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Industry analysis and investment decision‐making under conditions of uncertainty
Author(s) -
Feldman Stanley J.
Publication year - 1983
Publication title -
managerial and decision economics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.288
H-Index - 51
eISSN - 1099-1468
pISSN - 0143-6570
DOI - 10.1002/mde.4090040310
Subject(s) - cash flow , valuation (finance) , investment (military) , productivity , economics , quality (philosophy) , investment decisions , econometrics , business , actuarial science , industrial organization , microeconomics , production (economics) , finance , philosophy , epistemology , politics , political science , law , macroeconomics
Evaluating risk is a key element in successful investment decision‐making. A major risk in forecasting company performance is associated with projecting its cash flow streams by product‐line which in turn is strongly related to the expected industry outlook and likely variability about this outlook. This paper sets out a methodology for evaluating this aspect of the investment decision by developing measures of expected real cash flow growth (reward) and expected annual variability of this growth (risk). These measures are constructed for 77 industry classifications at the two and three digit SIC (Standard Industrial Classification) level of detail by solving a dynamic input–output model under various economic secenarios. Aside from producing results which are relevant for producing company valuation bands, the analysis strongly suggests that perceptions of which industries are ‘winners’ and which are ‘losers’ are significantly altered when both ‘reward’ and ‘risk’ are used as criteria as opposed to either one alone. Finally, the methodology also produces results which measures the sources of cash flow growth in terms of relative price performance, productivity, and demand for output. Since there measures can be used as indicators of the quality of industry real cash flow growth, industry performance can be further arrayed within the broad categories of winning and losing industries. Several examples of how this is done are offered.