Advanced Theory of Finance – Assignment Two
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Question One (a): Relevance of Existing Valuation Models for Non-Listed Firms
Advancement in financial technology has led to various innovative changes in the finance industry, particularly the valuation of non-listed firms. The valuation of these firms presents various challenges that make the use of traditional valuation models inapplicable, but not entirely useless. The fundamental principle that guides all valuation models is that value gains from future cash flows for the firm are discounted at an appropriate risk-controlled rate (Coulon, 2021). This principle remains valid across all firms regardless of their design or modes of operations. However, when applied to non-listed firms, these models require critical adjustments to meet the required outcomes.
The first model is the discounted cash flow (DCF) model and is largely used as the foundation of valuation (Tibúrcio Silva, 2023). This model is highly relevant for non-listed firms as it focuses on inherent value generation by relying on cash flows instead of market-based metrics. However, the challenge with this model is its applicability as it lacks market-based benchmarks to determine suitable discount rates. The Capital Asset Pricing Model (CAPM), which is commonly applied in estimating cost of equity, faces some notable challenges when applied to non-listed firms (Tibúrcio Silva, 2023). The problem with CAPM application is that beta coefficients are impossible to calculate directly from calculated form market data. In this case, practitioners are compelled to use proxy betas from comparable public companies by observing parameters such as financial leverage, business risks, and size premiums. However, the build-up method and arbitrage pricing theory (APT) is a better option in the valuation of non-listed firms as it provides more suitable frameworks for estimation of returns.
Relative valuation models such as price-to-earnings, enterprise value multiples and price-to-book also experience challenges when applied to non-listed firms (Carmo & Miguéis, 2022). These models are designed to meet market-derived multiples from comparable public companies, making them inefficient to apply to private companies. The problem is caused by the comparability assumption as non-listed firms differ in terms of size, growth patterns, liquidity, and governance styles (Tibúrcio Silva, 2023). Additionally, lack of active trading markets imply that traditional market-based multiples tend to overstate the value of non-listed firms, creating the need for discounted illiquidity and marketability constraints.
The asset-based valuation approach is highly recommended for non-listed firms mostly in industries where tangible assets make up the largest segment of firm value. Firms with large investment in real estate holdings, natural resources, or in manufacturing operations rely on this approach since their assets can be independently appraised (Sotti, 2018). However, the prevailing challenge in using the asset-based valuation model approach is the question of accuracy in valuing intangible assets, particularly brand value, customer relationships, and intellectual property (Carmo & Miguéis, 2022). The big problem is that non-listed firms do not have these intangible assets listed in book values.
In the recent past, there are several developments introduced to complement approaches meant for valuation of non-listed firms (Sotti, 2018). The option pricing model is one of these new approaches and it is used to value firms with significant growth opportunities or those based in volatile environments. The concept used in this approach is the acknowledgment that many non-listed firms, particularly technology firms, have valuable reap options that may be undervalued by traditional DCF models.
When applying existing valuation models to non-listed firms, practitioners should carefully consider the control of premiums and minority discounts. Contrary to public markets where shares trade at the interest of minorities, many non-listed firm valuations entail interest control, and invoking adjustments to reflect the value control (Carmo & Miguéis, 2022). Thus, while existing valuation models are useful in providing theoretical foundations, there is a need to modify them when valuing non-listed firms.
Question (b). Psychological Aspects in Valuation
Various psychological factors and cognitive biases largely influence the valuation of non-listed firms. Such factors and biases can lead to systematic differences from rational, fundamental-based valuation. In public markets, the use of continuous price discovery mechanisms correct individual biases. On the contrary, there is an amplification of the impacts of psychological factors in the private market environment as a result of infrequent transactions, lack of transparency in information, and inconsistency in continuous market feedback (Qadeer et al., 2021). In order to achieve more accuracy in the development of valuation frameworks and to improve in making investment decisions in private markets, it is crucial to understand these cognitive biases.
Anchoring bias is a prevalent problem associated with cognitive distortions in non-listed firm valuation. In most cases, valuers anchor their views and assessment processes on initial reference points, particularly past transaction multiples, book values or leadership projections, regardless of whether such anchors are inaccurate or outdated (Sakariyahu et al., 2024). Anchoring bias is a problem prevalent in private markets due to the limitation and stale nature of comparable transactions. The anchoring effect can last longer and affect the valuation process, inhibiting change processes for circumstances or new information (Pinci, 2024). Even when practitioners are aware of anchoring bias, making sufficient adjustments still remains a problem, resulting in clustered valuations that do not address critical economic needs.
