AI-Powered Geopolitical Risk Quantification: Investment Portfolio Hedging Strategies Using OSINT and LLM

In a rapidly changing global landscape, traditional geopolitical risk analysis methods are no longer sufficient to effectively protect investment portfolios. This article presents specific strategies and implementation plans for real-time quantification of geopolitical risk by combining OSINT and Large Language Models (LLM), and proactively hedging investment portfolios based on this information. This will be an innovative approach to capturing investment opportunities and managing risk in highly uncertain markets.

1. Geopolitical Risk: A Variable No Longer to Be Overlooked

Today's investment market is more significantly influenced by geopolitical factors than ever before. Various geopolitical events such as the war in Ukraine, the US-China trade conflict, instability in the Middle East, and resource crises due to climate change directly impact global supply chains, energy prices, exchange rates, and ultimately corporate profitability and stock prices. In the past, geopolitical risk largely remained within the realm of macroeconomic analysis, but it has now emerged as a key variable that determines the investment performance of individual companies and specific sectors.

The problem is that these risks are difficult to predict and cause rapid market fluctuations when they occur. Traditional analysis methods have limitations in terms of time lag in report issuance, analyst subjectivity, and processing vast amounts of data. For these reasons, investors often reacted belatedly after risks materialized, which sometimes led to massive losses. We urgently need a new approach that can detect geopolitical trends in real-time, convert them into objective and quantitative risk indicators, and proactively reflect them in portfolios.

2. Deep Dive: Geopolitical Risk Quantification Mechanism Using OSINT and LLM

Our solution lies in maximizing the synergy between OSINT (Open Source Intelligence) and LLM (Large Language Models). OSINT is responsible for collecting raw data from publicly accessible information sources (news articles, social media, government announcements, academic reports, satellite imagery, etc.), while LLM is the core engine that analyzes this vast unstructured data in real-time, extracting, structuring, and quantifying key information related to geopolitical risk.

• OSINT: Source of Real-time Data Acquisition
  • Definition: An information collection technique that utilizes all publicly available information sources, including newspapers, broadcasts, the internet (social media, blogs, websites), government and public institution data, and academic research.
  • Value in Use: Geopolitical events often reveal early signs within the flow of public information. OSINT is the fastest means to detect these signs. Changes in the tone of news articles, the frequency of specific keywords, and the social media activities of diplomats can provide early warnings of potential crises.
  • Technical Considerations: It is essential to build a pipeline that collects large-scale unstructured data using various APIs (NewsAPI, GDELT Project, Twitter API, etc.) and web scraping techniques. Given the vast amount of data, efficient collection and storage strategies (e.g., NoSQL databases) are crucial.
• LLM: Extracting Insights from Unstructured Data
  • Definition: An artificial intelligence model trained on vast amounts of text data, possessing human-like language understanding and generation capabilities. GPT-4, LLaMA, and Gemini are representative examples.
  • Value in Use: Unstructured text data (news articles, tweets, etc.) collected through OSINT is difficult to analyze on its own. LLM transforms this data as follows:
    • Named Entity Recognition (NER): Identifies countries, cities, people, organizations, and event names.
    • Event Extraction: Identifies the type, time, related parties, and outcomes of events that have occurred.
    • Sentiment Analysis: Determines the positive/negative/neutral attitude of news or public opinion on a specific topic or entity and assigns a sentiment score.
    • Summarization and Key Information Extraction: Extracts key summaries and important facts related to geopolitical risk from long articles.
    • Relationship Extraction: Identifies causal relationships or interactions between different entities or events.
  • Quantification Technique: Based on the information extracted by LLM, a numerical Risk Score is calculated by comprehensively considering the Severity, Probability of occurrence, and Impact on related assets of a specific event. For example, if the sentiment scores of articles indicating rising tensions in a specific region consistently decrease and the frequency of related keywords (e.g., 'military exercise', 'border dispute') increases, the risk score for assets related to that region would be raised.

3. Step-by-Step Guide: Building an AI-Powered Geopolitical Risk Hedging System

Now, let's look at the specific steps to actually build an investment portfolio hedging system using OSINT and LLM.

Step 1: Building an OSINT Data Collection Pipeline

Configure a pipeline that automatically collects data from various public information sources. Initially, you can start with news APIs and RSS feeds for specific keywords.

import requests
import json
import time

def fetch_news_api(api_key, query, language='ko', pages=1, page_size=100):
    """
    뉴스 API (예: NewsAPI)를 사용하여 뉴스 기사를 수집합니다.
    주의: NewsAPI는 상업적 사용 시 요금 정책이 있으므로, 사용 전에 반드시 확인하십시오.
    """
    all_articles = []
    base_url = "https://newsapi.org/v2/everything"
    
    for page in range(1, pages + 1):
        params = {
            "q": query,
            "language": language,
            "pageSize": page_size,
            "page": page,
            "apiKey": api_key
        }
        try:
            response = requests.get(base_url, params=params, timeout=10)
            response.raise_for_status() # HTTP 오류가 발생하면 예외 발생
            data = response.json()
            if data and data.get("articles"):
                all_articles.extend(data["articles"])
            if data.get("totalResults", 0) <= len(all_articles):
                break # 더 이상 기사가 없으면 종료
            time.sleep(1) # API rate limit 준수를 위해 지연
        except requests.exceptions.RequestException as e:
            print(f"Error fetching news: {e}")
            break
    return all_articles

def collect_rss_feeds(rss_urls):
    """
    RSS 피드로부터 기사를 수집합니다. (예시 코드는 Feedparser 라이브러리 필요)
    """
    # import feedparser # pip install feedparser
    all_entries = []
    # for url in rss_urls:
    #     feed = feedparser.parse(url)
    #     for entry in feed.entries:
    #         all_entries.append({
    #             "title": entry.title,
    #             "description": entry.summary,
    #             "url": entry.link,
    #             "publishedAt": entry.published
    #         })
    # print("RSS feed collection is conceptual; requires 'feedparser' library and implementation.")
    return all_entries

# 사용 예시 (실행하려면 YOUR_NEWSAPI_KEY를 실제 키로 대체해야 합니다)
# NEWS_API_KEY = "YOUR_NEWSAPI_KEY" 
# geopolitical_queries = ["미중 무역", "러시아 우크라이나", "중동 정세", "반도체 공급망"]
# collected_data = []
# for query in geopolitical_queries:
#     articles = fetch_news_api(NEWS_API_KEY, query, language='ko', pages=2)
#     collected_data.extend(articles)

# print(f"총 {len(collected_data)}개의 기사 수집 완료.")
# # 수집된 데이터를 저장 (예: JSON 파일)
# with open("geopolitical_news.json", "w", encoding="utf-8") as f:
#     json.dump(collected_data, f, ensure_ascii=False, indent=2)
    

Step 2: LLM-Based Information Extraction and Structuring

Input the collected article text into the LLM to extract key information related to geopolitical risk and structure it into a JSON format suitable for quantification. Prompt engineering is very