V-Square Quantitative Management

V-Square Quantitative Management is a systematic asset manager using quantitative models and algorithmic strategies for investment decisions.

V-Square Quantitative Management

V-SQUARE QUANTITATIVE MANAGEMENT is an SEC-registered investment adviser in Chicago, IL, registered since 2020. The firm manages approximately $1.4 billion in regulatory assets. It has 5 employees and 3 investment advisers.

General information

Firm type

Asset Manager

Year founded

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AUM

Undisclosed

Location

Region

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Country

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City

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Corporate office

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What makes V-Square Quantitative Management different from traditional asset managers?

V-Square Quantitative Management uses systematic, rule-based strategies rather than discretionary judgments. The firm relies on mathematical models, historical data, and algorithmic execution. This approach aims to eliminate emotional biases in trading. Common quantitative techniques include statistical arbitrage, trend following, and factor-based investing.

Does V-Square Quantitative Management manage assets for external clients?

The firm's client structure is not publicly disclosed. Quantitative asset managers often operate as proprietary trading firms or manage external capital through funds. Without confirmed regulatory filings or official communications, this remains speculative.

What asset classes does V-Square Quantitative Management typically trade?

Quantitative firms like V-Square commonly trade equities, futures, currencies, and fixed income. They may also engage in commodity trading. Specific instruments and market exposures are not disclosed.

Who leads V-Square Quantitative Management?

Leadership and key personnel of V-Square Quantitative Management are not publicly named. Quantitative firms often have a CIO or head of research driving strategy.

How does V-Square Quantitative Management source trading signals?

The firm uses quantitative models that analyze market data, such as price patterns, volumes, and volatility. Signals may come from statistical analysis, machine learning, or event-driven models. Without disclosed details, exact signals are unknown.

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