Stock-Returns-Prediction-ML-Textual-Analysis

Applying machine learning and textual analysis on macro and micro determinants to predict stock returns

Getting Started

1. Make sure your python is configured properly
   • In the .ipynb file, make sure the top right python library is the same as where you install your python libraries

There are 4 main folders : Data , Data-Processed , Data-Collection , Data-Model and Data-Report

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1. Data

Contains Stock and Global data. These unstructured data are extracted and preprocessed from investing.com and scraped using BeautifulSoup. Each csv file contains 3 columns : Date, Title and Text

Stock

Name	Rows
Apple	2289
Meta	2465
Tesla	1776

Global

Name	Rows
World
Politics
Coronavirus

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2. Data-Processed

Vader Analysis is performed on both stock and global data

Date	Title	Text	Processed Title	Processed Text	Sentiment Title	Sentiment Text	Positive Title	Positive Text