Semiconductor trend signals for equity research

Sell-side models update quarterly. Search interest on "semiconductor" and company names like TSMC can shift weekly. The gap between attention data and reported revenue is where trend signals earn a place in equity research workflows, not as a replacement for channel checks, but as an early warning layer.

What the data showed in mid-2026

A Trends MCP snapshot on June 17, 2026 illustrates the pattern.

Sector keyword "semiconductor" (Google Search):
- 12-month growth: +130.77% (normalized value rose from 13 to 30; estimated volume from 36,674 to 84,633 weekly searches)
- 3-month growth: -48.28% (attention cooled after a Q1 spike)
- YTD: flat (0% change from January baseline)

TSMC (Google Search):
- 12-month growth: +105.56% (volume from 156,512 to 321,718)
- YTD: -26.0% (attention declined from January peak despite strong fundamentals narrative)

The divergence matters. Sector-level interest can rise on macro AI capex themes while individual names cool as investors rotate within the group. A single ticker chart misses that rotation.

Which signals to track

Google Search interest

Best for: retail attention, product launch curiosity, export policy reactions.

Query example:

{
  "source": "google search",
  "keyword": "semiconductor",
  "percent_growth": ["12M", "3M", "YTD"]
}

Endpoint: POST https://api.trendsmcp.ai/api

Use company names for issuer-specific monitoring and thematic keywords (HBM, EUV lithography, chiplet) for sub-sector bets. Absolute volume estimates help distinguish a 2-point normalized move on a high-base keyword from a genuine attention spike.

News volume

Best for: media cycle intensity before earnings, policy headlines, supply chain disruptions.

{
  "source": "news volume",
  "keyword": "semiconductor",
  "percent_growth": ["30D", "3M"]
}

News volume often leads Reddit and Wikipedia by days during policy events (export controls, CHIPS Act milestones). It lags Google Search during consumer product launches where search spikes before journalists file stories.

Reddit subreddit growth

Best for: retail sentiment and niche technical discussion.

{
  "source": "reddit",
  "keyword": "semiconductors",
  "percent_growth": ["6M", "1Y"]
}

Keyword must be the subreddit name without the r/ prefix. Useful subreddits: semiconductors, stocks, wallstreetbets (for ticker-specific retail flows). Subscriber growth is a slow-moving signal compared to post velocity, but it captures sustained community interest.

Wikipedia page views

Best for: retail education spikes when a complex technology enters mainstream discourse.

{
  "source": "wikipedia",
  "keyword": "Semiconductor",
  "percent_growth": ["30D", "12M"]
}

Wikipedia spikes often coincide with viral news cycles rather than institutional research publication.

A practical monitoring workflow

Step 1: Define a watchlist. Map 5-15 keywords: 3-5 company names, 2-3 product themes, 1-2 macro cycle terms.

Step 2: Pull growth across periods. Run get_growth with ["7D", "30D", "3M", "12M"] for each keyword. Compare direction consistency: a name rising on 7D and 30D but falling on 12M suggests a recent reversal, not a sustained trend.

Step 3: Cross-source triangulation. For any keyword showing >20% growth on Google Search in 30D, pull the same keyword on news volume and Reddit. Agreement across two or more sources raises confidence that attention is broad, not a single-platform artifact.

Step 4: Anchor to events. Use custom date ranges to measure pre/post impact:

{
  "source": "google search",
  "keyword": "TSMC",
  "percent_growth": [
    {
      "name": "post-earnings",
      "recent": "2026-04-20",
      "baseline": "2026-04-10"
    }
  ]
}

Step 5: Log and compare to filings. Store weekly snapshots. When search interest diverges from management guidance language on the next call, that divergence becomes a research question, not a trading signal on its own.

Python example: sector dashboard pull

import requests

API_KEY = "YOUR_API_KEY"
WATCHLIST = ["semiconductor", "TSMC", "ASML", "HBM", "AI chip"]

results = []
for keyword in WATCHLIST:
    res = requests.post(
        "https://api.trendsmcp.ai/api",
        headers={"Authorization": f"Bearer {API_KEY}"},
        json={
            "source": "google search",
            "keyword": keyword,
            "percent_growth": ["30D", "3M", "12M"]
        }
    )
    results.append({"keyword": keyword, "growth": res.json()})

for row in results:
    print(row["keyword"], row["growth"]["results"])

For AI-native workflows, the same queries run through the MCP server at https://api.trendsmcp.ai/mcp so Claude, Cursor, or ChatGPT can pull fresh data during research sessions.

Limits of trend data in semis

Trend signals capture attention, not unit shipments. A flat Google Search line during a supply-constrained upcycle is common: buyers already know what they want and stop searching. Conversely, a spike in "semiconductor shortage" search does not confirm shortages persist; it may reflect media recycling old narratives.

Export policy keywords (chip ban, lithography restriction) produce sharp, short-lived spikes that mean little for 12-month revenue models. Weight 7D and 30D windows higher for policy events; weight 12M for structural theme monitoring.

Data gaps occur. High-volume keywords occasionally return data_unavailable during upstream pipeline maintenance. Retry after a few hours or switch to a related keyword (company legal name vs ticker abbreviation).

How this fits existing research stacks

Bloomberg and FactSet remain the source of record for financials. Trends MCP sits upstream of the model: a triage layer that flags which names deserve deeper channel work this week. The hedge fund alternative data and equity research alternative data pages cover broader integration patterns.

Sector rotation signals applies the same cross-platform growth framework across industries beyond semiconductors.