FICO's Trading Volume Surges 53.77% to $0.46 Billion, Ranking 252nd as AI Fraud Detection Signals Potential Tech Growth

, , . The company has drawn attention for its emerging role in AI-driven fraud detection, signaling potential growth in its technological capabilities.

Recent developments highlight FICO's strategic focus on leveraging artificial intelligence to enhance fraud prevention systems. This aligns with broader industry trends where financial institutions are prioritizing to mitigate risks. Analysts suggest that FICO’s advancements in this space could position it as a key player in the evolving landscape of financial cybersecurity, though market performance remains closely tied to regulatory and competitive dynamics.

To run this test robustly I need to pin down a few practical details: 1. Universe • Which exchange(s) should be scanned each day (e.g., all U.S. common stocks, only the S&P 500 constituents, the entire NASDAQ/NYSE universe, etc.)? • Any exclusions (ETFs, preferreds, ADRs)? 2. Ranking metric • Do we rank by share volume or by dollar volume (price × volume)? (Dollar volume is more common when sizing equal-weight baskets.) 3. Trade-price convention • Typical approach is: – Day t: rank after the close; – Day t + 1: buy the selected set at the next open; – Day t + 1: sell the same set at that day’s close. Is this acceptable? 4. Weighting • Equal-weight each name vs. value-weight by volume? (Equal weight is assumed unless you say otherwise.) 5. Transaction costs / slippage • Should we include a per-trade cost assumption (e.g., 2 bps in and out) or keep it frictionless for now? Once these are clear I can generate the daily signal file and run the back-test from 2022-01-03 (first trading day of 2022) to the latest available date.