Analyze US public company filings to summarize financial health, management commentary, changing risks, insider trades, and institutional holdings. Combine these findings into a structured research report with scores and investment implications.
SEC EDGAR filing analysis — 10-K, 10-Q, 8-K, proxy statements, insider Form 4. Extract key financials, risk factors, management discussion, and generate investment signals from US public company filings.
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Analyze US public company filings from SEC EDGAR to extract fundamental insights, risk signals, and investment-relevant information. Covers annual reports (10-K), quarterly reports (10-Q), current events (8-K), proxy statements (DEF 14A), and insider transactions (Form 4).
This skill provides the analytical framework for interpreting SEC filings. Data retrieval uses read_url tool with EDGAR URLs or yfinance Ticker objects for structured financial data.
| Filing | Frequency | Key Content | Signal Value |
|---|---|---|---|
| 10-K | Annual | Full-year financials, risk factors, MD&A, segment data | Comprehensive fundamental view |
| 10-Q | Quarterly | Quarterly financials, interim MD&A, legal updates | Trend confirmation / inflection detection |
| 8-K | Event-driven | Material events: M&A, CEO change, restatement, guidance | Catalyst / risk trigger |
| DEF 14A | Annual (proxy) | Executive comp, board composition, shareholder proposals | Governance quality signal |
| Form 4 | Within 2 days | Insider buys / sells | Insider conviction signal |
| 13F | Quarterly | Institutional holdings >$100M AUM | Smart money positioning |
| SC 13D/G | Event-driven | >5% ownership stake disclosure | Activist / strategic investor signal |
# Company filings search
# https://www.sec.gov/cgi-bin/browse-edgar?action=getcompany&CIK={ticker}&type={filing_type}
# Example: Apple 10-K filings
url = "https://www.sec.gov/cgi-bin/browse-edgar?action=getcompany&CIK=AAPL&type=10-K&dateb=&owner=include&count=10"
# EDGAR full-text search (EFTS)
# https://efts.sec.gov/LATEST/search-index?q={query}&dateRange=custom&startdt={start}&enddt={end}
import yfinance as yf
ticker = yf.Ticker("AAPL")
# Financial statements (derived from 10-K/10-Q)
income = ticker.financials # Annual income statement
income_q = ticker.quarterly_financials # Quarterly
balance = ticker.balance_sheet # Balance sheet
cashflow = ticker.cashflow # Cash flow statement
# Insider transactions (derived from Form 4)
insider = ticker.insider_transactions
# Institutional holders (derived from 13F)
institutions = ticker.institutional_holders
major = ticker.major_holders
Income Statement Focus:
Balance Sheet Focus:
Cash Flow Focus:
The MD&A section is the most qualitative and forward-looking part of the filing.
Key extraction targets:
Tone analysis signals:
# Simplified tone scoring
positive_words = ["growth", "improvement", "strong", "exceeded", "momentum", "opportunity"]
negative_words = ["challenging", "decline", "uncertainty", "headwind", "pressure", "risk"]
cautious_words = ["moderate", "cautious", "prudent", "measured", "selective"]
# Count frequency change vs prior filing
# Rising negative word count = deteriorating outlook
# Rising cautious words = management hedging
Risk factor change detection (10-K vs prior 10-K):
| Change Type | Signal | Action |
|---|---|---|
| New risk factor added | Material new risk identified | Deep dive on the specific risk |
| Risk factor removed | Risk resolved or deemed immaterial | Positive signal if genuine resolution |
| Language intensified | Risk escalating | Review exposure and hedging |
| Order changed (moved higher) | Risk priority elevated | Assess potential impact magnitude |
Common risk categories for US equities:
| Event Type | 8-K Item | Typical Price Impact | Time Sensitivity |
|---|---|---|---|
| Earnings pre-release | 2.02 | High | Immediate |
| M&A announcement | 1.01 | Very high | Immediate |
| CEO / CFO departure | 5.02 | Medium-high | Same day |
| Restatement | 4.02 | Very high (negative) | Immediate |
| Guidance revision | 7.01/8.01 | High | Same day |
