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Behavioral economics focuses on how investors behave, what their portfolios contain, and how they trade these investments (Koumou, 2019). According to the article by Chang et al. (2018), investors can be classified as either risk seekers or risk averters. In more recent times, a new group has been identified as Markowitz investors. The risk preferences of this kind of investor is more multifaceted. In fact, they tend to fluctuate based on their own personal leanings toward losses and gains. Furthermore, Ortobelli Lozza et al. (2018), identified an additional type of investor called prospect investors. These investors are neither risk averse nor risk seeking. Prospect investors tend to only partially diversify their portfolio and limit them to across a few holdings. No matter what type of classification an investor falls under, the diversification of one’s portfolio is key to his/her success.
Diversification is simply “choosing variety over uniformity” (De Giorgi & Mahmoud, 2016, p. 143). According to the authors, it is one of the important—if not the most important—investment principles when it comes to the area of finance. The goal of every investor should be to reduce overall risk while maximizing the return of his/her portfolio. As a result, risk should not increase if diversification is used effectively.
A problem the authors identified is that most investors do not have a diversified enough portfolio. To aid their decision making in diversification and portfolio optimization, they need to have some form of risk measure. Diversification has two key properties for the risk perception of a decision maker: convexity and monotonicity. With convexity, having diversification across two selections allows for the overall risk to remain lower than the worse one. Monotonicity ensures that the vector preorder aligns well with the risk order (De Giorgi & Mahmoud, 2016).
According to Ortobelli Lozza et al. (2018), the ideal level of diversification for a portfolio should tally higher than 300 stocks. This is based on the rules of the mean-variance portfolio theory. However, most investors only usually have a maximum of four. The authors’ study proved that “portfolio diversification increases with an investor’s risk aversion in a mean-variance framework only if all the asset returns have the same variance and common covariance” (Ortobelli Lozza et al., 2018, p. 4671). Another reliable way to measure portfolio diversification is through diversification returns. According to Koumou (2019), this is done by calculating the difference “between the weighted average of each asset variance and portfolio variance” (p. 1).
One principle of diversification, based on the capital asset pricing model (CAPM), is the mean-variance model. In short, this model supports that “a portfolio is well-diversified if and only if it is not exposed to unsystematic risks” (Koumou, 2019, p. 1). The author states the market portfolio would meet this criterion. Another principle to consider is that of the mean-variance (MV) model which proposes that an investor holds multiple securities across disparate industries. This will protect an investor if a single industry sees a significant loss.Risk aversion can be roughly defined as “the preference for a certain outcome with a possibly lower payoff over an uncertain outcome with equal or higher expected value” (De Giorgi & Mahmoud, 2016, p. 145). Risk averters, more often than not, will select a diversified portfolio over a less diversified portfolio. On the other hand, the diversification preference of risk seekers is to invest in a less diversified portfolio or even a single stock (Ortobelli Lozza et al., 2018). There are three types of risk aversion: weak, strong, and monotone. If someone has a weak risk aversion then he/she favors “the expected value of a random variable with certainty to the random variable itself” (De Giorgi & Mahmoud, 2016, p. 152). Those with a strong risk aversion typically have a dislike towards any increase in risk. Lastly, De Giorgi & Mahmoud (2016) define monotone risk aversion as “an aversion to convex order in risk” (p. 152).
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