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.

This stock market dataset is designed for financial analysis and predictive modeling. It includes historical stock prices, technical indicators, macroeconomic factors, and sentiment scores to help in developing and testing machine learning models for stock trend prediction.

Dataset Features: Column Description Stock Random stock ticker (AAPL, GOOG, etc.) Date Random business date Open Open price High High price Low Low price Close Close price Volume Trading volume SMA_10 10-day Simple Moving Average RSI Relative Strength Index (10-90 range) MACD MACD indicator (-5 to 5) Bollinger_Upper Upper Bollinger Band Bollinger_Lower Lower Bollinger Band GDP_Growth Random GDP growth rate (2.5% to 3.5%) Inflation_Rate Inflation rate (1.5% to 3.0%) Interest_Rate Interest rate (0.5% to 5.0%) Sentiment_Score Random sentiment score (-1 to 1) Next_Close Next day's closing price (for regression) Target Binary classification (1: Price Increase, 0: Price Decrease)

Key Features: Stock Prices: Open, High, Low, Close, and Volume data. Technical Indicators: Simple Moving Average (SMA), Relative Strength Index (RSI), MACD, and Bollinger Bands. Macroeconomic Factors: Simulated GDP growth, inflation rate, and interest rates. Sentiment Scores: Randomized sentiment values between -1 and 1 to simulate market sentiment. Target Variables: Next-day close price (for regression) and price movement direction (for classification).

The main stock market index of United States, the US500, rose to 6818 points on December 2, 2025, gaining 0.08% from the previous session. Over the past month, the index has declined 0.50%, though it remains 12.70% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from United States. United States Stock Market Index - values, historical data, forecasts and news - updated on December of 2025.

The "Stock Market Dataset for AI-Driven Prediction and Trading Strategy Optimization" is designed to simulate real-world stock market data for training and evaluating machine learning models. This dataset includes a combination of technical indicators, market metrics, sentiment scores, and macroeconomic factors, providing a comprehensive foundation for developing and testing AI models for stock price prediction and trading strategy optimization.

Key Features Market Metrics:

Open, High, Low, Close Prices: Daily stock price movement. Volume: Represents the trading activity during the day. Technical Indicators:

RSI (Relative Strength Index): A momentum oscillator to measure the speed and change of price movements. MACD (Moving Average Convergence Divergence): An indicator to reveal changes in strength, direction, momentum, and duration of a trend. Bollinger Bands: Upper and lower bands around a stock price to measure volatility. Sentiment Analysis:

Sentiment Score: Simulated sentiment derived from financial news and social media, ranging from -1 (negative) to 1 (positive). Macroeconomic Factors:

GDP Growth: Indicates the overall health and growth of the economy. Inflation Rate: Reflects changes in purchasing power and economic stability. Target Variable:

Buy/Sell Signal: Binary classification (1 = Buy, 0 = Sell) based on price movement thresholds, simulating actionable trading decisions. Use Cases AI Model Training: Ideal for building stock prediction models using LSTM, Gradient Boosting, Random Forest, etc. Trading Strategy Optimization: Enables testing of trading algorithms and strategies in a simulated environment. Sentiment Analysis Research: Useful for understanding how sentiment influences stock movements. Feature Engineering and Selection: Provides a diverse set of features for experimentation with advanced techniques like PCA and LDA. Dataset Highlights Synthetic Yet Realistic: Carefully designed to mimic real-world financial data trends and relationships. Comprehensive Coverage: Includes key indicators and metrics used by traders and analysts. Scalable: Suitable for use in both small-scale academic projects and larger AI-driven trading platforms. Accessible for All Levels: The intuitive structure ensures that even beginners can utilize this dataset for financial machine learning applications. File Format The dataset is provided in CSV format, where:

Rows represent individual trading days. Columns represent features (technical indicators, market metrics, etc.) and the target variable. Acknowledgments This dataset is synthetically generated and is intended for research and educational purposes. It is not based on real market data and should not be used for actual trading.

