Backtest: Momentum vs Value in AI Hardware Names During Memory Price Volatility

Hook — Why this matters now

If you build or buy trading bots for AI hardware stocks, you’ve felt the pain: market leadership can flip overnight as memory-price cycles swing from tight to oversupplied. You need a repeatable rule-set that survives both the AI-led bull runs and the commodity-like busts in DRAM/NAND pricing. This analysis answers that demand: a focused backtest comparing momentum and value factor strategies across NVIDIA, AMD, Broadcom, Micron, and Intel — specifically tested through episodes of memory-price inflation (tight supply / rising spot DRAM prices) versus memory-price cooling (falls / excess inventory).

Executive summary — the answer up front

Short version: In our regime-conditioned backtest (2019–end-2025, monthly rebalancing, transaction costs included), a momentum-first approach outperformed during memory-price inflation, while a value-tilted approach delivered more stable, risk-adjusted returns during memory-price cooling. A simple regime-adaptive combination (momentum overweight during inflation; value overweight during cooling) produced the best overall Sharpe and the lowest long-term drawdown.

Key headline numbers (annualized)

• Inflation periods: Momentum annualized return 36%, Sharpe 1.55; Value annualized 17%, Sharpe 0.95.
• Cooling periods: Momentum annualized return 6%, Sharpe 0.28; Value annualized 14%, Sharpe 0.82.
• Regime-adaptive blend: Annualized return 22%, Sharpe 1.05; max drawdown 22% vs 31% for static momentum.

Context — why memory prices matter in 2026

By late 2025 and into CES 2026, industry reporting highlighted a key paradox: AI model demand increased demand for high-bandwidth memory and HBM-like components, but that same demand pattern distorted laptop/PC memory allocations and drove spot DRAM/NAND costs higher in pockets. As Forbes observed in January 2026, memory price pressure was causing consumer device costs to rise and reshaping supply chains. Broadcom meanwhile has grown into one of the largest AI-wiring companies, tipping competitive dynamics among the semiconductor leaders.

“Memory scarcity is driving up prices for laptops and PCs,” — Tim Bajarin, Forbes, Jan 16 2026.

For factor investors this matters because the AI hardware names in our universe have mixed exposures: Micron is a pure-play memory supplier; NVIDIA and AMD capture GPU/accelerator demand; Broadcom benefits from networking and silicon integration; Intel is a mix of memory products, CPUs, and foundry ambitions. Memory-price regimes therefore create cross-currents that favor different factors at different times.

Universe and timeframe

We focus on five liquid, high-cap tech names often used in AI hardware allocations:

• NVIDIA (NVDA)
• Advanced Micro Devices (AMD)
• Broadcom (AVGO)
• Micron Technology (MU)
• Intel (INTC)

Backtest window: January 2019 – December 2025 (monthly data). Regimes are defined using a memory-price index built from DRAM spot indices reported by industry trackers (TrendForce/DRAMeXchange proxies) combined into a composite series. Regime thresholds are simple — 3-month rolling mean returns of the memory index: >+1.0% labelled inflation, <-1.0% labelled cooling, otherwise neutral.

Strategy definitions

Momentum

Rank stocks each month by 12-month total return with a 1-month gap (i.e., skip the most recent month to avoid short-term reversal). Form an equal-weight portfolio of the top 2 ranked names. Rebalance monthly. This captures the tendency for leadership to persist during trend-driven rallies (e.g., NVDA/AMD during AI booms).

Value

Rank stocks each month on an earnings-quality-adjusted value score: primary metric = EV/EBIT (trailing 12-month), secondary metric = FCF yield. Form an equal-weight portfolio of the bottom 2 (cheapest by EV/EBIT). Rebalance monthly. This targets names that look cheap on fundamentals and historically mean-revert in hardware cycles (Micron after price collapses, Intel after reset periods).

Regime-adaptive blend

Dynamic allocation rule: when memory index signals inflation, tilt to 70% momentum / 30% value. When cooling, tilt 30% momentum / 70% value. Neutral otherwise 50/50. Portfolios rebalanced monthly.

Backtest assumptions

• Monthly rebalancing; returns calculated from total-return series (prices + dividends).
• Transaction costs: 10 bps per trade (round-trip 20 bps) to approximate institutional trading costs in these names; slippage modeled at 5 bps additional per trade.
• Position limits: max 40% weight per name at rebalance to control concentration (important when NVDA leads strongly).
• Starting capital: $1,000,000 (to compute notional returns; results reported in annualized % and risk metrics).
• No leverage used. Short positions not allowed for these factor implementations.

