How do you view valuation? What does valuation mean to you? How do you calculate the valuation before deciding to buy a company’s stocks? Determining the accurate valuation holds significant importance for various purposes, such as:
- Facilitating well-informed investment choices
- Establishing equitable prices for products and services
- Evaluating the fiscal well-being of an organisation
Various valuation methods are available for use, contingent on the nature of the asset or business under evaluation. Several prevalent methods encompass:
Market approach: This method involves contrasting the asset or business with similar ones that have recently changed hands.
Income approach: This method gauges the asset or business’s worth by considering its income-generating potential.
Asset approach: This method computes the asset or business’s value based on the aggregate value of its individual assets.
However, all these approaches have one thing in common, i.e., they help to perceive the ‘valuation’ of a company, thus, already prompting you to broach this subject with a prejudiced mind.
What is the Bermuda Triangle of Valuation?
Aswath Damodaran, a Professor of Finance at the Stern School of Business at New York University views the concept of “valuation” unlike his peers. Damodaran brings forth his famous “Bermuda Triangle of Valuation” which explains why so many people go wrong while estimating the correct valuation of a business or stock.
In his theory of valuation, Damodaran explains why so many people arrive at the incorrect value of businesses or stocks while investing their money. He attributed the following reasons to people relying on the ruined valuation of their assets.
Anticipated value perception
A fixed mindset has helped no one and this is true of statisticians and investors too who estimate valuation based on certain preconceived notions. Confirmation bias represents a cognitive distortion where individuals actively seek out information that aligns with their pre-existing beliefs while neglecting contradictory data. In the context of valuation, this bias can lead to selective data analysis, the omission of critical factors, and the formation of inaccurate assumptions.
For instance, if you hold the belief that a company’s value stands at $100 million, you’re inclined to emphasise data supporting this valuation, such as robust revenue growth or market dominance. Conversely, you may downplay or disregard data conflicting with your assessment, such as high debt levels or profitability challenges.
To counter confirmation bias, it’s essential to embark on the valuation process with an open mindset, considering all pertinent information—both positive and negative. Self-awareness of your personal biases is crucial, along with implementing strategies to mitigate them.
Here are some strategies to mitigate confirmation bias during the valuation process:
Start with a neutral stance: Avoid preconceived notions or fixed valuations before conducting research.
Evaluate all relevant information: Examine both favourable and unfavourable factors, encompassing financial and non-financial aspects.
Acknowledge personal biases: Recognise your own predispositions and take steps to minimise their influence.
Seek external input: Encourage others to review your valuation and offer constructive feedback.
Approaching valuation as a scientific discipline
Charlie Munger’s concept of “physics envy” aptly characterises the yearning for unwarranted precision in the field of economics. Economics, being a complex system, defies simple formulas to address its myriad challenges.
One fundamental reason for this complexity is that economics falls under the purview of the social sciences, inherently more intricate than physical sciences. Physical systems, such as the solar system, adhere to relatively straightforward laws of physics. In contrast, social systems like economies are subject to a vast array of factors including human behaviour, cultural influences, and political dynamics.
Another layer of complexity arises from the continuous evolution of economics. The global economy undergoes rapid transformations, marked by emerging technologies and novel economic paradigms. This dynamic environment hinders the development of universally applicable economic theories.
Notwithstanding economics’ complexity, valuable insights and predictive abilities can still be derived. Economists employ diverse methodologies, including mathematical modelling, statistical analysis, and historical case studies.
However, it’s crucial to acknowledge that these methods rely heavily on the quality of data and underlying assumptions. Given the intricacies of the economy, a degree of uncertainty in economic predictions is inevitable.
Therefore, while the allure of simple and precise solutions to economic challenges may be strong, it is imperative to remain realistic about the limitations of economic science. Economics is an intricate and ever-evolving discipline, and there are no panaceas.
Here are some instances illustrating the perils of succumbing to “physics envy” in economics:
Excessive reliance on mathematical models: Mathematical models can aid in comprehending economic phenomena, but they must not replace critical thinking. Models are only as reliable as the data and assumptions on which they are built, often simplifying real-world complexities.
