Business Valuation in the Face of Information Scarcity: A Synthesis of Methods and Adaptive Perspectives

Valuations of unlisted companies are difficult because of unlisted company financial disclosures, comparable-company information and unlisted company transaction information can not be complete, outdated and it is difficult to find and verify company relevant information. Traditional methods like Discounted Cash Flow and market multiples approach remain effective but the accuracy is highly dependent on that assumption and evidence may not be equally accessible for every company. Machine learning can facilitate valuation by detecting financial market patterns, but the predictive models are unreliable when observations are scarce or not well matched to the focus company. Research on stochastic valuation further reveals that the form of distribution of a point estimator may be concealing important uncertainty. This paper overviews research on discounted cash flow valuation, enterprise valuation based on machine learning, private company comparable and uncertainty modeling.Based on the overview it then develops a conceptually InformationScarcity Adaptive Framework (ISAV) where the type, aggregation and reliability of evidence guide the choice and reporting of valuation methods. Precise estimation is not intended. Instead, the framework is designed to assist transparent method selection and presentation of valuation ranges. Contents of the framework involve steps like collection and verification of data, evaluation of financial statements and quality of data, selecting the suitable method, carrying out the valuation estimate through DCF, comparable multiples and machine learning whensufficient evidence is available, followed by estimate reconciliation, uncertainty analysis and transparent reporting. What makes this framework unique, is the introduction of an Information Completeness Score (ICS) which provides an initial indication by indicating whether the data is of high, medium or low quality, indicating to the analyst if more validation is needed before the valuation. ISAV framework also encourages explicit quantification of an estimate range to generate transparency about uncertainty, instead of providing a single valuation estimate as if it were exact. The article discusses the application of this framework to unlisted firms in India and the challenges faced owing to paucity of available data, while acknowledging that further empirical testing will be required to establish whether the framework delivers its promises in practice.