How I Can Explain 96% Of Your Portfolio’s Returns | Kiran Pande

Great article from Kiran Pande:

Since the 1960s, we’ve been dependent on a model called CAPM (capital asset pricing model) to understand the relationship between risk and return, despite the fact that its measure of risk only explains about 70% of return. This measure, beta, makes the assumption that the entirety of every stock’s return is due to its exposure to the market. Put simply, every stock’s returns will equal a factor of the S&P 500’s returns. Thus, if a stock’s beta is 2.0, it will double the S&P 500’s returns on a bull day and double its losses on a bear day. Obviously, this assumption is wrong almost every day, but the idea is that this factor is explaining most of a stock’s returns.

All returns not explained by beta in the CAPM model are called alpha. This is traditionally accepted as the level of skill and value added by a portfolio’s manager……

There is a whole laundry list of reasons not to use CAPM, beta, and alpha but here are some highlights…

  • 70% is not 100%, not even close
  • Beta is symmetrical, risk is not… downside risk is rarely the same as upside risk.
  • Since the market index used to calculate beta (usually the S&P 500) contains stocks whose returns are supposedly dependent upon beta, these stocks’ returns are somewhat dependent upon themselves.

These counterpoints do not render beta, alpha, and CAPM useless, but we can do much better. The Fama-French Three Factor model is the answer. Rather than a single factor (market performance), the model throws a size factor and a value factor into the mix, replacing much of the nebulous alpha term. With the addition of these factors, Fama and French boast that their model explains as much as 96% of returns with quantifiable measures.

Read more at How I Can Explain 96% Of Your Portfolio's Returns | Seeking Alpha.

The DNA of a diversified portfolio – Managed Funds – Futures Magazine

Before starting, we must define an end goal. Commonly, the initial, singular objective is to maximize performance. This answer is legitimate but raises additional questions.

The first question involves consistency of performance. Certain strategies such as trend-following have desirable risk properties but are intermittent in their returns, while strategies such as option selling may tend to produce consistent returns over most periods but occasionally experience large, sudden draw-downs. Optimizing for performance typically implies that you are optimizing for the average performance over the sample period, but this metric doesn’t account for the year-to-year variability around the average. The importance of consistency depends largely on the time horizons of both the portfolio designer and the investors. Shorter time horizons demand greater consistency of returns.

Another question is that of style, or desired correlation to a benchmark. Alternatively, you may wish to minimize correlation specifically to a particular benchmark. Many portfolio designers seek to replicate the style of trend-followers, yet also improve on the risk-adjusted performance, i.e., they seek “alpha” as well as “beta” (see “Manager lingo,” below). Other portfolios have become popular. For example, an index comprising short-term traders has been developed to reflect a uncorrelated return stream to standard trend-following benchmarks.

Additional and often overlooked objectives include optimizing for various return statistics, including skewness, kurtosis and draw-down measures. Such objectives can be difficult to incorporate into the optimization process accurately. For instance, even though many believe that draw-downs can be bounded a priori and that risk-management methodologies can be separated from the trading program itself, two primary determinants of draw-down magnitude are program style and time. Longer-lived programs generally will have experienced larger peak-to-valley draw-downs, reinforcing the adage: “Your worst draw-down is always ahead of you.” Hence, optimizing for maximum draw-down is an exercise in futility….

via The DNA of a diversified portfolio – Managed Funds – Futures Magazine.