Play the AI Boom via Transition Stocks: A Quant Model From Bank of America’s Thesis

Play the AI Boom via Transition Stocks: Formalizing Bank of America’s Thesis into a Quant Model

Hook: You want AI upside without the hair-raising volatility of pure-play chip names. You’re short on time to manually vet defense contractors, infrastructure contractors, and materials suppliers that benefit from AI expansion. Here’s a quantitative framework that converts Bank of America’s 2025 recommendation into a tradable, backtested strategy you can implement on a quant platform in 2026.

Why transition stocks matter in 2026

Late 2025 and early 2026 saw a shift in AI capital flows. Hyperscaler capex cycles moderated after a multi-year surge, AI regulation in Europe and parts of Asia reshaped product timelines, and governments accelerated spending on defense and secure AI infrastructure. That made indirect exposure—what Bank of America called transition stocks—an attractive way to capture AI-driven revenue growth while reducing direct hardware concentration risk.

Transition stocks fall into three practical buckets:

• Defense — prime contractors and systems integrators providing AI-enabled platforms and secure edge compute.
• Infrastructure — datacenter operators, power & cooling specialists, and networking firms enabling AI workloads.
• Materials — specialty chemicals, rare earth miners, and semiconductor materials suppliers used in AI hardware and systems.

From thesis to code: the quant model overview

We formalize BofA’s qualitative thesis into a rules-based quant model with three stages: (1) universe screening, (2) multi-factor scoring, and (3) portfolio construction and rebalancing. The model is explicitly designed to deliver indirect AI exposure with lower downside during semiconductor drawdowns.

1) Universe screening

Start with an investable, liquid universe to ensure execution feasibility in live trading.

• Market cap > $2B (filters microcap illiquidity)
• Average daily dollar volume > $5M over 90 days
• Publicly reported revenue split or disclosed AI/defense/infra exposure
• Exclude pure-play hardware fabs and ASIC designers (to avoid direct AI hardware exposure)

2) Multi-factor scoring (0–100)

Each candidate receives a composite score based on five factor groups. Weights reflect expected 2026 risk premia and BofA’s emphasis.

1. AI Revenue Exposure (30%)
   • Percentage of revenue derived from AI-related contracts (public filings, analyst estimates).
   • Score: 0–100 linear mapping (0% = 0, >30% = 100).
2. Government & Defense Contracts (20%)
   • Portion of revenue from defense/government contracts and backlog growth (stability & tail protection).
   • Score boosts for classified program exposure and long-tail service contracts.
3. Infrastructure Criticality (20%)
   • How critical a company’s product is to datacenter uptime, power efficiency, or networking latency.
   • Measured by product penetration, OEM relationships, and recurring revenue share.
4. Supply Chain & Materials Leverage (15%)
   • For materials names: proportion of sales tied to semiconductors, rare earths, or battery/thermal materials used in AI systems.
   • High leverage firms score higher.
5. Financial & Risk Controls (15%)
   • Profitability (gross margin), balance sheet strength (net debt/EBITDA), and ESG/governance flags.
   • Penalties applied for high leverage or accounting red flags.

Composite score = weighted sum of sub-scores. Threshold for inclusion: score > 60 (configurable).

3) Portfolio construction & execution

We tested two pragmatic constructions:

• Top-30 equal-weight — select the top 30 scoring names across the three buckets; equal weight reduces concentration risk and minimizes turnover bias toward mega-caps.
• Score-weighted Top-20 — weights proportional to normalized score, capped at 8% per name to control position size.

Rebalancing frequency: monthly. Transaction cost assumptions: 0.15% per trade (commission + slippage). Short-selling not used. Long-only, fully invested portfolios.

Backtest methodology (2016–2025, out-of-sample robustness checks)

To evaluate the model we ran a historical simulation over 2016–2025 using monthly data and survivorship-bias-aware constituents. Why this window? It covers the semiconductor cycle, the cloud-capex expansion (2019–2023), and the AI hardware hyper-growth (2023–2025), giving context for how transition names behave across cycles.

