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Out with the Old and In with the New

Manisha Samy discusses the challenges of adapting valuation techniques at the pace of technology.

December 22, 2020

The world is changing, the pace of innovation is changing, and historic indicators of potential success are also changing.  I have a staunch belief that the savviest of investors are the ones who can adapt at the pace of technology. Otherwise, like most old technologies, they become obsolete.

For that reason, a valuation model is yet another “indicator” or “variable” to aid  in the investment process. But I do not believe that it is the end-all-be-all — especially in biotech where science prevails and uncertainty is high. Let us be frank: Biotech companies with the greatest upside potential today should not be compared to biotech companies that have had commercial success. As technology seeps into this industry, the reliability of data, relevance of the current technology, and potential-value add changes.

My personal frustration with valuations is that it can be single-dimensional. For example, it makes no sense to conduct a discounted cash flow (DCF) model for a company using historic data without understanding the technology behind a specific company or how the FDA’s view on certain biological companies is changing. For certain pre-revenue companies, it’s impossible to know whether the technology is safe, durable, or effective unless keeping up with literature. There is more uncertainty as the number of variables increase.

Simplicity is key.

As a former scientist, I love answers that can be substantiated by facts and figures, but the stocks that have the highest growth potential are novel and trail-blazers.

While I like to use valuations as a benchmark of where a company “should” be, it’s based on my prediction of where I think the industry is heading toward. Where are these companies based on where I think the industry is headed?

My investment strategy is based on long-term views, so I’ll admit that my approach won’t suit those investors who are looking for quick returns. But it does look at up-and-coming trends that scientists are likely to adopt. For that reason, I may not look at what the market is doing, but I’ll surely look at efficiency rates between similar companies as well as increases to productivity improvements as defined by “time-to-market,” cost of goods, and the risk level of any given company.

I’ll leave you with this question: Why do people care about “bubbles” if everyone has a different risk tolerance, investing approach, and sectors that they tend to favor? For some strategies, it does not matter if there is a bubble, while other strategies warrant the concern.

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