Stable public markets are the secret to private equity (PE) optimism. An NLP analysis of publicly-traded PE firms’ Q1'24 earnings call transcripts reveals a significant rebound in PE confidence over the last two years. After peaking in Q3'21, PE executives' sentiment declined in 2022 as inflation, geopolitics and fed-driven volatility ended a post-COVID calm.

However, with rates finding footing, Global PMI at a 12-month high and volatility subsiding, PE sentiment in Q1'24 reflects renewed optimism. This quarter, PE executive sentiment surged to its second-highest level in 57 earnings seasons, underscoring the inverse relationship between market turbulence and PE industry morale.

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Source: S&P Global Market Intelligence Quantamental Research. Data as at 05/30/2024.

Other key takeaways:

• Publicly-traded PE firms’ executives spoke less about inflation, interest rates, and liquidity on Q1’24 earnings calls than they have in 10 quarters, suggesting reduced tension (but ongoing importance) around these topics.

• A secular increase in the focus on 'private wealth' and 'private credit' continued this quarter. PE firms are increasingly targeting high net worth individuals to fill fundraising gaps; private credit remains a focus for asset class diversification, despite an anticipated rise in defaults in 2024.

• Discussions of artificial intelligence in PE earnings calls doubled, in Q1’24 vs Q1’23. Executives are focusing on AI's broader implementation for value creation within portfolio companies, including applications in customer service automation, predictive analytics for risk assessment, and investments in AI-driven companies and infrastructure.                                                                                                     

Explore the data used to conduct this research

Textual Data Analytics (TDA)

TDA was launched in October 2019 and is productized from Quantamental Research’s previous publications with an advanced suite of analytics and metrics added in May 2022. It is an off-the-shelf NLP solution that tailors to our Machine-Readable Transcripts and outputs 800+ predictive and descriptive analytics for equity investing and various data science workflows. The analytics could be accessed via SQL, Snowflake or (DataBricks) Workbench.

Machine-Readable Transcripts

This dataset aggregates data from earnings calls delivered in a machine-readable format for NLP applications with metadata tagging. Among its key features, the data set captures the different segmentations of earnings calls in the follow ways:

• Sections (e.g., prepared remarks, sell-side analyst questions, responses to questions)
• Speaker types (e.g., executives, sell-side analysts, shareholders etc.)
• Professionals (e.g., Tim Cook) where the individual professional identifiers serve as a unique key that connects the transcripts data set with the S&P Global Market Intelligence’s Professionals and Sell-side Estimates data sets.