Metaverse Dynamics: Predictive Modeling of Roblox Stock Prices using Time Series Analysis and Machine Learning - Soeltan Abdul Ghaffar; Wilbert Clarence Setiawan (Bright Publisher, 2024)
Stock price prediction is a critical task in finance and investment, enabling investors to make informed decisions and capitalize on market opportunities. This paper explores the application of predictive modeling techniques to forecast the stock prices of Roblox Corporation, a prominent player in the gaming industry. Despite the growing interest in predictive analytics, there remains a research gap concerning the application of these techniques to specific companies, particularly within the gaming sector. To address this gap, we employ a comprehensive dataset spanning from March 2021 to June 2023, obtained from Yahoo Finance, to develop predictive models using both time series analysis and machine learning algorithms. Our analysis encompasses exploratory data analysis, model development, and evaluation, culminating in insights into Roblox's stock price dynamics and model performance. The evaluation of our predictive models reveals promising results, with a Mean Squared Error (MSE) of 1.22, Root Mean Squared Error (RMSE) of 1.10, and a high R-squared (R2) score of 0.998. These metrics indicate relatively low prediction errors and a strong explanatory power of the models in capturing the variance in Roblox's closing prices. The findings shed light on the unique challenges and opportunities in predicting stock prices within the gaming industry and contribute to the growing body of knowledge in finance and investment. Through our research endeavors, we aim to empower investors and stakeholders with actionable insights to navigate the complexities of financial markets and make informed decisions with confidence and agility.