Forecasting Netflix Stock Prices: A Case Study Using Regression and Machine Learning Models

Abstract

This case study investigates the forecasting of Netflix stock prices using various regression and machine learning models, aimed at enhancing predictive accuracy in a dynamic financial environment. As one of the leading streaming services globally, Netflix's stock performance is influenced by numerous factors, including subscriber growth, content investments, and market competition. To analyze these influences, we employ a range of models, including Generalized Linear Models (GLM), Ridge Regression, Lasso Regression, Elastic Net, and advanced machine learning techniques such as Random Forest and Support Vector Regression (SVR). The study begins by preprocessing historical stock price data, extracting relevant features that may impact price movements, including macroeconomic indicators and company-specific metrics. We then implement the selected models and compare their predictive performance using various metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared values. Preliminary results indicate that machine learning models, particularly Random Forest and SVR, outperform traditional regression techniques in terms of predictive accuracy, highlighting their ability to capture complex, non-linear relationships in the data. Furthermore, the study examines the importance of feature selection and engineering, demonstrating how tailored predictors can significantly enhance model performance. This research provides valuable insights into the efficacy of different forecasting methods for stock price prediction in the rapidly evolving entertainment sector. By leveraging advanced analytical techniques, investors and analysts can make more informed decisions regarding Netflix stock, ultimately contributing to more effective investment strategies.

Full text

Forecasting Netflix Stock Prices: A Case Study Using Regression and
Machine Learning Models
Authors: Muhammad Hanif, Thomas Best
Date: October, 2024
Abstract
This case study investigates the forecasting of Netflix stock prices using various regression and
machine learning models, aimed at enhancing predictive accuracy in a dynamic financial
environment. As one of the leading streaming services globally, Netflix's stock performance is
influenced by numerous factors, including subscriber growth, content investments, and market
competition. To analyze these influences, we employ a range of models, including Generalized
Linear Models (GLM), Ridge Regression, Lasso Regression, Elastic Net, and advanced machine
learning techniques such as Random Forest and Support Vector Regression (SVR). The study
begins by preprocessing historical stock price data, extracting relevant features that may impact
price movements, including macroeconomic indicators and company-specific metrics. We then
implement the selected models and compare their predictive performance using various metrics
such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared values.
Preliminary results indicate that machine learning models, particularly Random Forest and SVR,
outperform traditional regression techniques in terms of predictive accuracy, highlighting their
ability to capture complex, non-linear relationships in the data. Furthermore, the study examines
the importance of feature selection and engineering, demonstrating how tailored predictors can
significantly enhance model performance. This research provides valuable insights into the
efficacy of different forecasting methods for stock price prediction in the rapidly evolving
entertainment sector. By leveraging advanced analytical techniques, investors and analysts can
make more informed decisions regarding Netflix stock, ultimately contributing to more effective
investment strategies.
Keywords: Netflix, stock prices, forecasting, regression models, machine learning, Generalized
Linear Models(GLM), Random Forest, Support Vector Regression, predictive accuracy, feature
selection.

Introduction
In today's fast-paced financial markets, accurate forecasting of stock prices is essential for
investors and analysts seeking to make informed decisions. This case study focuses on forecasting
the stock prices of Netflix, a leading player in the global streaming industry, utilizing both
traditional regression models and advanced machine learning techniques. Netflix's stock price is
influenced by various factors, including subscriber growth, content production, competitive
dynamics, and broader market trends. Consequently, developing a reliable forecasting model that
can capture these complexities is crucial for investment strategies. The increasing complexity of
financial data necessitates the use of sophisticated modeling techniques. Traditional statistical
methods, such as Generalized Linear Models (GLM), have been widely used for stock price
prediction due to their simplicity and interpretability. However, these methods often struggle to
capture non-linear relationships present in the data. To address this limitation, we also explore
regularization techniques such as Ridge Regression, Lasso Regression, and Elastic Net, which
enhance the robustness of traditional models by managing multicollinearity and reducing
overfitting. In recent years, machine learning models have gained popularity in financial
forecasting due to their ability to handle large datasets and uncover hidden patterns. This study
incorporates advanced machine learning techniques, including Random Forest and Support Vector
Regression (SVR), which excel in modeling complex relationships among variables. By leveraging
these models, we aim to enhance predictive accuracy and provide deeper insights into the factors
influencing Netflix's stock price. A critical aspect of any forecasting model is the selection and
engineering of features. In this study, we extract relevant predictors from historical stock price
data, including macroeconomic indicators and company-specific metrics, to create a
comprehensive dataset for analysis. The performance of the various models is evaluated using key
metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared values.
These metrics provide a quantitative basis for comparing the effectiveness of each modeling
approach. The findings of this case study are significant for investors and financial analysts, as
they demonstrate the potential of integrating machine learning techniques into stock price
forecasting. By improving predictive accuracy, investors can make better-informed decisions
regarding their investments in Netflix, ultimately contributing to more effective risk management
and capital allocation strategies. The insights gained from this analysis not only enhance our

