DAXplus Minimum Variance
E559787
DAXplus Minimum Variance is a German stock market index designed to track a low-volatility subset of DAX-listed companies using minimum-variance optimization.
All labels observed (1)
| Label | Occurrences |
|---|---|
| DAXplus Minimum Variance canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T5973354 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
Target entity: DAXplus Minimum Variance Context triple: [Deutscher Aktienindex, hasVariant, DAXplus Minimum Variance]
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A.
DAX
DAX (DynamoDB Accelerator) is a fully managed, in-memory caching service designed to significantly speed up read performance for Amazon DynamoDB applications.
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B.
DAX
DAX is Germany’s leading blue-chip stock market index, tracking the performance of major companies listed on the Frankfurt Stock Exchange.
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C.
DAX
DAX (Data Analysis Expressions) is a formula and query language used in Microsoft Power BI, Excel Power Pivot, and Analysis Services for creating custom calculations and data models.
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D.
Markowitz
Markowitz is a locality in what is now Poland that is historically notable as the birthplace of the classical philologist Ulrich von Wilamowitz-Moellendorff.
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E.
MDAX
MDAX is a German stock market index that tracks the performance of 50 mid-cap companies listed on the Frankfurt Stock Exchange.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Target entity: DAXplus Minimum Variance Target entity description: DAXplus Minimum Variance is a German stock market index designed to track a low-volatility subset of DAX-listed companies using minimum-variance optimization.
-
A.
DAX
DAX is Germany’s leading blue-chip stock market index, tracking the performance of major companies listed on the Frankfurt Stock Exchange.
-
B.
DAX
DAX (DynamoDB Accelerator) is a fully managed, in-memory caching service designed to significantly speed up read performance for Amazon DynamoDB applications.
-
C.
DAX
DAX (Data Analysis Expressions) is a formula and query language used in Microsoft Power BI, Excel Power Pivot, and Analysis Services for creating custom calculations and data models.
-
D.
Markowitz
Markowitz is a locality in what is now Poland that is historically notable as the birthplace of the classical philologist Ulrich von Wilamowitz-Moellendorff.
-
E.
MDAX
MDAX is a German stock market index that tracks the performance of 50 mid-cap companies listed on the Frankfurt Stock Exchange.
- F. None of above. chosen
Statements (31)
| Predicate | Object |
|---|---|
| instanceOf |
factor index
ⓘ
low-volatility index ⓘ stock market index ⓘ |
| assetClass | equities ⓘ |
| componentExchange | Frankfurt Stock Exchange NERFINISHED ⓘ |
| componentType | large-cap stocks ⓘ |
| constraintType | constituent and sector limits ⓘ |
| country | Germany NERFINISHED ⓘ |
| currency | euro ⓘ |
| dataInput |
historical return covariance matrix
ⓘ
stock volatilities ⓘ |
| focus | low volatility stocks ⓘ |
| indexFamily | DAXplus NERFINISHED ⓘ |
| market | German stock market ⓘ |
| objective |
minimize portfolio variance subject to constraints
ⓘ
track a low-volatility subset of DAX-listed companies ⓘ |
| optimizationCriterion | minimum portfolio variance ⓘ |
| parentIndex | DAX NERFINISHED ⓘ |
| providerCountry | Germany NERFINISHED ⓘ |
| rebalancing | periodic rebalancing based on updated risk measures ⓘ |
| region | Europe ⓘ |
| riskCharacteristic | lower volatility than parent index ⓘ |
| selectionMethod |
minimum variance optimization
ⓘ
risk-based weighting ⓘ |
| targetInvestor |
low-volatility strategy users
ⓘ
risk-averse equity investors ⓘ |
| underlyingUniverse | DAX NERFINISHED ⓘ |
| useCase |
benchmark for low-volatility German equities
ⓘ
underlying for index-linked investment products ⓘ |
| weightingScheme |
optimized weights
ⓘ
volatility-based weighting ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Subject: DAXplus Minimum Variance Description of subject: DAXplus Minimum Variance is a German stock market index designed to track a low-volatility subset of DAX-listed companies using minimum-variance optimization.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.