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EBFI (Eco Bright Future) Mohanram G-Score : N/A (As of Sep. 2024)


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What is Eco Bright Future Mohanram G-Score?

Eco Bright Future does not have enough data to calculate Mohanram G-Score.


Eco Bright Future Mohanram G-Score Historical Data

The historical data trend for Eco Bright Future's Mohanram G-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.

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Eco Bright Future Mohanram G-Score Chart

Eco Bright Future Annual Data
Trend
Mohanram G-Score

Eco Bright Future Quarterly Data
Jun23 Sep23 Jun24 Sep24
Mohanram G-Score - - - -

Competitive Comparison of Eco Bright Future's Mohanram G-Score

For the Electrical Equipment & Parts subindustry, Eco Bright Future'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.


Eco Bright Future's Mohanram G-Score Distribution in the Industrial Products Industry

For the Industrial Products industry and Industrials sector, Eco Bright Future's Mohanram G-Score distribution charts can be found below:

* The bar in red indicates where Eco Bright Future's Mohanram G-Score falls into.



Eco Bright Future 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

Eco Bright Future  (OTCPK:EBFI) 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.


Eco Bright Future Mohanram G-Score Related Terms

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Eco Bright Future Business Description

Comparable Companies
Traded in Other Exchanges
N/A
Address
87 Avenue Norte, World Trade Center San Salvador, Calle el Mirador, San Salvador, SLV
Eco Bright Future Inc engages in the financial technology and blockchain business worldwide. It builds tools to help people in accessing the economy via mobile and digital services. It is also involved in digital asset tokenisation and digital asset trading, as well as digital wallet activities. The company is building an open source platform and developer infrastructure which enables everyone to access and participate in the global economy and Real-World Assets(RWA) tokenisation.