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iLearningEngines (iLearningEngines) Return-on-Tangible-Asset : -4.05% (As of Jun. 2023)


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What is iLearningEngines Return-on-Tangible-Asset?

Return-on-Tangible-Asset is calculated as Net Income divided by its average total tangible assets. Total tangible assets equals to Total Assets minus Intangible Assets. iLearningEngines's annualized Net Income for the quarter that ended in Jun. 2023 was $-2.9 Mil. iLearningEngines's average total tangible assets for the quarter that ended in Jun. 2023 was $71.7 Mil. Therefore, iLearningEngines's annualized Return-on-Tangible-Asset for the quarter that ended in Jun. 2023 was -4.05%.

The historical rank and industry rank for iLearningEngines's Return-on-Tangible-Asset or its related term are showing as below:

AILE' s Return-on-Tangible-Asset Range Over the Past 10 Years
Min: 6.19   Med: 14.09   Max: 21.99
Current: 11.21

During the past 3 years, iLearningEngines's highest Return-on-Tangible-Asset was 21.99%. The lowest was 6.19%. And the median was 14.09%.

AILE's Return-on-Tangible-Asset is ranked better than
79.89% of 2824 companies
in the Software industry
Industry Median: 1.29 vs AILE: 11.21

iLearningEngines Return-on-Tangible-Asset Historical Data

The historical data trend for iLearningEngines's Return-on-Tangible-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.

iLearningEngines Return-on-Tangible-Asset Chart

iLearningEngines Annual Data
Trend Dec20 Dec21 Dec22
Return-on-Tangible-Asset
- 6.19 21.99

iLearningEngines Semi-Annual Data
Dec20 Dec21 Jun22 Dec22 Jun23
Return-on-Tangible-Asset - - 9.72 29.86 -4.05

Competitive Comparison of iLearningEngines's Return-on-Tangible-Asset

For the Software - Infrastructure subindustry, iLearningEngines's Return-on-Tangible-Asset, along with its competitors' market caps and Return-on-Tangible-Asset data, can be viewed below:

* Competitive companies are chosen from companies within the same industry, with headquarter located in same country, with closest market capitalization; x-axis shows the market cap, and y-axis shows the term value; the bigger the dot, the larger the market cap. Note that "N/A" values will not show up in the chart.


iLearningEngines's Return-on-Tangible-Asset Distribution in the Software Industry

For the Software industry and Technology sector, iLearningEngines's Return-on-Tangible-Asset distribution charts can be found below:

* The bar in red indicates where iLearningEngines's Return-on-Tangible-Asset falls into.



iLearningEngines Return-on-Tangible-Asset Calculation

iLearningEngines's annualized Return-on-Tangible-Asset for the fiscal year that ended in Dec. 2022 is calculated as:

Return-on-Tangible-Asset=Net Income/( (Total Tangible Assets+Total Tangible Assets)/ count )
(A: Dec. 2022 )  (A: Dec. 2021 )(A: Dec. 2022 )
=Net Income/( (Total Assets - Intangible Assets+Total Assets - Intangible Assets)/ count )
(A: Dec. 2022 )  (A: Dec. 2021 )(A: Dec. 2022 )
=11.466/( (40.721+63.545)/ 2 )
=11.466/52.133
=21.99 %

iLearningEngines's annualized Return-on-Tangible-Asset for the quarter that ended in Jun. 2023 is calculated as:

Return-on-Tangible-Asset=Net Income/( (Total Tangible Assets+Total Tangible Assets)/ count )
(Q: Jun. 2023 )  (Q: Dec. 2022 )(Q: Jun. 2023 )
=Net Income/( (Total Assets - Intangible Assets+Total Assets - Intangible Assets)/ count )
(Q: Jun. 2023 )  (Q: Dec. 2022 )(Q: Jun. 2023 )
=-2.906/( (63.545+79.835)/ 2 )
=-2.906/71.69
=-4.05 %

* 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.

In the calculation of annual Return-on-Tangible-Asset, the net income of the last fiscal year and the average total tangible assets over the fiscal year are used. In calculating the quarterly data, the Net Income data used here is two times the semi-annual (Jun. 2023) net income data.


iLearningEngines  (NAS:AILE) Return-on-Tangible-Asset Explanation

Return-on-Tangible-Asset measures the rate of return on the average total tangible assets (total assets minus intangible assets). Tangible means physical in nature. Intangible Assets are assets that are not physical in nature, and typically "derive their value from legal or intellectual rights." Return-on-Tangible-Asset measures a firm's efficiency at generating profits from its tangible assets. It shows how well a company uses what it has to generate earnings. Return-on-Tangible-Assets can vary drastically across industries. Therefore, Return-on-Tangible-Asset should not be used to compare companies in different industries.


Be Aware

Like ROE and ROA, Return-on-Tangible-Asset is calculated with only 12 months data. Fluctuations in the company’s earnings or business cycles can affect the ratio drastically. It is important to look at the ratio from a long term perspective. Return-on-Tangible-Asset can be affected by events such as stock buyback or issuance, and by a company’s tax rate and its interest payment. Return-on-Tangible-Asset may not reflect the true earning power of the assets. A more accurate measurement is ROC % (ROC).

Many analysts argue the higher return the better. Buffett states that really high Return-on-Tangible-Asset may indicate vulnerability in the durability of the competitive advantage.


iLearningEngines Return-on-Tangible-Asset Related Terms

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iLearningEngines (iLearningEngines) Business Description

Comparable Companies
Traded in Other Exchanges
N/A
Address
6701 Democracy Boulevard, Suite 300, Bethesda, MD, USA, 20817
iLearningEngines Inc is an AI and automation platform that empowers its customers to productize their institutional knowledge by transforming it into actionable intellectual property that enhances outcomes for employees, customers and other stakeholders. Its platform enables enterprises to build intelligent Knowledge Clouds that incorporate large volumes of structured and unstructured information across disparate internal and external systems and to automate organizational processes that leverage these Knowledge Clouds to improve performance. The company combines its offerings with vertically focused capabilities and data models to operationalize AI and automation to effectively and efficiently address critical challenges facing its customers.