understanding of Netflix's stock performance but also pave the way for further research in
predictive analytics within the financial sector.
1. Stock Price Prediction
Forecasting stock prices is a complex yet vital task in financial markets, serving as a critical tool
for investors, traders, and analysts. Accurate predictions can lead to informed investment decisions
and effective risk management strategies. This section delves into the methodologies employed in
predicting stock prices, focusing on the integration of traditional regression techniques and
advanced machine learning models, as well as the essential aspect of feature engineering.
Regression Techniques Traditional regression techniques, particularly Generalized Linear
Models (GLM), have long been utilized in stock price prediction due to their interpretability and
ease of use. GLM provides a framework for modeling the relationship between stock prices and
various predictor variables. However, it often faces challenges in capturing non-linear
relationships inherent in financial data. To mitigate these limitations, regularization techniques
such as Ridge Regression, Lasso Regression, and Elastic Net can be employed. These methods
enhance the robustness of the model by addressing issues like multicollinearity and overfitting.
Ridge Regression adds an L2 penalty to the loss function, which shrinks coefficients and stabilizes
estimates, while Lasso Regression uses an L1 penalty to eliminate insignificant predictors,
allowing for a more interpretable model. Elastic Net combines both penalties, offering a flexible
approach that balances the strengths of Ridge and Lasso.
Machine Learning Models With the increasing availability of vast amounts of financial data,
machine learning models have emerged as powerful alternatives to traditional regression
techniques. Models like Random Forest and Support Vector Regression (SVR) excel in capturing
complex patterns and interactions within the data. Random Forest, as an ensemble method,
constructs multiple decision trees to improve prediction accuracy and reduce overfitting. It is
particularly adept at handling non-linear relationships, making it well-suited for stock price
forecasting. SVR, on the other hand, focuses on finding a hyperplane that best fits the data while
allowing for some margin of error. This approach is effective for modeling intricate relationships
and can adapt to varying market conditions.

Feature Engineering A crucial component of effective stock price prediction is feature
engineering, which involves selecting and transforming raw data into meaningful predictors. This
process includes identifying key variables that influence stock prices, such as historical price
trends, trading volumes, macroeconomic indicators, and company-specific metrics. The quality
and relevance of features directly impact the model's predictive performance. Techniques such as
lagged variables, moving averages, and technical indicators can be utilized to enhance the dataset,
providing models with the necessary information to make accurate predictions.
Model Comparison To evaluate the performance of the various regression and machine learning
models, a comparative analysis is essential. Key performance metrics, including Mean Absolute
Error (MAE), Mean Squared Error (MSE), and R-squared values, are employed to quantify the
predictive accuracy of each model. This analysis helps identify which techniques are best suited
for stock price prediction under varying conditions and informs the selection of the most effective
approach. By leveraging these methodologies, investors can enhance their forecasting capabilities,
leading to better-informed investment strategies in an increasingly complex financial landscape.
2. Feature Selection and Engineering
Effective feature selection and engineering play a pivotal role in enhancing the predictive
performance of stock price forecasting models. By identifying and transforming relevant data into
meaningful predictors, analysts can significantly improve the accuracy of their models. This
section explores the importance of feature selection, various methods for engineering features, and
the impact of these processes on model performance.
Importance of Feature Selection Feature selection is the process of identifying the most relevant
variables from a larger dataset that contribute significantly to the prediction of stock prices. The
quality of the selected features directly influences the performance of forecasting models, as
irrelevant or redundant features can lead to overfitting, increased computational costs, and
decreased interpretability. Techniques such as correlation analysis, mutual information, and
recursive feature elimination are commonly employed to assess the significance of features. By
narrowing down the list of predictors, analysts can enhance model accuracy while simplifying the
analysis process. Additionally, the selection of features should consider domain knowledge,
incorporating variables known to affect stock prices, such as macroeconomic indicators (e.g.,

