There's no crystal ball, but this researcher from Rice University is trying to see if some metrics work for economic forecasting. Photo via Getty Images

Research by Rice Business Professor K. Ramesh shows that the Fed appears to harvest qualitative information from the accounting disclosures that all public companies must file with the Securities and Exchange Commission.

These SEC filings are typically used by creditors, investors and others to make firm-level investing and financing decisions; and while they include business leaders’ sense of economic trends, they are never intended to guide macro-level policy decisions. But in a recent paper (“Externalities of Accounting Disclosures: Evidence from the Federal Reserve”), Ramesh and his colleagues provide persuasive evidence that the Fed nonetheless uses the qualitative information in SEC filings to help forecast the growth of macroeconomic variables like GDP and unemployment.

According to Ramesh, the study was made possible thanks to a decision the SEC made several years ago. The commission stores the reports submitted by public companies in an online database called EDGAR and records the IP address of any party that accesses them. More than a decade ago, the SEC began making partially anonymized forms of those IP addresses available to the public. But researchers eventually figured out how to deanonymize the addresses, which is precisely what Ramesh and his colleagues did in this study.

"We were able to reverse engineer and identify those IP addresses that belonged to Federal Reserve staff," Ramesh says.

The team ultimately assembled a data set containing more than 169,000 filings accessed by Fed staff between 2005 and 2015. They quickly realized that the Fed was interested only in filings submitted by a select group of industry leaders and financial institutions.

But if Ramesh and his colleagues now had a better idea of precisely which bellwether firms the Fed focused on, they still had no way of knowing exactly what Fed staffers had gleaned from the material they accessed. So the team decided to employ a measure called "tone" that captures the overall sentiment of a piece of text – whether positive, negative, or neutral.

Building on previous research that had identified a set of words with negatively toned financial reports, Ramesh and his colleagues examined the tone of all the SEC filings accessed by Fed staff between one meeting of the Federal Open Markets Committee (FOMC) and the next. The FOMC sets interest rates and guides monetary policy, and its meetings provide an opportunity for Fed officials to discuss growth forecasts and announce policy decisions.

The researchers then examined the Fed's growth forecasts to see if there was a relationship between the tone of the documents that Fed staff examined in the period between FOMC meetings and the forecasts they produced in advance of those meetings.

The team found close correlations between the tone of the reports accessed by the Fed and the agency’s forecasts of GDP, unemployment, housing starts and industrial production. The more negative the filings accessed prior to an FOMC meeting, for example, the gloomier the GDP forecast; the more positive the filings, the brighter the unemployment forecast.

Ramesh and his colleagues also compared the Fed's forecasts with those of the Society of Professional Forecasters (SPF), whose members span academia and industry. Intriguingly, the researchers found that while the errors in the SPF's forecasts could be attributed to the absence of the tonal information culled from the SEC filings, the errors in the Fed’s forecasts could not. This suggests both that the Fed was collecting qualitative information that the SPF was not—and that the agency was making remarkably efficient use of it.

"They weren’t leaving anything on the table," Ramesh says.

Having solved one mystery, Ramesh would like to focus on another; namely, how does the Fed identify bellwether firms in the first place?

Unfortunately, the SEC no longer makes IP address data publicly available, which means that Ramesh and his colleagues can no longer study which companies the Fed is most interested in. Nonetheless, Ramesh hopes to use the data they have already collected to build a model that can accurately predict which firms the Fed is most likely to follow. That would allow the team to continue studying the same companies that the Fed does, and, he says, “maybe come up with a way to track those firms in order to understand how the economy is going to move.”

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This article originally ran on Rice Business Wisdom and was based on research from K. Ramesh is Herbert S. Autrey Professor of Accounting at Jones Graduate School of Business at Rice University.

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Houston space tech co. rolls out futuristic lunar rover for NASA's Artemis missions

to the moon

Houston-based space exploration company Intuitive Machines just unveiled its version of a lunar terrain vehicle that’s designed to be used by astronauts in NASA’s Artemis moon discovery program.

Intuitive Machine recently rolled out its RACER lunar terrain vehicle (LTV) at Space Center Houston. RACER stands for Reusable Autonomous Crewed Exploration Rover.

The rover can accommodate two astronauts and nearly 900 pounds of cargo. In addition, it can pull a trailer loaded with almost 1,800 pounds of cargo.

Intuitive Machines will retain ownership and operational capabilities that will enable remote operation of the LTV between Artemis missions for about 10 years.

NASA chose Intuitive Machines and two other companies to develop advanced LTV capabilities.

“The objective is to enable Artemis astronauts, like the Apollo-era moonwalkers before them, to drive the rover, which features a rechargeable electric battery and a robotic arm, across the lunar surface, to conduct scientific research and prepare for human missions to Mars,” Intuitive Machines says in a post on its website.

