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AILE (iLearningEngines) Piotroski F-Score : N/A (As of Oct. 31, 2024)


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What is iLearningEngines Piotroski F-Score?

The zones of discrimination were as such:

Good or high score = 7, 8, 9
Bad or low score = 0, 1, 2, 3

iLearningEngines has an F-score of 5 indicating the company's financial situation is typical for a stable company.

The historical rank and industry rank for iLearningEngines's Piotroski F-Score or its related term are showing as below:


iLearningEngines Piotroski F-Score Historical Data

The historical data trend for iLearningEngines's Piotroski F-Score 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 Piotroski F-Score Chart

iLearningEngines Annual Data
Trend Dec20 Dec21 Dec22
Piotroski F-Score
N/A N/A N/A

iLearningEngines Semi-Annual Data
Dec20 Dec21 Jun22 Dec22 Jun23 Jun24
Piotroski F-Score Get a 7-Day Free Trial N/A - N/A - -

Competitive Comparison of iLearningEngines's Piotroski F-Score

For the Software - Infrastructure subindustry, iLearningEngines's Piotroski F-Score, along with its competitors' market caps and Piotroski F-Score 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 Piotroski F-Score Distribution in the Software Industry

For the Software industry and Technology sector, iLearningEngines's Piotroski F-Score distribution charts can be found below:

* The bar in red indicates where iLearningEngines's Piotroski F-Score falls into.


How is the Piotroski F-Score calculated?

* 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.

This Year (Dec22) TTM:Last Year (Dec21) TTM:
Net Income was $11.5 Mil.
Cash Flow from Operations was $-8.9 Mil.
Revenue was $309.2 Mil.
Gross Profit was $215.3 Mil.
Average Total Assets from the begining of this year (Dec21)
to the end of this year (Dec22) was (40.721 + 63.545) / 2 = $52.133 Mil.
Total Assets at the begining of this year (Dec21) was $40.7 Mil.
Long-Term Debt & Capital Lease Obligation was $9.7 Mil.
Total Current Assets was $45.1 Mil.
Total Current Liabilities was $15.3 Mil.
Net Income was $2.5 Mil.

Revenue was $217.9 Mil.
Gross Profit was $153.0 Mil.
Average Total Assets from the begining of last year (Dec20)
to the end of last year (Dec21) was (0 + 40.721) / 2 = $40.721 Mil.
Total Assets at the begining of last year (Dec20) was $0.0 Mil.
Long-Term Debt & Capital Lease Obligation was $8.5 Mil.
Total Current Assets was $39.4 Mil.
Total Current Liabilities was $8.6 Mil.

*Note: If the latest quarterly/semi-annual/annual total assets data is 0, then we will use previous quarterly/semi-annual/annual data for all the items in the balance sheet.

Profitability

Question 1. Return on Assets (ROA)

Net income before extraordinary items for the year divided by Total Assets at the beginning of the year.

Score 1 if positive, 0 if negative.

iLearningEngines's current Net Income (TTM) was 11.5. ==> Positive ==> Score 1.

Question 2. Cash Flow Return on Assets (CFROA)

Net cash flow from operating activities (operating cash flow) divided by Total Assets at the beginning of the year.

Score 1 if positive, 0 if negative.

iLearningEngines's current Cash Flow from Operations (TTM) was -8.9. ==> Negative ==> Score 0.

Question 3. Change in Return on Assets

Compare this year's return on assets (1) to last year's return on assets.

Score 1 if it's higher, 0 if it's lower.

ROA (This Year)=Net Income/Total Assets (Dec21)
=11.466/40.721
=0.28157462

ROA (Last Year)=Net Income/Total Assets (Dec20)
=2.521/0
=

iLearningEngines's return on assets of this year was 0.28157462. iLearningEngines's return on assets of last year was . ==> This year is higher. ==> Score 1.

Question 4. Quality of Earnings (Accrual)

Compare Cash flow return on assets (2) to return on assets (1)

Score 1 if CFROA > ROA, 0 if CFROA <= ROA.

iLearningEngines's current Net Income (TTM) was 11.5. iLearningEngines's current Cash Flow from Operations (TTM) was -8.9. ==> -8.9 <= 11.5 ==> CFROA <= ROA ==> Score 0.

Funding

Question 5. Change in Gearing or Leverage

Compare this year's gearing (long-term debt divided by average total assets) to last year's gearing.

Score 0 if this year's gearing is higher, 1 otherwise.

