A Risk Management Approach to Business Valuation - Essay Example

February 2002 A Risk Management Approach to Business Valuation Investment valuation is like selling an ageing automobile: econometrists love to cloak recommendations in numbers and words that we hope most people will not understand, and then look for escape routes in to eternity! Seriously, the propensity of the business environment to change succeeds in defeating the most elegant and sophisticated of historic data analyses. Yet the very nature of investments, whether for commercial or social objectives, is inextricably linked with how the future unfolds. Managements can learn from experience, and they can respond to pressures from important stake-holders as well. People and teams can influence future performance, though the cause and effect relationship lies more in the present than in the past. The ability of investment to produce future returns is fraught with probabilities. The alternative chosen for use of an asset is a matter of deliberation and discretion. It should therefore be possible to value an investment based on the risk-taking nature of the group that controls deployment of the asset. The matter is worth pursuing wherever there is value in predicting future cash flows from an investment, and when the past is known to be of low relevance for conditions in the future. It is also of interest because it could allow for the professional application of modeling to an area of widespread interest and concern amongst all communities of investors. The Limitations of the Historical Approach Though a quantile approach can ameliorate the confusing scatter of past events with respect to independent variables, major qualitative changes in their complex inter-relationships can result in the most disruptive discontinuities as we extend past trends in to horizons of the future. There are situations in which statistical validity is inadequate for a decision on a risk with very serious consequences. We need, as far as possible, to create comprehensive scenarios in which the outcome can be reasonably guaranteed if specified conditions are met. The imperatives of a risk management approach will not allow for the degree of uncertainty to which mere smoothing of past variations may be restricted. Legislation, regulation and social pressures from organized groups are different today from their past arraignment. They continue to change as well. We are asked to discontinue things to which we are accustomed, new challenges arise from technology and new opportunities as well. Investments with long gestation are par for many courses, yet the futility of past data grows as we travel ever more distant in to the future. However, risk management methods allow for both existential learning and for structured protection from hypothetically conceived risks. We can make decisive moves now to protect assets from future events. This infers that the value of the same asset can be different depending on the assumptions we make today about tomorrow. This is a real situation at ground zero for a business person in a line function, though it can be a nightmare without utility for the econometrist! We need a bridge between the theoretical elegance of mathematical finance and the practical realities of risk management in the business world. Independent variables, in changing combinations, have varying time lags before they take perceptible effect on statutory financial statements. Quantile regression methods can account for such variability when used by people internal to an organization and privy to secure data. However, the most important valuation operations have to take place with only information in the public domain, some of which may not be in numerical form. This is another crucial limitation to the use of historical numbers for predicting future cash flows from an investment today. The Risk Management Triad There are three major lines of management action that affect the risks of business and hence the probability of realization of revenues projected for the future from investments. Each of these involves qualitative initiatives by organizations, though the impact is direct and large on financial performance. They are deliberate steps that people can take to safeguard the continuity of normal operations even in the face of abrupt discontinuities and disturbances in the environment. There will be time periods during which entities which do not take any measures, survive without any disruption because the environment remains even and secure. However, the value of the measures comes in to sharp focus when there is a sudden adverse event. The efficacy of risk management becomes increasingly evident with the passage of time. Therefore, organizations that practice comprehensive risk management should show greater consistency of performance over time, than others that take chances and do not cover themselves for severe risks. The coverage referred to here is not by insurance alone, but by systematic preventive measures and readiness with a sequence of contingent actions as well. Hazard and Operability Studies (HAZOP) and Hazard and Critical Control Points (HACCP) are the two most common methods used internationally to identify risks that can arise from operations, and to plan for their management and response. Organizations in lines of business with known and visible risks often have internal specialists to deal with risk management, and there are specialist firms that offer consultancy and audit services as well. Business Continuity is a measure of the efficacy of an organization’s HAZOP, HACCP and related measures. The environmental impact of an organization is a second source of risk. Air, water and soil pollution, waste accumulation and its safe disposal and consumption of non-renewable resources are three important aspects of environmental management. Lapses on any of these parameters may cause the cessation of revenue flows from any class of investor. Some organizations seek the authority of the International Standards Organization and get ISO 14000 certification. Others use internal systems and control measures to manage their environmental imprints. Every step that reduces environmental impact secures future revenue, and the reverse is equally true! Product liability is the third area that severely impacts the continuity of revenue streams from investments. Litigation is expensive in itself, and judicial awards can be crippling. The damage to public image can also be as serious as the financial damages. Label text is covered by the ISO 14000 series, but organizations that wish to protect their operations may also take significant and ongoing stewardship measures. Most product liability events have some minor incidents preceding a major disruption. Organizations that use risk identification and analysis methods such as HAZOP and HACCP, the ones that seek ISO 14000 certification or equivalents and those who adopt strategies to contain product liability claims, should provide more stable returns on investment, than others that do not use such safe guards. However, since risk management is expensive, companies that ignore risk management should provide