Yongzhuo Chen, Yixuan Liang, Yiran Liu, Brian Hobbs, Michael Kane
{"title":"Valuing Post-Revenue Biopharmaceutical Assets with Pfizer's Current Portfolio as a Case Study","authors":"Yongzhuo Chen, Yixuan Liang, Yiran Liu, Brian Hobbs, Michael Kane","doi":"arxiv-2312.02250","DOIUrl":null,"url":null,"abstract":"This research paper addresses the critical challenge of accurately valuing\npost-revenue drug assets in the biotechnology and pharmaceutical sectors, a key\nfactor influencing a wide range of strategic operations and investment\ndecisions. Recognizing the importance of reliable valuations for stakeholders\nsuch as pharmaceutical companies, venture capitalists, and private equity\nfirms, this study introduces a novel model for forecasting future sales of\npost-revenue biopharmaceutical assets. The proposed model leverages historical\nsales data, a resource known for its high quality and availability in company\nfinancial records, to produce distributional estimates of cumulative sales for\nindividual assets. These estimates are instrumental in calculating the Net\nPresent Value of each asset, thereby facilitating more informed and strategic\ninvestment decisions. A practical application of this model is demonstrated\nthrough its implementation in analyzing Pfizer's portfolio of post-revenue\nassets. This precision highlights the model's potential as a valuable tool in\nthe financial assessment and decision-making processes within the biotech and\npharmaceutical industries, offering a methodical approach to identifying\ninvestment opportunities and optimizing capital allocation.","PeriodicalId":501355,"journal":{"name":"arXiv - QuantFin - Pricing of Securities","volume":"5 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - QuantFin - Pricing of Securities","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2312.02250","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This research paper addresses the critical challenge of accurately valuing
post-revenue drug assets in the biotechnology and pharmaceutical sectors, a key
factor influencing a wide range of strategic operations and investment
decisions. Recognizing the importance of reliable valuations for stakeholders
such as pharmaceutical companies, venture capitalists, and private equity
firms, this study introduces a novel model for forecasting future sales of
post-revenue biopharmaceutical assets. The proposed model leverages historical
sales data, a resource known for its high quality and availability in company
financial records, to produce distributional estimates of cumulative sales for
individual assets. These estimates are instrumental in calculating the Net
Present Value of each asset, thereby facilitating more informed and strategic
investment decisions. A practical application of this model is demonstrated
through its implementation in analyzing Pfizer's portfolio of post-revenue
assets. This precision highlights the model's potential as a valuable tool in
the financial assessment and decision-making processes within the biotech and
pharmaceutical industries, offering a methodical approach to identifying
investment opportunities and optimizing capital allocation.