In recent years, there has been a rise in the popularity of systematic investment techniques. This is largely a result of drawbacks from more traditional discretionary approaches. A few common drawbacks include a lack of investment discipline and transparency, human biases, and an absence of automated systematic processes that lead to limited scalability across assets, asset classes, and geographies. To address these roadblocks, a sound investment approach needs to provide firms with an automated, end-to-end solution that takes the investor through key steps of the investment process and is free of human judgment or bias.
In a recent webcast, our experts highlighted the broad spectrum of capabilities that investment professionals have within FactSet’s Quantitative Research Environment (QRE), a consolidated solution used to perform key steps in any systematic investment process, including factor construction, factor standalone performance analysis, and factor additivity analysis.
Download the webcast, Optimize Your Factor Research Workflows using FactSet’s Python-Based Quantitative Research Environment, for the full discussion.
This blog post is for informational purposes only. The information contained in this blog post is not legal, tax, or investment advice. FactSet does not endorse or recommend any investments and assumes no liability for any consequence relating directly or indirectly to any action or inaction taken based on the information contained in this article.
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