Methodology
How to Measure If a Trading Influencer Actually Beats the Market
A mobile-first framework to evaluate trading influencers with clear thresholds, benchmark matching, and risk-aware allocation rules.
Read article →InfluencerQ Research
From social noise to measurable signal.
InfluencerQ shows which creators generate repeatable alpha — and which ones just generate views.
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Influencer Studies
The Hidden Cost of Following Bad Trading Signals
Bad signals hurt more than hit rate suggests. This guide quantifies capital drag, behavior drag, and process drag with decision tables.
Market Analysis
Why Most YouTube Trading Calls Lose Money — A Data-Driven Look
A compact framework to test YouTube trading advice accuracy with bias checks, PnL leakage controls, and channel triage rules.