interest rates, inflation rates), industry trends, and company-specific metrics (e.g., earnings reports,
subscriber growth). This strategic approach ensures that the models capture relevant information
necessary for making informed predictions.
Feature Engineering Techniques Once the relevant features are identified, feature engineering
transforms raw data into a format suitable for modeling. Common techniques include creating
lagged variables, moving averages, and technical indicators. Lagged variables represent past
values of the stock price or other predictors, providing models with historical context. For example,
incorporating the stock price from the previous day as a predictor can help capture trends and
momentum in price movements. Moving averages smooth out price fluctuations over specific
periods, allowing analysts to identify underlying trends more effectively. Short-term and long-
term moving averages, such as the 50-day and 200-day averages, are often used to signal potential
buy or sell opportunities. Technical indicators, such as Relative Strength Index (RSI) and Moving
Average Convergence Divergence (MACD), provide additional insights into market conditions,
helping traders assess overbought or oversold scenarios.
Dimensionality Reduction In some cases, high-dimensional datasets can complicate the modeling
process. Dimensionality reduction techniques, such as Principal Component Analysis (PCA) or t-
distributed Stochastic Neighbor Embedding (t-SNE), can help by transforming the data into a
lower-dimensional space while retaining the essential characteristics. These techniques can
simplify models, reduce noise, and enhance interpretability, allowing analysts to focus on the most
critical components influencing stock prices.
Impact on Model Performance The impact of feature selection and engineering on model
performance can be evaluated through various metrics, including Mean Absolute Error (MAE),
Mean Squared Error (MSE), and R-squared values. By comparing models with and without certain
features, analysts can assess the contribution of each feature to the overall predictive capability.
This iterative process of refining features ensures that models remain robust and adaptable to
changing market conditions. By identifying relevant predictors, transforming raw data into
meaningful features, and employing dimensionality reduction techniques, analysts can
significantly enhance the predictive performance of their models. This process not only improves
accuracy but also facilitates a deeper understanding of the factors driving stock price movements,
ultimately leading to more informed investment decisions.

3. Comparative Analysis of Predictive Models
The effectiveness of stock price prediction largely hinges on the choice of modeling techniques.
In this section, we conduct a comparative analysis of various regression and machine learning
models, focusing on their predictive performance, strengths, and weaknesses. By examining
models such as Generalized Linear Models (GLM), Ridge Regression, Lasso Regression, Elastic
Net, and advanced machine learning approaches like Random Forest and Support Vector
Regression (SVR), we aim to identify the most effective strategies for forecasting Netflix stock
prices.
Traditional Regression Models Traditional regression techniques like GLM provide a foundation
for understanding relationships between stock prices and predictor variables. GLM is favored for
its interpretability and simplicity, allowing analysts to glean insights from the coefficients of the
model. However, GLM often struggles with non-linear relationships and multicollinearity issues,
which can hinder predictive accuracy. Ridge Regression addresses multicollinearity by
introducing an L2 regularization term, which penalizes large coefficients, thus stabilizing the
model. This can improve performance in scenarios where predictors are highly correlated.
Conversely, Lasso Regression employs an L1 penalty, which not only addresses multicollinearity
but also performs feature selection by shrinking some coefficients to zero. Elastic Net combines
both penalties, making it a versatile option for handling complex datasets. While these traditional
models can provide valuable insights, they may not fully capture the intricacies of stock price
movements, especially in a volatile market.
Machine Learning Models In contrast, machine learning models such as Random Forest and
SVR have emerged as powerful tools for forecasting stock prices. Random Forest operates by
constructing a multitude of decision trees and aggregating their predictions, effectively capturing
complex interactions and non-linear relationships in the data. This ensemble approach mitigates
the risk of overfitting, making Random Forest a robust choice for stock price prediction. Support
Vector Regression (SVR) is another formidable contender, particularly adept at handling high-
dimensional data. SVR identifies an optimal hyperplane that separates data points, allowing it to
model complex relationships effectively. It offers flexibility through different kernel functions,
enabling analysts to tailor the model to specific characteristics of the dataset.

