Date anchor: Today's date comes from the # currentDate system context. Compute ALL relative dates from it. Example: if today = 2026-03-14, then "prior month" = 2026-02-01 to 2026-02-28, "current month" (most recent complete) = February 2026, "three months ago" = December 2025. Never use training-data dates.

Group Benchmarking — Peer Comparison of Publicly Traded Dealer Groups

User Profile (Required)

Load the marketcheck-profile.md project memory file if exists. Extract: tracked_tickers (filter to dealer group: AN, LAD, PAG, SAH, GPI, ABG, KMX, CVNA), tracked_states, benchmark_period_months, country. If missing, ask for tickers to compare. US-only. Confirm profile.

Built-in Ticker → Dealer Group Mapping

AN    → AutoNation
LAD   → Lithia Motors
PAG   → Penske Automotive Group
SAH   → Sonic Automotive
GPI   → Group 1 Automotive
ABG   → Asbury Automotive Group
KMX   → CarMax
CVNA  → Carvana

User Context

Financial analyst or portfolio manager performing relative value analysis across publicly traded dealer group stocks. Goal: identify strongest operational fundamentals for long/short decisions, overweight/underweight calls, and earnings estimates.

Workflow: Full Peer Benchmarking

Step 1 — Collect KPIs for all 8 public dealer groups

Call mcp__marketcheck__get_sold_summary with:

• ranking_dimensions: dealership_group_name
• ranking_measure: sold_count
• ranking_order: desc
• top_n: 20
• date_from / date_to: current month

Also pull:

• ranking_measure: average_days_on_market (ranked asc)
• ranking_measure: average_sale_price (ranked desc)

Repeat all calls for prior month. → Extract only: dealership_group_name, sold_count, average_sale_price, average_days_on_market per group per period. Discard full response.

From results, extract data for all 8 publicly traded groups.

Step 2 — Calculate per-group metrics

For each group:

• Volume and MoM change %
• ASP and MoM change %
• DOM and MoM change (days)
• Efficiency Score = sold_count / avg_dom (higher = better capital efficiency)
• Efficiency MoM change %

Step 3 — Active inventory health

For each group, call mcp__marketcheck__search_active_cars with:

• dealer_group: the group name
• car_type: used, then new
• stats: price,dom
• rows: 0 → Extract only: num_found, price and dom stats per car_type per group. Discard full response.

Calculate:

• Days Supply (used) = active used / monthly used sold × 30
• Days Supply (new) = active new / monthly new sold × 30

Step 4 — Rank groups on each KPI

Create rankings (1 = best):

1. Volume Rank (highest volume = rank 1)
2. Volume Growth Rank (highest MoM % = rank 1)
3. Efficiency Rank (highest efficiency score = rank 1)
4. DOM Rank (lowest DOM = rank 1)
5. Days Supply Rank (lowest days supply = rank 1)
6. ASP Trend Rank (strongest ASP growth = rank 1)
7. Composite Rank = average of all individual ranks

Step 5 — Identify investment signals

For each group:

• BULLISH: Composite rank top 3, improving on 3+ metrics, volume growth > industry average
• NEUTRAL: Mid-pack composite rank, stable metrics
• CAUTION: Composite rank bottom 3 but with some positive trends
• BEARISH: Composite rank bottom 2, deteriorating on 3+ metrics

Step 6 — Relative value insights

Identify:

• Best momentum: Which group improved most in composite rank vs prior period?
• Best value play: Strongest operational metrics but may be undervalued
• Deteriorating fundamentals: Which group is declining fastest?
• Pair trade opportunity: Groups with diverging operational trajectories

Output

Present: composite performance rankings table (all 8 groups with signals), individual KPI rankings, KPI deep dive (leader/laggard gaps), investment thesis per ticker, and relative value opportunities (best momentum, pair trade signal, earnings risk).

Signal Logic

Metric	BULLISH	NEUTRAL	CAUTION	BEARISH
Volume MoM	> +3%	-1% to +3%	-3% to -1%	< -3%
ASP MoM	> +1%	-1% to +1%	-3% to -1%	< -3%
DOM Change	< -2 days	-2 to +2	+2 to +5	> +5 days
Days Supply (used)	< 35	35-55	55-75	> 75
Days Supply (new)	< 50	50-80	80-100	> 100
Efficiency MoM	> +5%	-2% to +5%	-5% to -2%	< -5%

Important Notes

• US-only: All data from get_sold_summary requires US market.
• The dealership_group_name field in MarketCheck may not exactly match — use fuzzy matching if needed.
• This benchmarking covers ALL 8 publicly traded groups regardless of which are in the analyst's tracked_tickers. The full peer set is needed for relative value analysis.
• Composite rank weights all KPIs equally by default. If the user has a specific priority (e.g., "I care most about efficiency"), adjust the weighting.
• Volume from MarketCheck represents listings activity, not closed transactions. Use as a proxy for retail velocity.
• Efficiency Score (volume / DOM) is the single best proxy for operational health and capital efficiency — it's the metric most correlated with gross profit per unit.
• DOM is a leading indicator of margin pressure — rising DOM precedes price cuts which precede margin compression in quarterly earnings.
• Always frame findings in terms of upcoming quarterly earnings and relative stock positioning.