The Modified Fundamental Portfolio. Konrad Droeske

Transcription

1 The Modified Fundamental Portfolio Konrad Droeske A thesis submitted in partial fulfilment of the requirements for the degree of BACHELOR OF APPLIED SCIENCE Supervisor: Roy Kwon Department of Mechanical and Industrial Engineering 1

2 ABSTRACT The fundamental index derived by Roger Arnott has been shown to outperform market capitalization-based indexes over a multitude of scenarios. Rather than using cap as a means allocating wealth, the fundamental index uses cap-indifferent metrics that attempt to form a truer representation of each company s current economic size. This study investigates whether a combination of cap-indifferent metrics, in combination with valuation ratios, predictors of future wealth generation, and a smaller basket of equities, can generate a superior mean-variance efficient return on investment. These adjustments to the fundamental index were found to deliver more mean-variance efficient returns in spite of higher trading costs. While fundamental metrics that gauge company size may present a more attractive option for the allocation of billions of dollars, using measures that predict revenue and seek lower valuations in the form of a smaller portfolio can offer significant benefits to individual investors. 2

3 TABLE OF CONTENTS 1.0 The Modified Fundamental Portfolio 1.1 Reasoning and methodology behind the fundamental index 1.2 Evaluated and researched existing methods for equity selection 1.3 Chose a handful of simple and effective metrics 1.4 A methodology similar to the fundamental index 1.5 Applied methodology to evaluate its effectiveness 1.6 Compared to the S&P500 and FTSE RAFI US Justification 2.1 Fundamental index works, but could it be improved? 2.2 Graham and Buffett 2.3 A new investment system for individual investors 3.0 Method of Approach 3.1 CompuStat 3.2 Microsoft Access 3.3 Microsoft Excel 4.0 Results 4.1 Returns 4.2 Volatility 4.3 Sharpe Ratio 4.4 Excess Return 5.0 Conclusion 6.0 References 7.0 Figures and Tables

4 1.0 The Modified Fundamental Portfolio 1.1 Reasoning and methodology behind the fundamental index The Fundamental Index by Roger Arnott was used as the initial resource for this study. It offered a superior alternative to indexes based on market capitalization by weighing companies by their actual economic footprint as defined by a set of fundamental criteria. This concept provided the inspiration for developing a smaller portfolio which followed some of the rules outlined by Arnott, but also included fundamental metrics that were indicators of low valuation and predictors of future earnings growth. 1.2 Evaluated and researched existing methods for equity selection Additional papers on fundamental analysis and technical analysis were reviewed. This led to the identification of a number of strategies and rules which attempt to predict stock price movement, identify undervalued securities, as well as minimize risk and transaction costs. The feasibility of each was evaluated and a select few were used for the new portfolio management strategy. During the research process, a large amount of time was also spent developing familiarity with contextual information that surrounds the world of finance. This includes 1

5 understanding the terminology and the conflicting points of view that remain between academics and practitioners. 1.3 Chose a handful of simple and effective The final approach required the selection of four simple and proven metrics capable of taking value, future earnings growth and risk management into consideration. Lower Price-to-Earnings (P/E) ratios tend to imply greater value, since you are paying less for each unit of net income earned by the company. When comparing companies, all things being equal, the one with the lower P/E will be the superior investment. However, this ratio can be industry specific, and companies without earnings will not have a defined P/E value. Book value is often described as what would be left if the company went bankrupt. Priceto-Book (P/B) represents the ratio between price and book value per share, with lower multiples implying greater value. Once again, this ratio can be very industry specific as it does not take the value of a company s intellectual property and brand into account. Return-On-Equity (ROE) was also selected and is considered by many to be one of the best indicators of investment efficiency. It is a measure of how each available dollar is 2

6 used to generate earnings for the company. This is also a favoured measure of legendary investor Warren Buffett (Investopedia, 2004). Lastly, Dividend Yield was used as a means of reducing the risk involved in the portfolio. Traditionally, companies that issue dividends have shareholder-friendly policies and strong balance sheets. The income received from these dividends ensures some degree of return in the absence of capital gains. In addition, a higher dividend yield may imply that the shares of a company are undervalued. 1.4 A methodology similar to the fundamental index This study derived its methodology heavily from the fundamental index. The four chosen criteria were each weighted equally and scaled from 0 to 1 based on their respective maximum and minimum values (see Table 5). A score was then calculated for each stock by averaging these numbers, and then the 20 highest were placed in a portfolio for that particular year. Much like how a fundamental index allocates capital, the amount of each stock to be purchased was based on their individual scores in proportion to the combined sum of all them. 3

