## Windham Insights Series

## Portable Alpha

The conventional approach to building investment portfolios, which relies on the typical hierarchy of investment decisions, imposes constraints on active management. Under most circumstances, these constraints are unnecessary and produce mean-variance inefficient portfolios. By using portable alphas, managers can strike an efficient balance between alpha and beta exposures to create mean variance-efficient portfolios for their clients. Portfolio managers can take advantage of a new approach to portfolio composition designed to eliminate inefficient constraints by using portable alphas. Portable alphas allow managers to strike an efficient balance between active and passive exposures to create mean-variance efficient portfolios. What are alpha and beta? There are two potential sources of return and risk in an actively managed portfolio: alpha and beta. Any investment portfolio can be decomposed into an alpha portfolio and a beta portfolio. One type of return is passive return, or beta, which is the compensation for bearing the systematic risks embedded in each asset class. The beta portfolio is selected by allocating assets based on passive benchmarks. The portfolio of exposures to passive benchmarks is the investor’s beta portfolio, which is sometimes called the “policy portfolio.” The other type is active return, or alpha, which is expected return earned [...]

## Risk Regimes

It has been shown time and time again that volatilities and correlations are unstable. Depending on the sample used to estimate them, they may differ significantly—which can lead investors to underestimate their exposure to loss, and to form portfolios that are not sufficiently resilient to turbulent markets. To better manage risk, we propose a methodology for partitioning historical returns into distinct samples that are characteristic of quiet and turbulent markets. By separating these samples, investors can: Stress test portfolios by evaluating their exposure to loss during turbulent conditions Structure portfolios that are more resilient to turbulent markets Shift their portfolios’ risk profiles dynamically to accord with their assessment of the relative likelihood that market conditions will be quiet or turbulent To identify risk regimes, we use a statistical procedure to isolate periods in which returns are unusual. A period may qualify as unusual because two assets whose returns are positively correlated generate returns that move in opposite directions. Periods with unusual returns represent statistical outliers and are typically associated with turbulent markets. We create a subsample of these outliers and estimate volatilities and correlations from these turbulent subsamples. We also estimate volatilities and correlations from the remaining returns, which [...]

## The Hidden Cost of Active Management

Investors are well aware of the incremental transaction costs managers incur as they seek to replace securities perceived to be overvalued with those perceived to be undervalued. Moreover, it is no secret that active funds charge much higher fees than passive funds designed to track market indexes. What investors may not be as aware of, however, is that there is a hidden cost associated with most active funds. The typical active fund is more than 90% correlated with the market, yet their relatively high active management fee is applied not only to the fund’s active component, but to its market component as well. Rather than pay active fees on total assets, including those that provide market exposure, an investor could achieve essentially the same result before fees by allocation most of the portfolio to an index fund with the residual allocated to a pure alpha fund that nets out market exposure. The following example illustrates this hidden cost. Table 1 shows the monthly returns and values of a hypothetical actively managed fund and an index fund, assuming an initial investment of $10 million. The index fund serves as the benchmark for the active fund. In this example, the active [...]

## The Windham Labs Guide to Asset Allocation [Infographic]

With index funds now providing full access to global markets, a thoughtful asset allocation approach is more important than ever. Windham makes it painless to meet your clients investment goals while managing their unique constraints. Let this infographic be your guide to a comprehensive asset allocation strategy. Want to learn more about asset allocation? Download our Whitepaper! Asset Allocation Whitepaper

## Time Diversification

As mentioned by Windham CEO Mark Kritzman in Episode #51 of the Meb Faber Show, time diversification is the common assumption that investing over the long-term is safer than investing over shorter periods. For example, suppose you were going to buy a house in three months and needed to pay $100,000 in cash. In the meantime, would you be more inclined to invest that amount in a riskless asset, such as a Treasury bill, or in a risk asset, such as an S&P 500 index fund? Alternatively, suppose you wanted to buy that house in 10 years. How would you invest in the meantime? Typical investors would choose the riskless investment for the three-month horizon, and the riskier investment for the 10 year horizon. Keep in mind that the only difference between these scenarios is the length of the investment horizon. The Argument for Time Diversification Time diversification is the notion that above-average returns tend to offset below-average returns over long investment horizons. If returns are independent from one year to the next, the standard deviation of annualized returns diminishes with time. Consequently, the distribution of annualized returns converges as the investment horizon increases. Figure A shows a 95% confidence [...]

