Going into another week of earnings, we offer a sneak peek into the large cap energy firms. Robust production, softening demand, and fears of a slowing US economy are set against a backdrop of trade tensions and industrial uncertainty that have left energy firms exposed. Amenity’s NLP models examined two early energy reporters: Kinder Morgan (KMI) and Schlumberger (SLB), finding both companies’ net sentiment on fundamentals turning south.

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October 21, 2019

Earnings Preview: KMI & SLB Kick-off Energy Sector

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Earnings Preview: KMI & SLB Kick-off Energy Sector

Going into another week of earnings, we’re writing with a sneak peek into large cap energy firms. Robust production, softening demand, and fears of a slowing US economy are set against a backdrop of trade tensions and industrial uncertainty that have left energy firms exposed. We offer an early first look at some metrics that provide situational awareness to energy investors. Ahead of the ramp up in results, we provide what may be an early signal of the sector’s trajectory going into earnings.

Kinder Morgan and Schlumberger

We examined two early energy reporters: Kinder Morgan (KMI) reported earnings on 16 October and Schlumberger (SLB) reported on 18 October. Last quarter, Amenity’s NLP models found the net sentiment on fundamentals of calls for Kinder Morgan and Schlumberger to be decidedly middle of the pack compared to the rem. With that in mind, we wanted to offer our readers an early snippet of analysis that finds both companies’ net sentiment on fundamentals turning south. If this is a harbinger of things to come, we wanted our clients to see it as early as possible.

Energy Firms Sentiment Scores Q3 to Present: Market Cap $10B

The figure above visualizes company level sentiment scores for energy firms with market capitalizations over $10 billion on a timeline spanning from the last quarter to present. The data on the left side of the figure represent sentiment scores for earnings call transcripts, which we left unlabelled for readability (please reach out for the full data set). The lines indicate sentiment trajectories of those companies that have reported results this quarter -- so far, ony Kinder Morgan and Schlumberger.

To verify the robustness of our findings, we took a look under the hood in our Transcript Viewer, which allows our clients to engage directly with text to see how and what we classify as significant extractions and into what event types and key drivers they fall. For both companies, we observe moderate up-ticks in the amount of deceptive language employed by management, which suggests a lack of clarity coming from management in answering tough questions. We also observe a drop off in tailwind commentary, indicating that management may believe the tides are turning less favorable.

Curiously, the only substantive area in which we find no drop off in positivity and no influx of negativity relates to capital returns (buybacks and dividends). Schlumberger CFO Simon Ayat proudly reported the company “spent $79 million to repurchase 2.2 million shares” in the quarter. Kinder Morgan Chairman Richard Kinder emphasized the company will “opportunistically buy back [their] stock” while CEO Steven Kean highlighted a “25% year-over-year dividend increase.” Whatever the headwinds dragging down sentiment may be, it seems management at both firms remain devoutly focused on shareholder returns.

Interested in running these types of analyses with our platform?

Clients can use the NLP platform to examine both Kinder Morgan and Schlumberger earnings calls.

Not a Client? Request a demo today to find out how you can analyze earnings call transcripts and other financial documents with our text analytics platform. Spot outliers, identify critical insights, and understand key drivers.

About Amenity

Amenity Analytics is the industry leader in providing insights from unstructured text by using Natural Language Processing (NLP) assisted by Artificial Intelligence (AI) and Machine Learning (ML). Amenity’s NLP system is a sector-agnostic, language-dependent tool for quantitative text analysis that is deployed across the financial services industry and beyond.

This communication does not represent investment advice. Transcript text provided by S&P Global Market Intelligence.

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