The S&P 500 is made of 500 stocks – duh. But each stock is not equally important. Perkins Elmer (PKI) has shown a nice steady rise since May 2013 and is a component of the S&P.
If equally weighted, PKI would represent 0.2% of the index. However, the S&P 500 is a capitalization-weighted index – a type of market index whose individual components are weighted according to their market capitalization, so that larger components carry a larger percentage weighting. The value of a capitalization-weighted index can be computed by adding up the collective market capitalizations of its members and dividing it by the number of securities in the index. As such, PKI represents 0.03% of the S&P 500, or roughly 1/100 of the representation of Apple.
The top ten weights of the S&P are the ten largest capitalized companies, and represent almost 18% of the entire index.
Apple (AAPL) 2.8%
Exxon Mobile (XOM) 2.5%
Google (GOOG) 2.1%
Microsoft (MSFT) 1.7%
Johnson & Johnson (JNJ) 1.6%
General Electric (GE) 1.5%
Chevron (CVX) 1.3%
Wells & Fargo (WFC) 1.3%
J.P Morgan (JPM) 1.3%
Proctor & Gamble (PG) 1.3%
Being cap-weighted, the S&P 500 certainly provides a solid representation of the economy as a whole. On one hand, Apple is surely more representative than, say, Jabil Circuit (JBL). However, on another hand, a large proportion of Apple’s market cap is its ungodly cash balance. Apple grew its massive cash hoard to a new record-breaking level last quarter taking the total of cash and marketable securities to $158.8 billion, or roughly a third of its entire market cap. Therefore, is it properly positioned in the S&P 500?
These cap-weighted portfolios automatically rebalance as security prices fluctuate, and thus easy to maintain, only when new companies become large enough to merit inclusion in an index or when others disappear through merger, failure, or relative changes in capitalization. However, cap weighting may lead to suboptimal portfolio return characteristics. Mathematically, cap weighting gives additional weight to rising stocks and reduces weights in stocks that have traded lower. Therefore, by definition, cap-weighted equity index funds have the tendency to overweight overvalued securities and underweight undervalued ones. This mismatch leads to a natural performance drag in cap-weighted and other price-weighted portfolios.
Traditional indices characterized by capitalization weighting are based upon the theories of the Capital Asset Pricing Model (CAPM). This model recognizes that all investors in a given market are exposed to systematic market risk. For investors who had neither the time for alpha generation nor the inclination that active managers can persistently capture excess returns, the cap-weighted market portfolio was the only sensible passive portfolio.
Recent years have seen much of the CAPM criticized or even rejected on both theoretical and empirical grounds, yet a trillion-dollar industry has emerged on investing in or benchmarking to cap-weighted indexes. CAPM says that a “market portfolio” is mean–variance optimal. While CAPM is still taught in business schools as a valuable conceptual tool, the state of the art in return modeling is the multi-factor framework based on the Arbitrage Pricing Model (APT) which reflects the sensitivity of the underlying asset to economic factors.
Capitalization-based, but equal-weight portfofolios have different problems. An equal-weighted portfolio containing the Russell 1000 stocks gives as much weight to the 1000th largest company as to the largest company, and gives no weight whatsoever to the 1001st largest company.
Based on APT, financial theorists now believe that there are numerous sources of equity premia, some risk-based and some behavior-based. These factor-based premiums, which appear to be robust over time and economically significant, can be identified and are associated with value, growth, momentum, volatility, capitalization and other fundamental or technical factors.
A paper on Fundamental Indexation (Arnott, Hsu, Moore, 2005) in the Financial Analyst Journal quantified the difference between a straight up cap-weighted return versus a similar portfolio weighted and built with intuitive fundamental factors (BV, income, revenue, sales, dividends, and employment). They concluded that sourcing particular equity premia yielded both a benefit in retun as well as in some cases volatility and correlation to other asset classes. Such difference was persistent under many market environments.
Although our process is somewhat different, the concepts presented by Arnott, Hsu, and Moore are similar to the methods used by Bloodhound. Our system’s methodology allows a user to seek and identigy specific factors that lead to performance enhancement – whether it be for alpha generation or smarter beta. Our simulation engine allows users to quickly and precisely model equal-weighted portfolios based on customized fundamental and technical factors.
Equal-weighted portfolios eliminate the dominating effect that one stock can claim. Strategies can be built and validated with a finite number of holdings. While not weighted by fundamantal factors, Bloodhound’s simulation engine utilizes ranking methodologies to identify the most appropriate holdings.
Bloodhound’s point-in-time dataset contains fundamental and technical data since 1987 for over 40,000 U.S. and Canadian equities and ADRs. Every data element remains in original, ‘as reported’ form. This adherence to data integrity enables investors to generate historical simulations completely free of survivorship, selection and restatement biases.
Systematic, factor-based indexes have been found to deliver consistent, significant benefits relative to standard cap-weighted indexes. As such, a more efficient system exists than the existing mass-produced, financial products. Bloodhound enables the customization of low cost, liquid, transparent, easy-to-implement strategies that either reflect broad market exposure or identify unique sources of equity premia.