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
Google Scholar :
https://tinyurl.com/3wf47tjf
Scribd :
https://tinyurl.com/ypttfu7e
DOI :
https://doi.org/10.5281/zenodo.10040460
Abstract :
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.
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.
