Master’s Thesis · Credibility of Earnings Headline Sources (cehs-llm)
Same earnings numbers, different source — does it change what retail investors believe and do?
Designed and ran a 2×2 between-subjects LLM pretest (GPT-5-mini, temperature = 1.0) generating 520 simulated retail-investor responses. Stimuli varied by source (Wall Street Journal vs. Reddit r/stocks) and benchmark wording (verifiable GICS industry average vs. subjective “selected peers”). Headlines were pre-screened for sentiment neutrality via FinBERT; sample size set by Monte Carlo power analysis targeting 80% power.
What I found. WSJ headlines came with a ~51-point credibility boost over Reddit — and almost all of the “who said it” effect on buying decisions flows through that trust alone. The twist: vague “peers” wording actually made investors want to buy more, even while they flagged it as more biased. They see the cherry-picking — and still bite. Altogether, these design cues explain about 42.5% of what drives buy intent. That’s a lot for one packaging tweak.
A WSJ label buys trust that no benchmark wording can replicate — but the “right” benchmark wording still nudges investment decisions. For investor relations, financial communications, and platform design: channel strategy and benchmark presentation are not neutral packaging. First LLM-supported experiment in this niche, with full reproducibility (frozen prompts, hardwired data, make-driven pipeline).
See the full GitHub project here — code, data, and thesis PDF.