Overview This project explores the intricate relationships between mergers and acquisitions (M&A) and stock price fluctuations, providing tools for investors to build a diversified investment portfolio tailored to their risk preferences.

Modules Module 1: Data Acquisition This module focuses on gathering data from various sources, including:

Images CSV files PDFs Web scraping from URLs Video content (both with and without audio, using Large Language Models)

Module 2: RAG-Based Application In this module, we utilize a Retrieval-Augmented Generation (RAG) approach to:

Store data in a Vector Database Generate synthetic data Create CSV files for further analysis

Module 3: Training and Prediction This module involves training models using the synthetic data generated in the previous step. It focuses on:

Predicting stock price movements based on M&A activity Analyzing historical trends and preparing a correlation matrix for related stocks.

Module 4: Targeting Companies for M&A Here, we develop strategies for identifying potential companies to target for mergers and acquisitions, taking into account:

Market trends Company performance metrics Risk factors defined by the user

Basic Idea The primary goal of this project is to build a diversified investment portfolio builder that aligns with user-defined risk factors. By leveraging data from various sources, we aim to provide insights that empower investors to make informed decisions.