Model Evaluation Metrics To assess the predictive performance of these models, we employ key
evaluation metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-
squared values. MAE provides an average of the absolute errors, offering a clear view of the
model's accuracy. MSE, on the other hand, emphasizes larger errors, providing insight into model
performance while penalizing outliers. R-squared values measure the proportion of variance
explained by the model, highlighting its explanatory power.
Results and Insights The comparative analysis reveals that machine learning models generally
outperform traditional regression techniques in terms of predictive accuracy, particularly in
capturing non-linear dynamics and interactions among variables. For instance, Random Forest and
SVR consistently demonstrate lower MAE and MSE values compared to GLM, Ridge, Lasso, and
Elastic Net, indicating their superior ability to forecast stock prices. While traditional regression
models provide valuable insights, advanced machine learning techniques such as Random Forest
and SVR offer enhanced predictive performance, particularly in complex financial markets. By
integrating these methodologies into stock price prediction, analysts can develop more effective
investment strategies and improve decision-making processes in an increasingly dynamic
environment.
Conclusion
In the realm of financial markets, accurately forecasting stock prices is crucial for informed
investment decisions and effective risk management strategies. This study has explored various
modeling techniques, including traditional regression methods and advanced machine learning
approaches, to predict the stock prices of Netflix. Through a comprehensive analysis, we have
identified the strengths and limitations of each method, emphasizing the importance of model
selection and feature engineering in enhancing predictive accuracy. The integration of traditional
models such as Generalized Linear Models (GLM), Ridge Regression, Lasso Regression, and
Elastic Net offers a foundational understanding of the relationships between stock prices and
various predictors. While these models provide interpretability and insights into the underlying
dynamics, they often fall short in capturing non-linear interactions and complex patterns within
the data. The introduction of regularization techniques, such as Ridge and Lasso, addresses some
limitations, particularly concerning multicollinearity and overfitting. However, they may not fully
exploit the potential of advanced analytics in volatile market conditions. On the other hand,

machine learning models like Random Forest and Support Vector Regression (SVR) demonstrate
remarkable capabilities in predicting stock prices by effectively handling large datasets and
uncovering hidden relationships. Their ability to model non-linear dynamics and interactions
provides a significant advantage in the ever-changing financial landscape. The comparative
analysis conducted in this study revealed that machine learning approaches consistently
outperformed traditional regression techniques, as evidenced by lower Mean Absolute Error
(MAE) and Mean Squared Error (MSE) values. Moreover, feature selection and engineering
emerged as critical components in developing robust predictive models. By carefully selecting
relevant predictors and transforming raw data into meaningful features, analysts can enhance
model performance and gain deeper insights into the factors influencing stock prices. This iterative
process of refining features ensures that models remain adaptable to changing market conditions,
ultimately leading to better-informed investment strategies. By integrating traditional regression
techniques with advanced machine learning models, analysts can significantly improve forecasting
accuracy, enabling them to navigate the complexities of financial markets more effectively. The
findings of this research not only contribute to the understanding of Netflix's stock performance
but also pave the way for further exploration of predictive analytics in finance. As markets continue
to evolve, the ongoing development and refinement of these methodologies will play a vital role
in shaping investment strategies and enhancing decision-making processes in the financial sector.
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