Since the days of Benjamin Graham, fundamental equity analysis has offered tremendous insight into the fair valuation of companies.
Many investment strategies leverage these insights. Indeed, models like discounted cashflow analysis or relative value have become so ubiquitous that they are taught in entry-level corporate finance and investment courses.
In the last few decades, several aspects have changed about how these models should be deployed except one thing: these models take time to construct. Fundamental equity analysis has never been scalable. To cover more companies, firms must hire more analysts.
In this presentation, Lee Davidson will discuss how advances in machine learning and robotics may be poised to up-end that paradigm very soon.
Warwick Hotel
14 rue de Lausanne, Geneva
Lee Davidson CFA, is the head of quantitative research for Morningstar and leads a team dedicated to developing innovative statistical models and data points. Prior to assuming his current role in 2015, he was a senior analyst on the quantitative research team. Before that, he was an analyst on Morningstar’s passive strategies manager research team, covering European-domiciled commodities, Asian-equity, and fixed-income exchange-traded funds. He joined Morningstar in 2010 as a product consultant. Lee holds a bachelor’s degree in economics from the University of Chicago and a master’s degree in business administration from the University of Chicago Booth School of Business. He also holds the Chartered Financial Analyst® designation
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