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GRID (TSE:5582) Total Debt per Share : 円8.20 (As of Dec. 2024)


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What is GRID Total Debt per Share?

円8.20 (As of Dec. 2024)

Total Debt per Share is calculated as total debt divided by Shares Outstanding (EOP). Total debt is calculated as Long-Term Debt & Capital Lease Obligation plus Short-Term Debt & Capital Lease Obligation. GRID's Total Debt Per Share for the quarter that ended in Dec. 2024 was 円8.20.


GRID Total Debt per Share Historical Data

The historical data trend for GRID's Total Debt per Share 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.

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GRID Total Debt per Share Chart

GRID Annual Data
Trend Jun21 Jun22 Jun23 Jun24
Total Debt per Share
44.33 53.52 29.81 15.17

GRID Quarterly Data
Jun21 Jun22 Mar23 Jun23 Sep23 Dec23 Mar24 Jun24 Dec24
Total Debt per Share Get a 7-Day Free Trial Premium Member Only 25.80 22.30 18.76 15.17 8.20

GRID Total Debt per Share Calculation

GRID's Total Debt Per Share for the fiscal year that ended in Jun. 2024 is calculated as:

GRID's Total Debt Per Share for the quarter that ended in Dec. 2024 is calculated as:

* 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.


GRID Total Debt per Share Related Terms

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GRID Business Description

Industry
Traded in Other Exchanges
N/A
Address
3-11-7 Kitaaoyama, 6th Floor Ao Building, Minato-ku, Tokyo, JPN, 107-0061
GRID Inc is engaged in the Development, sale, maintenance, and operation support of planning optimization systems using artificial intelligence. The company solves complex problems by using digital twin technology, which reproduces real space in digital space, with AI optimization technology.

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