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Value and Marketability - Determinants of the discount for lack of marketabilityThe valuation of private companies is a matter of interest for many stakeholders, including valuation professionals, auditors, courts, and tax authorities. The matter is deceptively complex, notably because there is no consensus on the nature, size, and determinants of the so-called discount for lack of marketability (DLOM). The DLOM can be defined as an amount or percentage deducted from the value of an ownership interest to reflect the relative absence of marketability. Indeed, most valuation methods lead to value indications for a marketable interest, and it is generally accepted that investors attach a lower price to assets that are not readily marketable. The DLOM is oftentimes oversimplified as the difference in value between an illiquid (unlisted) stock and an all-else-equal liquid (listed) security. This value gap is important but ill understood. Leading scholars have noted time and time again that fair market value calculations often boil down to taking a marketable value estimate and reducing that amount by a contrived percentage. In practice, DLOMs of 20% to 40% are routinely used for valuing private businesses. The extant literature has proposed various DLOM estimation methods that fall into two broad categories: theoretical and empirical models. All of these models have been challenged, either because they require the input of information that cannot be objectively determined for private companies (theoretical models), or because estimates based on the comparisons between liquid and illiquid valuation subjects are by nature always imperfect and thus prone to discussion (empirical models). Nevertheless, and in the absence of better information, the empirical models, especially, have received lots of attention and the averages presented in these studies are often used in practice without much formal reasoning or economic justification. In order to shed more light on the determinants of the DLOM we have turned to an alternative source of information that can bring additional insights. Specifically, we have turned to court decisions that decide on private company valuations, including the DLOM to be applied. The court typically justifies its decision by referring to how the specific company is situated, and the rights and obligations attached to the valuation subject. This contextual information provides more background than the pure financial information that can be found in traditional data sources. This method which combines elements of qualitative and quantitative analysis has allowed us to demonstrate that the company’s ownership structure, its operations, the transfer restrictions on shares, the exit possibilities for shareholders, and the level of control attached to the valuation subject have a significant impact on the DLOM.
Pharmaceutical net price transparency across European markets: Insights from a multi-agent simulation modelWith pharmaceutical health policy striving for fair and sustainable pricing under increasing budgetary pressures, public stakeholders are more and more willing to be involved in transparent access decision-making related to novel medicines, considered by them to be a societal good. Full net price transparency (NPT) is believed by many to promote price competition and to increase equity by making pharmaceutical products accessible to all. Using agent-based simulations, we find that a full NPT system implemented across EU countries would not be viable. This while, acting as rational economic agents, a group of middle- and lower-income countries would not be willing to give up their confidential agreements with the pharmaceutical industry. Even partial NPT would delay access predominantly in middle- to lower-income countries. Hence, we conclude that implementing net price transparency across Europe would be challenging to reach from a political perspective. Especially in lower-income countries there would remain a plea to be left free to negotiate confidential discounts with drug manufacturers. This while, counterintuitively, in those countries NPT will be seen to be unjust while violating Ramsey pricing and distributive justice principles.
What leaders get wrong about data-driven decisionsData-driven decision-making has been hailed as an antidote to the biases of human intuition. Companies have more data than ever, but surveys cast doubt on the effectiveness of data-driven decisions in organizations. The majority of executives say their data analytics initiatives produce disappointing results, and only about a quarter of executives say their data analytics projects produce actionable insights. There is a clue to the problem in the name “data-driven decision-making.” It’s data-driven.
Do people understand the benefit of diversification?Diversification—investing in imperfectly correlated assets—reduces volatility without sacrificing expected returns. Although the expected return of a diversified portfolio is the weighted average return of its constituent parts, the variance of the portfolio is less than the weighted average variance of its constituent parts. Our results suggest that very few people have correct statistical intuitions about the effects of diversification. The average person in our data sees no benefit of diversification in terms of reducing portfolio volatility. Many people, especially those low in financial literacy, believe diversification actually increases the volatility of a portfolio. These people seem to believe that the unpredictability of individual assets compounds when aggregated together. Additionally, most people believe diversification increases the expected return of a portfolio. Many of these people correctly link diversification with the concept of risk reduction but seem to understand risk reduction to mean greater returns on average. We show that these beliefs can lead people to construct investment portfolios that mismatch investors’ risk preferences. Furthermore, these beliefs may help explain why many investors are underdiversified.