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Biomedical Investments Assessment using “Regression Analysis”

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Today many Entrepreneurs really find it ever more risky to invest in Biomedical firms as technologies are advancing so rapidly. The decision to invest is ever more complex unless one has a good knowledge of the operations and a firms past success record and how its securities trade on the market. Due to innovation and new paradigms shifts, many Biotech companies are in early stage development and tend to be small, developing only a few promising projects focused in digital genetic developments in pharmaceutical and medical devices. Most of these companies incur losses through extremely costly R & D processes due to development and clinical regulatory lead time to market until they demonstrate regulatory efficacy for early market adopters. As a result, investments in Biotech companies are often volatile. Because of this dynamic, Biotech companies tend to find partners for financial support, usually through Venture Capital, Universities, Pharmaceutical companies or the Government.

While pharmaceutical companies also experience the costly and lengthy R&D process in new innovative Biotech developments, including the ups and downs during clinical trials, they are more capable of withstanding volatility given their maturity and size in their respective markets. The question nonetheless arises with regard to investment in biomedical research, “How much is truly required to bring a product to market and what will be the ROI on such a new technology? What will be the benefit of the technology to the medical community and resultant revenues to the biomedical firm and to the investor?

The new “Risk Assessment” today can be obtained using “Quantitative Economic Analysis”. It can validate assumptions in marketing plans for early stage companies that lack financial history. An Economic Analysis for Venture Capital Groups or early stage companies, seeking to raise capital, can show quantitative clarity when considering emerging new technologies, valuations on acquisitions and mergers in the healthcare sector. This can assist in creating and securing investor’s confidence when a firm needs to estimate time to market if market demand factors change. In other words, a “best fit regression analysis” of marketing plan assumptions can more accurately determine the demand elasticity or better said, “Time & Capital required on new technologies to reach market acceptance and revenue forecasts”. Overcoming this lag time before adoption by market leaders is often where Investment bankers fall short of the amount of capital they initially raise based on assumptions in the marketing plan to see these new technologies gain traction to bring the ROI forecasts to fruition. With “Regression Analysis” of a marketing plan assumptions, a firm can consider a more accurate calculation of time to market on revenue forecasting to deliver the ROI.

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