AI Financial Analysis with Chinese LLMs

August 15, 2026 • 13 min read • By TokenEase Research

Financial analysts spend countless hours reading earnings reports, parsing SEC filings, and synthesizing market data into actionable insights. Chinese LLMs like DeepSeek, GLM-4, and Qwen are now capable of performing many of these tasks at scale, extracting key metrics, identifying trends, and generating professional-grade research reports in minutes rather than days.

This guide explores how to build AI-powered financial analysis workflows using Chinese LLMs, from automated earnings summaries to multi-factor valuation models.

            Why Chinese LLMs for Finance?

            Chinese LLMs demonstrate strong performance on structured data reasoning and numerical analysis. DeepSeek-V4 scores highly on math and logic benchmarks, while GLM-4 excels at long-document comprehension, making both well-suited for parsing lengthy financial filings.

1. Financial LLM Capability Comparison

Model	Numerical Reasoning	Document Comprehension	Context Window	Cost (per 1M tokens)
DeepSeek-V4	9.0/10	8.8/10	128K	$0.14
Qwen2.5-72B	8.5/10	8.5/10	128K	$0.50
GLM-4-9B	8.0/10	9.0/10	128K	$0.06
Kimi K2.5	8.2/10	9.2/10	256K	$0.50

Recommendation: Use DeepSeek-V4 for numerical modeling and quantitative analysis. Use Kimi K2.5 or GLM-4 for long-form document analysis (10-K, annual reports). Use GLM-4 for cost-sensitive batch processing.

2. Automated Earnings Report Analysis

The most immediate application is automating earnings call summaries and financial statement analysis:

2.1 Earnings Transcript Summarizer

import requests
import json

def analyze_earnings(transcript_text, model="deepseek"):
    """Generate structured analysis from earnings transcript."""
    
    prompt = f"""You are a senior equity analyst. Analyze the following earnings call transcript and provide structured insights.

Transcript:
{transcript_text[:30000]}

Provide your analysis in the following JSON structure:
{{
  "executive_summary": "3-4 sentence overview of key takeaways",
  "revenue_analysis": {{
    "reported_revenue": "...",
    "yoy_growth": "...",
    "vs_consensus": "beat/miss and magnitude",
    "segment_breakdown": "..."
  }},
  "margin_analysis": {{
    "gross_margin": "...",
    "operating_margin": "...",
    "margin_trends": "..."
  }},
  "guidance": {{
    "revenue_guidance": "...",
    "eps_guidance": "...",
    "management_tone": "optimistic/cautious/neutral"
  }},
  "key_risks": ["risk 1", "risk 2"],
  "key_opportunities": ["opportunity 1", "opportunity 2"],
  "analyst_rating": "BUY/HOLD/SELL with conviction level 1-5"
}}

Be precise with numbers. If a metric is not mentioned, state "Not discussed"."""

    response = requests.post(
        "https://tokenease.io/v1/chat/completions",
        headers={
            "Authorization": f"Bearer {api_key}",
            "Content-Type": "application/json"
        },
        json={
            "model": model,
            "messages": [
                {"role": "system", "content": "You are a professional equity research analyst with 15 years of experience."},
                {"role": "user", "content": prompt}
            ],
            "temperature": 0.2,
            "max_tokens": 2500,
            "response_format": {"type": "json_object"}
        },
        timeout=90
    )
    
    return json.loads(response.json()["choices"][0]["message"]["content"])
        

2.2 Financial Statement Ratio Analysis

def analyze_financial_ratios(balance_sheet, income_stmt, cash_flow):
    """Analyze financial health from structured statement data."""
    
    prompt = f"""Analyze the following financial statements and calculate key ratios. Flag any concerning trends.

Balance Sheet (current + prior year):
{balance_sheet}

Income Statement:
{income_stmt}

Cash Flow Statement:
{cash_flow}

Calculate and analyze:
1. Profitability: ROE, ROA, Gross Margin, Operating Margin, Net Margin
2. Liquidity: Current Ratio, Quick Ratio, Cash Ratio
3. Leverage: Debt-to-Equity, Interest Coverage, Debt-to-Assets
4. Efficiency: Asset Turnover, Inventory Turnover, Receivables Turnover
5. Valuation context: P/E implied range based on growth

For each ratio:
- State the calculated value
- Compare to industry averages if you can infer the sector
- Flag as STRONG / NORMAL / CONCERNING
- Explain the trend direction

Output as structured JSON."""

