LQA solves the fundamentals of financial risk assessment by combining Bloomberg’s powerful data and advanced methods to provide daily security analysis of more than 5 million securities across multiple asset classes, including non-current business transactions. .
Water quality assessment: BQL includes LQA
To expand the application challenge of LQA and help clients analyze the amount of money accurately and at its level, Bloomberg has recently integrated the functionality of LQA into the Bloomberg Query Language (BQL).
BQL is the latest generation of the Bloomberg API that enables companies to easily access and analyze Bloomberg’s structured, structured, and structured data. With BQL, businesses can easily compute in the Bloomberg cloud and integrate large amounts of data while capturing real-time information through easy-to-learn expressions.
The integration of LQA in BQL increases the financial analysis of the business market so that businesses can quickly analyze the current situation, estimate the expected time to get an opportunity to do it, evaluate the price reduction from the reference price, or evaluate the market size at a given price. the environment based on the problems of the end of the money, among other cases of use.
Historically, in order to analyze the history of salary income based on the salary price in the index, it was necessary to download a large amount of data and then spend time importing it to Excel. Now, LQA through BQL supports this by extracting the requested information and all calculations are performed directly at Bloomberg Data Centers to help companies optimize the use of data.
The following sections provide examples of how LQA through BQL can be used to achieve quality assessment:
- Performing current and historical analysis on financial metrics that support BQL’s ability to integrate.
- Promote business research/analysis to assist in portfolio building, rebalancing, performance benchmarking, and value analysis.
Critical analysis: Integrated liquidity metrics
LQA through BQL enables users to analyze the amount of money (for example, the amount of money, the estimated cost of return) for the selected environment (for example, one security, watch list, profile, index) and easily combine the results based on the actual requirements to get the information that is needed from. big data environment. This reduces the amount of data that needs to be checked, which greatly reduces the time and effort required to develop the research.
For example, the chart below shows how a client can use LQA through BQL, analysis of the percentage of income by sector (according to Bloomberg FI Classification System, BCLASS, Level 2), and the long-term bucket of Bloomberg Pan-European High . Yield Index, as of May 31, 2023. With a short period of time, it is clear that there are concerns about the financial institutions of the Financial Institutions.