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Greater Bay Area AI Computing Tech Co (OTCPK:GBAIF) Liabilities-to-Assets Explanation
Liabilities-to-Assets is a solvency ratio indicating how much of the company’s assets are made of liabilities. It can vary greatly across different industries, as they have different capital structure. A high Liabilities-to-Assets ratio (more leveraged) suggests that the company might have potential solvency problems, or even a signal of financial distress. Conversely, a low Liabilities-to-Assets ratio usually indicates a healthy financial situation. However, it may also suggest that the company is not expanding or not making good use of debt.
Greater Bay Area AI Computing Tech Co Liabilities-to-Assets Related Terms
Greater Bay Area AI Computing Tech Co Liabilities-to-Assets Historical Data
* 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.
Greater Bay Area AI Computing Tech Co Liabilities-to-Assets Chart
Greater Bay Area AI Computing Tech Co Liabilities-to-Assets Competitor Comparison
For the Information Technology Services subindustry, Greater Bay Area AI Computing Tech Co's Liabilities-to-Assets, along with its competitors' market caps and Liabilities-to-Assets 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.
Greater Bay Area AI Computing Tech Co Liabilities-to-Assets vs Software Industry
For the Software industry and Technology sector, Greater Bay Area AI Computing Tech Co's Liabilities-to-Assets distribution charts can be found below:
* The bar in red indicates where Greater Bay Area AI Computing Tech Co's Liabilities-to-Assets falls into.
Greater Bay Area AI Computing Tech Co Liabilities-to-Assets Calculation
Liabilities-to-Assets ratio measures the portion of the total liabilities to the total asset. It indicates the leverage of the company, and the amount of debt the company uses in its operation.
Liabilities-to-Assets ratio is calculated by dividing total liabilities by total asset.
* 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.
Greater Bay Area AI Computing Tech Co (GBAIF) has a Liabilities-to-Assets of 0.69 as of Dec. 2025. Liabilities-to-Assets equals total liabilities divided by total assets. It measures financial leverage. View historical data on Greater Bay Area AI Computing Tech Co and its competitors.
Is Greater Bay Area AI Computing Tech Co's Liabilities-to-Assets too high?
Greater Bay Area AI Computing Tech Co's current Liabilities-to-Assets is 0.69. Overall, Greater Bay Area AI Computing Tech Co has a GF Score™ of 41/100, reflecting its overall financial health beyond just this single metric.
How does Greater Bay Area AI Computing Tech Co's Liabilities-to-Assets compare to competitors?
Greater Bay Area AI Computing Tech Co's Liabilities-to-Assets of 0.69 can be compared against companies in the Software industry. See the competitive comparison table and distribution chart on this page for a detailed peer-by-peer breakdown.
What is a good Liabilities-to-Assets for a Software company?
A good Liabilities-to-Assets depends on the Software industry context. However, Liabilities-to-Assets 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 Liabilities-to-Assets mean?
A high Liabilities-to-Assets can signal that a stock is expensive relative to its fundamentals. Liabilities-to-Assets equals total liabilities divided by total assets. It measures financial leverage. View historical data on Greater Bay Area AI Computing Tech Co and its competitors. Greater Bay Area AI Computing Tech Co's current Liabilities-to-Assets is 0.69. However, context matters — high-growth companies often justify higher valuations. Always evaluate alongside other metrics like GF Score™ and GF Value™.
Is Greater Bay Area AI Computing Tech Co stock overvalued right now?
Greater Bay Area AI Computing Tech Co (GBAIF) has a current Liabilities-to-Assets of 0.69. The current Liabilities-to-Assets is 0.69. Greater Bay Area AI Computing Tech Co's overall GF Score™ is 41/100 with 5 warning signs to review. Investors should evaluate multiple metrics — including profitability, growth, and financial strength — before making a decision.
How is Liabilities-to-Assets calculated?
Liabilities-to-Assets is calculated from a company's financial statements. For Greater Bay Area AI Computing Tech Co (GBAIF), the current Liabilities-to-Assets is 0.69 as of Dec. 2025. 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.
Greater Bay Area AI Computing Tech Co Ltd, formerly Guangdong Hong Kong Greater Bay Area Holdings Ltd is engaged in the development of residential and urban renewal projects in the Greater Bay Area. The principal activities of the Group are the development, sales, and operation of residential properties, commercial trade and logistics centers, and trading business in Mainland China. The segments of the company are Infrastructure business, AI computing power services, and Integration and delivery of high-performance server equipment. It derives maximum revenue from AI computing power services segment engaged in the construction, delivery, and operation of large-scale training and inference computing clusters. It has a geographic presence in Hong Kong, Macao, Taiwan, Chinese mainland.
41GF Score
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