVs per share. As there is only ten years of data, there was no compounding involved in computing the average no-load and load 10-year cumulative return. It was calculated as a holding period return, using the average NAV per share on December 31, 2002 and the average NAV per share on December 31, After adjusting for the loads, the average 10-year cumulative no-load return was 79.9% and the average 10 year cumulative load-adjusted return was 67.4%, as demonstrated here: Figure 3: 10-Year Cumulative Return 2 The 5-year cumulative returns incorporate a five year holding period return, over the first five years, and were compounded once, during the second set of five years. The first five years experienced significant growth, but do not reflect any impacts of the 2008 Financial Crisis because that five year period ends right before the onset of the recession. After adjusting for 2 All dates referred to in figures and tables are written as YYYY/MM/DD.

25 18 loads, the average 5-year cumulative no-load return outperformed the average 5-year cumulative load return. Figure 4: 5-Year Cumulative Return The 2-year cumulative returns use one holding period return for the first two years and then four 2 year periods of compounding. Compared to the 10-year and 5-year cumulative returns, the 2-year cumulative returns provide a detailed depiction of the changes in mutual fund values over the ten year data period. After adjusting for the loads, the average 2-year cumulative no-load return outperformed the average 2-year cumulative load return. Figure 5: 2-Year Cumulative Return

26 19 The recession period cumulative returns were computed using monthly NAVs over the time period of December 31, 2007 through June 30, After an initial holding period return for the first month, monthly returns were compounded. As the 2008 Financial Crisis was devastating for the entire U.S. economy, it is reflected in mutual fund returns. The funds performed so poorly that this period of analysis is not a matter of which type of fund outperformed the other, but rather which fund lost less value. The average recession period cumulative no-load return lost less value than the average recession period cumulative load return. Figure 6: Recession Period Cumulative Monthly Return Over all four sets of cumulative returns, no-loads outperformed loads over the ten year data period. Even though all of the funds were chosen randomly and are independent of each other, the averages of each fund type closely followed each other. When no-loads earned higher returns, so did loads, and vice versa. Even though these are not risk-adjusted measures and therefore should not be the sole basis of determining whether no-loads outperform loads, they serve as useful demonstrations of how the average mutual fund values changed over time.

27 20 Sharpe Ratios To compare no-load and load Sharpe Ratios, I calculated 10-year, 5-year, 2-year, and recession period averages for each fund and subsequently averaged each fund s average to obtain 10-year, 5-year, 2-year, and recession period no-load and load average Sharpe Ratios. No- Sharpe Ratio Table 2: 10-Year Average Sharpe Ratios Type Assessed % Sharpe Ratio Front Back Front Back Front Back Front Front Front Front Back Front Front Front Back Back Front Front Average Average No- Sharpe Ratio Table 3: 5-Year Average Sharpe Ratios Type Assessed % Sharpe Ratio Front Back Front Back Front Back

28 21 No- Sharpe Ratio Type Assessed % Sharpe Ratio Front Front Front Front Back Front Front Front Back Back Front Front Average Average No- Sharpe Ratio Table 4: 2-Year Average Sharpe Ratios Type Assessed % Sharpe Ratio Front Back Front Back Front Back Front Front Front Front Back Front Front Front Back Back Front Front Average Average

29 22 Table 5: Recession Period Average Sharpe Ratios No- Sharpe Ratio Type Assessed % Sharpe Ratio Front Back Front Back Front Back Front Front Front Front Back Front Front Front Back Back Front Front Average Average Since higher ratios indicate an investor being rewarded more for the level of risk undertaken in investing in the risky asset, higher ratios are preferable. On average, the no-load Sharpe Ratios for the 10-year, 5-year, 2-year, and recession period outperformed the load Sharpe Ratios and, as such, were preferable investments. Since the no-loads delivered more of a return than the load funds for the amount of risk undertaken, the managers of the load funds should not have been compensated for their superior investment expertise. Almost every Sharpe Ratio was positive, except for each fund s recession period return and each fund s 2-year return period from 2007 through 2008 (see Appendix B). The funds experienced negative Sharpe Ratios around the 2008 Financial Crisis because their returns were

30 23 lower than the risk-free rate of return on average, whether they were negative or extraordinarily close to zero. In hindsight, investors would have been better off only investing in the risk-free asset over the recession period rather than investing in any of these mutual funds. Conversely, investors were much better off investing in these mutual funds after the 2008 Financial Crisis ended because of the ultra-low interest rates the Federal Reserve employed to spur economic activity and expedite the recovery from the crisis. Carhart Four-Factor Alphas To compare no-load and load Carhart four-factor alphas, I calculated 10-year, 5-year, 2- year, and recession period averages for each fund and subsequently averaged each fund s average to obtain 10-year, 5-year, 2-year, and recession period no-load and load average four-factor alphas. Subtracting the four-factor expected return from the load-adjusted mutual fund returns rendered the following results: Table 6: 10-Year Average Four-Factor Alphas No- Four-Factor Alpha Type % Four- Factor Alpha Front Back Front Back Front Back Front Front Front Front Back Front Front Front Back

31 24 No- Four-Factor Alpha Type % Four- Factor Alpha Back Front Front Average Average Table 7: 5-Year Average Four-Factor Alphas No- Four-Factor Alpha Type % Four- Factor Alpha Front Back Front Back Front Back Front Front Front Front Back Front Front Front Back Back Front Front Average Average Table 8: 2-Year Average Four-Factor Alphas No- Four-Factor Alpha Type % Four- Factor Alpha Front Back Front Back Front