    # API call with low temperature for consistency
    return call_llm_api(prompt, temperature=0.1, max_tokens=3000)
        

3. Multi-Source Research Synthesis

Modern financial analysis requires synthesizing news, analyst reports, and market data. Here is a pipeline that combines multiple sources:

def synthesize_research(ticker, news_articles, analyst_ratings, price_data):
    """Synthesize multiple data sources into a unified research view."""
    
    prompt = f"""Synthesize the following research inputs for {ticker} into a coherent investment thesis.

Recent News (last 30 days):
{chr(10).join([f"- {a['title']}: {a['summary']}" for a in news_articles[:10]])}

Analyst Ratings:
{chr(10).join([f"- {r['firm']}: {r['rating']} (PT: {r['target']})" for r in analyst_ratings])}

Price Action (last 30 days):
- High: ${price_data['high']}
- Low: ${price_data['low']}
- Current: ${price_data['current']}
- Volume trend: {price_data['volume_trend']}

Provide:
1. Investment thesis (bull and bear cases)
2. Key catalysts to watch (next 90 days)
3. Risk factors ranked by severity
4. Suggested position sizing rationale
5. Entry and exit price levels with reasoning

Be balanced. Acknowledge uncertainties. Do not make specific buy/sell recommendations without proper disclaimers."""

    return call_llm_api(prompt, temperature=0.3, max_tokens=2500)
        

4. Automated Report Generation

For institutional clients or internal use, generate full research reports:

def generate_research_report(company_profile, financial_data, market_data, model="deepseek"):
    """Generate a full-page equity research report."""
    
    sections = {
        "investment_thesis": "Write a compelling investment thesis in 3 paragraphs",
        "business_overview": "Describe the business model, competitive position, and moat",
        "financial_analysis": "Analyze 3-year revenue, margin, and cash flow trends",
        "valuation": "Provide DCF and comparable company valuation framework",
        "risk_factors": "List and explain top 5 risks",
        "catalysts": "Identify upcoming events that could move the stock"
    }
    
    report = {}
    for section, instruction in sections.items():
        prompt = f"""{instruction} for {company_profile['name']} ({company_profile['ticker']}).

Company: {company_profile['description']}
Sector: {company_profile['sector']}
Market Cap: {company_profile['market_cap']}

Financial Highlights:
{financial_data}

Market Context:
{market_data}

Write in professional equity research style. Include specific numbers and percentages. Length: 200-300 words."""
        
        report[section] = call_llm_api(prompt, temperature=0.3, max_tokens=600)
    
    return report
        

5. Real-Time Market Sentiment Analysis

Track market sentiment across news and social sources:

def analyze_market_sentiment(news_batch, model="deepseek"):
    """Analyze sentiment and extract key themes from financial news."""
    
    prompt = f"""Analyze the following financial news batch and provide structured sentiment analysis.

News Items:
{chr(10).join([f"{i+1}. {n['source']}: {n['headline']}" for i, n in enumerate(news_batch)])}

Provide:
1. Overall market sentiment score (-1.0 to +1.0)
2. Sector sentiment breakdown (tech, finance, energy, healthcare, etc.)
3. Top 5 emerging themes or narratives
4. Notable sentiment shifts vs. previous period
5. Key entities mentioned (companies, people, policies)
6. Risk events flagged

Output as JSON."""

    return call_llm_api(prompt, temperature=0.2, max_tokens=1500, response_format="json")
        

6. Performance Benchmarks

We tested Chinese LLMs on real financial analysis tasks against human analyst outputs:

Task	DeepSeek-V4	Qwen2.5-72B	GLM-4	Human Baseline
Earnings summary accuracy	87%	84%	81%	92%
Ratio calculation correctness	94%	91%	89%	98%
Trend identification	82%	79%	80%	88%
Report generation speed	45s	38s	22s	4-8 hours
Cost per full report	$0.15	$0.40	$0.05	$500-2000

7. Risk Management and Compliance

When using AI for financial analysis, adhere to these principles:

Human oversight: AI outputs should always be reviewed by a licensed analyst before client distribution
Data freshness: LLMs have knowledge cutoffs. Supplement with real-time data feeds for time-sensitive analysis
Regulatory compliance: Ensure AI-generated research meets FINRA/SEC disclosure requirements
Hallucination checks: Always verify numerical claims against primary sources (SEC filings, company IR)
Model transparency: Document which AI model was used and its known limitations

8. Building a Production Pipeline

A complete financial analysis pipeline typically includes:

Data ingestion: Pull earnings transcripts, filings, and news via APIs (EDGAR, Bloomberg, Alphavantage)
Preprocessing: Clean and structure data, chunk long documents to fit context windows
LLM analysis: Run multiple parallel prompts for different analysis dimensions
Post-processing: Validate numerical outputs, check for hallucinations, format for distribution
Distribution: Generate PDF reports, email alerts, or dashboard updates

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