Was sind einige der häufigsten Bewertungsfehler und -verzerrungen, die Sie bei der Bewertung eines Startups vermeiden sollten?

While valuing startups, there must be a careful assessment of the life cycle of the startup. For example, very young startups, especially Pre-Revenue or early stage startups are usually “priced” based on negotiations with investors as financial models are usually not appropriate in identifying their values.

Valuing a startup based on market data is fraught with problems, not because market data doesn't exist but because of the opposite - most market data is not relevant and only acts as confusing 'noise' in the valuation process. Why is market data irrelevant? Because of Survivorship Bias. Market data comes from companies that have survived long enough that their data is in the public domain. But, 90% of startups fail by Year 10. So the data from those companies that survived for a long time is irrelevant in a startup valuation. Some would suggest that, to mitigate this problem, the valuer should collect and validate as much market as possible but that misses the point - market data is largely irrelevant in valuing a startup.

Ensuring accurate valuation for startups is crucial, and steering clear of common pitfalls is key. One common mistake lies in over-reliance on projections without a thorough understanding of market dynamics or realistic growth trajectories. Additionally, anchoring bias, where valuations are disproportionately influenced by initial impressions or external benchmarks, can skew assessments. It's essential to maintain objectivity, scrutinize assumptions, and actively mitigate biases to arrive at a more robust valuation that aligns with the startup's true potential.

Traditional valuation methodology and tools (transaction databases) are largely irrelevant when valuing startups or venture investments. Any entity that is pre-revenue or pre-profit is actually not yet a business - a business has sales, customers, and results. Startups and venture investments are most often valued on the size of their addressable market and the strength of the team. With a startup, the strength of the team is really where the "value" is at

In the healthcare/pharma space, value is often based on data. Not necessarily market data but data about the specific drug, device, etc. Clinical trials, and other events generate data about effectiveness, feasibility, safety, etc. The more data, the more value because data chips away at product risk - and we haven't even started talking about commercialization yet. Most founders greatly underestimate these risks, valuation analysts cannot.

A common problem when valuing startups is that the valuer suffers from Overconfidence Bias and over-values the startup. Overconfidence Bias is subconscious, born from the enthusiasm of the startup founder, transmitted by their influence on the valuer, and amplified by the valuer's desire to satisfy his client with a good valuation. Unfortunately, Overconfidence Bias cannot be rationally mitigated with data because: (1) the bias is fundamentally irrational (subconscious); and (2) most market/historical data is irrelevant to valuing a startup. The risk of falling prey to Overconfidence Bias may be partially mitigated by sensitivity or multi-scenario analysis, and by applying higher-than-normal discount rates to dampen future expectations.

This stems from an excessive sense of optimism or confidence in your judgments or forecasts, impacting both startup founders and investors and resulting in unrealistic expectations or valuations. For example, there might be an exaggerated estimation of the startup's growth potential, profitability, or market share, or a downplaying of risks, costs, or competition. To counter overconfidence bias, it's crucial to validate assumptions and projections against historical data, industry benchmarks, or feedback from experts or customers. Moreover, recognising the uncertainty and volatility of the startup landscape and employing sensitivity analysis or discount rates can aid in adjusting valuation accurately.

Overconfidence bias should be overcome by using simulations, probability scenarios and sometimes, options to ensure that your assumptions are tested against future uncertainties.

Overconfidence bias is a pervasive issue in business valuation, particularly in the startup ecosystem, where optimism can cloud judgment. Founders and investors often project ideal scenarios without adequately accounting for market realities, leading to inflated valuations that can result in significant financial losses. To mitigate this risk, it is crucial to adopt a disciplined approach to valuation that incorporates rigorous scenario analysis and external validation. Utilizing a combination of quantitative metrics and qualitative insights from industry experts can provide a more balanced perspective, ultimately fostering more sustainable investment decisions and realistic growth expectations.

Startup valuations often suffer from 2 biases that are linked and mutually-reinforcing: Anchoring Bias and Confirmation Bias. Anchoring Bias causes the valuer to focus and rely too much on the first significant piece of information encountered in the valuation process - e.g., the valuation of a competitor - and ignore other relevant factors. Confirmation Bias causes the valuer to seek, interpret, or remember information that confirms pre-existing beliefs, and ignore or reject information that contradicts them. As a result, a valuer's Confirmation Bias will cause him to be blind to the limitations or detrimental effects of the initial information that he focused on through his Anchoring Bias, thereby skewing the valuation results.

If the large startups are the only source of reliable data available, you may want to consider applying a “Size Discount” to reflect realistic valuations and thus avoiding the Survivorship Bias.

Most startups exist in a "binary" world. That is, either they make it to the next month, next quarter, next year and become something viable or they don't and no one ever hears about them again. These are the majority of companies but if you never hear about them, how do you reflect this in your valuation? For every failed startup that you DO hear about, there are hundreds that you DON'T.

Confirmation bias is a significant challenge in business valuation, particularly in the startup ecosystem where data can be scarce and subjective interpretations abound. Investors and founders alike must cultivate a disciplined approach to valuation that prioritizes empirical evidence over personal beliefs. Engaging in rigorous scenario analysis and employing multiple valuation methodologies can mitigate the effects of confirmation bias. Additionally, fostering a culture of constructive dissent within teams can help surface alternative viewpoints, ultimately leading to more accurate and reliable valuations. Embracing diverse perspectives not only enhances decision-making but also aligns with best practices in due diligence.

Valuation gaps can often be deal breakers in many investment scenarios. In my experience, the approach that works to effectively bridge these gaps involves: 1. Use Multiple Valuation Methods: It's essential to use a variety of methods to get a well-rounded view of a startup's value. 2. Forward-looking Analysis: Focusing on what lies ahead for the startup is key to understanding its true potential. 3. Assess Risk and Opportunity: Looking beyond the numbers to external factors is critical in valuation. 4. Creative Deal Structuring: Innovative structures like earn-outs can align differing valuation expectations. 5.Transparent Communication: Open and honest discussions are vital for understanding and resolving valuation differences.

Valuation Gaps arise in M&A transactions due to a variety of subconscious decision-making biases that affect both business founders/sellers and investors/buyers in the valuation process: Overconfidence Bias, Confirmation Bias, Anchoring Bias and Survivorship Bias. When these biases come into play, valuations get skewed and do not reflect the inherent value of the business. It is difficult to overcome this problem because the biases often cause either or both of the parties to become entrenched and inflexible in their positions. The most effective way to resolve these issues is with a conscious effort toward open and meaningful communication, negotiation and compromise with the other party.

Valuation gaps often stem from psychological biases rather than factual differences. Overconfidence, confirmation, and anchoring biases skew perceptions and entrench positions. As Warren Buffett said, "Price is what you pay. Value is what you get." Focusing on transparent, meaningful negotiation can bridge gaps and align valuations with true worth.

*Financial Statements* While evaluating the financial statements of startups for valuation or for creating valuation models, a detailed assessment is necessary. Startups are usually unregulated and their financial statements may have higher levels of misstatements such as founders’ discretionary spends, personal expenses charged to business or unrealistic salaries to founders (a company must have realistic payment towards a CEO). These should be normalised while creating financial models. Also look for areas where accounting regulations are different. For example, a major expense for startups may be Research & Development or Marketing spends. these may be treated as assets for valuation purpose rather than expenses.

Consider the role of intangibles like brand value and intellectual property, often underestimated in traditional valuations. These elements can drive disproportionate future value, as shown by Apple's brand dominance. Jeff Bezos emphasized, "Your brand is what other people say about you when you're not in the room." Incorporating these factors ensures a holistic and forward-looking startup valuation.