AI Stock Valuation Engine – InvestorsCraft

Why AI Valuation?

You already weigh discounted‑cash‑flow models and price charts, but both leave blind spots. DCF anchors long‑term worth yet lags real‑time news, while technical patterns often ignore economics altogether. Our AI Valuation Engine closes that gap bylearning the relationship between a company’s latest fundamentals and its next six‑to‑twelve‑month price delta. Instead of guessing the absolute “true” price, the model focuses on the change—the incremental rerating that should logically follow a shift in revenue, margins, leverage, or capital intensity. You can therefore act onwhere the stock is likely heading, not just where it “ought” to settle someday

Data Universe & Feature Engineering

Breadth That Matches Your Watch‑List

Pillar	Details
Coverage	Approximately 10 000 listed equities across North America, Europe, and Japan since 2005
Raw Inputs	Quarterly and annual filings, segment disclosures, share‑based‑comp adjustments, intraday price/volume, and macro factors such as CPI and the 10‑year yield
Engineered Signals	Growth rates, trend‑adjusted margins, rolling ROIC, leverage ratios, working‑capital turn, buy‑back intensity, proprietary “moat” quality metrics, and over 300 additional features
Label	Risk‑adjusted excess return versus the MSCI ACWI over the next 6–12 months

Lagged variables (one to eight quarters), rolling z‑scores, calendar seasonality buckets, and peer‑ranked percentiles help the network “see” both longitudinal trends and cross‑sectional context.

Model Architecture

A Stack Built for Economic Signal and Statistical Rigor

Bi‑directional LSTM Encoder — captures the long‑term interplay among growth, margin, leverage, and capital intensity—essential for understanding cause and effect, not mere correlation.
Cross‑Sectional Attention Layer — benchmarks each ticker against industry peers in the same quarter, sharpening relative‑value awareness.
LightGBM Stack — digests learned embeddings alongside sparse or one‑off events (impairments, tax reversals), providing tree‑based interpretability and higher robustness on discontinuous inputs.
Bayesian Ridge Ensemble & Isotonic Calibration — converts model outputs into probability‑weighted price deltas (%ΔP) and aligns stated confidence with realized hit rates.
SHAP Driver Tags — surfaces the top explanatory factors behind each signal, turning a potential black box into a transparent decision aid.

Performance Highlights

Regime	Excess IRR vs S&P 500	Hit Rate	Max Draw‑Down	Context
2006 – 2020 Out‑of‑Sample	robust +5.4 pp/yr	63 %	−21 %	Before transaction costs
2021 – Q1 2023 Paper Trade	solid +4.9 pp/yr	60 %	−19 %	Stress‑tested in a volatile macro window
Q2 2023 – Today Live	encouraging +3.3 pp/yr	58 %	−17 %	Includes real execution slippage

Past results do not guarantee future returns, yet the consistency underscores statistical validity. Metrics are market‑neutral.

Using the Dashboard

Key Fields

Field	Meaning	Typical Range
AI Target Δ%	Model‑implied price change over the next 6–12 months	−40 % to +80 %
AI Rating	Five‑bucket signal from Strong Sell to Strong Buy	Discrete
Confidence	Posterior probability that the direction of Δ% is correct	50 % to 85 %
Driver Tags	Top SHAP factors (e.g., “FCF ↑”, “Debt/EBITDA ↓”)	3 per stock

Practical Tips

Pair with Position Sizing Rules —larger AI Target Δ% and higher confidence warrant bigger bets, but cap exposure to avoid concentration risk.
Monitor Divergence —a widening gap between AI valuation and market price can flag entry points; shrinking gaps may hint at exit windows.
Cross‑Validate with DCF —when both the intrinsic model and AI delta align, conviction is naturally higher. Divergence invites deeper research.
Leverage Driver Tags —use the SHAP descriptors to verify the fundamentals behind the signal.

Update Workflow

Quarterly Full Retrain — after 90 %+ of constituents file, the engine re‑estimates all weights.
Weekly Drift Tune‑ups — lighter recalibrations ingest fresh macro, price, and volume data.
Hot‑Fix Triggers — material restatements, M&A, or spin‑offs prompt immediate re‑evaluation.

Limitations & Risk Factors

Risk	Implication	Mitigation
Data Quality	Restated filings or erroneous inputs can distort forecasts.	Automatic anomaly detection flags outliers for manual review.
Regime Shifts	Structural changes may weaken learned relationships.	Quarterly retrains incorporate new economic regimes quickly.
Crowding	Alpha can compress if many adopt similar signals.	Continual feature R&D and ensembling maintain edge.
Black‑Box Perception	Limited transparency can deter discretionary managers.	SHAP driver tags and factor downloads enhance clarity.
Execution Slippage	Real trades may fill away from theoretical mid‑quotes.	Live metrics already incorporate typical bid‑ask spreads.

Bottom Line

The InvestorsCraft AI Valuation Engine turns raw, time‑stamped fundamentals intostatistically significant, forward‑looking price deltas you can act on today. Use its signals to sharpen your watch‑list, time entries and exits, and cross‑check qualitative narratives—always in concert with disciplined risk management and complementary research.

InvestorsCraft