LLM-based ESG-Focused Portfolio Optimization Service

This repository hosts the LLM-based ESG-Focused Portfolio Optimization Service project. By integrating ESG (Environmental, Social, Governance) criteria and advanced portfolio optimization techniques, this service provides users with a personalized ESG-driven investment portfolio. Using a large language model (LLM) to process and evaluate text data, the service enables investors to assess companies' ESG scores and generate optimized portfolios based on their preferences.

About

KWU 8th Industry-Academic Cooperation SW Project & AI Convergence Graduation Project

• Adviser: 조민수 교수 (Department of Information Convergence at KWU)
• Affiliated company: 빌리언스랩 (표수진 박사)
• Contributer: Team KWargs (백지헌(PM), 김나연, 장한재, 신해리)

Project Introduce focused on UI/UX

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Table of Contents

1. Project Overview
2. Key Features
3. Installation
4. Usage
5. Project Structure
6. Future Extensions
7. License

Project Overview

In recent years, ESG has become a critical metric for assessing a company's long-term sustainability and stability. However, traditional ESG evaluation methods often lack transparency and accessibility for non-expert investors. This project addresses these issues by providing a user-centered, transparent ESG evaluation system using an LLM, which performs text-based assessments and applies the Black-Litterman model for portfolio optimization. Through this, the system generates customized portfolios that balance ESG factors and financial returns according to user preferences.

Core Objectives

1. Technical Implementation: Develop a pipeline using LLM for ESG assessment and produce an optimized portfolio.
2. User-Centered Service: Provide personalized portfolio construction with ESG evaluation based on user-selected criteria and preferences.

Key Features

1. Text Data Collection and Processing

   • Collect articles related to selected companies from various sources using Selenium and BeautifulSoup, storing the data in MongoDB for efficient management.
   • Preprocess the collected data, anonymizing company names and filtering irrelevant content to ensure high data quality.

2. LLM-based ESG Scoring

   • Use an LLM (via the OpenAI API) to evaluate collected articles based on ESG relevance, extracting and labeling content by ESG factors and sentiment.
   • Fine-tune a KoElectra model for classification, allowing independent ESG assessments without API dependency.

3. Comprehensive ESG Evaluation

   • Utilize user input for adjusting weights on ESG factors (environmental, social, governance) to customize scoring.
   • Adjust scores dynamically based on investment style preferences: financial-centered, ESG-centered, or balanced.
   • Apply a scoring model that integrates multiple ESG rating agencies’ criteria to build a comprehensive score, enabling evaluation for companies outside major agency coverage.

4. Portfolio Optimization via Black-Litterman Model

   • Calculate market data and covariance matrices for selected companies using Yahoo Finance data.
   • Integrate user ESG-adjusted scores into the Black-Litterman model, generating customized expected returns based on user preferences.
   • Apply cvxopt for quadratic programming to calculate optimized portfolio weights, considering constraints like maximum asset weight.

5. User Interface with Streamlit

   • A user-friendly interface allows users to input ESG preferences and visualize optimized portfolios.
   • Interactive sliders for ESG weights and investment styles, providing an intuitive way to construct personalized portfolios.
   • Visualization of portfolio metrics, including expected return, volatility, and Sharpe ratio.

Installation

1. Clone the Repository:

   git clone https://github.com/fairyofdata/LLM-ESG-POS.git
   cd LLM-ESG-POS

2. Install Required Packages:

   pip install -r requirements.txt

3. Run Streamlit Application:

   streamlit run app.py

Usage

1. Data Collection: Configure company tickers and data sources for article collection.
2. Set ESG Preferences: Use the Streamlit UI to define weights for environmental, social, and governance factors.
3. Investment Style Selection: Choose from financial-centered, ESG-centered, or balanced portfolio options.
4. View Portfolio Results: See portfolio composition and performance metrics, including expected return and volatility.

Project Structure

├── data/                   # Data and ESG score tables
├── src/                    # Source code for ESG scoring and portfolio optimization
│   ├── data_processing.py   # LLM-based text processing
│   ├── esg_scoring.py       # ESG scoring functions
│   ├── portfolio_optimization.py  # Optimization with Black-Litterman
│   └── ui/                  # Streamlit UI code
├── app.py                  # Main application script
├── README.md               # Project documentation
└── requirements.txt        # List of dependencies

Future Extensions

1. Expanded ESG Evaluation: Integrate additional ESG rating sources and add real-time news updates for responsive scoring.
2. Scalability for Non-listed Companies: Develop scoring mechanisms that leverage LLMs to assess startups and non-public companies.
3. Improved Constraints in Optimization: Include sector-specific constraints for better portfolio diversification.

License

This project is licensed under the MIT License. See the LICENSE file for details.