Model N reported $1.17B in Market Capitalization this June of 2024, considering the latest stock price and the number of outstanding shares.Data for Model N | MODN - Market Capitalization including historical, tables and charts were last updated by Trading Economics this last December in 2025.

The dataset contains a total of 25,161 rows, each row representing the stock market data for a specific company on a given date. The information collected through web scraping from www.nasdaq.com includes the stock prices and trading volumes for the companies listed, such as Apple, Starbucks, Microsoft, Cisco Systems, Qualcomm, Meta, Amazon.com, Tesla, Advanced Micro Devices, and Netflix.

Data Analysis Tasks:

1) Exploratory Data Analysis (EDA): Analyze the distribution of stock prices and volumes for each company over time. Visualize trends, seasonality, and patterns in the stock market data using line charts, bar plots, and heatmaps.

2)Correlation Analysis: Investigate the correlations between the closing prices of different companies to identify potential relationships. Calculate correlation coefficients and visualize correlation matrices.

3)Top Performers Identification: Identify the top-performing companies based on their stock price growth and trading volumes over a specific time period.

4)Market Sentiment Analysis: Perform sentiment analysis using Natural Language Processing (NLP) techniques on news headlines related to each company. Determine whether positive or negative news impacts the stock prices and volumes.

5)Volatility Analysis: Calculate the volatility of each company's stock prices using metrics like Standard Deviation or Bollinger Bands. Analyze how volatile stocks are in comparison to others.

Machine Learning Tasks:

1)Stock Price Prediction: Use time-series forecasting models like ARIMA, SARIMA, or Prophet to predict future stock prices for a particular company. Evaluate the models' performance using metrics like Mean Squared Error (MSE) or Root Mean Squared Error (RMSE).

2)Classification of Stock Movements: Create a binary classification model to predict whether a stock will rise or fall on the next trading day. Utilize features like historical price changes, volumes, and technical indicators for the predictions. Implement classifiers such as Logistic Regression, Random Forest, or Support Vector Machines (SVM).

3)Clustering Analysis: Cluster companies based on their historical stock performance using unsupervised learning algorithms like K-means clustering. Explore if companies with similar stock price patterns belong to specific industry sectors.

4)Anomaly Detection: Detect anomalies in stock prices or trading volumes that deviate significantly from the historical trends. Use techniques like Isolation Forest or One-Class SVM for anomaly detection.

5)Reinforcement Learning for Portfolio Optimization: Formulate the stock market data as a reinforcement learning problem to optimize a portfolio's performance. Apply algorithms like Q-Learning or Deep Q-Networks (DQN) to learn the optimal trading strategy.

The dataset provided on Kaggle, titled "Stock Market Stars: Historical Data of Top 10 Companies," is intended for learning purposes only. The data has been gathered from public sources, specifically from web scraping www.nasdaq.com, and is presented in good faith to facilitate educational and research endeavors related to stock market analysis and data science.

It is essential to acknowledge that while we have taken reasonable measures to ensure the accuracy and reliability of the data, we do not guarantee its completeness or correctness. The information provided in this dataset may contain errors, inaccuracies, or omissions. Users are advised to use this dataset at their own risk and are responsible for verifying the data's integrity for their specific applications.

This dataset is not intended for any commercial or legal use, and any reliance on the data for financial or investment decisions is not recommended. We disclaim any responsibility or liability for any damages, losses, or consequences arising from the use of this dataset.

By accessing and utilizing this dataset on Kaggle, you agree to abide by these terms and conditions and understand that it is solely intended for educational and research purposes.

Please note that the dataset's contents, including the stock market data and company names, are subject to copyright and other proprietary rights of the respective sources. Users are advised to adhere to all applicable laws and regulations related to data usage, intellectual property, and any other relevant legal obligations.

In summary, this dataset is provided "as is" for learning purposes, without any warranties or guarantees, and users should exercise due diligence and judgment when using the data for any purpose.