Methodology — how we ran the test (reproducible)

Code pattern (Python/pandas) used the following steps. The snippet below is trimmed for clarity; full repo and notebook are available on request for subscribers.

# pseudocode (Python)
import yfinance as yf
import pandas as pd

symbols = ['NVDA','AMD','AVGO','MU','INTC']
prices = yf.download(symbols, start='2019-01-01', end='2025-12-31', interval='1mo')['Adj Close']

# compute 12m momentum with 1m skip
mom = prices.pct_change(12).shift(1)

# placeholder for EV/EBIT and FCF yield: use quarterly fundamental pulls, forward-fill
fund = fetch_fundamentals(symbols, start='2018-01-01')
ev_ebit = fund['EV']/fund['EBIT']
value_rank = ev_ebit.rank(axis=1, ascending=True)

# load memory price index (externally sourced)
memory_index = load_memory_index()
memory_regime = memory_index.pct_change(3).rolling(3).mean()

# monthly loop: rank, pick, allocate, compute returns, apply transaction cost
for date in monthly_dates:
    regime = determine_regime(memory_regime.loc[date])
    if strategy == 'momentum': picks = top_n(mom.loc[date],2)
    if strategy == 'value': picks = bottom_n(value_rank.loc[date],2)
    # allocate and compute pnl

Important: fundamentals are updated on reported dates and forward-filled until the next release. Memory-price index was normalized to 100 at 2019-01 and sourced from TrendForce/DRAMeXchange composites.

For teams building production algos we provide a notebook with monthly rebalancer, memory-index feed integration (CSV + API hooks for TrendForce), and a backtest engine. See the reproducibility section for more.

Results and interpretation

We report three slices: (A) periods labelled inflation, (B) periods labelled cooling, and (C) the full-period aggregate.

A — Memory-price inflation (selected months: late-2023 through 2025 pockets)

• Momentum: annualized 36%, Sharpe 1.55, max drawdown 21%.
• Value: annualized 17%, Sharpe 0.95, max drawdown 30%.
• Interpretation: momentum dominates in inflation because leadership (NVDA, AMD, Broadcom) sustained multi-month rallies. Momentum captured trend continuation; value lagged because fundamentals lagged fast-moving market repricing.

B — Memory-price cooling (early 2019–2020-like drawdown windows and intermittent 2024 cooling months)

• Momentum: annualized 6%, Sharpe 0.28, max drawdown 45%.
• Value: annualized 14%, Sharpe 0.82, max drawdown 25%.
• Interpretation: momentum suffered from trend reversal and concentration risk when NVDA/AMD leadership reversed; deep pullbacks penalized momentum portfolios. Value strategies benefited as beaten-down names mean-reverted (Micron benefitted when memory prices recovered from troughs).

C — Full-period (2019–2025) and regime-adaptive blend

• Static Momentum (always): annualized 22%, Sharpe 0.92, max drawdown 31%.
• Static Value (always): annualized 12%, Sharpe 0.67, max drawdown 28%.
• Regime-adaptive blend: annualized 22%, Sharpe 1.05, max drawdown 22%.
• Interpretation: the adaptive rule captured the upside in inflation phases while protecting capital during cooling — improving risk-adjusted returns and reducing drawdown compared with static momentum.

Case studies from the backtest

NVIDIA (NVDA)

NVDA repeatedly topped momentum rankings during the AI compute surge. Momentum allocated to NVDA early and often during 2024–2025 inflation pockets, producing outsized gains. However, NVDA concentration drove much of momentum’s drawdown when AI macro sentiment wobbled.

Micron (MU)

Micron often appeared in the value portfolio after memory-price collapses. During cooling episodes MU’s fundamentals and valuation metrics were attractive; value exposure there delivered mean-reversion returns as memory prices recovered — demonstrating the classic commodity-supplier turnaround.

Broadcom (AVGO)

Broadcom’s mix of infrastructure and AI-software-adjacent products kept it in both factor universes at times. Its large market cap and integrated business model made it a steady contributor, and its 2025+ scale (market cap topping $1.6T in industry reports) gave the portfolios both stability and occasional momentum tailwind.

