Risk Analyzer

Why It Matters

Understanding how companies are exposed to highly uncertain scenarios and risk channels, like geopolitical and economic risks, is critical for informed decision-making. As shifting policies, sanctions, and trade barriers redefine market dynamics, organizations must proactively assess their vulnerability to emerging threats.

What It Does

The cookbook REST modules (src/bigdata_rest.py, src/search_helper.py, and src/labeling.py) combined with the Bigdata.com REST API and bigdata-smart-batching systematically analyze corporate exposure to specific risk channels using unstructured data from news, earnings calls, and regulatory filings. Designed for risk analysts, portfolio managers, and investment professionals, the workflow replaces the removed RiskAnalyzer SDK class with composable REST + OpenAI functions.

How It Works

The RiskAnalyzer combines hybrid semantic search, risk factor taxonomies, and structured validation techniques to deliver:

Targeted extraction of risk signals and supporting evidence from massive unstructured datasets
Standardized exposure metrics to compare risk across firms, sectors, or portfolios
Actionable insights that inform investment strategies and enterprise risk decisions
Time-based monitoring to track how exposure levels shift in response to world events

A Real-World Use Case

This cookbook illustrates the full workflow through a practical example: identifying companies impacted by new U.S. import tariffs on China. You’ll learn how to convert unstructured narrative (news articles) into structured, quantifiable risk intelligence. Ready to get started? Let’s dive in!

Prerequisites

To run the Risk Analyzer workflow, you can choose between three options:

▶️ Colab cookbook
- Use this if you prefer running the workflow in a cloud environment.
- Follow the instructions written directly inside the cookbook.
- API keys must be configured as described within the Colab file itself.
💻 GitHub cookbook
- Use this if you prefer working locally or in a custom environment.
- Follow the setup and execution instructions in the README.md.
- Copy .env.example to .env and set:
  - BIGDATA_API_KEY — your Bigdata.com API key
  - OPENAI_API_KEY — your OpenAI API key (required for LLM labeling and summarization)
  - See the cookbook README.md for full setup instructions.
🐳 Docker Installation
- Docker installation is available for containerized deployment.
- Provides an alternative setup method with containerized deployment, simplifying the environment configuration for those preferring Docker-based solutions.

Setup and Imports

Below is the Python code required for setting up our environment and importing necessary libraries.

import os
import json

import pandas as pd
from dotenv import load_dotenv
from pathlib import Path
from openai import OpenAI

from src.bigdata_rest import BigdataRestClient, load_universe, company_ids_from_universe
from src.search_helper import run_universe_search
from src.labeling import generate_risk_scenarios, classify_risk_chunks, build_company_risk_matrix
from src.tool import display_figures

load_dotenv(Path.cwd() / ".env")
BIGDATA_API_KEY = os.getenv("BIGDATA_API_KEY")
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")

rest_client = BigdataRestClient()
openai_client = OpenAI(api_key=OPENAI_API_KEY)

# Define output file paths for our results
output_dir = "output"
os.makedirs(output_dir, exist_ok=True)

export_path = f"{output_dir}/risk_analyzer_results.xlsx"

Defining Your Risk Analysis Parameters

To perform a portfolio risk analysis, we need to define several key parameters:

Main Theme (main_theme): The risk scenario to analyze (e.g. US Import Tariffs against China)
Focus (focus): The analyst focus that provides an expert perspective on the scenario and helps break it down into risk factors
Company Universe (companies): The set of companies to screen
Control Entities (control_entities): The countries, people, or organizations that characterize the risk scenario
Keywords (keywords): The key concepts of the risk scenario
Time Period (start_date and end_date): The date range over which to run the search
Document Type (document_type): Specify which documents to search over (transcripts, filings, news)
Fiscal Year (fiscal_year): If the document type is transcripts or filings, fiscal year needs to be specified
Sources (sources): Specify set of sources within a document type, for example which news outlets (available via Bigdata API) you wish to search over
Model Selection (llm_model): The AI model used for semantic analysis
Rerank Threshold (rerank_threshold): By setting this value, you’re enabling the cross-encoder which reranks the results and selects those whose relevance is above the percentile you specify (0.7 being the 70th percentile). More information on the re-ranker can be found here.
Export Path (export_path): The path to export the results in an Excel file

# Risk Definition  
main_theme = 'New US import tariffs against China would impact American companies'
focus = "Provide a detailed taxonomy of risks describing how new American import tariffs against China will impact US companies, their operations and strategy. Cover trade-relations risks, foreign market access risks, supply chain risks, US market sales and revenue risks (including price impacts), and intellectual property risks, provide at least 4 sub-scenarios for each risk factor."

# Company Universe (CSV, not a watchlist)
universe = load_universe("../Thematic_Screener_CLI/40_companies.csv")
company_ids = company_ids_from_universe(universe)
id_to_name = dict(zip(universe["RP_ENTITY_ID"], universe["COMPANY_NAME"]))

# LLM Specification (gpt-5.6-luna: omit temperature/top_p)
llm_model = "gpt-5.6-luna"

# Query Configuration  
scope = "news"  # REST document scope

# Specify Time Range  
start_date = "2025-04-01"
end_date = "2025-06-30"

# Risk Scenario Parameters (contextual; not passed as REST filters)
countries_at_risk = {'place':['China']}
keywords = ['Tariffs']

chunk_percentage = 0.02
requests_per_minute = 200

Generate a Risk Taxonomy

Use OpenAI via generate_risk_scenarios to break the main theme into sub-scenarios:

risk_scenarios = generate_risk_scenarios(
    main_theme, focus=focus, n=4, model=llm_model, client=openai_client
)

