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RELX (RELX) LT-Debt-to-Total-Asset : 0.35 (As of Dec. 2023)


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What is RELX LT-Debt-to-Total-Asset?

LT Debt to Total Assets is a measurement representing the percentage of a corporation's assets that are financed with loans and financial obligations lasting more than one year. The ratio provides a general measure of the financial position of a company, including its ability to meet financial requirements for outstanding loans. It is calculated as a company's Long-Term Debt & Capital Lease Obligationdivide by its Total Assets. RELX's long-term debt to total assests ratio for the quarter that ended in Dec. 2023 was 0.35.

RELX's long-term debt to total assets ratio declined from Dec. 2022 (0.37) to Dec. 2023 (0.35). It may suggest that RELX is progressively becoming less dependent on debt to grow their business.


RELX LT-Debt-to-Total-Asset Historical Data

The historical data trend for RELX's LT-Debt-to-Total-Asset can be seen below:

* For Operating Data section: All numbers are indicated by the unit behind each term and all currency related amount are in USD.
* For other sections: All numbers are in millions except for per share data, ratio, and percentage. All currency related amount are indicated in the company's associated stock exchange currency.

* Premium members only.

RELX LT-Debt-to-Total-Asset Chart

RELX Annual Data
Trend Dec14 Dec15 Dec16 Dec17 Dec18 Dec19 Dec20 Dec21 Dec22 Dec23
LT-Debt-to-Total-Asset
Get a 7-Day Free Trial Premium Member Only Premium Member Only 0.32 0.44 0.43 0.37 0.35

RELX Semi-Annual Data
Jun14 Dec14 Jun15 Dec15 Jun16 Dec16 Jun17 Dec17 Jun18 Dec18 Jun19 Dec19 Jun20 Dec20 Jun21 Dec21 Jun22 Dec22 Jun23 Dec23
LT-Debt-to-Total-Asset Get a 7-Day Free Trial Premium Member Only Premium Member Only Premium Member Only Premium Member Only Premium Member Only Premium Member Only Premium Member Only Premium Member Only Premium Member Only Premium Member Only Premium Member Only Premium Member Only 0.43 0.39 0.37 0.36 0.35

RELX LT-Debt-to-Total-Asset Calculation

RELX's Long-Term Debt to Total Asset Ratio for the fiscal year that ended in Dec. 2023 is calculated as

LT Debt to Total Assets (A: Dec. 2023 )=Long-Term Debt & Capital Lease Obligation (A: Dec. 2023 )/Total Assets (A: Dec. 2023 )
=6562.025/18882.278
=0.35

RELX's Long-Term Debt to Total Asset Ratio for the quarter that ended in Dec. 2023 is calculated as

LT Debt to Total Assets (Q: Dec. 2023 )=Long-Term Debt & Capital Lease Obligation (Q: Dec. 2023 )/Total Assets (Q: Dec. 2023 )
=6562.025/18882.278
=

* For Operating Data section: All numbers are indicated by the unit behind each term and all currency related amount are in USD.
* For other sections: All numbers are in millions except for per share data, ratio, and percentage. All currency related amount are indicated in the company's associated stock exchange currency.


RELX  (NYSE:RELX) LT-Debt-to-Total-Asset Explanation

LT Debt to Total Asset is a measurement representing the percentage of a corporation's assets that are financed with loans and financial obligations lasting more than one year. The ratio provides a general measure of the financial position of a company, including its ability to meet financial requirements for outstanding loans. A year-over-year decrease in this metric would suggest the company is progressively becoming less dependent on debt to grow their business.


RELX LT-Debt-to-Total-Asset Related Terms

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RELX (RELX) Business Description

Address
1-3 Strand, London, GBR, WC2N 5JR
RELX PLC is a diversified information and analytics company. The firm operates across four main areas: scientific, technical, and medical; risk; legal; and exhibitions. It generates maximum revenue from the Scientific, Technical and Medical and Risk segment. Scientific, Technical and Medical segment provides information and analytics that help institutions and professionals progress science, advance healthcare and improve performance. Risk segment provides customers with information-based analytics and decision tools that combine public and industry-specific content with advanced technology and algorithms to assist them in evaluating and predicting risk and enhancing operational efficiency.