AInvestor Recommendation: Definition and How It Is Calculated
The AInvestor Recommendation is the actionable label (e.g., Strong Buy, Buy, Hold, Sell, Strong Sell) produced by combining model scores, fair value comparisons, and confidence/data checks to guide investors.
Definition
An AInvestor Recommendation summarizes multiple analytic signals into a single buyer/seller stance. It is intended to help prioritize further research and is not investment advice.
How the recommendation is calculated (step-by-step)
- Inputs: primary inputs are the AInvestor Score (0–100), one or more fair value estimates (weighted fair value), current market price, confidence level produced by data validation checks, and industry trend sentiment (positive, neutral, or negative).
- Compute Fair Value Ratio (FVR): FVR = weightedFairValue / currentPrice. FVR > 1 implies the stock may be undervalued, FVR < 1 implies overvaluation.
- Data quality & confidence: verify required data (price, fair value, and score) and use the confidence level. If confidence is low or key inputs missing, the recommendation is downgraded or marked as not actionable.
- Base decision rules: combine score and FVR using threshold logic. Example base logic:
- Strong Buy: Score ≥ 80 and FVR > 1.05
- Buy: Score ≥ 70 and FVR > 1.05
- Hold: Score between 50–69 or FVR between 0.65–1.05
- Sell: Score between 35–49 or FVR < 0.9
- Strong Sell: Score < 35 and FVR < 0.75
- Industry trend adjustments: apply sector sentiment to borderline cases only, to avoid over-weighting sector influence:
- Positive industry trend: upgrade borderline cases (Buy → Strong Buy if score > 75; Hold → Buy if score > 65 and FVR > 0.95; Sell → Hold if score > 65)
- Negative industry trend: downgrade borderline cases (Strong Buy → Buy; Buy → Hold)
- Neutral industry trend: no adjustment applied
- Output: the produced recommendation includes: label (Strong Buy/Buy/Hold/Sell/Strong Sell), reason string (score, FVR, and industry trend summary), the fair value ratio, confidence level, industry sentiment, and any data-quality flags for downstream UI use.
Practical notes
- Recommendations are automated and should be used as a starting point for research, not as sole decision criteria.
- Different products or viewers may vary thresholds or weighting; always check the component breakdown.
- The UI surfaces confidence and breakdowns so users can understand which inputs drove the recommendation.
See How AInvestor Recommendations Perform
Curious about how these recommendations work in practice? Explore the stocks we recommend and check their backtest performance: