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On October 7, 2025,
(APP) surged 7.64% with a trading volume of $7.35 billion, ranking 10th in daily equity turnover. The stock's performance was driven by strategic developments in its mobile advertising platform and partnerships with emerging tech firms, which analysts noted could enhance long-term revenue visibility despite macroeconomic uncertainties.Recent disclosures highlighted AppLovin's expansion into AI-driven ad optimization tools, positioning it to capture a larger share of the fragmented digital advertising market. Executives emphasized progress in reducing user acquisition costs through machine learning algorithms, a factor that may stabilize margins amid rising competition in the app monetization sector.
Market participants observed that the stock's volatility correlated with broader trends in consumer discretionary spending, though AppLovin's focus on in-app purchases showed resilience compared to traditional advertising models. Regulatory developments in data privacy frameworks were also cited as potential catalysts for near-term strategic adjustments.
To run this back-test accurately, I need a bit more detail on the intended portfolio construction: 1. Universe: • Do we draw the “top-500 by daily dollar volume” from the entire U.S. equity universe each day, or from a narrower list (e.g., S&P 1500 constituents)? 2. Portfolio formation: • Should the 500 names be held in an equal-weighted basket each day (i.e., 0.2 % per name), or is another weighting scheme preferred? 3. Practical simplification: • Our current back-test engine works on one ticker at a time. To evaluate a 500-stock basket we can: a) Approximate the strategy with a representative ETF (e.g., SPY or VTI) if that meets your goal, or b) Build a synthetic “index” of the 500 stocks and back-test that index. This requires generating and storing daily prices for all 500 constituents. Please let me know which approach you’d like to take (and any other preferences), and I’ll set up the back-test accordingly.

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