Quantifying the Impact of Economic Environment on Business Performance and Value

1. Quantifying the Impact of Economic Environment onBusiness Performance and Value Ashok Bhardwaj Abbott Ph.D. West Virginia University Morgantown, WV

2. Ashok Bhardwaj Abbott Ashok B. Abbott is an Associate Professor of Finance at West Virginia University in Morgantown, West Virginia. Professor Abbott received his MBA in Finance at Virginia Polytechnic Institute and State University (VPI&SU) in 1984, followed by a Ph.D. in finance also at VPI&SU, in 1987. His Ph.D. dissertation title was "The valuation effects of tax legislation in corporate sell-offs".  He has published extensively in scholarly research journals and made presentations at national and international conferences. He serves on the editorial boards of The Business Valuation Review and The Value examiner. His focus area of research and consulting in valuation is the level of price adjustments (discounts/premiums) appropriate for liquidity, marketability, and identifying passive appreciation for the interests being appraised. Professor Abbott consults for valuation divisions of well-known firms, (Standard & Poor's, Duff & Phelps, Willamette Management Associates, and Houlihan Valuation Advisors, among others). You can see his full CV at www.be.wvu.edu/faculty_staff/cv/ashok_abbott_cv.pdf. • Please do not hesitate to ask any questions. • Email: ashok.abbott@gmail.com • Phone 304 692 1385

3. Blood Sweat and Tears Vs. Dumb Luck • Business performance is a combination of two components, in varying degrees • Efforts of Owner/Managers (active), • And • Impact of external factors and market forces (passive). • It is very common for managers to take credit when the business performs well and blame the environmental factors when the going gets tough.

4. 10-K Report of a Major U.S. Retailer • We are highly susceptible to the state of macroeconomic conditions and consumer confidence in the United States. • All of our stores are currently located within the United States, making our results highly dependent on U.S. consumer confidence and the health of the U.S. economy…. • Deterioration in macroeconomic conditions and consumer confidence could negatively affect our business in many ways, including slowing sales growth or reduction in overall sales, and reducing gross margins. …. • Deterioration in macroeconomic conditions can adversely affect cardholders' ability to pay their balances, … resulting in higher bad debt expense. • Weather conditions where our stores are located may impact consumer shopping patterns, which alone or together with natural disasters in areas where our sales are concentrated, could adversely affect our results of operations.

5. Placing Performance Attribution in Context • This performance measurement matrix has wide applications. A few cases are • Management Compensation, including performance bonuses. • Business interruption claims, damages assigned to proximate cause. • Appreciation of separate property or marital estate during a marriage in case of divorce.

6. Core Issues: Identifying Causation and Quantifying Factor Impact • Causal factors • Which factors outside the control of the owner manager(s)of the business, if any, significantly impacted the (passive) changes in revenues of the business? • Performance Impact • What proportion of the change in business performance (Revenues/NOPAT/ EBIT/NI) can be explained by these external factors outside the control of the manager(s)?

7. Correlation and Causality • Subject of discussion since Aristotle • cum hoc ergo propter hoc, • (for "with this, therefore because of this“) • Post hoc ergo propter hoc •  (for "after this, therefore because of this").

8. Correlation • the state or relation of being  correlated;  • specifically:  a relation existing between phenomena or things or between mathematical or statistical variables which tend to vary, be associated, or occur together in a way not expected on the basis of chance alone 

9. Correlation • Correlation, by definition, is bi-directional. If x and y are positively correlated higher values of y are observed with higher values of x. Conversely if x and y are negatively correlated higher values of y are observed with lower values of x. Observation of correlation between x and y may suggest three potential causal pathways. • 1. Changes in x may be causing changes in y • 2. Changes in y may be causing changes in x • 3. Changes in a third factor z may be causing changes in both x and y

10. Real vs. Spurious Correlation • Correlation can provide powerful evidence for a cause-and-effect relationship between a treatment and benefit, a risk factor and a disease, or a social or economic factor and various outcomes. • Causality can be inferred theoretically and confirmed empirically using a statistical causality test. • Correlation without causality is spurious.

11. Correlation :Necessary but not Sufficient • Empirically observed correlation is a necessary but not sufficient condition for causality. • Causation without correlation is unlikely. • Causal pathway needs to be established theoretically and tested empirically. • Correlation is observed Causality is inferred.

12. Causation • Connection between two events or states such that one produces or brings about the other; where one is the cause and the other its effect. Also called causality. • Causality is unidirectional.

13. Correlation does not imply Causation • Correlation does not imply causation is a phrase often used in science and statistics to emphasize that a correlation between two variables does not necessarily imply that one causes the other. • Aristotle discerned two modes of causation: proper (prior) causation, and • accidental (chance) causation.