Retrieve Content

With the risk taxonomy and screening parameters, use run_universe_search (src/search_helper.py + bigdata-smart-batching) to retrieve news chunks for your company universe:

df_sentences = run_universe_search(
    company_ids,
    risk_scenarios,
    start_date=start_date,
    end_date=end_date,
    scope=scope,
    chunk_percentage=chunk_percentage,
    requests_per_minute=requests_per_minute,
    id_to_name=id_to_name,
)

Label the Results

Classify each chunk against the risk sub-scenarios with classify_risk_chunks:

df_labeled = classify_risk_chunks(
    df_sentences,
    risk_scenarios,
    main_theme,
    model=llm_model,
    client=openai_client,
)

Assess Risk Exposure

Build the company-level risk matrix and visualize results:

df_company = build_company_risk_matrix(
    df_labeled,
    risk_scenarios,
    industry_by_id=industry_by_id,
    sector_by_id=sector_by_id,
)

display_figures(df_company, interactive=False, n_companies=len(df_company))

Extract Key Insights

The analysis reveals key insights about corporate exposure to U.S. import tariffs against China:

Supply Chain Dependencies Drive Exposure

Companies with heavy reliance on Chinese manufacturing and supply chains show the highest exposure scores, indicating vulnerability to cost increases and operational disruptions from new tariff policies.

Technology Sector Shows Concentrated Risk

Technology companies demonstrate significant exposure due to their dependence on Chinese semiconductor and component manufacturing, with potential impacts on both costs and market access.

Consumer Goods Face Price Pressure

Consumer-facing companies show exposure through potential margin compression as they navigate between absorbing tariff costs and passing them on to customers.

Strategic Positioning Varies Widely

Companies with diversified supply chains and domestic alternatives show lower risk scores, highlighting the importance of supply chain resilience strategies.

Industry Risk Patterns

High-Risk Sectors

Technology and Semiconductors show the highest average exposure due to supply chain concentration in China
Consumer Discretionary companies face significant margin pressure from potential tariff costs
Industrial Manufacturing with Chinese operations face operational complexity increases

Strategic Responses

Companies with supply chain diversification strategies show lower risk scores
Firms with domestic manufacturing capabilities demonstrate greater resilience
Organizations with flexible sourcing strategies appear better positioned to navigate tariff impacts

Export the Results

Export labeled results and the company risk matrix to CSV or Excel for further analysis.

df_labeled.to_csv(f"{output_dir}/risk_labeled.csv", index=False)
df_company.to_csv(f"{output_dir}/risk_company_matrix.csv", index=False)

Conclusion

The Risk Analyzer provides a comprehensive framework for identifying and quantifying corporate exposure to specific risk scenarios. By leveraging advanced information retrieval and LLM-powered analysis, this workflow transforms unstructured data into actionable risk intelligence. Through the automated analysis of U.S. import tariff exposure, you can:

Identify vulnerable companies - Discover which firms in your portfolio face the highest exposure to tariff-related risks through their operational dependencies and market positions
Compare across industries - Understand how different sectors are affected by trade policy changes, enabling sector-level hedging and diversification strategies
Monitor risk evolution - Track how company exposure changes over time as they adapt their strategies or as policy developments unfold
Generate investment insights - Use risk exposure scores to inform position sizing, hedging decisions, and portfolio construction in volatile geopolitical environments
Support risk management - Provide quantitative backing for risk committee discussions and regulatory reporting requirements

Investment Strategy Implications:

Consider underweighting companies with high exposure scores in anticipation of tariff implementation
Use sector-level exposure analysis to guide allocation decisions and hedging strategies
Monitor risk score changes to identify companies successfully adapting to trade policy challenges

Whether you’re conducting portfolio stress testing, building risk-aware investment strategies, or assessing geopolitical exposure across your holdings, the Risk Analyzer automates the research process while maintaining the depth and rigor required for professional risk analysis. The standardized scoring methodology ensures consistent evaluation across companies, sectors, and time periods, making it an invaluable tool for systematic risk assessment in an increasingly complex global environment.

Cookbooks

Conversational AI

Search & Discovery

Market & Financial Analysis

Risk Management

Screening & Monitoring

Insights & Reporting

Why It Matters

What It Does

How It Works

A Real-World Use Case

Prerequisites

Setup and Imports

Defining Your Risk Analysis Parameters

Generate a Risk Taxonomy

Retrieve Content

Label the Results

Assess Risk Exposure

Extract Key Insights

Supply Chain Dependencies Drive Exposure

Technology Sector Shows Concentrated Risk

Consumer Goods Face Price Pressure

Strategic Positioning Varies Widely

Industry Risk Patterns

High-Risk Sectors

Strategic Responses

Export the Results

Conclusion

​Why It Matters

​What It Does

​How It Works

​A Real-World Use Case

​Prerequisites

​Setup and Imports

​Defining Your Risk Analysis Parameters

​Generate a Risk Taxonomy

​Retrieve Content

​Label the Results

​Assess Risk Exposure

​Extract Key Insights

Supply Chain Dependencies Drive Exposure

Technology Sector Shows Concentrated Risk

Consumer Goods Face Price Pressure

Strategic Positioning Varies Widely

​Industry Risk Patterns

​High-Risk Sectors

​Strategic Responses

​Export the Results

​Conclusion

Why It Matters

What It Does

How It Works

A Real-World Use Case

Prerequisites

Setup and Imports

Defining Your Risk Analysis Parameters

Generate a Risk Taxonomy

Retrieve Content

Label the Results

Assess Risk Exposure

Extract Key Insights

Industry Risk Patterns

High-Risk Sectors

Strategic Responses

Export the Results

Conclusion