Abstract
A number of market factors are generally acknowledged for having the potential to add alpha over the benchmark indexes over time. They include value, quality, price momentum, and low volatility. Exchange-traded funds have commoditized these factors, making them available at a very nominal fee. In contrast, nontraditional or alternative research data can be used to improve performance in both actively and passively managed portfolios, but obtaining and using these data can be challenging for many investment professionals. Challenges include knowing the sourcing methods for alternative data, understanding the mechanisms by which alternative data can and cannot generate alpha, identifying which types of investments benefit the most from nontraditional data, and learning how to implement alternative data strategies in real-world portfolios. This article is a primer for investment professionals seeking to learn more about how to best use alternative data sources, such as web scraping, as a new form of fundamental data for tactically or quantitatively managed active portfolios or as alternate selection and weighting strategies for tracking tolerant smart beta applications. TOPICS:Exchange-traded funds and applications, analysis of individual factors/risk premia, big data/machine learning, portfolio construction
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