Model N reported $538.4M in Assets for its fiscal quarter ending in March of 2024. Data for Model N | MODN - Assets including historical, tables and charts were last updated by Trading Economics this last December in 2025.

Model N reported $290.06M in Debt for its fiscal quarter ending in March of 2024. Data for Model N | MODN - Debt including historical, tables and charts were last updated by Trading Economics this last December in 2025.

Model N reported $2.66M in EBITDA for its fiscal quarter ending in March of 2024. Data for Model N | MODN - Ebitda including historical, tables and charts were last updated by Trading Economics this last December in 2025.

This dataset contains synthetic stock price data for five companies across 900 days in 2023. The data is designed to resemble real-time financial data, providing key insights for those interested in stock market analysis, trend predictions, and data visualization projects. Each entry includes a stock price, trade volume, and daily percentage change in price, making it suitable for various financial forecasting or machine learning models.

Columns:

Date: The specific day the stock data was recorded (YYYY-MM-DD format). Company: The name of the company. Companies included are: TechCorp HealthPlus FinancePro GreenEnergy AutoInnovate Stock Price (USD): The stock price of the company on the given date. Volume: The number of shares traded on the given date. Percentage Change: The percentage change in stock price compared to the previous day’s price. Usage: This dataset is ideal for:

Stock price trend analysis Time-series forecasting Machine learning models for predicting stock prices Data visualization and financial analytics practice License: The dataset is synthetic and does not contain any personal or proprietary information. It is free to use for educational and research purposes.

About the Google Stock Price Dataset

The Google Stock Price Dataset consists of two CSV (Comma Separated Values) files containing historical stock price data for training and evaluation. Each row in the dataset represents a trading day, and the columns provide various information related to Google's stock for that day.

Columns:

Date: The date of the trading day in the format "YYYY-MM-DD."

Open: The opening price of Google's stock on that trading day.

High: The highest price reached during the trading day.

Low: The lowest price reached during the trading day.

Close: The closing price of Google's stock on that trading day.

Adj Close: The adjusted closing price, accounting for any corporate actions (e.g., stock splits, dividends) that may affect the stock's value.

Volume: The trading volume, representing the number of shares traded on that trading day.

Time Period: The train dataset spans from January 1, 2010, to December 31, 2022, providing twelve years of daily stock price information for model training. The test dataset spans from January 1, 2023, to July 30, 2023, providing seven month of daily stock price data for model evaluation.

Data Source:

The dataset was collected from Yahoo Finance (finance.yahoo.com), a reputable and widely-used financial data platform.

Use Case:

The Google Stock Price Dataset can be utilized for various purposes, such as predicting future stock prices, analyzing historical stock trends, and building machine learning models for financial forecasting.

Potential Applications:

Time Series Analysis: Explore stock price patterns and seasonality. Financial Modeling: Develop predictive models to forecast stock prices. Algorithmic Trading: Create trading strategies based on historical stock data. Risk Management: Assess potential risks and volatilities in the stock market.

Citation:

If you use this dataset in your research or analysis, please provide proper attribution and citation to acknowledge the source.

License: This dataset is provided under the Creative Commons CC0 1.0 Universal (CC0 1.0) Public Domain Dedication, making it freely available for use without any restrictions or attribution requirements.

