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Mitigating Risk


No person on earth could have predicted the explosion of British Petroleum's Deep Horizon offshore drilling rig a day before it happened, not even those who caused the accident. Many unfortunate circumstances coalesced in order to pave the way for one of the most financially devastating events in that company's history. The cataclysmic explosion and the enormous environmental catastrophe that resulted had a devastating effect on the value of BP's market value, as it suffered severe financial penalties as the result of causing the largest marine oil spill in history.

This example, illustrates a fundamental law in economics. Since there is no way to predict future occurrences with 100% certainty, there is no investment in the financial world that does not come with some degree of risk, whether it is cash stored under a mattress or a US Treasury bill. Manufacturing and biochemical companies can be particularly predisposed to the outcomes of "random" events, partially because of their capital structure and their reliance on future political and governmental regulations.

At the same time, not all "random" events are detrimental to a company's bottom line. Drug or chemical discoveries that occur accidentally or incidentally (Teflon by Dupont or ePTFE by W.L Gore, for example) can have a very positive effect on a company's future income. Similarly, random events that affect competitors can allow for penetration into novel markets for companies that are poised to do so. While future events cannot be predicted with absolute certainty, estimates can be created that relate all current information in order to form a probability density function that measures the potential benefit of taking on additional risk. As a result, evaluations of a company's development workforce, it's supply chain, and overall ability to respond to new market conditions, are very important factors in determining latent value. Relatedly, it is often the smaller and leaner companies that are better able to respond more rapidly to changes in the market; so long as the barriers to entry are not significantly prohibitive.

Mathematical Basis

If the market is taken to be at equilibrium, the relationship between risk and reward should be linear, where incurring additional risk should increase the potential return by an equivalent value for all intervals. Non-linearity, in this case, is the result of market inefficiencies or information that exists, but is not fully known or understood. MVS employs several techniques for modeling future "unknown" or "random" events that will have a definitive outcome. One simple example is with regard to future drug or therapy commercialization. Since this is a future event that is considered to be unknown with two likely results, the hypothesis generates a bimodal distribution of the future value of the business sector in question. As different factors pertaining to approval or rejection become apparent or publicly available, the bimodal distribution gradually transforms into a Gaussian distribution. This is true for a wide range of operational factors, and as a result, many cumulative distribution functions take on multimodal characteristics when solving for future values.

Although the market mechanism is very efficient, part of the strategy with security selection is with finding companies where the average return will stray from this equilibrium line and result in a positive value for alpha. Diagram below:

Graphical Interpretation

At MVS, we apply a mathematical approach to assess risk: The mean value of the cumulative distribution function that results from the Valuation Metrics is calculated at a certain future time. This value represents the average, anticipated return, while the variance of the CDF at this time corresponds to the anticipated risk of purchasing the security. The average return and variance of the security form a point on the two-dimensional graph and the angle is measured from the x-axis to the point. If this angle is greater than the 45 degree "risk-free" rate of return, a positive alpha is observed and is quantified.

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