BMPA (BMP AI Technologies) Debt-to-EBITDA : -2.71 (As of Sep. 2023)

Author: Vera Yuan Vera Yuan
Vera Yuan
Vera Yuan
Director of Data and Quant Analytics at GuruFocus
Focused on building reliable datasets, financial models, and research tools for value-minded investors. Committed to turning complex data into practical guidance for value-investing and long-term wealth.
Reviewed by: Charlie Tian Charlie Tian
Charlie Tian
Charlie Tian
Founder & CEO of GuruFocus
Dr. Charlie Tian is the founder and CEO of GuruFocus.com, a leading global investment research platform established in 2004. With a Ph.D. in physics, Dr. Tian transitioned from science to finance, applying a data-driven, disciplined approach to value investing.

What is BMP AI Technologies Debt-to-EBITDA?

BMP AI Technologies BMPA Debt-to-EBITDA is -2.71 as of Sep. 2023.

Debt-to-EBITDA measures a company's ability to pay off its debt.

BMP AI Technologies's Short-Term Debt & Capital Lease Obligation for the quarter that ended in Sep. 2023 was $0.13 Mil. BMP AI Technologies's Long-Term Debt & Capital Lease Obligation for the quarter that ended in Sep. 2023 was $0.00 Mil. BMP AI Technologies's annualized EBITDA for the quarter that ended in Sep. 2023 was $-0.05 Mil. BMP AI Technologies's annualized Debt-to-EBITDA for the quarter that ended in Sep. 2023 was -2.71.

A high Debt-to-EBITDA ratio generally means that a company may spend more time to paying off its debt. According to Joel Tillinghast's BIG MONEY THINKS SMALL: Biases, Blind Spots, and Smarter Investing, a ratio of Debt-to-EBITDA exceeding four is usually considered scary unless tangible assets cover the debt.

The historical rank and industry rank for BMP AI Technologies's Debt-to-EBITDA or its related term are showing as below:

BMPA's Debt-to-EBITDA is not ranked *
in the Software industry.
Industry Median: 1.085
* Ranked among companies with meaningful Debt-to-EBITDA only.

BMP AI Technologies  (OTCPK:BMPA) Debt-to-EBITDA Explanation

In the calculation of Debt-to-EBITDA, we use the total of Short-Term Debt & Capital Lease Obligation and Long-Term Debt & Capital Lease Obligation divided by EBITDA. In some calculations, Total Liabilities is used to for calculation.

Be Aware

A high Debt-to-EBITDA ratio generally means that a company may spend more time to paying off its debt.

According to Joel Tillinghast's BIG MONEY THINKS SMALL: Biases, Blind Spots, and Smarter Investing, a ratio of Debt-to-EBITDA exceeding four is usually considered scary unless tangible assets cover the debt.

BMP AI Technologies Debt-to-EBITDA Related Terms

BMP AI Technologies Debt-to-EBITDA Historical Data

* Premium members only.

The historical data trend for BMP AI Technologies's Debt-to-EBITDA 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.

BMP AI Technologies Debt-to-EBITDA Chart

BMP AI Technologies Annual Data
Trend
Debt-to-EBITDA

BMP AI Technologies Quarterly Data
Sep22 Sep23
Debt-to-EBITDA 0.00 -2.71

BMPA vs SPQS, BWMG, HAS: Debt-to-EBITDA Comparison

For the Software - Application subindustry, BMP AI Technologies's Debt-to-EBITDA, along with its competitors' market caps and Debt-to-EBITDA 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.

BMP AI Technologies Debt-to-EBITDA vs Software Industry

For the Software industry and Technology sector, BMP AI Technologies's Debt-to-EBITDA distribution charts can be found below:

* The bar in red indicates where BMP AI Technologies's Debt-to-EBITDA falls into.


BMP AI Technologies Debt-to-EBITDA Calculation

Debt-to-EBITDA measures a company's ability to pay off its debt.