The company tapped the expertise of Apollo-era moonwalkers Charlie Duke and Harrison Schmitt to design the pickup-truck-sized RACER. Intuitive Machines engineered the LTV in partnership with Atlas Devices, AVL, Barrios, Boeing, CSIRO, FUGRO, Michelin, Northrop Grumman, and Roush.

“This [project] strategically aligns with the Company’s flight-proven capability to deliver payloads to the surface of the Moon under [NASA’s] Commercial Lunar Payload Services initiative, further solidifying our position as a proven commercial contractor in lunar exploration,” says Steve Altemus, CEO of Intuitive Machines.

Astronauts at NASA’s Johnson Space Center are testing the static prototype of the company’s LTV. Meanwhile, the fully electric mobile demonstration LTV will undergo field testing later this month near Meteor Crater National Park in Arizona.

NASA expects to choose an LTV provider or providers in 2025.

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Houston accelerator names inaugural cohort to propel digital transformation in energy

building tech

Houston-based Venture Builder VC has kicked off its NOV Supernova Accelerator and named its inaugural cohort.

The program, originally announced earlier this year, focuses on accelerating digital transformation solutions for NOV Inc.'s operations in the upstream oil and gas industry. It will support high-potential startups in driving digital transformation within the energy sector, specifically upstream oil and gas, and last five months and culminate in a demo day where founders will present solutions to industry leaders, potential investors, NOV executives, and other stakeholders.

The NOV Supernova Accelerator will work to cultivate relationships between startups and NOV. They will offer specific companies access to NOV’s corporate R&D teams and business units to test their solutions in an effort to potentially develop long-term partnerships.

“The Supernova Accelerator is a reflection of our commitment to fostering forward-thinking technologies that will drive the future of oil and gas,” Diana Grauer, director of R&D of NOV, says in a news release.

The cohort’s focus will be digital transformation challenges that combine with NOV’s vision and include data management and analytics, operational efficiency, HSE (Health, Safety, and Environmental) monitoring, predictive maintenance, and digital twins.

Startups selected for the program include:

  • AnyLog, an edge data management platform that replaces proprietary edge projects with a plug-and-play solution that services real-time data directly at the source, eliminating cloud costs, data transfer, and latency issues.
  • Equipt, an AI-powered self-serve platform that maximizes Asset & Field Service performance, and minimizes downtime and profit leakages.
  • Geolumina's platform is a solution that leverages data analytics to enhance skills, scale insights, and improve efficiency for subsurface companies.
  • Gophr acts as the "Priceline" of logistics, using AI to provide instant shipping quotes and optimize dispatch for anything from paper clips to rocket ships.
  • IoT++ simplifies industrial IoT with a secure, AI-enabled ecosystem of plug-and-play edge devices.
  • Kiana's hardware-agnostic solution secures people, assets, and locations using existing Wi-Fi, Bluetooth, UWB, and cameras, helping energy and manufacturing companies reduce risks and enhance operations.
  • Novity uses AI and physics models to accurately predict machine faults, helping factory operators minimize downtime by knowing the remaining useful life of their machines.
  • Promecav is redefining crude oil conditioning with patented technology that slashes water use and energy while reducing toxic exposure for safer, cleaner, and more sustainable oil processing.
  • RaftMind's enterprise AI solution transforms how businesses manage knowledge. Our advanced platform makes it easier to process data and unlock insights from diverse sources.
  • Spindletop AI uses edge-based machine learning to make each well an autonomous, self-optimizing unit, cutting costs, emissions, and cloud dependence.
  • Taikun.aicombines generative AI with SCADA data to create virtual industrial engineers, augmenting human teams for pennies an hour.
  • Telemetry Insight’s platform utilizes high-resolution accelerometer data to simplify oilfield monitoring and optimize marginal wells for U.S. oil and gas producers via actionable insights.
  • Visual Logging utilizes fiber optic and computer vision technology to deliver real-time monitoring solutions, significantly enhancing data accuracy by providing precise insights into well casing integrity and flow conditions.

“Each startup brings unique solutions to the table, and we are eager to see how these technologies will evolve with NOV’s support and expertise,” Billy Grandy, general partner of Venture Builder VC, says in the release. “This partnership reflects our ongoing commitment to nurturing talent and driving innovation within the energy sector.”

Venture Builder VC is a consulting firm, investor, and accelerator program.

“Unlike mergers and acquisitions, the venture client model allows corporations like NOV to quickly test and implement new technologies without committing to an acquisition or risking significant investment,” Grandy previously said about the accelerator program.

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This article originally ran on EnergyCapital.