Since one of the best ways to manage risk is to avoid excessively risky investments from the outset, Smart PortfoliosTM starts by screening-out mutual funds and ETFs that have consistently failed to compensate investors for the risk that they exhibit. From the remainder are selected "universes" of investments that individually have attractive risk characteristics and that collectively are diversified and meet the risk profile of the investor. Investments in these pre-screened "universes" are dynamically optimized using Smart Technology© to achieve lower than market benchmark volatility and above benchmark risk-adjusted returns.

Smart PortfoliosTM manages risk at each step in the process from the selection of individual investments to the complete portfolio design. The DPOTM model incorporates the best tool available for quantifying risk, through the successor to standard deviation and value-at-risk, called Expected Shortfall. This is a more accurate measure of risk. It enables viewing of potential extreme events (outliers) that can adversely affect portfolio performance.

Studies have shown that over 90% of the quarterly variation in returns¹ among different investment managers is due to allocation among different asset classes. Nevertheless, Wall Street spends enormous resources on individual security research while virtually ignoring the opportunity to improve returns through asset allocation. Why? Tradition, out-of-date learning, belief that new theories don't add much if markets are efficient, (and, possibly, laziness) are all partial explanations.

For more than 50 years investment professionals have relied on a concept called Modern Portfolio Theory (MPT), developed circa 1952, that won its developers the Nobel Prize in Economics. MPT demonstrated that risk could be reduced and returns enhanced through asset diversification. The theory is applied to asset allocation via a methodology called Mean-Variance Optimization (MVO). MVO was a major advance in portfolio optimization, and is founded on four basic elements: measuring risk, forecasting returns, diversification and data management. These remain important today but they have all been upgraded. MVO makes unsupportable assumptions, such as that price changes are random, and lacks the significant advancements in technology and mathematics made in recent years. Extreme Value Theory represents a long-needed improvement to MPT.

– ¹Brinson, Beebower & Singer, Financial Analyst Journal '91, '94

Diversification is the secret sauce of investing because it allows the investor to reduce volatility (risk) and create a more consistent stream of returns. The driver behind diversification is the “dependency structure” among securities, most commonly referred to as correlation. If two securities are highly correlated the reduction of risk achieved by allocating to both of them is minimal because both securities will move in the same direction during up and down markets. If two securities have low or negative correlation then portfolio volatility is reduced because one security is going up while the other is moving less or in a different direction.

Calculating the dependency has historically been done using “linear correlation,” which is the average relationship between two securities over a defined period of time. Correlation is a poor measurement of dependency because markets are dynamic and prices change daily, as do the risk and return characteristics of individual securities. The daily change in a security's price alters its relationship with other securities. This calls for a tool that constantly adjusts for those dynamically changing relationships, and that tool or method, called Copula Dependency, is also an integral part of the Dynamic Portfolio OptimizationTM engine.

Much attention is given to the importance of investing in multiple asset classes (stocks, bonds, real estate, alternatives, etc.), disparate investment strategies (value, core, growth), different sized companies (large cap, mid cap, small cap), and various financial marketplaces (domestic, emerging markets, international). With all the possible choices, how can anyone know where and when to invest? An investor ought to invest in multiple sectors is to reap the rewards of diversification, but it can require a large financial outlay to own assets in all sectors and asset classes. Fortunately, investors can gain access to most sectors cost effectively using mutual funds, exchange-traded funds, and closed-ended funds. Knowing which sectors are most advantageous to own and when to own them is what Smart PortfoliosTM does best.

Traditional allocation models make a series of five "bets", based on long-term averages, to estimate the optimal asset mix, using a top-down approach. The first bet is estimating how much investment should be made in each asset class (equity, fixed income, real estate, commodities, and cash & equivalents). The next three bets, known collectively as style box selection, focus on sectors as they relate to market capitalization (large cap, mid cap, small cap), strategy (value, core, growth), and country (domestic, emerging market, international). The final bet is choosing which security(s) to own for each targeted style box. This traditional approach is out-dated because of its reliance on long-term averages, poor risk analysis, and the subjective judgment of the financial professional.

Smart's Dynamic Portfolio Optimization follows a scientific, bottom-up approach process. The first step is to calculate the current and forecasted risk and return of an individual security using DPOTM's advanced mathematics. The second step is to dynamically measure the relationship (correlation) between pairs of securities. The final step is to rank the pairs to determine the optimal portfolio mix based on current market conditions.

Sectors Graph