7 1.5 Applied methodology to evaluate its effectiveness Based on the portfolios from each year, an overall return was calculated from 1997 to 2006 that included capital gains and dividends, but subtracted trading costs. Due to the high turnover rate involved with this new system, trading costs were increased from 1 (from the fundamental index) to 4 percent. The assumption was that every transaction, buy and sell, would incur a two percent fee. 1.6 Compared the results to that of the S&P500 and the FTSE RAFI US 1000 These results were then compared to those of the FTSE RAFI US 1000 and the S&P500. In order to properly evaluate the various strategies, many of the same methods used to compare the fundamental index to the S&P500 were altered to include this study s methodology. 4

8 2.0 Justification 2.1 Fundamental index works, but could it be improved? For the last 50 years, indexes have been weighted based on the market capitalizations of each company, with the S&P 500 being the most notable example. The number of these indexes has been expanded to include a variety of markets, levels of capitalization, and combinations thereof, but have still relied on the hypothesis that market prices are more or less efficient. It was assumed that in the long run, we could only hope to match the results of the entire market less trading costs unless we increased our risk tolerance. Recently, Roger Arnott pioneered the concept of a fundamental-based index, which weighs a portfolio based on its current economic strength rather than its market capitalization. Since market capitalization takes future growth into account, it potentially skews the overall investment towards overvalued stocks. As a result, equities trading for less than their intrinsic value are underutilized. By weighing an index fundamentally, we avoid the speculation involved in placing disproportionate capital in the projected growth of companies and invest according to the reality of the current economy. The results of this novel method were substantial. A fundamental index of US equities outperformed the S&P by over 2% with similar levels of volatility. In the international arena, a fundamental index outperformed the MSCI EAFE by over 2.5% with even lower levels of volatility. Given that these results could be attained by passively allocating 5

9 capital based on the economic footprint of these companies, this study aims to develop a similar methodology that uses metrics capable of identifying undervalued securities. 2.2 Graham and Buffett The value based approach, as defined by Benjamin Graham, and later improved upon by Warren Buffett, has stood the test of time. Like the fundamental index, their methodologies seek to remove speculation from the realm of stock picking, and focus on investing in companies priced beneath their intrinsic value. By combining these teachings with the simplicity and effectiveness of the fundamental index, it could be possible to create a more mean-variance efficient model. Rather than weigh a portfolio based on economic footprint, the study adopts commonly used indicators of value to develop a similar system of objective capital allocation. 2.3 A new investment system for individual investors By limiting the size of each portfolio to a maximum of 20 equities, individual investors will be able mimic the returns of the study without having to rely on institutional adoption. The fundamental index relied on the creation of a mutual fund in order to provide ordinary investors the opportunity to capitalize on its effectiveness. If a system 6

10 for investment could maintain mean-variance efficiency with a much smaller sample, its adoption and implementation could be much more rapid and flexible. 7

11 3.0 Method of Approach 3.1 CompuStat The entire basis this study was to use financial information to develop an investment methodology that could generate market beating returns over the long run. In order to accomplish this, it was necessary to acquire a set of data that contained financial information, for the entire North American market, over the course of a number of years (See Table 6). Each of the four metrics were be broken down into expressions that indicated what financial data was needed. A query in CompuStat provided a file containing all of the required fields, which were then saved and imported into Microsoft Access. From here, the data could then be distilled and organized in such a way as to suit the needs of this study. 3.2 Microsoft Access A query was created in Microsoft Access that used expressions to calculate the values the four metrics from the initial set of financial data (See Table 3). These were then rescaled from 0 to 1 and converted into weighted scores. In order to preserve the integrity of our scores, the upper and lower limits of the metrics were set to realistic levels (See Table 5). 8