BMP AI Technologies's Debt-to-EBITDA for the fiscal year that ended in . 20 is calculated as

Debt-to-EBITDA=Total Debt / EBITDA
=(Short-Term Debt & Capital Lease Obligation + Long-Term Debt & Capital Lease Obligation) / EBITDA
=( + ) / N/A
=N/A

BMP AI Technologies's annualized Debt-to-EBITDA for the quarter that ended in Sep. 2023 is calculated as

Debt-to-EBITDA=Total Debt / EBITDA
=(Short-Term Debt & Capital Lease Obligation + Long-Term Debt & Capital Lease Obligation) / EBITDA
=(0.13 + 0) / -0.048
=-2.71

* 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 Debt-to-EBITDA, the EBITDA of the last fiscal year is used. In calculating the annualized quarterly data, the EBITDA data used here is four times the quarterly (Sep. 2023) EBITDA data.

Frequently Asked Questions Learn more about Debt-to-EBITDA →
What does a Debt-to-EBITDA of -2.71 mean?
BMP AI Technologies (BMPA) has a Debt-to-EBITDA of -2.71 as of Sep. 2023. Debt-to-EBITDA ratio represents the ratio of total debt to total earnings before interest, taxes, depreciation and amortization. View historical data on BMP AI Technologies.
Is BMP AI Technologies' Debt-to-EBITDA too high?
BMP AI Technologies' current Debt-to-EBITDA is -2.71.
How does BMP AI Technologies' Debt-to-EBITDA compare to SPQS and BWMG?
BMP AI Technologies' Debt-to-EBITDA of -2.71 can be compared against companies in the Software industry. The industry median Debt-to-EBITDA is 1.09. See the competitive comparison table and distribution chart on this page for a detailed peer-by-peer breakdown.
What is a good Debt-to-EBITDA for a Software company?
The median Debt-to-EBITDA among Software companies is 1.09, based on 1,716 companies in the industry. Companies in the top quartile (top 25%) have a Debt-to-EBITDA significantly above this median, while those in the bottom quartile fall well below. However, Debt-to-EBITDA should not be evaluated in isolation — investors should consider it alongside profitability, growth, and financial strength metrics. Use the industry distribution chart on this page to see where any company falls relative to its peers.
What does a high Debt-to-EBITDA mean?
A high Debt-to-EBITDA can signal that a stock is expensive relative to its fundamentals. Debt-to-EBITDA ratio represents the ratio of total debt to total earnings before interest, taxes, depreciation and amortization. View historical data on BMP AI Technologies. For the Software industry, the median Debt-to-EBITDA is 1.09 — values significantly above this may indicate overvaluation, while values below may suggest a bargain or underlying issues. BMP AI Technologies's current Debt-to-EBITDA is -2.71. However, context matters — high-growth companies often justify higher valuations. Always evaluate alongside other metrics like GF Score™ and GF Value™.
Is BMP AI Technologies stock overvalued right now?
BMP AI Technologies (BMPA) has a current Debt-to-EBITDA of -2.71. The current Debt-to-EBITDA is -2.71. Investors should evaluate multiple metrics — including profitability, growth, and financial strength — before making a decision.
How is Debt-to-EBITDA calculated?
Debt-to-EBITDA is calculated from a company's financial statements. For BMP AI Technologies (BMPA), the current Debt-to-EBITDA is -2.71 as of Sep. 2023. GuruFocus calculates this using data sourced from SEC filings and annual reports. See the calculation section and 30-year financial data on this page for the full breakdown.

BMP AI Technologies Business Description

Address 10409 Pacific Palisades Avenue, Las Vegas, NV, USA, 89144-1221
BMP AI Technologies Inc focuses on delivering next-generation AI solutions across key commercial sectors, including enterprise SaaS, intelligent automation, and data-driven decision-making tools. The company integrates seamlessly with file systems, CRMs, and knowledge bases, enabling businesses in sectors such as financial services, healthcare, legal, and government to deploy explainable, high-trust AI solutions with minimal technical overhead.