An Index Innovation:
The First 130/30 Strategy Index
Advances in risk management, trading, and portfolio construction have made it possible to construct new kinds of enhanced indexes that replicate active investment strategies.
MIT Professor Andrew W. Lo, PhD, and Pankaj N. Patel, CFA, Managing Director and Head of the Credit Suisse Quantitative Equities Group, recognized that an investment strategy could be designed as an index for use as a benchmark. This insight motivated them to create the Credit Suisse 130/30 Index, the first dynamic strategy index of its kind.
The Credit Suisse 130/30 Index replicates a 130/30 investment strategy in a disciplined, risk-controlled manner. The index also renders the strategy transparent—by using openly defined portfolio construction and optimization techniques. The index is dynamic in that it is rebalanced each month.
The principles and investment process of the Credit Suisse index were described in Lo and Patel’s award-winning paper, "130/30: The New Long-Only," in the Winter 2008 issue of The Journal of Portfolio Management.
How the Credit Suisse 130/30 Index works
The methodology of the Credit Suisse 130/30 Index has two main components: the calculation of expected alpha scores for each large-cap stock in the universe and portfolio optimization. The expected alpha scores quantify the performance prospects for each stock; portfolio optimization attempts to ensure that the long/short portfolio maintains risk characteristics similar to the long-only large-cap universe.
The investment process built into the Credit Suisse 130/30 Index is designed to strictly manage certain parameters of investment risk. It includes four steps (shown below), repeated on the third Friday of each month.
Investment Process: Credit Suisse 130/30 Index
1. Establish Universe: The largest 500 U.S. stocks (excluding ADRs) are selected. Then, any stocks trading below $5.00 per share or with low trading volume are excluded.
2. Calculate Expected Alpha Scores: Each stock in the Universe is assigned an expected alpha score (or ranking) using a comprehensive ten-factor model developed by Credit Suisse’s Quantitative Equities Group. The alpha scores measure each stock’s potential for adding to, or subtracting from, the return of the index portfolio. (Expected alpha is a forecast of a stock’s risk-adjusted return.)
The ten-factor model is composed of 50 distinct measures, which are grouped into ten categories: traditional value, relative value, historical growth, expected growth, profit trends, accelerating sales, earnings momentum, price momentum, price reversal, and small size. Each stock in the investment universe is scored on each of the ten composite factors; these scores are then combined to generate an overall forecast measure, in the form of a single alpha score.
3. Optimize Portfolio: To create the index portfolio, the alpha scores for each stock are used to develop a covariance matrix. Long and short positions are set to 130% and 30%, respectively. The maximum active weight for each stock is set to ± 0.40% of the overall portfolio (implying an annualized tracking error in the 1.5% –3% range). In addition, the index portfolio is constructed such that its beta is set to 1.0—in line with the overall universe of large-cap equities—and the annual portfolio turnover is limited to 100%.
4. Apply Equity Weights: The assigned weights for each stock in the universe—determined during the portfolio optimization process—are used to construct the 130/30 portfolio. The resulting index portfolio is thus returned to its 130/30 long/short structure, with a net exposure of 100% to the overall universe of large-cap stocks, and a beta of 1.0 to that universe.
Covariance matrix
To create an efficient, or optimized, portfolio requires several points of information for each security in that portfolio: the security’s expected return and its relationship (covariance) with all the other securities in the portfolio. A covariance matrix shows the degree to which the expected return for each security in the portfolio moves in relationship to every other security in the portfolio. A positive covariance means that the expected returns for a set of securities move in a similar direction, while a negative covariance means that their returns move inversely. If the securities in a portfolio have a high covariance with one another, they do not produce a well-diversified portfolio. A covariance matrix can be an important input to a portfolio optimization process.
Tracking error
A measure of the amount of deviation that a portfolio shows over time, relative to its benchmark index. Tracking error quantifies the degree to which the portfolio differs from its index or benchmark, by measuring the standard deviation between the two values. If tracking error is measured historically, it is called 'ex post' tracking error. If a model is used to predict tracking error, it is called 'ex ante' tracking error. Ex post tracking error is useful for performance reporting; ex ante is generally used by portfolio managers as an input to a portfolio risk management process.
Leverage
In general, leverage refers to borrowing. In investments, leverage can refer to the use of borrowed funds to enhance the returns of an investment. In a more general sense, leverage can refer to the enhancement of returns through the use of a variety of financial instruments.
Efficient Portfolio
A portfolio that provides the greatest expected return for a given level of risk. An efficient portfolio is mathematically calculated and takes into account the expected return for each security and its standard deviation. An information-efficient portfolio can be understood as a portfolio that takes full advantage of the value-added information provided by a stock-ranking system in determining the portfolio's composition and weighting.
Alpha
A measure of an investment’s performance relative to its benchmark. Specifically, alpha measures risk-adjusted return – the return generated by a security, relative to the return you would expect based on its beta. An investment returning more than the return that would be expected based on its volatility is said to generate positive alpha; an investment returning less than its expected return is said to generate negative alpha.
Beta
A measure of an investment’s price variability, relative to the market in which that investment trades. Over time, an investment with a beta of 1.5 will move, on average, 1.5 times the movement of its market. Betas can change over time, and they can be different for different types of price and market movement. For instance, an investment can be described as having a greater downward than upward beta if the investment moves downward more strongly in a down market than it moves upward in an up market.
Short selling
In an investment portfolio, selling short is the act of selling securities not already owned, in the hope of profiting by buying them back at a lower price. Typically, short sellers borrow stocks from broker-dealers and immediately sell those borrowed shares in the open market, collecting the sale price from the buyer. In order to profit, the investor must buy the shares (to return them to the broker-dealer) on the open market at a lower price than the investor got by selling them.
Long-only portfolio constraint
A long-only portfolio is restricted by its investment charter to purchasing securities and selling securities it already owns. A long-only portfolio is prohibited from borrowing shares for the purpose of selling them (i.e., selling short).
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