Congressional Stock Trading Performance by Sector
Congressional Stock Trading Performance by Sector is less about copying individual tickers and more about spotting where policy attention and capital are clustering. The catch is timing: under the STOCK Act, disclosures can land weeks after the trade, so this data works best as a trend signal, not a day-trade trigger.
Below is a sector-wise Congress stock trading analysis using recent trading activity plus a decision framework you can use to judge whether a sector tilt is investable. For a deeper dive into sector-level flows, check out our Congressional Sector Flows page, or if you prefer more granular data, explore Congressional Trades by Name or Symbol.
Tables + Analysis
Table 1: Congressional trading activity by sector (recent sample)
Source: CongressEdge sector analysis (trades, buys, sells, top tickers).
| Sector | Trades | Buys | Sells | Buy % | Share of Trades | Most Traded Names |
|---|---|---|---|---|---|---|
| Information Technology | 103 | 83 | 19 | 80.6% | 48.6% | NVDA, GOOGL, AAPL |
| Health Care | 26 | 17 | 9 | 65.4% | 12.3% | UNH, LLY, MCK |
| Financials | 24 | 15 | 9 | 62.5% | 11.3% | GS, BLK, SCHW |
| Consumer Staples | 17 | 10 | 7 | 58.8% | 8.0% | PG, WMT, PEP |
| Industrials | 12 | 7 | 5 | 58.3% | 5.7% | HON, ETN, GD |
| Consumer Discretionary | 8 | 6 | 2 | 75.0% | 3.8% | HD, MCD, YUM |
| Crypto | 6 | 5 | 1 | 83.3% | 2.8% | IBIT, COIN, MSTR |
| Energy | 6 | 4 | 1 | 66.7% | 2.8% | CVX, WMB, VLO |
| Communication Services | 4 | 2 | 2 | 50.0% | 1.9% | CMCSA, T, VZ |
| Utilities | 3 | 1 | 2 | 33.3% | 1.4% | DUK, WEC, XEL |
| Real Estate | 3 | 2 | 1 | 66.7% | 1.4% | EQIX, AMT, PLD |
What stands out: nearly half of tracked activity sits in Information Technology, and it is also the most bullish by buy volume. If you are looking for “which sectors benefit from congressional stock trading,” this kind of concentration is the first clue, but you still need to separate signal from crowded mega-cap behavior.
Table 2: Sector sentiment ranking (net buys)
Source: derived from the same buys and sells counts above.
| Rank | Sector | Net Buys (Buys − Sells) | Read |
|---|---|---|---|
| 1 | Information Technology | +64 | Strong accumulation bias |
| 2 | Health Care | +8 | Consistent net buying |
| 3 | Financials | +6 | Mild accumulation |
| 4 | Consumer Discretionary | +4 | Small but positive |
| 5 | Crypto | +4 | Small sample, high tilt |
| 6 | Consumer Staples | +3 | Defensive, mixed |
| 7 | Energy | +3 | Modest, mixed |
| 8 | Real Estate | +1 | Low activity |
| 9 | Industrials | +2 | Low to moderate |
| 10 | Communication Services | 0 | No clear bias |
| 11 | Utilities | −1 | Net selling pressure |
How to use it: treat this as a heatmap for “congressional trading impact on market sectors.” Your highest-confidence signals usually show up where you have both (1) higher volume and (2) sustained net buying, not just a single bullish week. Also remember disclosures can be delayed, so confirm the sector trend still matches current price action.
Table 3: Sector watchlist (what to monitor and how to check it)
Source: Tickers and sector leaders from CongressEdge.
| Sector | What Congress is clustering into | What to check before acting | Best “decision use” |
|---|---|---|---|
| Info Tech | NVDA, GOOGL, AAPL | Is this just mega-cap momentum? Watch valuation and the earnings calendar. | Trend confirmation — not a standalone buy signal |
| Health Care | UNH, LLY, MCK | Policy headline risk, FDA timelines, and reimbursement catalysts | Identify policy-sensitive names to track |
| Financials | GS, BLK, SCHW | Rate expectations, credit cycle inflection, regulatory headlines | Sector rotation and macro context |
| Energy | CVX, VLO, WMB | Crude trend, geopolitical risk, and inventory data | Hedge timing or rotation signal |
| Utilities (bearish) | DUK, WEC, XEL | Rate sensitivity and defensive capital flows | “Risk-off” sentiment check |
| Crypto (small sample) | IBIT, COIN, MSTR | Volatility regime shifts and regulatory news flow | Early risk appetite indicator |
Why this matters: academic work suggests aggregate congressional trading can relate to future market returns at a broad level, which supports using this data as a macro or sector signal rather than a single-name copy trade.
Table 4 (Illustrative): How a sector tilt can drive “performance”
Illustrative example only. This shows how a strategy can “look smart” just by being concentrated in the winning sector of the year.
| Sector Tilt Strategy | Weight in Tech | Tech Return | Non-Tech Return | Approx Portfolio Return |
|---|---|---|---|---|
| Even-weight sectors | 10% | +30% | +10% | +12% |
| Moderate tech tilt | 30% | +30% | +10% | +16% |
| Heavy tech tilt | 50% | +30% | +10% | +20% |
Takeaway: a lot of “congressional stock market performance” narratives can be explained by sector exposure. For example, funds that track lawmakers can diverge heavily by sector, and performance can reflect that tilt as much as “information edge.”
Conclusion / Callout
If you want a clean read on congressional stock trading performance by sector, start with two filters: concentration (where the trades are) and consistency (net buying over time). In the latest sample, Information Technology dominates both. Use the data as a sector radar, confirm with current market conditions, and do not ignore disclosure lag.