Neglecting human behaviour: Economic theory often assumes individuals act rationally, driven solely by self-interest. In reality, human decisions are influenced by emotions, biases, and social norms, introducing unpredictability.
Contextual oversight: Economic theories may be developed devoid of the specific circumstances in which they are applied, leading to impractical and unrealistic conclusions.
It is vital to bear in mind that economics is a social science, and social systems inherently embody greater intricacy than physical systems. Economic theories should be employed judiciously, and economists should remain humble about the boundaries of their knowledge.
Relying on complex numbers and models
There's an inherent allure in grappling with complexity. Delving into intricate systems like the economy or the human brain can be captivating. Nonetheless, it's crucial to bear in mind that the efficacy of complex models hinges on the bedrock of their assumptions. Moreover, as complexity increases, so does the propensity for assumptions to proliferate.
Complex models pose several pitfalls. They can deceive us into a false sense of certainty. The assumption that exhaustive considerations of influencing factors render a model infallible can lead to complacency. Yet, even minuscule errors in our foundational assumptions can yield substantial prediction inaccuracies.
Another peril of complexity lies in the difficulty of error identification and rectification. Elaborate models can obfuscate the origins of errors, hindering efforts to enhance their precision. Furthermore, overly intricate models may prove unwieldy in practical applications. Their complexity can render them inscrutable, impeding decision-makers from utilising these models effectively to inform their choices. Hence, exercising caution when employing complex models is imperative. Acknowledging their inherent limitations and refraining from excessive reliance on them is essential.
Here are some guidelines for the effective use of complex models:
Scrutinise assumptions: Thoroughly comprehend the model's underlying assumptions. Evaluate the realism of these assumptions in the context of the real world.
Recognise limitations: No model is without its flaws. Familiarise yourself with the known limitations and potential sources of error within the model.
Diversify information sources: Avoid relying solely on the model for decision-making. Incorporate other factors, including personal judgment and the experiences of others.
Stay adaptable: Be prepared to update the model as needed. As new information emerges, be ready to adjust the model's assumptions or parameters to enhance its accuracy.
Believe in both stories and numbers
Valuation preferences vary among individuals, with some favouring the quantitative aspect, while others lean towards the qualitative dimension. Nonetheless, the synergy of both aspects is essential for a successful valuation.
The quantitative side of valuation entails leveraging financial data to assess a company's worth. This encompasses scrutinising the company's revenue, expenses, assets, and liabilities. The quantitative facet holds significance as it furnishes a numerical foundation for valuing a company.
On the other hand, the qualitative side of valuation revolves around comprehending the company's business model, its competitive landscape, and the calibre of its management team. The qualitative component is indispensable as it offers an understanding of the company's narrative and its potential.
Both the quantitative and qualitative facets of valuation are indispensable in crafting precise and dependable valuations. The absence of the quantitative aspect would result in valuations rooted in speculation and intuition. Conversely, a valuation solely reliant on financial data would fall short of capturing the comprehensive value of a company.
Here are instances illustrating the integration of the quantitative and qualitative aspects in valuation:
Comparable company analysis: This method entails juxtaposing the target company with similar firms recently sold. To execute this method effectively, a profound grasp of the industry's key value drivers and the identification of genuinely comparable companies are paramount.
Discounted cash flow (DCF) analysis: This approach gauges a company's value based on its projected future cash flows. To wield this method effectively, it is imperative to comprehend the company's business model and establish realistic assumptions about its future growth and profitability.
In both instances, the quantitative and qualitative facets of valuation coalesce to yield more precise and dependable outcomes.
Moreover, it's vital to acknowledge that valuation is not an exact science. There is no one-size-fits-all approach, and diverse valuation methods can yield disparate results. Hence, employing multiple valuation techniques and considering all pertinent information, spanning both quantitative and qualitative realms, is crucial.
By harnessing both the quantitative and qualitative aspects of valuation, and by employing a multifaceted approach, one can generate valuations that are more precise and reliable.