Zeta Global Surges 3.03% on Speculative Tech Bets as $330M Volume Ranks 338th in Market Activity

Zeta Global Holdings (ZETA) surged 3.03% on October 9, 2025, with a trading volume of $0.33 billion, ranking 338th in market activity. The stock’s performance reflects renewed investor interest amid broader market volatility.

Analysts noted that Zeta’s gains were driven by speculative positioning in high-beta tech assets, though limited liquidity—evident from its mid-tier trading volume—suggests the move remains concentrated in momentum-driven short-term strategies. Institutional activity showed mixed signals, with no major institutional stake adjustments reported in recent filings.

Strategic back-test parameters for Zeta would require clarifying key execution frameworks: defining the equity universe (e.g., Russell 3000 vs. broader exchanges), timing conventions for entry/exit (pre-market vs. intraday), and portfolio constraints like position limits or slippage assumptions. Current tools necessitate either a simplified proxy (e.g., SPY) or a phased multi-asset test to handle a 500-ticker portfolio, with the latter requiring extended data integration.

To run this back-test rigorously I need to pin down a few practical details and make sure the required data universe is feasible with our current tool-set. Market universe: Do you want all U.S. common stocks (NYSE + NASDAQ)? Or a predefined universe such as Russell 3000, S&P 1500, etc.? Ranking & execution convention: Rank stocks on yesterday’s (t-1) dollar trading volume and buy at today’s open, then exit at today’s close? Or rank on today’s intraday volume and buy at the close, exiting next day at the close? Weighting & sizing: Equal-weight the 500 names each day? Any limit on position size / trading capacity? Transaction assumptions: Slippage or commission to include? If none, I’ll assume zero. Practical scope: Our current single-ticker back-test engine can’t directly hold 500 tickers simultaneously, so I’ll have to run a portfolio back-test through a custom workflow. If you’re OK with a representative approximation—e.g., back-testing the strategy on a broad-based ETF (SPY) as a proxy—or limiting the universe to the top 50–100 names, we can proceed immediately. Otherwise we’ll need to orchestrate an external multi-asset back-test, which will take longer and require additional data pulls. Let me know your preferences on the above points so I can configure the test correctly (and as efficiently as possible).