VERSES AI (STU:J9A) Mohanram G-Score : N/A (As of Sep. 2024)

VERSES AI Mohanram G-Score Chart

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Competitive Comparison of VERSES AI's Mohanram G-Score

For the Software - Infrastructure subindustry, VERSES AI's Mohanram G-Score, along with its competitors' market caps and Mohanram G-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.

VERSES AI's Mohanram G-Score Distribution in the Software Industry

For the Software industry and Technology sector, VERSES AI's Mohanram G-Score distribution charts can be found below:

* The bar in red indicates where VERSES AI's Mohanram G-Score falls into.

VERSES AI Mohanram G-Score Calculation

The calculation of the Mohanram G-score consists of eight criteria. Assign one point for each criterion met, then add up all the points to get the G-Score.

Profitability

Question 1. Return on Assets (ROA)

ROA % is calculated as Net Income divided by its average Total Assets over a certain period of time. It measures how well a company uses its asset to generate earnings.

Score 1 if ROA > ROA Industry Median, 0 otherwise.

Question 2. Cash ROA

Cash ROA equals to Cash Flow from Operations divided by average Total Assets. It measures how well a company uses its asset to generate cash.

Score 1 if Cash ROA > Cash ROA Industry Median, 0 otherwise.

Question 3. CFO and Net Income

Score 1 if CFO > Net Income, 0 otherwise.

Earnings Predictability

Question 4. Earnings Variability

Earnings Variability is measured as the variance of a firm's ROA in the past five years.

Score 1 if Earnings Variability < Earnings Variability Industry Median, 0 otherwise.

Question 5. Sales Growth Variability

Sales Growth Variability is measured as the 5-year variance in sales growth.

Score 1 if Sales Growth Variability < Sales Growth Variability Industry Median, 0 otherwise.

Accounting Conservatism

Question 6. Research & Development Intensity

Research & Development Intensity is calcualted by Research & Development divided by the beginning Total Assets.

Score 1 if Research & Development Intensity > Research & Development Intensity Industry Median, 0 otherwise.

Question 7. CAPEX Intensity

CAPEX Intensity is calcualted by Capital Expenditure divided by the beginning Total Assets.

Score 1 if CAPEX Intensity > CAPEX Intensity Industry Median, 0 otherwise.

Question 8. Advertising Expenditure Intensity

Advertising Expenditure Intensity is calcualted by Advertising Expenditure divided by the beginning Total Assets. Note that Advertising Expenditure is not reported as a seperate line item for many companies, thus Selling, General, & Admin. Expense is used in this calculation.

Score 1 if Advertising Expenditure Intensity > Advertising Expenditure Intensity Industry Median, 0 otherwise.

* 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.

* Note that all the Industry Median used for comparison in his original research, are substituted with Sector Median due to the limitation of data within certain countries.

Good or high score = 6, 7, 8
Bad or low score = 0, 1

VERSES AI  (STU:J9A) Mohanram G-Score Explanation

Partha Mohanram is the John H. Watson Chair in Value Investing at Rotman and the Acting Vice-Dean of Research Strategy and Resources.

In 2000, he wrote a research paper called "Separating Winners from Losers Among Low Book-to-Market Stocks Using Financial Statement Analysis".

This paper tests whether a strategy based on financial statement analysis of low book-to-market (growth) stocks is successful in differentiating between winners and losers in terms of future stock performance. Based on the research, a strategy based on buying high G-score (6, 7 or 8) firms and shorting low G-score (0 or 1) firms consistently earns significant excess returns. Further, the results do not support a risk based explanation for the book-to-market effect as the strategy returns positive returns in all years, and firms that ex-ante appear less risky have better future returns.

To conclude, one can use a modified fundamental analysis strategy (G-score) to identify mispricing and earn substantial abnormal returns.

VERSES AI Mohanram G-Score Related Terms

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VERSES AI Business Description