higher returns in the short-term. The Benefits of a Stochastic Approach Risk management measures take long periods to unfold fully. HAZOP and HACCP are incremental in approach, seeking marginal improvements periodically. There are new risks to be identified in each round of analysis, and organizations have to travel along learning curves. Environmental management is highly dependant on technology. Standards get increasingly difficult to meet over time. Product liability claims can arise decades after use, and various levels of courts may give contradictory awards. Stochastic modeling may help us measure the effectiveness of risk management in compressed time frames. The recognition that present actions have more bearing on projected cash flows for future periods than historical trends, leads automatically to the consideration of stochastic methodology for forecasting. We can construct mathematical relationships between discrete threats to operations that have occurred recently, the ability of the organization to respond and to recover from the adverse events, and to use the quantified relationship to predict the probable course of future events based on the frequency of occurrence of such adverse events. A stochastic model will have the advantage that we can study combinations of independent but inter-related management actions in a manner that numbers representing historical performance cannot. The stochastic approach will also allow us to see each organization as a separate entity, and to relate each of their risk management measures in terms of impact on financial performance. Formal Research Objectives 1. To compare stochastic and quantile regression for efficacy in predicting investment valuation as determined by the financial performance of commercial and industrial organizations. 2. To develop effective models to predict future financial performance of individual organizations, which operate in relatively uncertain conditions. 3. To relate responsible corporate behavior in terms of risks, environmental pollution and harmful product effects, to more even financial performance. Approach The first step could be to select sectors for the study. Pesticides, tobacco and electronic are three areas of high turbulence, both in terms of risks and environmental impact. The proposal is to study these three areas of the international economy. The next step is to select a bourse which can be used to track company statements and financial results. The New York Stock Exchange seems to be a natural choice for the purpose of this study. The third stage would be to select two firms in each sector, that is, a total of 6 firms for detailed study. One firm in each sector should have a high profile for risk management measures, whereas its counterfoil should be one known more for aggressive short-term volume and profit growth. Each of the six firms could be studied for the 2000-2005 period, with a view to developing stochastic models, and to compare such equations with the results of regression analysis and conventional stock valuation on the bourse. It is proposed to focus on Markov Chains for the specific type of stochastic modeling in this study. Candidates Syngenta and Dow could be the two firms from the pesticides business. Dow has inherited the mantle of the Bhopal disaster, and is the brand leader of a notorious organo-phosphorus product. Syngenta of Swiss origin is a leader in the development of safer products, and has the image of a more responsible player in this field. Altria has a strong record of legal defense of its tobacco business, and is known for superior financial performance. It may be compared with a smaller domestic tobacco company that has largely used price competition to survive in this segment. Such a company remains to be identified. Apple is a leader in recycling and waste management and should be compared with Dell, which has adopted a more focused approach to the core of electronic engineering. These two companies would be sound choices for detailed study from the world of computers. Reviews There is a large body of work covering quantile regression for financial valuation, and this will be reviewed to learn of its accuracy and any shortfalls. Similarly, there is considerable information of stochastic modeling. It is proposed to focus on Markov Chains and past experience in the practical applications of this system. The companies chosen for detailed study will also have to be examined for their past history and critical present actions. The project must make an attempt to seek the cooperation of the companies themselves, since some ongoing management actions may not have been announced on the bourse. Though six firms have been selected for detailed study, the actual choice could change to an extent depending upon the total experience of the bourse. There is therefore the need for an exhaustive review of financial performance trends of all members of the bourse. Companies with exceptional records of either turbulence or of stability may be chosen in preference to the ones named at this stage. The project would also have to review the performance of NASDAQ members, as there could be some differences for newer firms in high-technology areas. The foregoing underscores the need for conducting the literature review at the start of the project, as its conclusions can affect the further course of work. However, the aims of the project should remain intact as stated in this document. Limitations The findings of this project, though it can suggest a modeling system, may not have automatic relevance for the future. There are some basic changes underway with respect to restrictions on pesticide use, intellectual property rights and patterns of nicotine addiction that can affect the valuation of firms in related fields of business in untold ways. The companies would need, in reality, to repeat the modeling exercise each time there are major shifts in the set of independent variables. The modeling developed for the companies chosen for this project may not be relevant for all players in the sectors. Generic pesticide producers, online traders in tobacco and software firms may all enjoy sustained and superior financial performance without using the models that this project will develop. However, the stochastic approach to valuation could emerge as a valid and a better alternative to quantile regression regardless of the type of business conducted by a firm. The basic preoccupation of this work is with the present and with the actions that managements take now to affect future values. Association with the selected companies will therefore have much to do with the quality of the planned output. The inspiration for the kind of Markov Chains that could be appropriate in a given business situation can best come through insights of professionals who are deeply involved in routine operations. Stochastic modeling may prove some hunches to be incorrect at least in degree, but the leads are best provided internally. Econometrists can measure independent variable impact but not identify the variables themselves! Works Cited Kijima Masaaki, 1997, Markov Processes for Stochastic Modeling, CRC Press Read More