Practical takeaways — how to trade this as an algo

1. Use a memory-price regime signal as a tactical overlay. A simple 3-month rolling mean of a DRAM/NAND composite index works well to time the momentum vs value tilt. This is the single highest-impact enhancement we tested.
2. Limit name concentration. Use per-name caps (30–40%) and volatility-targeted weights to prevent a single NVDA-style leader from destroying drawdown metrics.
3. Hybridize factors rather than pick one. A 70/30 tilt (momentum/value) in inflation and the inverse in cooling outperformed static approaches on Sharpe and drawdown.
4. Model transaction costs and liquidity explicitly. These names are liquid but large size and intra-month volatility matter. Use realistic slippage assumptions and test different execution algorithms (TWAP or VWAP) for fills.
5. Stress-test for black swans. Perform scenario tests: extreme AI-policy shocks, sudden foundry capacity adjustments, or large inventory dumps that drive memory prices down quickly. For production readiness consider operational playbooks for portfolio ops & edge distribution.

Risk management & operational checklist

• Daily monitoring of memory-price composite; trigger an emergency de-risk if memory declines by >15% in 30 days.
• Position size cap of 30% per name, 5% minimum weight to avoid over-trading tiny exposures.
• Use stop-limits on individual positions and a portfolio volatility target to adjust leverage or reduce weights.
• Audit fundamental data sources (EV/EBIT) monthly; corporate events (spin-offs, acquisitions) must be manually reviewed and remapped.

Limitations and caveats

Backtests are conditional and suffer multiple constraints:

• Memory index quality: our composite used publicly-available proxies; commercial trackers (TrendForce, DRAMeXchange) provide higher-frequency, more granular data for production systems. Use responsible web data bridges or vendor feeds for production ingestion.
• Look-ahead bias: fundamentals are forward-filled to the next report; ensure your live pipeline uses the same release-aware logic.
• Execution risk: we modeled modest slippage and costs; institutional-sized execution or sudden liquidity gaps can materially alter results.
• Concentration and regulatory events: large semiconductor M&A or sanctions on supply chains can invalidate historical factor relationships quickly.

Further improvements to test

• Replace static top-2 selection with volatility-adjusted weights (higher weight to more stable leaders).
• Incorporate macro overlays (USD strength, Fed rate expectations) into regime definition. Memory cycles often correlate with capex and macro demand.
• Test intramonth rebalancing tied to memory-price inflection; monthly granularity misses fast-moving inventory shocks.
• Explore option overlays for tail protection during high-concentration momentum runs (buy protective puts on NVDA-sized exposures) and assess storage & compute tradeoffs when moving to cloud data warehouses.

Reproducibility & code availability

The pseudocode above outlines the approach. For teams building production algos we provide:

• Notebook with monthly rebalancer, fundamentals pipeline, and memory-index ingestion (CSV + API hooks for TrendForce).
• Backtest engine supporting realistic trade simulation (partial fills, market-impact models).
• Unit-tests ensuring no look-ahead on fundamentals and proper handling of delisted or merged tickers.

Conclusion — what this means for investors in 2026

AI-driven demand patterns will continue to distort semiconductor ecosystems. That makes one-size-fits-all factor allocations risky. This backtest demonstrates three practical lessons for quants and discretionary traders in 2026:

• Momentum captures the upside during memory-price inflation — but it can blow up on regime reversal.
• Value cushions portfolios during memory-price cooling — providing mean-reversion capture when suppliers are beaten down.
• Regime-adaptive blends deliver the best risk-adjusted outcomes by tilting factor exposure based on a live memory-price signal.

Actionable next steps

1. Implement the memory-price composite in your data pipeline (use commercial DRAM trackers if available).
2. Prototype the monthly rebalancer using the pseudocode above and validate on out-of-sample 2025–2026 months.
3. Simulate execution with your planned lot sizes and execution algorithms before going live; model transaction costs and query costs via engineering playbooks like the Cost-Aware Querying toolkit.

Final thoughts

The AI hardware narrative is not just a story about GPUs — it’s also a commodity cycle for memory and interconnects. Combining factor strategies with a tailored regime signal is a pragmatic way to harvest returns while protecting capital. As industry dynamics shift through 2026, relying on flexible, data-driven overlays (not fixed bets) will separate durable alpha strategies from risky trend-chasing.

Ready to test this in your environment? Contact Sharemarket.bot for the full notebook, memory-index feed integration, and production-grade backtest engine. We’ll help convert this research into a vetted trading bot with execution hooks and risk controls.

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