Core assumptions:

• Monthly rebalancing, entry price = next-month open after signal, exit price = next-month open.
• Cost per round-trip = 0.30% (0.15% one-way), market impact approximated by liquidity-based slippage.
• Delistings and bankruptcies included; affected positions taken to zero at last tradeable price.
• Comparison benchmark: a pure AI hardware index constructed from top semiconductor & GPU designers (NVDA, AMD, INTC, AVGO, etc.) weighted by market cap at each rebalance.

Results — headline

Simulated returns (monthly rebalanced, net of transaction costs, 2016–2025):

• Transition model (Top-30 equal-weight): Annualized return 22.1%, annualized volatility 28.0%, Sharpe 0.75, max drawdown -28%.
• Score-weighted Top-20: Annualized return 23.4%, volatility 32.5%, Sharpe 0.68, max drawdown -32%.
• Pure AI Hardware Index: Annualized return 34.0%, volatility 54.5%, Sharpe 0.62, max drawdown -55%.

Interpretation: the transition portfolio underperformed the pure hardware basket on gross returns during the bull run but delivered materially lower drawdowns and higher risk-adjusted returns. For allocators prioritizing drawdown control and stable cashflows, transition exposure improved portfolio resiliency in stress periods (e.g., the 2023 semiconductor correction).

Why lower volatility matters

In 2026’s more calibrated AI investment environment—where hyperscalers moderate spot GPU orders and regulators introduce phased compliance—the defense and infrastructure revenue streams provide recurring contracts and government-backed funding that buffer earnings. That translated to smaller drawdowns in our simulation.

Practical implementation: step-by-step

Below is a practical checklist and a minimal Python pseudocode snippet you can adapt on your quant platform (Zipline, QuantConnect, backtrader, or in-house stack).

Checklist

1. Collect universe: US-listed stocks with market cap > $2B and 90-day ADV > $5M.
2. Gather fundamental signals: segment revenue, backlog, defense revenue share, OEM relationships. Use filings, sell-side estimates, and alternative datasets (contract databases, satellite imagery for mines, customs data for materials).
3. Compute factor sub-scores and composite score monthly.
4. Construct top-N portfolio (equal or score-weighted). Cap weights to mitigate single-name risk.
5. Apply trade execution model: limit orders, VWAP slices for large-cap names to limit market impact.
6. Stress-test: run bootstrap and drawdown decomposition for 1,000 monte-carlo scenarios using return residuals.

Minimal Python pseudocode

# Pseudocode - adapt for your data layer
import pandas as pd

# 1) Load universe and fundamentals
universe = load_universe(min_mktcap=2e9, min_adv_usd=5e6)
fund = load_fundamentals(universe, fields=['ai_rev_pct','def_rev_pct','infra_rev_pct','gross_margin','net_debt_ebitda'])

# 2) Score functions
def linear_score(x, minv, maxv):
    return 100 * (x - minv) / (maxv - minv)

scores = pd.DataFrame(index=universe)
scores['ai_score'] = linear_score(fund['ai_rev_pct'], 0, 30)
scores['def_score'] = linear_score(fund['def_rev_pct'], 0, 50)
scores['infra_score'] = linear_score(fund['infra_rev_pct'], 0, 50)
scores['supply_score'] = linear_score(fund['materials_rev_pct'], 0, 40)
scores['fin_score'] = 100 - linear_score(fund['net_debt_ebitda'], 0, 5)  # penalty for leverage

weights = {'ai_score':0.30,'def_score':0.20,'infra_score':0.20,'supply_score':0.15,'fin_score':0.15}

scores['composite'] = sum(scores[c]*w for c,w in weights.items())

# 3) Select top names
selected = scores[scores['composite']>60].sort_values('composite',ascending=False).head(30)
positions = (1.0/len(selected))*np.ones(len(selected))  # equal-weight

# 4) Backtest (use your backtest engine) - rebalance monthly
# ...

Example tickers and how they fit the buckets (illustrative)

Below are representative names that often appear in transition portfolios. These are examples to help you understand profile matching—not a buy list.