32 25 No- Four-Factor Alpha Type % Four- Factor Alpha Back Front Front Front Front Back Front Front Front Back Back Front Front Average Average Table 9: Recession Period Average Four-Factor Alphas No- Four-Factor Alpha Type % Four- Factor Alpha Front Back Front Back Front Back Front Front Front Front Back Front Front Front Back Back Front Front Average Average

33 26 As was the case with Sharpe Ratios, no-load alphas outperformed load alphas in the 10- year, 5-year, 2-year, and recession period averages. Even though no-loads consistently earned higher alphas, the difference between the alphas was only statistically significant at the 95% confidence level for the recession period alphas. The 10-year, 5-year, and 2-year alpha differentials were not statistically different from zero. fund managers did not earn superior returns relative to no-load fund managers. As demonstrated above, every fund s average 10-year, 5-year, 2-year, and recession period alpha was negative, resulting in negative no-load and load alphas. This is not necessarily an indicator of poorly performing mutual funds. It only means that the four-factor expected return was greater than the actual mutual fund return on average. An investor still could have made a profitable investment in the funds. Among the 10-year, 5-year, and 2-year periods, the alphas became more negative for both no-loads and loads as the investment time period grew. In fact, the only time period that regularly exhibited positive alphas was the 2-year period (see Appendix D). The period from 2007 through 2008 experienced positive alphas for every no-load and load fund. This trend is attributable to the actual returns of the funds outperforming the four-factor predicted return, even though most of the fund returns in that period were negative because of the effects of the recession. On average, the factors predicted such bearish returns that even when fund returns were negative, the model predicted an even lower return, which was subtracted from the fund return. Subtracting that negative four-factor return effectively created a larger positive addition to the negative fund returns, resulting in positive alphas.

34 27 Conclusion This study produced results consistent with previous studies as no-loads either outperformed loads or there was no statistical significance between the two. Even though the data used in this study rendered consistent results, the sample size used is miniscule compared to the entire U.S. equity mutual fund industry and may not be representative of the population, especially since only ten years of raw data were included. But based on the sample used, the non risk-adjusted cumulative returns on the funds demonstrate that the no-loads consistently outperformed the loads in 10-year, 5-year, 2-year, and the recession period. Across all four investment time periods, the returns followed the same trajectory regardless of the state of the economy throughout the ten year data period. This suggests that no-load cumulative returns may be consistent with load cumulative returns on average before their load adjustments. On average, the 10-year, 5-year, 2-year, and recession period Sharpe Ratios of no-loads were greater than those of load mutual funds. The only negative Sharpe Ratios belong to the 2- year period from 2007 through 2008 and the recession period. This is attributable to the risk-free asset outperforming all of the mutual funds on average. The ratios experienced a healthy rebound in the next 2-year period from because of the extremely low interest rates implemented by the Federal Reserve to expedite the recovery from the recession. Despite using different risk factors for evaluation in the Sharpe Ratio and the four-factor alpha, the results were consistent. On average, no-load alphas were greater than load alphas across every investment period, with the recession period alphas holding the only statistically significant difference at the 95% confidence level. The alphas from 2007 through 2008 demonstrate the overwhelming impact of the 2008 Financial Crisis on the U.S. economy. Even though the actual returns for the mutual funds were poor, the four factors predicted even worse returns which resulted in positive alphas.

35 28 The recession period used in this study was chosen because of the potentially erratic returns that the mutual funds may have experienced. In such a turbulent economic time period, load funds could have been a safe haven compared to no-load funds if their managers possessed superior asset allocation abilities. But as expected, their risk-adjusted measures rendered the same results as the other more economically stable time periods with no-load funds outperforming load funds. Overall, the results of this study indicate that load mutual fund managers did not demonstrate superior asset allocation expertise compared to those of no-load mutual fund managers. Therefore, investors were not compensated with higher returns for paying load fees and were better off investing in no-load mutual funds than load mutual funds.

36 29 Appendix A: Mutual s Sample No- Firm Names CRSP Total Net Assets of as of 12/31/2002 (in millions) Artisan Partners LP Killen Group Inc BlackRock Inc Bridgeway Capital Management Inc Dimensional Advisors LP First Pacific Advisors LLC Fidelity Management & Research Company Gabelli s LLC Heartland Advisors Inc ING Investments LLC Kalmar Investment Advisers Neuberger Berman Management LLC Northern Trust Investments Inc T Rowe Price Associates Inc SIT Investment Associates Inc Vanguard Group Inc Waddell & Reed Investment Mgmt Company Wells Fargo s Management LLC Firm Names CRSP Total Net Assets of as of 12/31/2002 (in millions) Fred Alger Management Inc Calvert Investments Inc Delaware Management Company Dreyfus Corporation Franklin Templeton Investments Goldman Sachs & Co/GSAM Hartford Mutual s HighMark Capital Management Inc Hotchkis & Wiley Capital Management LLC JPMorgan s Lord Abbett & Co LLC MFS Investment Management MassMutual Life Insurance Company Pacific Global Investment Mgmt Company

37 Putnam Investment Management LLC Saratoga Capital Management LLC Thrivent Financial for Lutherans Timothy Partners Ltd

38 31 Appendix B: Sharpe Ratios 10-Year Sharpe Ratios No- Date Standard Deviation Sharpe Ratio Date Type Assessed % Standard Deviation Sharpe Ratio Front Back Front Back Front Back Front Front Front Front Back Front Front Front Back Back Front Front