Similarly to how we prioritize our own research, we choose speakers for our QES conferences primarily on the quality of their papers. We went through thousands of academic papers published in the last 12 months and applied a hybrid machine learning plus domain expert approach to identify six academic researchers – all have new, innovative, and cutting edge research. We also presented our latest research on Systematic Alpha from Risk Arbitrage (SARA) and introduced our Port@ Analytics platform.
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This report summarizes the suite of sophisticated global stock selection models that we have developed during the past two years in a number of areas ranging from alternative data, machine learning, NLP (Natural Language Processing), to corporate governance.
We review the key drivers of crude oil – production, demand (economic growth and sentiment surveys), and inventory. More importantly, we confirm our observations using a suite of alternative data (e.g., second hand ship price, satellite imagery) and geopolitical risk (including global trade conflicts). Overall, our macro models are neutral on the commodity, but model uncertainty is high.
Wolfe Research Vice Chairman of QES Research, Yin Luo, hosted a webcast to discuss corporate governance and future firm performance, quantifying management efficiency, big data, and machine learning, and to introduce the Capri Model.
Welcome to the May edition of our monthly newsletter. The aim is to make it easier to access all of our published research in the past month. We summarize our most topical papers to keep you abreast of our latest work. We also highlight the latest news and investment themes with respect to Quantitative Investing, Economics, and Portfolio Strategy (QES).
Executive compensation and management entrenchment have attracted tremendous attention from shareholders, economists, regulators, the media, and the public at large. In this research, we empirically test the relationship between key corporate governance attributes (e.g., pay and compensation, tenure and turnover, corporate entrenchment, executive age, and management efficiency) and future stock performance, globally (US, Europe, Asia, and EM). For example, we find that higher pay does lead to better performance, but only to certain extent. A stable management team with long tenure is often associated with higher subsequent returns. Our ICE (Investor Confidence on Executives) also plays an important role in stock selection.
Wolfe Research Vice Chairman of QES Research, Yin Luo, hosted a webcast to discuss an overview of our macro research, text mining of central bank announcements, Dodge construction data, and the third derivative in forecasting commodity price - second-hand ship price index.
Wolfe Research Vice Chairman of QES Research, Yin Luo, hosted a webcast to discuss systematic approaches to model risk arb, big data and machine learning, models and portfolio construction techniques for risk arb managers, and investable SARA strategies.
The vast majority of risk arbitrage funds are managed by specialist managers who have experience and expertise of M&A transactions, legal and accounting rules, and are well connected. In this research, we take a very different approach. We attempt to model the three key dimensions of risk arbitrage – deal success probability, duration, and premium – quantitatively, using alternative Big Data and sophisticated machine learning techniques.
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