12 Another field was created to form the average of all of these figures. The averages served as the basis of our equity selection, with higher numbers representing the most attractive investment opportunities. These figures were also used to determine the amount of capital to be allocated to each stock within each particular year. Smaller queries were then created listing the top scoring equities for each year in ascending order. These were exported to Microsoft Excel for further analysis. 3.3 Microsoft Excel The top twenty scores from each of the smaller queries formed the portfolio for each respective year. Using the information from the queries, each individual stock s contribution towards the portfolio was then calculated based on its capital gains, dividend yield and capital allocation. The returns were summed, and trading costs subtracted. These yearly return were collected and placed in a table that could allow easy assessment of the study s performance against others. The overall results, spanning from 1997 to 2006, were compared to those of the fundamental index and the S&P500. The geometric return, volatility, Sharpe ratio, and excess return, were all calculated, in order to identify the strengths and weaknesses of each strategy. 9

13 4.0 Results 4.1 Returns From 1997 to 2006, the new fundamental portfolio did not experience a single year of negative growth (See Table 1). The results were surprising considering the severe depreciation the entire market experienced in the early 2000s. In true Graham/Buffett fashion, the methodology avoided growth companies in favour of undervalued picks from established industries resulting in a geometric return of 18.28% and an excess return of 7.93 pps. The same time period saw much lower levels of growth from the S&P 500 and the FTSE RAFI US 1000, with each seeing an annual geometric return of 8.42% and 10.63% respectively. A single dollar invested in the modified fundamental index would have yielded $5.36. If invested in the S&P 500, the appreciation in value would have reached $2.24, while a dollar place in the RAFI US 1000 would have grossed $2.74 (See Table 2). 10

14 4.2 Volatility Standard deviation was used as this study s measure of volatility. While the returns of the modified fundamental portfolio were higher, levels of volatility remained lower than that of the benchmark indexes. From 1997 to 2006, it was calculated to be 14.07%, while the standard deviations of the S&P 500 and the FTSE RAFI US 1000 during the same time period were found to be 19.14% and 15.83% respectively (See Table 2). 4.3 Sharpe Ratio The fundamental index uses the Sharpe ratio as a means of identifying excess return per unit of risk. It is defined as follows: Where R is asset return and R f is the benchmark return. For the purposes of this study, R was set to 10%. In the case of the modified fundamental portfolio, the study finds a Sharpe ratio of Over the same period, the S&P 500 experiences a ratio of , while the FTSE RAFI 11

15 US 1000 performs at (See Table 2). The S&P 500 suffer from three years of consecutive negative returns from 2000 to 2002, while the FTSE RAFI US 1000 experienced negative returns in 2002 (See Table 1). This had a significant impact on the performance of these ratios. 4.4 Excess Return This measure determines the difference between a portfolio s geometric return and that of a risk free benchmark. Once again, this study uses a 10% compounded annual return as the benchmark for comparison. The findings of the study are such that the modified fundamental portfolio delivers an 8.28 pps advantage over the benchmark. Meanwhile, the FTSE RAFI US 1000 only returns pps above the standard, and the S&P 500 is pps below. All of these findings occur over the 1997 to 2006 timeframe. 12

16 5.0 Conclusion Based on a 10 year period, beginning in 1997 and ending in 2006, this study was able to create a portfolio that outperformed the fundamental index and the S&P 500 with lower levels of volatility. By using metrics that emphasized the identification and purchase of high value equities, it was possible to construct a portfolio that delivered consistently superior returns without an adverse effect on risk levels. The utilization of P/E, P/B, ROE and Dividend Yield as metrics may have provided insights that were previously unavailable in the fundamental index. These metrics were able to objectively target equities with low valuations, efficient use of reinvested funds, solid balance sheets, and a strong commitment to shareholder interests. In addition, rather than allocating capital based on the size of an organization, this study rescaled the values of the selected metrics and then averaged the results. This, in combination with a limit of 20 stocks, provided a series of scores capable of establishing the necessary proportions for a superior mean-variance efficient portfolio. When put into practice, the fundamental index s use company size as a means of allocating capital was replaced with a similar methodology whose objective was to find the best possible investment opportunities on the market and build a portfolio that allocated capital based on greatest potential return. 13

