Deepnoid (XKRX:315640) Total Assets : ₩33,247 Mil (As of Mar. 2024)

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What is Deepnoid Total Assets?

Deepnoid's Total Assets for the quarter that ended in Mar. 2024 was ₩33,247 Mil.

During the past 12 months, Deepnoid's average Total Assets Growth Rate was 195.70% per year. During the past 3 years, the average Total Assets Growth Rate was 44.40% per year.

During the past 5 years, Deepnoid's highest 3-Year average Total Assets Growth Rate was 44.40%. The lowest was 8.90%. And the median was 26.65%.

Total Assets is connected with ROA %. Deepnoid's annualized ROA % for the quarter that ended in Mar. 2024 was -30.03%. Total Assets is also linked to Revenue through Asset Turnover. Deepnoid's Asset Turnover for the quarter that ended in Mar. 2024 was 0.01.

Deepnoid Total Assets Historical Data

The historical data trend for Deepnoid's Total Assets 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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Deepnoid Total Assets Chart

Deepnoid Annual Data

Trend

Dec18

Dec19

Dec20

Dec21

Dec22

Total Assets

8,337.02

5,832.86

11,314.70

18,590.79

14,527.30

Deepnoid Quarterly Data
	Mar20	Sep20	Dec20	Mar21	Jun21	Sep21	Dec21	Mar22	Jun22	Sep22	Dec22	Mar23	Jun23	Sep23	Dec23	Mar24
Total Assets	Get a 7-Day Free Trial											11,455.16	11,528.13	10,286.19	34,424.06	33,246.70

Deepnoid Total Assets Calculation

Total Assets are all the assets a company owns.

From the capital sources of the assets, some of the assets are funded through shareholder's paid in capital and retained earnings of the business. Others are funded through borrowed money.

Deepnoid's Total Assets for the fiscal year that ended in Dec. 2022 is calculated as

Total Assets	=	Total Equity (A: Dec. 2022 )	+	Total Liabilities (A: Dec. 2022 )
	=	9966.349	+	4560.953
	=	14,527

Deepnoid's Total Assets for the quarter that ended in Mar. 2024 is calculated as

Total Assets	=	Total Equity (Q: Mar. 2024 )	+	Total Liabilities (Q: Mar. 2024 )
	=	25676.653	+	7570.045
	=	33,247

Deepnoid (XKRX:315640) Total Assets Explanation

Total Assets is connected with ROA %.

Deepnoid's annualized ROA % for the quarter that ended in Mar. 2024 is

ROA %	=	Net Income (Q: Mar. 2024 )	/	( (Total Assets (Q: Dec. 2023 )	+	Total Assets (Q: Mar. 2024 ))	/ count )
	=	-10161.288	/	( (34424.057	+	33246.697)	/ 2 )
	=	-10161.288	/	33835.377
	=	-30.03 %

Note: The Net Income data used here is four times the quarterly (Mar. 2024) data.

In the article Joining The Dark Side: Pirates, Spies and Short Sellers, James Montier reported that In their US sample covering the period 1968-2003, Cooper et al find that firms with low asset growth outperformed firms with high asset growth by an astounding 20% p.a. equally weighted. Even when controlling for market, size and style, low asset growth firms outperformed high asset growth firms by 13% p.a. Therefore a company with fast asset growth may underperform.

Total Assets is linked to total revenue through Asset Turnover.

Deepnoid's Asset Turnover for the quarter that ended in Mar. 2024 is

	Asset Turnover
=	Revenue (Q: Mar. 2024 )	/	( (Total Assets (Q: Dec. 2023 )	+	Total Assets (Q: Mar. 2024 ))	/ count )
=	358.057	/	( (34424.057	+	33246.697)	/ 2 )
=	358.057	/	33835.377
=	0.01

Therefore, if a company grows its Total Assets faster than its Revenue, the Asset Turnover will decline. This might be a warning sign for the business.

Deepnoid Total Assets Related Terms

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Deepnoid (XKRX:315640) Business Description

Industry

GURUFOCUS.COM » STOCK LIST » Healthcare » Healthcare Providers & Services » Deepnoid Inc (XKRX:315640) » Definitions » Total Assets

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Traded in Other Exchanges

N/A

Address

No.1305, 55, Digital-ro 33-gil, Guro-gu, Seoul, KOR, 08376

Website

https://www.deepnoid.com

Deepnoid Inc is a doctor-driven medical AI platform. It is an open platform that supports doctor-led medical imaging AI model research efficiently. It uses both the image processing module and neural network module provided by default for free, and can effectively use computing resources in the form of a cloud.

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