14. Correlation is ObservedCausation is Inferred • The adage ‘correlation is not causation’ is often tossed as a Hail Mary pass, obscuring that correlation is a necessary (but not sufficient) condition for causation. • Correlation suggests causation, but any claim of causation relies on one or more premise(s) that flow from domain knowledge.

15. Causality in Context • Causality cannot be defined in terms of statistical associations alone but needs to be supported by an underlying logical nexus. • Correlation by itself does not prove causation, but it is extremely unlikely that causation can exist without correlation.

16. Criteria for Causation • Hill ( 1965) provides a comprehensive list of criteria for determining if a finding of correlation leads to a determination of causation. While these criteria were initially suggested for use in epidemiological study, they provide valuable guidance in a broad range of disciplines, such as economics, social, and behavioral sciences. • Austin Bradford Hill, “The Environment and Disease: Association or Causation?,” Proceedings of the Royal Society of Medicine, 58 (1965): 295-300.

17. Hill’s Criteria for Causation • Temporal :Cause always precedes the outcome • Strength: The stronger the association, the more likely it is that the relation of "A" to "B" is causal.  • Consistency: if a relationship is causal, we would expect to find it consistently among different populations and different time periods. • Plausibility: There needs to be some theoretical basis for positing an association between one phenomenon and another.  • Coherence: The association should be compatible with existing theory and knowledge. 

18. Logical Nexus formulation • Consumption is funded by current and anticipated income (consumer credit). • Income elasticity of consumption is a widely accepted economic construct. • Desired Consumption creates demand. • Demand generates revenues. • Revenues generate value.

19. Granger Causality Test • Clive Granger* ( 1969) defined the causality relationship based on two principles: • The cause happens prior to its effect. • The cause has unique information about the future values of its effect. • The statistical test devised by Granger is widely used to confirm causality. • * Nobel Laureate Economics 2003

20. Stationarity and Time Series • In the presence of a significant common trend in time series data, correlation can appear when there is no relationship between the two variables. • Therefore, it is critical to confirm that the series being analyzed are stationary. This is often done using an Augmented Dickey Fuller test.

21. Establishing Causality • Economic factors commonly used to measure economic activity are identified as logical drivers of performance. • Economic data series are tested for stationarity, and, if necessary, transformed to stationary series before causality testing can proceed. • Granger causality test is used to identify relevant drivers for performance measures. • Factor Drivers meeting statistical significance criteria are identified and ranked in order of strength of causation.

22. Estimating Elasticity • Factor elasticities are estimated for identified performance drivers (causal factors) identified earlier. • Elasticity estimates are corrected for serial auto correlation using an autoregressive procedure. • Elasticity estimates measure the percentage change in performance measure for a unit percentage change in the causal factor.

23. Factor Contribution Analysis • Change in causal factor beginning and ending levels is calculated on a percentage basis. • This factor value change is multiplied by the estimated elasticity for the causal factor to calculate the contribution of the factor to overall change in revenue. • The proportional change in value of the revenue attributable to the change in causal factor is calculated by dividing the attributed change by the total change in revenue.

24. Collinearity among Economic Measures • Economic factors exhibit significant correlation among themselves. (collinearity) • This makes simultaneous consideration of multiple factor impact complicated as collinearity creates difficulties in interpreting observed coefficients. • This complex task can be tackled by ranking the causal variables in order of their strength and forming combinations of causal factors with lower correlations among the explanatory variables. • The combination that performs the best is the one in which the aggregate explanatory power is highest combined with significant impact of the individual variables.

25. Partial Elasticities for Multiple Factor Models • Advanced statistical techniques are available to assess partial impact of additional factors. • Total factor impact can not exceed 100% of the change in the underlying measure. • Think of stacking coupons in a store sale. • The Akaike information criterion (AIC) is a measure of the relative quality of statistical models, including regression models for a given set of data. • Alternative models proposed for measuring the impact on performance are ranked by AIC and the model with the lowest AIC value is the model closest to the ‘True” model.

26. Economic Environment • Analysis period covers years 1992-2014 • Quarterly data published by • Bureau of Economic Analysis • U.S. Census • Federal Reserve Bank of St. Louis Twenty three years, 92 quarters of data.

27. Changes in Economic Environment

28. Income and Consumption

29. Consumption and Debt Service

30. Fueled by Cheap Credit

31. Economic Variables Identification Drivers of Consumption • Population • Income • Employment • Interest Rates • Debt Service Burden • Inflation • Consumer Credit Outstanding

32. Grocery Sales Elasticity Estimates

33. Passive Component in Grocery sales

34. Jewelry Sales Elasticity Estimates

35. Passive Component in Jewelry Sales

36. Questions? • Please send your questions, comments and observations to • Email: ashok.abbott@mail.wvu.edu Phone 304 692 1385 • I make every effort to respond quickly.