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On September 17, 2025, , , . The stock’s performance was influenced by sector-specific dynamics and operational updates tied to its core rail logistics business.
Recent developments highlighted include renewed focus on infrastructure modernization initiatives, which analysts suggest could enhance long-term efficiency. Additionally, regulatory updates in the freight sector were noted as potential catalysts for near-term operational flexibility, though no immediate earnings revisions were indicated in the latest filings.
Market participants observed mixed sentiment following a mid-week earnings call, where management emphasized capital allocation strategies toward network optimization. While dividend stability was reaffirmed, investors remained cautious about macroeconomic headwinds affecting commodity demand cycles.
To run this back-test properly I need to pin down a couple of practical details: 1. Universe: • Do you want all U.S.-listed common stocks (≈ 4,000 names), only constituents of a particular index (e.g., S&P 1500), or another universe? 2. Data frequency and execution price: • The usual implementation is: – At each trading-day close, rank the
by that day’s dollar trading volume (price × shares). – Buy the equal-weight basket of the top 500 names at the next day’s open (or close) and hold until the end of that session. • Is this sequencing acceptable, or would you prefer a different convention (e.g., trade at the same-day close)? 3. Transaction costs / slippage: • Should I include an estimate (e.g., 2 bps per side), or assume frictionless trading? 4. Benchmark: • Would you like the results compared with a benchmark (e.g., SPY) for context? Once I have these details I can generate the data-retrieval plan and run the back-test.
Hunt down the stocks with explosive trading volume.

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