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DeepSpatial (XCNQ:DSAI) LT-Debt-to-Total-Asset : 0.00 (As of Mar. 2024)


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What is DeepSpatial 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. DeepSpatial's long-term debt to total assests ratio for the quarter that ended in Mar. 2024 was 0.00.

DeepSpatial's long-term debt to total assets ratio declined from Mar. 2023 (0.45) to Mar. 2024 (0.00). It may suggest that DeepSpatial is progressively becoming less dependent on debt to grow their business.


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

The historical data trend for DeepSpatial'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.

DeepSpatial LT-Debt-to-Total-Asset Chart

DeepSpatial Annual Data
Trend Jun20 Jun21 Jun22 Jun23
LT-Debt-to-Total-Asset
- 0.01 0.02 0.51

DeepSpatial Quarterly Data
Dec19 Mar20 Jun20 Sep20 Dec20 Mar21 Jun21 Sep21 Dec21 Mar22 Jun22 Sep22 Dec22 Mar23 Jun23 Sep23 Dec23 Mar24
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 0.45 0.51 0.52 0.15 -

DeepSpatial LT-Debt-to-Total-Asset Calculation

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

LT Debt to Total Assets (A: Jun. 2023 )=Long-Term Debt & Capital Lease Obligation (A: Jun. 2023 )/Total Assets (A: Jun. 2023 )
=0.611/1.189
=0.51

DeepSpatial's Long-Term Debt to Total Asset Ratio for the quarter that ended in Mar. 2024 is calculated as

LT Debt to Total Assets (Q: Mar. 2024 )=Long-Term Debt & Capital Lease Obligation (Q: Mar. 2024 )/Total Assets (Q: Mar. 2024 )
=0/1.557
=0.00

* 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.


DeepSpatial  (XCNQ:DSAI) 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.


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

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DeepSpatial Business Description

Traded in Other Exchanges
Address
77 King Street West, Suite 3000, Toronto, ON, CAN, M5K 1G8
DeepSpatial Inc is an artificial intelligence, technology SaaS company at the forefront of geospatial artificial intelligence and geographic informational systems, specializing in providing robust location intelligence solutions for transforming existing location data into business outcomes. AI powered solutions to businesses by leveraging the power of geospatial data. The company has built products that target four specific customer needs across industry verticals. These are geodemographic customer profiling, price recommendation engine, inventory management and supply chain analytics, and sentiment monitoring to measure and improve brand health.

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