| Credit agreement change | 1.01 | Low-medium | Monitor |
| Share repurchase program | 8.01 | Low positive | Background signal |
| Dividend change | 8.01 | Medium | Same day |
# High-priority 8-K events
if item == "4.02": # Restatement
signal = "strong_negative" # Restatements destroy trust
action = "review_all_prior_financials"
elif item == "2.02" and surprise_direction == "negative":
signal = "negative" # Earnings pre-announcement miss
elif item == "5.02" and role in ["CEO", "CFO"]:
signal = "uncertainty" # C-suite departure = governance risk
elif item == "1.01" and event_type == "acquisition":
signal = "evaluate" # M&A: acquirer usually -2 to -5%, target +20-40%
| Pattern | Signal | Confidence |
|---|---|---|
| Cluster buying: 3+ insiders buying within 30 days | Strong bullish | High |
| CEO/CFO large open-market purchase (>$500K) | Bullish | High |
| Insider buying after price decline >20% | Contrarian bullish | Medium-high |
| Cluster selling at all-time highs | Neutral to mildly bearish | Low (may be pre-planned) |
| CFO selling >50% of holdings | Bearish | Medium |
| 10b5-1 plan sales | Neutral | Low (pre-programmed) |
Key distinctions:
# Insider signal scoring
def score_insider_activity(transactions, lookback_days=90):
buys = [t for t in transactions if t.type == "Purchase" and t.days_ago <= lookback_days]
sells = [t for t in transactions if t.type == "Sale" and t.days_ago <= lookback_days]
buy_value = sum(t.value for t in buys)
sell_value = sum(t.value for t in sells)
# Filter out 10b5-1 plan sales
organic_sells = [s for s in sells if not s.is_10b5_1]
if len(buys) >= 3 and buy_value > 1_000_000:
return "strong_bullish"
elif buy_value > sell_value * 2:
return "bullish"
elif len(organic_sells) >= 3 and sell_value > 5_000_000:
return "bearish_watch"
else:
return "neutral"
Key metrics:
Institutional quality tiers:
# 13F change detection
def analyze_13f_changes(current_holders, prior_holders):
new_positions = current_holders - prior_holders # New entries
closed_positions = prior_holders - current_holders # Exits
# Flag: multiple Tier 1 funds initiating
tier1_new = [h for h in new_positions if h.tier == 1]
if len(tier1_new) >= 2:
signal = "strong_smart_money_accumulation"
return signal
filing_score = {
"financial_health": 0, # -2 to +2: based on 10-K/10-Q financials
"management_tone": 0, # -2 to +2: MD&A sentiment shift
"risk_factor_change": 0, # -2 to +2: new risks vs resolved risks
"insider_activity": 0, # -2 to +2: net insider buying/selling
"institutional_flow": 0, # -2 to +2: 13F position changes
"event_catalyst": 0, # -2 to +2: recent 8-K impact
}
# Total range: -12 to +12
# > +6: strong fundamental bullish
# +2 to +6: mild bullish
# -2 to +2: neutral
# < -2: fundamental caution
## SEC Filing Analysis — [Ticker]
### Filing Summary
- **Latest 10-K/10-Q**: [date], [period]
- **Recent 8-K events**: [list material events]
- **Insider activity (90d)**: [net buy/sell summary]
### Financial Health
- Revenue trend: [accelerating / stable / decelerating]
- Margin trajectory: [expanding / stable / compressing]
- FCF conversion: [strong / adequate / weak]
- Balance sheet: [net cash / moderate leverage / high leverage]
### MD&A Tone Shift
- vs prior filing: [more optimistic / unchanged / more cautious]
- Key language changes: [specific quotes or paraphrases]
### Risk Factor Changes
- New risks: [list any new risk factors added]
- Intensified risks: [list risks with stronger language]
- Resolved risks: [list removed risk factors]
### Insider & Institutional Signals
- Insider net activity: [cluster buy / neutral / cluster sell]
- Institutional positioning: [accumulation / stable / distribution]
### Composite Signal
| Dimension | Score (-2~+2) | Basis |
|-----------|---------------|-------|
| Financial health | +1 | Revenue accelerating, margins stable |
| Management tone | -1 | More cautious language in MD&A |
| ... | ... | ... |
### Investment Implication
- Direction: [bullish / bearish / neutral]
- Confidence: [high / medium / low]
- Key monitoring: [next earnings date, upcoming 8-K triggers]
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