Futuristic cityscape interwoven with stock charts and circuit boards, representing the intersection of technology and finance in stock prediction.

Decoding NVIDIA's Stock Future: A Beginner's Guide to Predicting Price Swings

Kai Mendoza in Business & Economy December 2025 • 4 min read.

"Unlock the secrets of stock market forecasting with our easy-to-understand analysis of NVIDIA's potential next-day stock prices. From ARIMA to LSTM, discover which model reigns supreme."

Forecasting stock prices is a daunting task, yet it holds immense value for investors, traders, and financial institutions. In today's rapidly evolving technological landscape, NVIDIA has risen to prominence as a key innovator across numerous sectors. Given its significant impact, understanding and predicting NVIDIA's stock performance is more crucial than ever.

This guide explores four distinct forecasting models—Autoregressive Integrated Moving Average (ARIMA), Multilayer Perceptron Network (MLP), Long Short-Term Memory (LSTM) networks, and ARIMA integrated with the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model—to evaluate their effectiveness in predicting NVIDIA's next-day stock prices.

Drawing on five years of stock data sourced from Yahoo Finance, from April 12, 2019, to April 11, 2024, we will provide a detailed analysis of NVIDIA's stock performance. By simplifying these complex models, we aim to equip you with the knowledge to better understand stock market predictions.

Why is Predicting Stock Prices So Challenging?

Futuristic cityscape interwoven with stock charts and circuit boards, representing the intersection of technology and finance in stock prediction.

Predicting stock prices has long been a subject of intense research and fascination within financial markets. The ability to forecast price movements accurately can translate into significant financial gains for investors, traders, and financial institutions. However, the stock market is a complex, dynamic environment influenced by a multitude of factors, making accurate predictions exceptionally difficult.

Numerous researchers have turned to statistical and deep learning methods to tackle this challenge, seeking to enhance the accuracy of stock price predictions. These methods range from traditional time series analysis to sophisticated neural networks, each with its strengths and limitations.

Statistical Methods: These include techniques like ARIMA, which identifies linear trends in data.
Deep Learning: Models such as LSTM can capture complex, nonlinear patterns.
Hybrid Approaches: Combining different methods to leverage their individual strengths.

Despite the variety of approaches, the inherent volatility and complexity of financial markets mean that no single method guarantees perfect predictions. Understanding the strengths and weaknesses of different models is crucial for making informed decisions.

The Future of Stock Prediction: What's Next?

In conclusion, while our analysis of various time series models reveals similar performance characteristics across different methodologies, the ARIMA-GARCH model shows promise. The ongoing quest for more accurate stock predictions continues, driven by the potential for substantial financial rewards. Future research and development in this field will likely focus on refining existing models, incorporating new data sources, and leveraging the latest advancements in artificial intelligence to navigate the complexities of the stock market.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: https://doi.org/10.48550/arXiv.2405.08284,

Title: Predicting Nvidia'S Next-Day Stock Price: A Comparative Analysis Of Lstm, Mlp, Arima, And Arima-Garch Models

Subject: econ.em cs.lg stat.ap

Authors: Yiluan Xing, Chao Yan, Cathy Chang Xie

Published: 13-05-2024

Everything You Need To Know

What are the primary methods used to predict NVIDIA's stock prices?

The primary methods explored for predicting NVIDIA's stock prices include Autoregressive Integrated Moving Average (ARIMA), Multilayer Perceptron Network (MLP), Long Short-Term Memory (LSTM) networks, and ARIMA integrated with the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model. These models use historical data to identify patterns and forecast future price movements. ARIMA is a statistical method ideal for linear trends. LSTM is a deep learning model designed to capture complex, nonlinear patterns, making it suitable for volatile markets. The ARIMA-GARCH model combines ARIMA with GARCH to address volatility, offering a potential advantage in forecasting.

Why is predicting NVIDIA's stock price considered a valuable undertaking?

Predicting NVIDIA's stock price is highly valuable because of the potential for significant financial gains for investors, traders, and financial institutions. Accurate predictions allow for better investment decisions, maximizing profits and minimizing losses. As NVIDIA is a key innovator, understanding its stock performance offers insight into the technology sector's future, providing a competitive edge in the financial market. Correct predictions have direct implications on investment returns and strategic financial planning, making it a crucial area of study.

What is the role of ARIMA and how does it work in predicting stock prices?

ARIMA, or Autoregressive Integrated Moving Average, is a statistical method used to predict NVIDIA's stock prices. It works by analyzing historical stock data to identify and model linear trends and patterns. ARIMA combines three components: Autoregression (AR), which uses past values of the stock price; Integration (I), which involves differencing the data to make it stationary; and Moving Average (MA), which uses past forecast errors. By understanding the relationships between these components, ARIMA forecasts future stock prices, offering a foundational approach in financial analysis.

How do LSTM networks compare to other methods, like ARIMA, in forecasting NVIDIA's stock prices?

LSTM (Long Short-Term Memory) networks, a type of deep learning model, differ significantly from statistical methods like ARIMA in their approach to forecasting NVIDIA's stock prices. While ARIMA focuses on linear trends, LSTM excels at capturing complex, non-linear patterns and long-term dependencies within the data. LSTMs are particularly effective in handling the volatility and complexities of financial markets, as they can retain information over extended periods. This makes them well-suited for analyzing intricate market behaviors and identifying subtle shifts that may not be apparent through simpler methods like ARIMA.

What is the ARIMA-GARCH model, and why does it show promise in stock price prediction?

The ARIMA-GARCH model combines the Autoregressive Integrated Moving Average (ARIMA) with the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model to forecast stock prices. ARIMA handles the time series data and GARCH models the volatility or changes in variance over time, which is a key aspect of financial markets. This hybrid approach allows the model to capture both linear trends and the volatility clustering often seen in stock prices, providing a more comprehensive and potentially more accurate prediction of NVIDIA's stock performance. The combination of ARIMA and GARCH specifically targets the unpredictable nature of the market, and offers a promising direction in the quest for improved stock price forecasting.