17 6.0 References Abarbanell, J. S., Bushee, B.J. (1997). Fundamental Analysis, Future Earnings, and Stock Prices, Journal of Accounting Research, 35(1). Arnott, R. D. (2008). The fundamental index: A better way to invest. Hoboken, NJ: John Wiley & Sons, Inc. Arnott, R. D., Hsu, J., & Moore, P. (2005). Fundamental Indexation. Financial Analysts Journal, 61(2), Beneish, M.D., Lee, C.M.C., Tarpley, R.L. (2001). Contextual Fundamental Analysis through the Prediction of Extreme Returns. Review of Accounting Studies. Chan, K., Hameed, A., Tong, W. (2000). Profitability of Momentum Strategies in International Equity Markets. Journal of Financial and Quantitative Analysis, 35(2). Graham, B. G. (1973). The intelligent investor: The definitive book on value investing. New York, NY: HarpersCollins. Investopedia Staff. (2004). Warren Buffett: How does he do it. Retrieved Jan 1, 2009, from Konno, H., Yamamoto, R. (2003). Minimal concave cost rebalance of a portfolio to the efficient frontier. Lo, A.W., Mamaysky, H., Wang, J. (2000). Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation. The Journal of Finance, 4. 14

18 7.0 Figures and Tables Table 1 Index/Portfolio Performance (1997 to 2006) Portfolio/Index Fundamental Portfolio 21.96% 7.28% 13.14% 8.13% 28.66% 10.27% 42.87% 39.43% 1.02% 17.27% S&P % 28.58% 21.04% -9.10% % % 28.69% 10.88% 4.91% 15.79% FTSE RAFI US % 29.84% 4.00% 15.01% 8.10% % 9.57% 6.92% 2.81% 19.64% Table 2 Return Characteristics of Alternative Indexing Metrics, Portfolio/Index Ending Value of $1 Geometric Return Volatility Sharpe Ratio S&P 500 ( ) 10.53% 15.10% S&P 500 ( ) % 19.14% Composite ( ) 12.47% 14.70% FTSE RAFI US 1000 Total Return Index ( ) % 15.83% Reference ( ) 10.35% 15.20% Fundamental Portfolio ( ) % 14.07% Table 3 Modified Fundamental Portfolio Metrics and Expressions Price to Book Value: [Price - Calendar Year - Close ($&c)]/(([assets - Total (MM$)]-[Intangibles (MM$)]-[Liabilities - Total (MM$)])/[Common Shares Outstanding (MM)]) Dividend Payout: [Dividends per share - Ex-date ($&c)]/[price - Calendar Year - Close ($&c)] P/E: [Price - Calendar Year - Close ($&c)]/[eps (Basic)] ROE: [Net Income (Loss) (MM$)]/[Stockholders' Equity - Total (MM$)] Table 4 - Fundamental Index Book Value Trailing 5-Yr Average Cash Flow Trailing 5-Yr Average Gross Sales Trailing 5-Yr Average Gross Dividends Table 5 Lower and Upper Limits Metrics P/B Dividend Payout P/E ROE Max = Max = 20 Max = Max = 20 Max = Max = 20 Max = Max = 20 Max = Max = 20 Max = Max = 20 Max = Max = 20 Max = Max = 20 Max = Max = 20 Max =

19 Table 6 CompuStat Query Variables Variables SMBL CONAME Data1 Data2 Data3 Data4 Data5 Data6 Data12 Data14 Data21 Data24 Data25 Data26 Data29 Data30 Data33 Data57 Data58 Data60 Data78 Data98 Data172 Data180 Data181 Data216 Ticker Company Name Cash and Short-Term Investments (MM$) Receivables Total (MM$) Inventories Total (MM$) Current Assets Total (MM$) Current Liabilities Total (MM$) Assets - Total (MM$) Sales (NET) (MM$) Depreciation and Amortization (MM$) Dividends Common (MM$) Price Calendar Year Close ($&c) Common Shares Outstanding (MM) Dividends per share Ex-date ($&c) Employees (M) Property, Plant & Equip Capital Ex (MM$) Intangibles (MM$) EPS (Diluted) Excl. Exta. Items ($&c) EPS (Basic) Exclude Extra. Items ($&c) Common Equity Total (MM$) Inventories Finished Goods (MM$) Order Backlog (MM$) Net Income (Loss) (MM$) Change in Working Capital Total (SoC)(MM$) Liabilities Total (MM$) Stockholders Equity Total (MM$) 16

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