Book of Wise Investors

For almost every field of studies, from engineering to businesses, there exists the need for acquiring some basic concepts to apply them in suitable situations for that field. In the field of investing and asset allocation, there is no exception also. The following concepts are required to know if one were to manage own portfolio of assets better, detailed mathematical operations of calculating them will be beyond the scope of this blog post or that they are easily available by the links referenced below. While it is a fact that when it comes to Warren Buffett value investing style of seeing stocks as businesses, there is no need to alpha, beta, variance, etc, the same cannot be said if you were to diversify into uncountable number of stocks or many different assets within an asset class, or managing a portfolio of different types of asset classes.

1. Variance and standard deviation

As most will have heard before, variance and standard deviation are statistical definitions of risk. Bear in mind that there are many meanings of risk as will be explained and elaborated later. What the numbers of standard deviation means in lay man terms is simply the degree of dispersion of the asset’s prices around its arithmetic mean over a chosen time period.

You can see it this way, in general, a relatively high standard deviation of returns means that there is a high chance that actual return when cashing out from that asset will be different from its mean over holding period and vice versa. Variance is the square of standard deviation, because the changes in price of assets will sometimes be higher or lower than its arithmetic mean, hence the need to square the differences between the return at each point of time and mean over the time period chosen, before adding together and the average of the sum is then called variance, square root variance is then called standard deviation.

A caveat is that standard deviation differs a lot depending on your chosen time period. This is not surprise, considering that the mean of the asset returns depends on the period of time used to calculate it. For the case of stocks, it is a common knowledge that standard deviation is much higher when measure over weeks and months than is when measure over years and decades.

2. Covariance

The value of covariance quantifies the degree to which two distinct assets move together. This value between two pairs of assets has deep significance when it comes to deceasing volatility of a portfolio of assets without decreasing expected returns – the ultimate objective of asset allocation anyway. You can interpret covariance this way, the higher the covariance between two pairs of assets within the same asset class or two different asset classes, the higher the correlation between those assets or asset classes and the higher the volatility, and of course vice versa.

3. Correlation

The correlation coefficient measures and quantifies the degree of positive or negative association between two sets of data. A positive correlation does not mean causation; it may mean that both are caused by the same causes. But finding correlation is essential if one wants to find causation. One of the main objectives behind asset allocation is to discover pairs of assets or asset classes that have quite stable and negative correlations with each other, or at least low correlations. The rationale behind is simple, so that the overall values of your portfolio will remain stable even though there is great volatility in today financial markets.

Correlation is simple to understand conceptually, in the simplest sense; a positive correlation means that between two data series, one move in the same direction as the other one at the same time. In other words, when the first asset increase in price, the second one also increase in price and vice versa. A zero correlation means that the two sets of data are totally unrelated to each other. The degree or extent of correlation is indicated by a value ranging from -1 to +1.

4. Alpha

Alpha gives you an idea of the degree in which that asset can generate returns lower, equal to, or higher than the expected returns, given the volatility of this asset relative to its market benchmark index. Put it in another words, it measures excess return if an asset relative to its beta.

5. Beta

In layman terms, beta gives an indication of how much percent change of the asset price for a given percentage in price of the market for that asset class as a whole. If you came across modern portfolio theory, beta is actually the asset’s market risk. Every financial asset, especially stocks, consists of market risk and company specific risk. Put it another way, beta can be interpreted as the extent of an asset’s market, systematic risk that cannot be diversified away. Beta of greater than one means that asset price will change in price greater than the percentage change in the overall market price of all assets in that particular asset class. Less than one is of course asset price changes less than broad market price changes and beta of one is when asset has same percentage change in price with the same price change in market.

6. Sharpe Ratio

Named after a Nobel Laureate, William F. Sharpe, this ratio is nothing fantastic or rocket science. It is a ratio of the mean return of an asset over its standard deviation. As a result, Sharpe ratio is also known as reward-to-variability ratio. Sharpe ratio is similar to alpha in the sense that both measures excess return of an asset over a risk free return benchmark, just that one is comparing to its beta and another is to its own standard deviation.

Related posts:

1. A simple asset allocation model for deciding between small and large caps
2. Using drawdown as a measure of investment risk
3. Why doing asset allocation between large and small caps is better than not doing so
4. Three different and essential ways to measure an asset rate of return