Gearing (This Year: Dec22)=Long-Term Debt & Capital Lease Obligation/Average Total Assets from Dec21 to Dec22
=9.713/52.133
=0.18631193

Gearing (Last Year: Dec21)=Long-Term Debt & Capital Lease Obligation/Average Total Assets from Dec20 to Dec21
=8.532/40.721
=0.20952334

iLearningEngines's gearing of this year was 0.18631193. iLearningEngines's gearing of last year was 0.20952334. ==> This year is lower or equal to last year. ==> Score 1.

Question 6. Change in Working Capital (Liquidity)

Compare this year's current ratio (current assets divided by current liabilities) to last year's current ratio.

Score 1 if this year's current ratio is higher, 0 if it's lower

Current Ratio (This Year: Dec22)=Total Current Assets/Total Current Liabilities
=45.05/15.341
=2.93657519

Current Ratio (Last Year: Dec21)=Total Current Assets/Total Current Liabilities
=39.361/8.619
=4.56677109

iLearningEngines's current ratio of this year was 2.93657519. iLearningEngines's current ratio of last year was 4.56677109. ==> Last year's current ratio is higher ==> Score 0.

Question 7. Change in Shares in Issue

Compare the number of shares in issue this year, to the number in issue last year.

Score 0 if there is larger number of shares in issue this year, 1 otherwise.

iLearningEngines's number of shares in issue this year was 134.97. iLearningEngines's number of shares in issue last year was 134.97. ==> There is smaller number of shares in issue this year, or the same. ==> Score 1.

Efficiency

Question 8. Change in Gross Margin

Compare this year's gross margin (Gross Profit divided by sales) to last year's.

Score 1 if this year's gross margin is higher, 0 if it's lower.

Gross Margin (This Year: TTM)=Gross Profit/Revenue
=215.28/309.17
=0.69631594

Gross Margin (Last Year: TTM)=Gross Profit/Revenue
=153.033/217.867
=0.70241478

iLearningEngines's gross margin of this year was 0.69631594. iLearningEngines's gross margin of last year was 0.70241478. ==> Last year's gross margin is higher ==> Score 0.

Question 9. Change in asset turnover

Compare this year's asset turnover (total sales for the year divided by total assets at the beginning of the year) to last year's asset turnover ratio.

Score 1 if this year's asset turnover ratio is higher, 0 if it's lower

Asset Turnover (This Year)=Revenue/Total Assets at the Beginning of This Year (Dec21)
=309.17/40.721
=7.59239704

Asset Turnover (Last Year)=Revenue/Total Assets at the Beginning of Last Year (Dec20)
=217.867/0
=

iLearningEngines's asset turnover of this year was 7.59239704. iLearningEngines's asset turnover of last year was . ==> This year's asset turnover is higher. ==> Score 1.

Evaluation

Piotroski F-Score= Que. 1+ Que. 2+ Que. 3+Que. 4+Que. 5+Que. 6+Que. 7+Que. 8+Que. 9
=1+0+1+0+1+0+1+0+1
=5

Good or high score = 7, 8, 9
Bad or low score = 0, 1, 2, 3

iLearningEngines has an F-score of 5 indicating the company's financial situation is typical for a stable company.

iLearningEngines  (NAS:AILE) Piotroski F-Score Explanation

The developer of the system is Joseph D. Piotroski is relatively unknown accounting professor who shuns publicity and rarely gives interviews.

He graduated from the University of Illinois with a B.S. in accounting in 1989, received an M.B.A. from Indiana University in 1994. Five years later, in 1999, after earning a Ph.D. in accounting from the University of Michigan, he became an associate professor of accounting at the University of Chicago.

In 2000, he wrote a research paper called "Value Investing: The Use of Historical Financial Statement Information to Separate Winners from Losers" (pdf).

He wanted to see if he can develop a system (using a simple nine-point scoring system) that can increase the returns of a strategy of investing in low price to book (referred to in the paper as high book to market) value companies.

What he found was something that exceeded his most optimistic expectations.

Buying only those companies that scored highest (8 or 9) on his nine-point scale, or F-Score as he called it, over the 20 year period from 1976 to 1996 led to an average out-performance over the market of 13.4%.

Even more impressive were the results of a strategy of investing in the highest F-Score companies (8 or 9) and shorting companies with the lowest F-Score (0 or 1).

Over the same period from 1976 to 1996 (20 years) this strategy led to an average yearly return of 23%, substantially outperforming the average S&P 500 index return of 15.83% over the same period.


iLearningEngines Piotroski F-Score Related Terms

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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.

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