Model N reported $803K in EBIT for its fiscal quarter ending in March of 2024. Data for Model N | MODN - Ebit including historical, tables and charts were last updated by Trading Economics this last December in 2025.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

What are the most successful trading algorithms? (NTAP Stock Forecast)

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

The price of Tesla shares traded on the Nasdaq stock exchange remained rather stable between July 2010 and January 2020. With the beginning of 2020, the price of Tesla shares increased dramatically and stood at 381.59 U.S. dollars per share in November 2021. Since then, the price of Tesla shares has fluctuated significantly and reached its peak at 444.72 U.S. dollars per share in September 2025. Why did Tesla's stock value go up in 2020? Despite the effects of the pandemic, Tesla share prices experienced a massive increase in 2020. Tesla kept increasing its output levels throughout the year, except for the second quarter, and released its new vehicle, the Tesla Model Y. Additionally, when the company was added to the S&P 500 index in December 2020, it instilled further trust in investors. In 2020, Tesla was the top-performing stock on the S&P 500 index, and two years later, in 2024, it ranked among the ten largest companies on the index by market capitalization. Steady growth in the last decade Founded in 2003, Tesla primarily focuses on designing and producing electric vehicles, as well as energy generation and storage systems. Since then, Tesla's revenue has steadily increased, reaching nearly 98 billion U.S. dollars in 2024. Most of the revenue came from automotive sales in 2024. Tesla's first electric car, the Roadster, was sold between 2008 and 2012. Currently, the company offers four primary electric vehicles: Model 3, Model Y, Model S, and Model X.

Stocks of video game retailer GameStop exploded in January 2021, effectively doubling in value on a daily basis. At the close of trading on January 27, GameStop Corporation's stock price reaching 86.88 U.S. dollars per share - or +134 percent compared to the day before. On December 30, 2020, the price was valued at 4.82 U.S. dollars per share. The cause of this dramatic increase is a concerted effort via social media to raise the value of the company's stock, intended to negatively affect professional investors planning to ‘short sell’ GameStop shares. As professional investors started moving away from GameStop the stock price began to fall, stabilizing at around 11-13 U.S. dollars in mid-February. However, stock prices unexpectedly doubled again on February 24, and continued to rise, reaching 66.25 U.S. dollars at the close of trade on March 10. The reasons for this second increase are not fully clear. At the close of trade on January 29, 2025, GameStop shares were trading at nearly 27.5 U.S. dollars. Who are GameStop? GameStop are a retailer of video games and associated merchandise headquartered in a suburbs of Dallas, Texas, but with stores throughout North America, Europe, Australia and New Zealand. As of February 2020 the group maintained just over 5,500 stores, variously under the GameStop, EB Games, ThinkGeek, and Micromania-Zing brands. The company's main revenue source in 2020 was hardware and accessories - a change from 2019, when software sales were the main source of revenue. While the company saw success in the decade up to 2016 (owing to the constant growth of the video game industry), GameStop experienced declining sales since because consumers increasingly purchased video games digitally. It is this continual decline, combined with the effect of the global coronavirus pandemic on traditional retail outlets, that led many institutional investors to see GameStop as a good opportunity for short selling. What is short selling? Short selling is where an investor effectively bets on a the price of a financial asset falling. To do this, an investor borrows shares (or some other asset) via an agreement that the same number of shares be returned at a future date. They can then sell the borrowed shares, and purchase the same number back once the price has fallen to make a profit. Obviously, this strategy only works when the share price does fall – otherwise the borrowed stocks need to be repurchased at a higher price, causing a loss. In the case of GameStop, a deliberate campaign was arranged via social media (particularly Reddit) for individuals to purchase GameStop shares, thus driving the price higher. As a result, some estimates place the loss to institutional investors in January 2021 alone at around 20 billion U.S. dollars. However, once many of these investors had 'closed out' their position by returning the shares they borrowed, demand for GameStop stock fell, leading to the price reduction seen early in early February. A similar dynamic was seen at the same time with the share price of U.S. cinema operator AMC.

In 2025, ** percent of adults in the United States invested in the stock market. This figure has remained steady over the last few years and is still below the levels before the Great Recession, when it peaked in 2007 at ** percent. What is the stock market? The stock market can be defined as a group of stock exchanges where investors can buy shares in a publicly traded company. In more recent years, it is estimated an increasing number of Americans are using neobrokers, making stock trading more accessible to investors. Other investments A significant number of people think stocks and bonds are the safest investments, while others point to real estate, gold, bonds, or a savings account. Since witnessing the significant one-day losses in the stock market during the financial crisis, many investors were turning towards these alternatives in hopes for more stability, particularly for investments with longer maturities. This could explain the decrease in this statistic since 2007. Nevertheless, some speculators enjoy chasing the short-run fluctuations, and others see value in choosing particular stocks.

