Free Earnings Call Evasiveness API
When management dodges analyst questions, it's often a leading indicator of trouble. Our AI scores how directly executives answer questions on a 1-10 scale.
Endpoint
GET https://securitiesdb.com/api/v1/stocks/{ticker}/earnings-transparencyHow It Works
We analyze earnings call transcripts using large language models to score management responses on a 1-10 evasiveness scale:
1-3: Very Direct — management gives clear, quantitative answers
4-6: Somewhat Evasive — vague language, redirections
7-8: Evasive — obvious deflections, scripted non-answers
9-10: Highly Evasive — actively avoids answering
The API also returns the most evasive quote from the call, a trend over quarters, and an AI analysis of the deflection pattern.
Python Example — Flag Evasive Companies
import requests
watchlist = ["AAPL", "TSLA", "META", "AMZN", "GOOGL"]
flagged = []
for ticker in watchlist:
r = requests.get(f"https://securitiesdb.com/api/v1/stocks/{ticker}/earnings-transparency")
if r.status_code != 200:
continue
d = r.json()["data"]
score = d["summary"]["latest_score"]
trend = d["summary"]["trend"]
if score >= 7 or trend == "becoming_more_evasive":
flagged.append({
"ticker": ticker,
"score": score,
"label": d["summary"]["latest_label"],
"trend": trend,
"quote": d["latest_deflection_quote"][:80] + "..."
})
print(f"Flagged {len(flagged)} companies for evasiveness:")
for f in flagged:
print(f" {f['ticker']}: {f['score']}/10 ({f['label']}) — Trend: {f['trend']}")
print(f" Quote: \"{f['quote']}\"")Research Background
Academic research has shown that linguistic features of earnings calls predict future stock performance. Companies with more evasive management tend to:
- Miss future earnings estimates at higher rates
- Have greater negative earnings surprises
- Show declining operational performance
- Experience increased analyst downgrades
Sources: Larcker & Zakolyukina (2012), Hobson et al. (2012), Cohen et al. (2020)