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A commonly stated concern with the efficient frontier theory is that is breaks down due to behavioral concerns with the market participants. The participants do not always maximize utility, information is not always readily available, and people do not always make decisions based solely on mean and standard deviations of returns.[1] Because of these concerns, it is necessary to discuss the behavioral implications for our framework.

We start with the definition of common behavioral errors associated with information processing and then move on to the types of informational errors.

Definition: "Information processing – errors in information processing can lead investors to misestimate the true probabilities of possible events or associated rates of return."[2]

The different types of informational processing errors are:

• Forecasting errors

• Overconfidence

• Conservatism

• Sample size neglect and representativeness[3]

People often have problems forecasting the future. The most typical concern is using the most recent information to forecast the future. As risk professionals, we see this every day as everyone thinks that the most recent years of information reflect the best and most reliable information. In reality, forecasting is much more complex than that. In our model, we rely on forecasting techniques, but concentrate on methods that use a minimum of five years of information and often 10 years or more of information if it is available. This reduces any forecasting errors and relies on data methods, which are more consistent than human forecasts.

Overconfidence is another common behavioral trait that is difficult to overcome. People often believe they forecast better than they actually do and are often unwilling to recognize that blind spot. This is where a robust process and using several independent experts can reduce the bias that comes from overconfidence. Any one person can have his or her own biases, even experts. So involving a team of experts and a process to reduce the bias is critical to getting a more accurate estimate of risk.

Sometimes a process or framework can be too slow to react to new information. A slow response often occurs in insurance where there is an unrecognized change in a company's risk profile. The client history and the industry data are naturally slow to reflect trends, and large volumes of data are required to finally identify new information. This phenomenon is the counterbalance to being too fast to react. The conservatism bias is best handled by involving business experts in the process to question and comment on changes in the business and to get a common understanding on how those changes are reflected in the modeling work.

Sample size bias is usually pretty well handled by expert modelers. They understand that small sample sizes are less credible than large ones and therefore provide less usable information within a forecast. This can be difficult to communicate, however, so it should be noted that communication of the biases of sample size neglect and representativeness is just as important as realizing them.

We next consider behavioral biases. It has been stated that "Behavioral biases largely affect how investors frame questions of risk versus return, and therefore make risk-return trade-offs."[4]

The main types of behavioral biases are:

• Framing

• Mental accounting

• Regret avoidance

• Prospect theory[5]

Framing is the way a question is posed about risk. The question can be posed as "Will you lose $50 million under a worst-case scenario?" or be posed as "Will you stand to make $5 million on the expected basis under the same scenario?" Different questions can lead to different responses, even in seemingly rational people. The way we approach framing is to include the positive and the negative, as well as several other scenarios to provide a range of responses. This can be information overload at first, but after the framework is understood, it provides key information to avoid the framing bias.

Oftentimes people segregate risks based on a particular belief or internal structure within an organization, saying it is fine to take risk in this particular area but not in another one. This is called mental accounting. Organizations are plagued with mental accounting as different divisions; regions, locations, and management all create some level of mental accounting for an organization. The only way to minimize this bias is to have the C-level executives dictate the level of risk they want to adhere to as an organization; otherwise the line-level managers will all view risk through their own lenses. Consultants can often point out this bias within a company, but a company that is not already aware of this bias can fail to use any risk framework appropriately.

Another large corporate risk is regret avoidance. This is the phenomenon that losing a bet on a scenario with long odds is more painful than losing the same amount on a game with a better expected outcome. This is illustrated in the saying "No one ever got fired by hiring IBM." Large corporations have different cultures and approaches to this bias. Some companies in Silicon Valley make an extra effort to avoid this bias and to create a risk-taking culture. Either way, this is a concern for our analysis. Any option we present, no matter how risk reducing to the organization, will look suboptimal to the current one based on our behavioral biases.

Prospect theory does not apply as well in a corporate environment as in a personal one. In prospect theory the change in wealth from one's current wealth is what is important, not the absolute wealth. For an organization, each employee has his or her own "wealth" and access to company funds. Many are limited in this area, and any change in wealth for the company is not often felt by the employee. There is a disengagement from the wealth of the corporation. This does not mean there is a certain level of bias in the corporation.

As we have shown, there are several behavioral considerations to make in any risk framework. We have tried to comment on how we address those concerns, but are sure there are many other successful ways to handle these biases. The key consideration here is to be aware of the biases and to make sure the organization addresses these issues as part of its enterprise risk management program.

  • [1] behavioral-finance.asp
  • [2] Zvi Bodie, Alex Kane, and Alan Markus, Investments, 8th ed. (New York: McGraw-Hill, 2008), 385.
  • [3] Ibid., 386.
  • [4] Ibid., 387.
  • [5] Ibid., 387-388.
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