Model N reported $67.74M in Operating Expenses for its fiscal quarter ending in March of 2024. Data for Model N | MODN - Operating Expenses including historical, tables and charts were last updated by Trading Economics this last December in 2025.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

How accurate is machine learning in stock market? (FHN Stock Forecast)

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Horizo​​n Broadening Prospects? (HBNC)

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

In the dynamic landscape of financial markets, accurate forecasting of stock indices remains a pivotal yet challenging task, essential for investors and policymakers alike. This study is motivated by the need to enhance the precision of predicting the Shanghai Composite Index’s opening price spread, a critical measure reflecting market volatility and investor sentiment. Traditional time series models like ARIMA have shown limitations in capturing the complex, nonlinear patterns inherent in stock price movements, prompting the exploration of advanced methodologies. The aim of this research is to bridge the gap in forecasting accuracy by developing a hybrid model that integrates the strengths of ARIMA with deep learning techniques, specifically Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks. This novel approach leverages the ARIMA model’s proficiency in linear trend analysis and the deep learning models’ capability in modeling nonlinear dependencies, aiming to provide a comprehensive tool for market prediction. Utilizing a comprehensive dataset covering the period from December 20, 1990, to June 2, 2023, the study develops and assesses the efficacy of ARIMA, LSTM, GRU, ARIMA-LSTM, and ARIMA-GRU models in forecasting the Shanghai Composite Index’s opening price spread. The evaluation of these models is based on key statistical metrics, including Mean Squared Error (MSE) and Mean Absolute Error (MAE), to gauge their predictive accuracy. The findings indicate that the hybrid models, ARIMA-LSTM and ARIMA-GRU, perform better in forecasting the opening price spread of the Shanghai Composite Index than their standalone counterparts. This outcome suggests that combining traditional statistical methods with advanced deep learning algorithms can enhance stock market prediction. The research contributes to the field by providing evidence of the potential benefits of integrating different modeling approaches for financial forecasting, offering insights that could inform investment strategies and financial decision-making.

All data acquired on December 11th 2023

1) Ticker: Stock symbol identifying the company.

2) Company: Name of the company.

3) Sector: Industry category to which the company belongs.

4) Industry: Specific sector or business category of the company.

5) Country: Country where the company is based.

6) Market Cap: Total market value of a company's outstanding shares.

7) Price: Current stock price.

8) Change (%): Percentage change in stock price.

9) Volume: Number of shares traded.

10) Price to Earnings Ratio: Ratio of stock price to earnings per share.

11) Price to Earnings: Price-to-earnings ratio based on past earnings.

12) Forward Price to Earnings: Expected price-to-earnings ratio.

13) Price/Earnings to Growth: Ratio of P/E to earnings growth.

14) Price to Sales: Ratio of stock price to annual sales.

15) Price to Book: Ratio of stock price to book value.

16) Price to Cash: Ratio of stock price to cash per share.

17) Price to Free Cash Flow: Ratio of stock price to free cash flow.

18) Earnings Per Share This Year (%): Percentage change in earnings per share for the current year.

19) Earnings Per Share Next Year (%): Percentage change in earnings per share for the next year.

20) Earnings Per Share Past 5 Years (%): Percentage change in earnings per share over the past 5 years.

21) Earnings Per Share Next 5 Years (%): Estimated percentage change in earnings per share over the next 5 years.

22) Sales Past 5 Years (%): Percentage change in sales over the past 5 years.

23) Dividend (%): Dividend yield as a percentage of the stock price.

