1. Purpose & Scope

The Global Clear, Transparent Stock-selection Engine (GCTSE) provides a fully documented, rules-based framework to identify high-conviction equity positions. Integrating seamlessly with the Global Algorithmic Trading Software (GATS), GCTSE combines multi-horizon momentum metrics, rigorous normalization, qualitative overlays, and GATS-aligned risk controls.

2. Objectives

1. Transparency & Reproducibility: A clear, auditable process for stock ranking and selection.
2. Momentum Capture: Leverage durable (12‑month) and recent (6‑month) price trends.
3. Relative Outperformance: Benchmark against the S&P 500 via a relative-strength component.
4. Qualitative Filtering: Apply catalyst-based overlays to mitigate idiosyncratic risks.
5. Risk Integration: Embed volatility-scaled stops and GATS position-sizing rules.

3. Core Components

3.1 Data Ingestion & Preprocessing

• Sources: End-of-day adjusted close prices via Bloomberg or Refinitiv APIs.
• Adjustments: Account for splits, dividends, spin-offs.
• Quality Checks: Remove data errors, fill sparse gaps, and flag extreme outliers (>±15% daily moves).

3.2 Momentum Calculations

3.3 Normalization & Z-Scoring

To compare metrics on a common scale, transform each series into a z-score over a rolling 252‑day window:

3.5 Qualitative Overlay & Catalyst Filters

From the top 10% by Score, apply:

1. Earnings Revision: ≥1 upward analyst revision in last quarter.
2. Structural Themes: Alignment with AI, cloud, reopening, etc.
3. Risk Exclusions: Exclude pending litigation, regulatory uncertainty, or major corporate actions.

3.6 Backtesting & Robustness Checks

• Walk-Forward Analysis: Rolling 3-year in-sample, 1-year out-of-sample tests.
• Transaction Costs: Model 0.10% fees + 0.05% slippage per trade.
• Metrics: Annualized returns, volatility, max drawdown, Sharpe ratio against benchmarks.

3.7 GATS Risk Management Integration

1. Position Sizing (1% Unit-of-Risk):

4. Workflow Diagram

(See flowchart illustrating steps from data ingestion through final selection.)

5. Implementation Roadmap

1. Phase 1 (Weeks 1–4): Develop data pipeline and scoring module; internal testing.
2. Phase 2 (Weeks 5–12): Live paper-trading pilot; gather performance metrics.
3. Phase 3 (Weeks 13–16): Production deployment; integrate into GATS UI; user training.
4. Phase 4 (Ongoing): Quarterly reviews, parameter updates, and enhancements.

6. Governance & Maintenance

• Documentation: Version-controlled rulebook in GAI knowledge base.
• Change Management: Quarterly review board with research, compliance, operations.
• Audit Trail: Automated logs of data snapshots, scores, and selections.

7. Next Steps

• Allocate IT and research resources for implementation.
• Conduct cross-functional workshops for validation.
• Define success metrics and reporting cadence.

About the Author
Dr. Glen Brown, President & CEO of Global Accountancy Institute, Inc. and Global Financial Engineering, Inc., holds a Ph.D. in Investments and Finance and over 25 years of expertise in quantitative trading systems. He leads development of GATS and GCTSE, blending rigorous analytics with systematic risk controls.

Risk Disclaimer
This document is for informational purposes only and does not constitute investment, legal, or tax advice. Past performance does not guarantee future results. Implementers should perform independent due diligence and consult qualified advisors.

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