## Defining an Asset Class

Asset allocation is one of the most important decisions faced by investors, however there are no universally accepted criteria that define exactly what an asset class is. Some investments take on the status of an asset class because managers feel that investors are more inclined to allocate funds to products if they are defined as an asset class, rather than merely as an investment strategy. Alternatively, the investment industry tends to overlook investment categories that legitimately qualify as an asset class because investors are reluctant to defy tradition.What are the real consequences of NOT defining an asset class?The imprecision about the nature of an asset class reduces the efficiency of the asset allocation process in at least two ways.1. If dissimilar investments are wrongly grouped together into an asset class, the portfolio will not be diversified efficiently. 2. If an asset class is inappropriately partitioned into redundant components, the investor will be required to deploy resources unproductively to analyze irrelevant expected returns, standard deviations, and correlations.Furthermore, the investor may waste additional resources in search of relevant investment managers. For these reasons, it is important to establish criteria for the purpose of identifying legitimate asset classes.The list [...]

## Factor Methods: Part Two

In the first part of this series, we discussed how to perform factor analysis and the challenges that come with it. Catch up here. CROSS-SECTIONAL REGRESSION ANALYSIS As we learned in the first post, factor analysis reveals covariation in returns, and challenges us to identify the sources of covariation. Cross-sectional regression analysis, on the other hand, requires us to specify the sources of return and challenges us to affirm that these sources correspond to differences in return. We proceed as follows. Based on our intuition and prior research, we hypothesize attributes that we believe correspond to differences in stock returns. For example, we might believe that highly leveraged companies perform differently from companies with low debt, or that performance varies according to industry affiliation. In either case, we are defining an attribute—not a factor. The factor that causes low-debt companies to perform differently from high-debt companies most likely has something to do with interest rates. Industry affiliation, of course, measures sensitivity to factors that affect industry performance (such as military spending or competition). Once we specify a set of attributes that we feel measure sensitivity to the common sources of risk, we perform the following regression. We regress the returns [...]

## Factor Methods: Part One

Financial analysts are concerned with common sources of risk that contribute to changes in security prices, called factors. By identifying these factors, analysts may be able to control a portfolio’s risk more efficiently, and perhaps even improve its return. This post will discuss the first of two common approached used to identify factors. The first, called factor analysis, allows analysts to isolate factors by observing common variations in the returns of different securities. These factors are merely statistical constructs that represent some underlying source of risk (which may or may not be observable). The second approach, called cross-sectional regression analysis, requires that we define a set of attributes that measure exposure to an underlying factor and determine whether or not differences across security returns correspond to differences in these security attributes. We'll get to that next. FACTOR ANALYSIS Let us first begin with an analogy that will highlight the insight behind factor analysis. Suppose we wish to determine whether or not there are common sources of intelligence in students, based on the grades of 100 students in the following nine courses: algebra, biology, calculus, chemistry, composition, French, geometry, literature, and physics. First, we compute the correlation between the algebra grades [...]

## Risk Budgets

For decades, investment managers have evaluated portfolios according to their likelihood of loss, or “value at risk.” Value at risk (VaR) is generally understood to describe the maximum loss an investment could incur at a given confidence level over a specified investment horizon. Below is an example of how to solve for value at risk. Suppose we estimate a portfolio’s expected return and standard deviation to equal 7.10% and 18.20%, and assume that returns are lognormally distributed. It is logical to estimate the probability that this portfolio will suffer a loss of at least 20% in any given year. We begin by converting the periodic expected return and standard deviation into their continuous counterparts (shown below). The continuous expected return and standard deviation equal 5.44% and 16.87% respectively. Next, we convert -20.00% to its continuous counterpart, which equals -22.31% [ln(0.80)], and calculate the area to the left of -22.31% under the normal distribution. We do so by first dividing the distance between -22.31% and 5.44% by the continuous standard deviation (18.67%), which equals 1.645. This value is called the normal deviate, and it means that -22.31% is 1.645 standard deviation units below the continuous expected return of 5.44%. When we [...]

## Mismeasurement of Risk

Investors tend to consider risk as an outcome—how much could be lost at the end of an investment period? Risk is typically measured as the probability of a given loss or the amount that can be lost with a given probability at the end of their investment horizon. This perspective considers only the result at the end of the investment horizon, ignoring what may happen within the portfolio along the way. We argue that exposure to loss throughout an investment horizon is important to investors, and propose two new ways of measuring risk: within-horizon probability of loss and continuous value at risk (VaR). Using these risk measures, we reveal that exposure to loss is often substantially greater than investors assume. Where is the danger in measuring risk at the end of an investment period? Financial analysts worry that means and variances used in portfolio construction techniques are estimated with error. These errors bias the resultant portfolio towards asset for which the mean is over-estimated and variance is underestimated, which may lead analysts to invest in the wrong portfolio. Additionally, financial analysts worry that higher moments, such as skewness and kurtosis, are misestimated. In that case, extreme returns occur more [...]