24) Return on Assets (%): Percentage return on total assets.

25) Return on Equity (%): Percentage return on shareholder equity.

26) Return on Investment (%): Percentage return on total investment.

27) Current Ratio: Ratio of current assets to current liabilities.

28) Quick Ratio: Ratio of liquid assets to current liabilities.

29) Long-Term Debt to Equity: Ratio of long-term debt to shareholder equity.

30) Debt to Equity: Ratio of total debt to shareholder equity.

31) Gross Margin (%): Percentage difference between revenue and cost of goods sold.

32) Operating Margin (%): Percentage of operating income to revenue.

33) Profit Margin: Percentage of net income to revenue.

34) Earnings: Net income of the company.

35) Outstanding Shares: Total number of shares issued by the company.

36) Float: Tradable shares available to the public.

37) Insider Ownership (%): Percentage of company owned by insiders.

38) Insider Transactions: Recent insider buying or selling activity.

39) Institutional Ownership (%): Percentage of company owned by institutional investors.

40) Float Short (%): Percentage of tradable shares sold short by investors.

41) Short Ratio: Number of days it would take to cover short positions.

42) Average Volume: Average number of shares traded daily.

43) Performance (Week) (%): Weekly stock performance percentage.

44) Performance (Month) (%): Monthly stock performance percentage.

45) Performance (Quarter) (%): Quarterly stock performance percentage.

46) Performance (Half Year) (%): Semi-annual stock performance percentage.

47) Performance (Year) (%): Annual stock performance percentage.

48) Performance (Year to Date) (%): Year-to-date stock performance percentage.

49) Volatility (Week) (%): Weekly stock price volatility percentage.

50) Volatility (Month) (%): Monthly stock price volatility percentage.

51) Analyst Recommendation: Analyst consensus recommendation on the stock.

52) Relative Volume: Volume compared to the average volume.

53) Beta: Measure of stock price volatility relative to the market.

54) Average True Range: Average price range of a stock.

55) Simple Moving Average (20) (%): Percentage difference from the 20-day simple moving average.

56) Simple Moving Average (50) (%): Percentage difference from the 50-day simple moving average.

57) Simple Moving Average (200) (%): Percentage difference from the 200-day simple moving average.

58) Yearly High (%): Percentage difference from the yearly high stock price.

59) Yearly Low (%): Percentage difference from the yearly low stock price.

60) Relative Strength Index: Momentum indicator measuring the speed and change of price movements.

61) Change from Open (%): Percentage change from the opening stock price.

62) Gap (%): Percentage difference between the previous close and the current open price.

63) Volume: Total number of shares traded.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

NXN Nuveen New York Select Tax-Free Income Portfolio Common Stock

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Model N reported $281.2M in Loan Capital for its fiscal quarter ending in March of 2024. Data for Model N | MODN - Loan Capital including historical, tables and charts were last updated by Trading Economics this last November in 2025.

The accelerated progress in artificial intelligence encourages sophisticated deep learning methods in predicting stock prices. In the meantime, easy accessibility of the stock market in the palm of one’s hand has made its behavior more fuzzy, volatile, and complex than ever. The world is looking at an accurate and reliable model that uses text and numerical data which better represents the market’s highly volatile and non-linear behavior in a broader spectrum. A research gap exists in accurately predicting a target stock’s closing price utilizing the combined numerical and text data. This study uses long short-term memory (LSTM) and gated recurrent unit (GRU) to predict the stock price using stock features alone and incorporating financial news data in conjunction with stock features. The comparative study carried out under identical conditions dispassionately evaluates the importance of incorporating financial news in stock price prediction. Our experiment concludes that incorporating financial news data produces better prediction accuracy than using the stock fundamental features alone. The performances of the model architecture are compared using the standard assessment metrics —Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Correlation Coefficient (R). Furthermore, statistical tests are conducted to further verify the models’ robustness and reliability.