Burlington’s Volume Plunge Drags It to 440th in Trading Activity

Generated by AI AgentAinvest Volume Radar
Thursday, Sep 25, 2025 6:19 pm ET1min read
BURL--
Aime RobotAime Summary

- Burlington (BURL) saw 37.46% lower trading volume ($0.26B) on 9/25/2025, ranking 440th with 1.73% share price decline.

- Subdued retail sector activity and shifting market sentiment drove reduced speculative interest in the stock.

- Volume-based trading strategy back-testing requires precise parameters for universe selection, timing, and weighting schemes.

- Strategy validation will determine performance metrics accuracy, including annualized returns and Sharpe ratios.

On September 25, 2025, BurlingtonBURL-- (BURL) traded with a volume of $0.26 billion, a 37.46% decline from the previous day’s volume, ranking 440th among stocks by trading activity. The shares closed 1.73% lower, reflecting subdued investor interest amid broader market dynamics.

The company’s performance appears influenced by sector-specific pressures and market sentiment shifts. While no direct earnings or operational updates were highlighted in the provided materials, the significant drop in trading volume suggests reduced short-term speculative activity. Investors may be reassessing exposure to retail sector equities following macroeconomic signals or sector rotation trends.

Back-testing for a volume-based trading strategy involving the top 500 U.S. stocks by trading activity requires precise parameter definitions. Key considerations include the selection of the trading universe, timing of volume measurements, execution assumptions, and weighting schemes. For instance, using prior-day volume data to rank stocks at market close and entering positions at the next day’s open aligns with a one-day holding period. Transaction costs and re-balancing frequencies further shape the strategy’s risk-return profile. Implementation of these parameters will determine the accuracy of performance metrics such as annualized returns, volatility, and Sharpe ratios.

Final execution of the back-test depends on confirmation of the outlined framework, including the choice of a primary-listed universe, execution timing, and weighting methodology. Once validated, the process will generate a comprehensive performance analysis alongside visual equity curve representations.

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