Executives Exuberant Amid “Rightsizing” Workforce - An NLP Analysis of the Q4’23 Earnings Season

AI, geopolitics, labor ‘rightsizing’ (and other layoff euphemisms), and a sanguine tone characterized the Q4’23 earnings season. Nvidia is riding the AI wave and pulling its connected network  along with it. An NLP analysis of earnings call transcripts was used to quantify the discussion.

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Figure 1: NVIDIA’s Connected Company Returns 3 weeks post Q4’2023 earnings.

^{Source: S&P Global Market Intelligence Quantamental Research. Data as Data as at  03/18/2024.}

Key takeaways:

• Sentiment is on track to hit all-time highs, beating the previous record post the Covid-19 pandemic in Q2’21. Talk of financials hit 5-year highs, while the topics of inflation and interest rates continued a multi-quarter decline. Profitability mentions may be related to cost cutting measures, as talk of layoffs (and related terms) increased by 24%.
• The geopolitical discussion has turned domestic on S&P 500 firms’ earnings calls. Mentions of the presidential election have increased 10-fold QoQ, while mentions of the Israel-Hamas conflict saw a commensurate 10-fold QoQ decrease.
• The AI hype is starting to calm. Mentions of the topic are steady for the third quarter, compared to the parabolic increase in Q2’23.

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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.