Overconfidence bias is common in private firm valuation and it manifests when valuers showcase excessive confidence in their ability to predict future cash flows and risks. The illusion of control escalates this bias as investors believe that they have the control over outcomes through active participation in investing in private companies (Sakariyahu et al., 2024). Overconfidence breeds underestimation of discount rates, overestimation of growth opportunities, and ignorance on downside scenarios. In addition, there is a confirmation bias which further aggravates this issue. Confirmation bias asserts that valuers only seek information that supports their initial assessments while disregarding any arising contradictory evidence. In the non-listed firms setting, confirmation bias usually leads to overreliance on optimistic projections without questioning business plans.
Availability bias is another type of bias that greatly impacts non-listed firm’s valuation by exerting more pressure on previous transactions. In private markets, there is low transaction frequency and recent transaction records do not receive proportionate attention in valuation comparisons. This bias leads to momentum effects in private valuations posing a risky bubble effect. Availability bias also makes valuers assume that small samples of comparable transactions represent the overall nature of the broader market, ignoring the larger perspective of possible outcomes (Gupta & Garg, 2025).
Loss aversion and endowment effect are other factors that further increase complications in the valuation of non-listed private firms. The knowledge created in loss aversion is that people feel losses more acutely than they feel about the same level of gains. Such feelings are attributed to conservative valuation approaches in which investors avoid overpaying, or sellers avoid prices below their psychological expectations (Gupta & Garg, 2025). In the case of endowment effect, business owners tend to overvalue their companies on the basis of ownership. Overvaluation of firms contributes to systematic overvaluation by sellers, hence, failure of merger and acquisition contracts. In most cases, these biases are witnessed in family-owned businesses where there is the existence of greater emotional attachment instead of rational economic reasoning.
Mitigation of cognitive biases requires implementation of several practices in an organization. First, organizations should develop structured decision-making processes that employ several scenarios and require documentations of important assumptions that can help in mitigating the issue of overconfidence and anchoring biases. The second solution is the implementation of reference class forecasting to help in providing more realistic base rates for important valuation attributes. This solution can help firms analyze historical performance of comparable firms or transactions, thus reducing the effects of availability bias. The third solution is implementation of Monte-Varlo simulation and scenario analysis to help mitigate overconfidence (Gupta & Garg, 2025). Firms can achieve this solution by factoring in uncertainty and considering various possible outcomes. The last solution is the use of expert elicitation techniques to collect various opinions and mitigate individual biases. The application of behavioral finance insights into private firm valuation is crucial as it shows that human psychology is important in value determination when applied alongside traditional financial metrics.
References
Carmo, C., & Miguéis, M. (2022). Voluntary sustainability disclosures in non-listed companies: An exploratory study on motives and practices. Sustainability, 14(12), 7365.
Coulon, Y. (2021). Small Business Valuation Methods: How to Evaluate Small, Privately-Owned Businesses. Springer Nature.
Gupta, I., & Garg, V. (2025). Overconfidence and Its Consequences in Financial Markets. In Unveiling Investor Biases That Shape Market Dynamics (pp. 195-224). IGI Global Scientific Publishing.
Pinci, P. M. (2024). Dynamics of Heterogeneity in Asset prices: Analysing Investor Behavior: Examining the Impact of Fundamentalist and Chartist Across Industry Portfolios (Master’s thesis, Universidade NOVA de Lisboa (Portugal)).
Qadeer, A., Rizvi, S. K. A., & Ahmad, A. (2021). General assessment of behavioral preferences of investors: A qualitative study. Journal of Business & Economics, 13(1), 35-43.
Sakariyahu, R., Paterson, A., Chatzivgeri, E., & Lawal, R. (2024). Chasing noise in the stock market: an inquiry into the dynamics of investor sentiment and asset pricing. Review of Quantitative Finance and Accounting, 62(1), 135-169.
Sotti, F. (2018). The value relevance of consolidated and separate financial statements: Are non-controlling interests
relevant?. African Journal of Business Management, 12(11), 329-337.
Tibúrcio Silva, C. A. (2023). Discounted Cash Flow: theoretical and practical aspects of its use. Advances in Scientific & Applied Accounting, 16(3).