Comparative Analysis of Stock Price Prediction Models: Generalized Linear Model (GLM), Ridge Regression, Lasso Regression, Elasticnet Regression, and Random Forest – A Case Study on Netflix

Authors : Cyril Neba C.; Gillian Nsuh; Gerard Shu F.; Philip Amouda A.; Adrian Neba F.; Aderonke Adebisi; P. Kibet.; F.Webnda

Volume/Issue : Volume 8 - 2023, Issue 10 - October

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The primary objective was to develop a robust model for predicting the adjusted closing price of Netflix, leveraging historical stock price data sourced from Kaggle. Through in-depth Exploratory Data Analysis, we examined a dataset encompassing essential daily metrics for February 2018, including opening price, highest price, lowest price, closing price, adjusted closing price, and trading volume. Our research aims to provide valuable insights and predictive tools that can assist investors and market analysts in making informed decisions. The dataset presented a unique challenge, featuring a diverse mix of quantitative and categorical variables, making it an ideal candidate for a Generalized Linear Model (GLM). To address the characteristics of the data, we employed a GLM with a gamma(normal) family and a log link function, a suitable choice for modeling positive continuous data with right-skewed distributions. The study also expands beyond the GLM framework by incorporating Ridge Regression, Lasso Regression, Elasticnet Regression, and Random Forest models, enabling a comprehensive comparison of their predictive capabilities. Based on the RMSE values, including the Volume variable did not significantly improve the performance of the model in predicting Netflix stock prices. However, the difference between the RMSE values of the two models was small and may not be practically significant. Therefore, it was reasonable to keep the Volume variable in the model as it could potentially be a useful predictor in other scenarios. The analysis of the five models used for predicting the Netflix stock price based on the Root mean Squared Errors showed that the Lasso model performed the best. The Elastic Net model had the second-best performance, then the Ridge model, followed by the Random Forest Model and finally the GLM model. Overall, all five models demonstrated some level of accuracy in predicting the stock price, but the Lasso and Elastic Net models stood out with the best performance. These findings can be useful in guiding investment decisions and risk management strategies in the stock market.

Keywords : Stock Price Prediction, Generalized Linear Model (GLM), Ridge Regression, Lasso Regression, Elasticnet Regression, Random Forest, RMSE, Netflix.


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