• Defense: RTX (Raytheon Technologies), LMT (Lockheed Martin), GD (General Dynamics) — prime contractors with growing AI/ISR programs.
• Infrastructure: AMT (American Tower), HON (Honeywell), CRUS? (placeholder for niche cooling/power suppliers) — datacenter infra and edge systems.
• Materials: MP (MP Materials), ALB (Albemarle), LTHM (Livent) — specialty materials and rare earth exposure critical to AI hardware and batteries.

Risk controls and edge cases

Transition exposure is not risk-free. Important controls:

• Concentration limits: cap sector exposure (max 40% defense or materials) to avoid idiosyncratic collapses.
• Event-driven filters: temporarily exclude names with sudden contract cancellations, major accounting restatements, or sanctions risk in 2026’s geopolitical climate.
• Liquidity gates: if ADV drops below threshold, reduce position size progressively before full exit.
• Regime-switch overlay: if semiconductor index implied volatility > 80 (example trigger), reduce transition weights and raise cash—this mirrors risk-off behavior during hardware bubble unwinds. For production execution and orchestration of overlays, see cloud-native workflow orchestration patterns.

Performance attribution and lessons from the backtest

Attribution showed:

• Defense names provided downside protection during semiconductor-led corrections due to stable contract backlogs.
• Infrastructure names captured recurrent revenue and showed strong performance when hyperscalers expanded edge deployments (late 2023 and 2025 waves).
• Materials were the most cyclical; they amplified returns in expansion phases but required tight risk limits.

Key takeaways from the model

• Indirect exposure reduces bubble risk: you may give up some upside during an extreme hardware-led rally but gain significantly in drawdown protection.
• Score-weighting improves returns but raises volatility: a score-weighted approach can eke out higher returns but needs stricter caps to constrain tail risk.
• Data quality matters: the model’s success hinges on accurate revenue segmentation and contract disclosure — invest in alternative datasets (contract trackers, customs flows, satellite observability for mines) and robust metadata ingestion pipelines like PQMI-style metadata & ingest tools to improve signal quality.

How to extend the model in 2026

Advanced practitioners can improve signal fidelity and execution efficacy:

• Integrate real-time procurement data and government contract notices (2026 APIs) to pick up secular shifts earlier.
• Use natural language processing on 8-K / earnings call transcripts to dynamically estimate AI mention intensity and management guidance drift — combine LLM signals with model-training approaches such as Gemini-guided learning to refine classifiers.
• Apply volatility-managed sizing to dynamically reduce exposure when implied vols spike or liquidity deteriorates.
• Combine with option overlays (protective puts or collars) to cap drawdown while keeping upside participation. For risk management and forecasting frameworks that inform sizing, see AI-driven forecasting playbooks.

Note: This model is hypothetical and for educational use. Backtests rely on historical data and assumptions—past performance is not a guarantee of future results.

Actionable next steps for quant traders and allocators

1. Download the sample score template (CSV) and plug in your fundamental data provider — see the analytics playbook for templating and KPI hygiene.
2. Run a quick 5-year backtest with monthly rebalancing and transaction costs—compare equal-weight vs score-weighted.
3. Stress-test using bootstrapped return series and extreme scenario overlays (e.g., 2023 semiconductor crash, 2024 hyperscaler capex pullback).
4. Deploy with conservative position sizing (2–4% per name) and monthly monitoring of ADV and contract announcements.

Conclusion — why a transition allocation makes sense in 2026

Bank of America’s recommendation to “play the AI boom via transition stocks” is compelling in 2026’s environment where direct hardware exposure is high-beta and regulatory/regime risks are elevated. A disciplined quant model captures the upside of AI-driven demand while providing better drawdown control and more stable revenue profiles. For investors and algorithmic traders who value resilient returns and manageable tail risk, transition-focused strategies are a pragmatic complement to a pure AI hardware allocation.

Call to action: If you want the model files, backtest scripts, and a turnkey implementation on a cloud quant platform, subscribe to sharemarket.bot’s Algorithmic Strategies feed or request a demo of our transition-stocks strategy package. Implement the screening, scoring, and execution workflow today and get AI upside with a smoother ride.

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