Tim is the global Director of Fundamental Research at Thomson Reuters. He held the same position at StarMine, a company acquired by Thomson Reuters in 2008.With more than 20 years of experience conducting fundamental analysis, Tim is widely regarded as an expert helping fundamental investors apply innovative quantitative research to their investment process. Tim previously worked at several buy-side firms, most recently at Transamerica Investment Management as an equity analyst and portfolio manager.
Tim is a Chartered Financial Analyst and holds an undergraduate degree in Electrical Engineering and graduate degree in Business Administration.
Director of Fundamental Research
This presentation will discuss the innovative research behind the new StarMine Credit Risk Model, a robust, multi-factor predictor of financial distress. This timely, quant-driven approach incorporates a Structural (Merton) framework, forward-looking financial Ratios Analysis and an innovative unstructured Text Mining approach from sources such as news, footnotes and transcripts to provide a more predictive measure of credit risk. Fixed income investors will learn how this research applies to their default and credit risk analysis, while equity managers will learn how this approach is superior to the widely used Altman Z-Score as a measure of bankruptcy risk.
Diversification may be the last free lunch on “Wall Street”. Most approaches diversify by position size, geography, sectors, market cap or asset classes. In this presentation, Mr. Gaumer will show the benefits of diversifying by market factors such as value, momentum, quality, etc., rather than tilting a portfolio toward just one. He will also show which factors work best in your region and how best to combine them into a single, alpha-generating, multi-factor model. This presentation will also share some of Thomson Reuters research findings on they’ve learned so far about factor momentum and factor timing.
• How investment managers can gain insights and advantages through three categories of the “big data” content ecosystem: text and social media, sensors, and transactions
• How investment managers can extract new relationships by linking big data content sets
• How investment managers can use unstructured content sets, such as text mining, to help predict such “abnormal events” as bankruptcies and uncover environmental, social, and governance factors
• How the financial community is overcoming the challenges of getting new big data content sets online
Factor performance (value, momentum, quality, etc.) varies significantly over time. In this presentation, we’ll share what we’ve learned so far about factor timing, with applicability to both quantitative and traditional investment processes.
• When and where does it make sense for investors to vary the emphasis placed on these factors?
• Using leading macro-economic signals to adjust factor weights.
• Institutional holdings give some hints with regard to factor popularity and rotation.
• Does factor momentum still work?
• Is it better to simply stick with static factor weights?
Since 2006, I have made roughly 80 presentations to over 50 CFA Institute Member Societies globally.