# Teaching Portfolio Theory: A tool for demonstrating the diversification effect and related issues

Professor **Steve Cook**

Department of Accounting and Finance, Swansea University Business School

s.cook@swan.ac.uk

Published May 2013

Analysis of risk and return for portfolios is a key component of many undergraduate and postgraduate *Principles of Finance* and *Corporate Finance *modules. While the depth of the analysis of risk and return may vary across modules and levels, it is (almost certainly always) introduced via consideration of a two asset example. To do this the two relevant risky assets need to be defined along with their expected returns, the variance (or standard deviation) of these returns, the proportion of the portfolio accounted for by each asset and, importantly, the correlation between the returns on the assets. These can be denoted as follows:

- The two assets:
*x*and*y*. - The expected returns on the two assets: E[
*r*], E[_{x}*r*]._{y} - The variance of the returns on the two assets: σ
_{x}^{2}, σ_{y}^{2}. - The proportion of the portfolio accounted for by each asset: λ
_{x}, λ_{y}. (Note λ_{x}+ λ_{y}= 1). - The correlation between the returns on the assets: ρ
_{x,y}.

With these terms defined, calculation of the expected return on the portfolio, denoted as E[*r _{p}*], is straightforward as it is simply the weighted sum of the expected return on the individual assets:

E[*r _{p}*] = λ

_{x}E[

*r*] + λ

_{x}_{y}E[

*r*]

_{y}Derivation of the variance of the returns on the portfolio, denoted as σ_{p}^{2}, is more involved, although it is quite intuitive: the variance depends the variances of the individual assets, the amount of each asset in the portfolio and the co-movement of the returns on the assets. As with the expected return, the variance of the return of the portfolio is a familiar feature in the presentation of this material and using the above notation is given as:

σ_{p}^{2} = λ_{x}^{2} σ_{x}^{2} + λ_{y}^{2} σ_{y}^{2} + 2λ_{x} λ_{y} ρ_{x}_{,y} σ_{x} σ_{y}

The expected return for the portfolio is then plotted against the standard deviation of the return for the portfolio to provide a standard risk-return diagram. This diagram is central to the analysis of risk and return and can be used to discuss a range of issues including:

- Identification of the opportunity set of alternative portfolios of the two assets;
- Identification of the efficient set of alternative portfolios of the two assets;
- ‘Dominated’ portfolios;
- The diversification effect.

The use of risk-return diagrams is clearly essential to the analysis of this material given the manner in which it assists the understanding of a variety of underlying concepts. As a consequence, I have constructed a simple Excel spreadsheet to allow the calculation of risk-return schedules for hypothetical assets with hypothetical returns, variances of returns and correlations between returns. I feel this is a very useful tool to help demonstrate all of the above issues, particularly the diversification effect and its dependence upon ρ_{x}_{,y}. With the spreadsheet provided, students can experiment with different values of ρ_{x}_{,y} to see how the diversification effect increases as ρ_{x}_{,y} moves from its upper limit of +1 towards its lower limit of -1. In the version of the spreadsheet available here, the values for the relevant returns, variances and correlation are those presented in an example provided in a particular popular *Corporate Finance* text (Hillier *et al*., 2003). Obviously, the flexibility of the spreadsheet is such that it can be used to replicate examples in textbooks, or extend such examples, or provide completely new examples to complement those seen elsewhere. During lectures and tutorials use can be made to the spreadsheet to generate alternative risk-return diagrams to illustrate and discuss a variety of issues central to portfolio theory, including those listed above.

**Download the Excel spreadsheet**

### References

Hiller, D., Ross, S., Westerfield, R. and Jaffe, J. (2013) *Corporate Finance* (Second European Edition), London: McGraw-Hill.

#### Other case studies by Steve Cook:

- Forecast Combination: A tool for teaching and demonstrating the underlying issues
- Time Series Decomposition: A practical example using a classic data set
- Understanding the construction and interpretation of forecast evaluation statistics using computer-based tutorial exercises
- Basic matrix algebra for economists

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