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Dec 12

A CRO guide to deal with financial amnesia

Posted by abiao at 11:33 | Others | Comments(0) | Reads(5135)
A guest post from Anton Kwaijtaal: A CRO guide to deal with financial amnesia.

1. Don’t fear the risk of falling behind
Whether it is the risk of falling behind, peer group pressure or ill-defined incentive schemes, there exists a tendency to choose direction based on the illusion of control when there is actually too much uncertainty. Instead, questions should be asked as to whether decisions based on more or less unfounded assumptions should be made at all. Unfounded and inappropriate assumptions are dangerous because of at least two well-known biases. First, we tend to be over-confident in our ability to make financial and economic probability models. The second bias is our tendency to favour information that confirms our beliefs or hypotheses. This is called the confirmation bias. Moreover, by using hyperbolic discounting we reveal a strong tendency to make choices that are inconsistent over time. In other words, we make choices today that our future self would prefer not to make, despite using the same reasoning. Therefore, CRO’s and all other professionals should minimize their bold assumptions about how the economy works. We know much less than we think we know. Warren Buffet, the highly successful investor, sets strict restraints on using assumptions. He nevertheless makes above average profits.

2. Use real risk indicators
The volatility is wrong when you really need it. When reading this sentence most risk managers immediately think about skewness, kurtosis or perhaps about extreme losses. However, it is necessary to take it one step further. Most of the risk indicators, also in a regulatory context, are based on statistics. In most circumstances this is a second moment, named "variance" or "volatility". The volatility is however an affect heuristic driven indicator. It has no real correlation with the actual risk. The affect heuristic leads people to have a low perception of risk when we feel positive about the economy (and the other way around). However, during long periods of bull markets – driven by debt accumulation – actual risk (e.g. the probability of a deep debt crisis) increases, but our perception of risk reduces.

What you are really interested in is the consequence of market shocks when it actually goes terribly wrong. In this way you correlate risk with the probability of survival of your firm. The use of volatility is a good example of attribute substitution. A complex problem (what are the consequences of a serious meltdown) is replaced with a less complex problem (what is the observed volatility of the market over the last few months/years), at which point the answer to the less complex problem is seen as the solution to the original problem. Risk indicators should be correlated with actual risk, not with indicators such as (implied) volatility. A better risk indicator is the price to profit ratio of stocks, which reveals – in combination with debt levels – a lot about instability accumulating in an economy.

3. Fit models to data, not data to models
There is a combination of eagerness to use complex models and too high a dependence on (recent) data that makes the use of models tricky at the very least. The quantitative models used in the financial sector are not fit for their purpose. For the models to perform reasonably well they need more regime shifts and more chaos components. For example, when we add debt to macro-economic models, they become very unstable. The economy and the financial markets follow an almost chaotic process. This, however, makes models almost impossible to calibrate. Additions, such as jump diffusion, copulas and stochastic volatilities are well-intentioned attempts to bring the models closer to reality, but this is still not close enough. We know reality is much more unstable. But, we don't like ambiguity, so we replace this with clear models. However, in the end they are still based on the implausible assumption of a stable repeating data generating process. Complex models also challenge our biased cognitive abilities. This especially holds true for the interpretation of model results. It is better to use simple models and perform many back- and stress tests and to focus on the underlying data, including data from past debt crises.

4. Listen to alternative stories
According to Shiller, the human mind thinks in terms of stories, with internal logic and dynamics that appear as a unified whole. Taleb calls it "explanations (stories) bind facts together". There is a direct link between the content of stories, the collective confidence and the booms and busts of the financial markets. The spread of stories, and thus the collective confidence or pessimism, could be compared to an epidemic, which tends to spread extremely quickly and without warning. This is why the economy follows an almost chaotic process. Collective confidence does not necessary mean a strong economy; even worse, it can lead to growing instability. One should remember that it does not matter what something looks like, it's how it behaves that counts. What makes it even more confusing is that the models seem to prove the story. The estimations based on data seem to be statistically significant, but in reality this is false. The underlying process changes when an economy tips! The CRO should not blindly follow the herd. Thinking in advance about other stories will improve the chance of survival when the stories start to change. Directly related to this topic is the use of scenario thinking in risk management. With proper scenarios, which are at the very heart of risk management – the minimization of unbearable loss ­– will be more successful.

5. Conduct behavioural self-assessments
As we have already seen, the brain makes decisions based on simplifications or so-called rules of thumb. These heuristics and biases have a tendency to deviate our decision-making from rationality and are at the root of our structurally making the same mistakes over and over again. Even if models work correctly, the resulting decision can still be irrational, usually because of (unconscious) emotions. Emotions and behaviour play a large role in decision-making. Seemingly rational decisions are actually driven by fear, loss aversion and affective forecasting. For example, people act completely differently when they are confronted with a loss than when they find themselves in a profit situation. This is a well-known and important aspect of Prospect Theory that is known as "aversion to a sure loss". Many more of these emotional aspects, that make us decide depending on the emotional state we're in, are known. Understanding all of this, it seems strange that no one in financial institutions is formally given the role of monitoring the behaviour and emotions of the senior management. Perhaps supervisory boards should consider hiring behavioural specialists. At the very least, the senior management and thus the CRO, should start conducting behavioural self-assessments.

Anton is the chief editor/publisher of Quant Magazine, aiming to be the personification of a new culture in finance


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