Regression Model Demo

Exploring Stock Market Predictions with Regression Analysis

In this demo, regression analysis is applied to stock market data to predict future returns and develop a trading algorithm. The process includes:

Library Importation: Essential libraries, including the statsmodels API, are imported to aid in building the regression model.
Data Preparation: A dataset from 2000 to the present is used. It comprises total returns and forward returns for varying days.
Data Splitting: The dataset is divided into a training set (70%) and a test set (30%) based on a 2017 cutoff to mitigate overfitting.
Feature Selection: Past returns from 1 to 20 days serve as features, with the forward 1-day return as the target variable.
Data Standardization: Train data is fit-transformed to prevent test data leakage.
Model Building: A constant is added to the regression model, which is trained using ordinary least squares.
Model Evaluation: Coefficient significance and R-squared values provide insight into the model's efficacy.
Trading Strategy Creation: Predictions are compared to actual values, guiding a strategy of going long on positive returns.
Performance Evaluation: The strategy's performance against benchmarks like the S&P 500 is assessed, showcasing a comparable outcome with slightly better risk-adjusted returns.

This demo provides an understanding of using regression for investment prediction and evaluation.