Alpha Generation with RavenPack Job Analytics

There is a strong link between human capital and business success. A company’s ebb and flow in hiring trends, the shifts in most wanted skills or the hiring locations can provide investors with valuable insights into performance and investment prospects.

Our newly released Job Analytics dataset analyzes the entire body of job postings sourced directly from employer websites using state-of-the-art Natural Language Processing (NLP) technology. It provides data on positions, locations, and job descriptions and allows tracking hiring trends and patterns, benchmark skills and more.

This paper demonstrates how investors can benefit from job posting data using RavenPack Job Analytics. We find that:

• Monthly hiring growth is positively correlated with future stock performance. Additionally, companies with higher hiring growth that hire in similar locations outperform the rest. A long/short sector-neutral portfolio delivers an Information Ratio of 1.1 and Annualized Returns of 2.7% with a weekly holding period.

• A strategy focused on the monthly growth of the most demanded positions for each sector generates better performance than the overall growth strategy. The resulting portfolio achieves an Information Ratio of 1.1 and Annualized Returns of 2.2% with a weekly holding period.

• Job descriptions carry orthogonal alpha. Companies with a stable distribution of soft skills outperform others, leading to a strategy with an Information Ratio of 0.8 and Annualized Returns of 1.9% with a monthly holding period.

• The combined strategy shows robust performance over different trading horizons. With an effective holding period of two weeks, the strategy delivers an Information Ratio of 1.4 with Annualized Returns of 2.4%. The Information Ratio remains 0.9 with a monthly holding period.