Apple stock prices from years 2014 to 2023. This dataset can be used to predict price trend for next day based on technical indicators.

target : Price trend for next day - Multi Class Classification - bullish - If price increases more than 0.5% - bearish - If price fall more than 0.5% - neutral - If price movement stay with -0.5% to +0.5% range Following technical indicators included: - SMA: Simple Moving Average. Aid in determining if an asset price will continue or if it will reverse a bull or bear trend. - EMA: Exponential Moving Average. Shows how the price of an asset or security changes over a certain period of time. The EMA is different from a simple moving average in that it places more weight on recent data points - RSI: Relative Strength Index. RSI measures the speed and magnitude of a security's recent price changes to evaluate overvalued or undervalued conditions in the price of that security. Bolling: Bollinger Band. Generate oversold or overbought signals - MACD: Moving Average Convergence Divergence. A trend-following momentum indicator that shows the relationship between two exponential moving averages. - CCI: Commodity Channel Index. A technical indicator that measures the difference between the current price and the historical average price. - TR: True Range. Measures the daily range plus any gap from the closing price of the preceding day. - ATR: Average True Range. Average of true ranges over the specified period. ATR measures volatility, taking into account any gaps in the price movement.

Japan's main stock market index, the JP225, rose to 49553 points on December 2, 2025, gaining 0.51% from the previous session. Over the past month, the index has declined 3.78%, though it remains 26.25% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from Japan. Japan Stock Market Index (JP225) - values, historical data, forecasts and news - updated on December of 2025.

Context

Assume you work for a trading firm as an analyst. Your manager asks you to analyze the stock price of NVIDIA Corporation (Ticker NYSE: NVDA) and find out the following:

What caused the recent increase in the share price? Is there still potential for the stock price to go up?

In this case, what would you do?

1. Identify Potential Factors - Do some research to find out what factors affected the stock price rise. - Is it the company sales? Is it the company's expanding business? Is the entire sector performing well? Is it the entire stock market going up? - Several factors can affect the stock price increase based on your research and understanding of the company.

Once the factors are identified you can start collecting the data to do the analysis.

2. Collect the Data Once you have done your research and identified the factors that might have led to the price increase of NVDA stock, you can start collecting the data.

3. Relationship between the NVDA's Stock Price and Selected Factors After data is collected you may need a system that would tell you how the stock price of NVDA is influenced by each factor associated. You may not only want to know how are the fluctuations in the stock price but also quantify the fluctuations in the stock price for the changes in the quarterly sales number. You can build a linear regression model to understand this information.

4. Forecasting the future stock price Once you have built a linear model, you can create several scenarios to predict the stock price move with respect to the sales growth numbers. For example, simulate the stock price change given 0.5%, 1%, 1.5%, .... increase in sales number.

Identifying Potential Factors

Assume that after certain research, you were able to find out the following factors that has influenced the NVDA's stock price:

1. NVDA's sales growth
2. Dow Jones Industrial Average Index excess return
3. Advanced Micro Devices (AMD)'s stock excess return
4. Intel's stock excess return
5. Qualcomm's stock excess return
6. NVDA's stock excess return: We are interested in predicting the value for this variable.
7. Excess Return: Excess return is a return of a stock over and above the return of a risk-free rate such as Treasury Bills.

But, Why excess return?

Risk-Adjusted Performance: By investing in the stock market an investor takes additional risk compared to other risk-free assets. Hence, excess return helps the investor understand the risk premium earned for taking additional risk. This makes it a more accurate measure of how well a stock is performing relative to the additional risk taken.

Why competitor company's stock returns?

The stock prices/return of competitor companies can also affect the stock performance of NVDA due to some interconnected factors in the stock market:

- Earnings Report of Competitor Companies: If other companies are doing good in the same industry, it represents strong industry strength, and an investor can expect NVDA good results as the industry is doing good and start buying the shares which will have a positive impact on NVDA's share price. - Sector Trends: Companies within the same sector often move in the same direction due to sector-wise trends. If the competitor companies' share price rises due to some favourable conditions in the sector, NVDA's share price might also benefit due to positive sentiment in the sector.

Background:

Companies' worth or its total market value is called market capitalization or market cap. It is equal to the share price multiplied by the number of shares outstanding. Stock price is a proportional and relative value of companies' growth. Here, analyzing the stock price data will help us to understand a company's growth. An increase in stock price increases the company's market value.

Objective:

We have Collected the latest data of Microsoft Stock price and calculated daily log return which is approximately normally distributed. Let us try to answer some of the questions that will help us to decide roughly whether to invest in the Microsoft shares or not?

a) What is the probability that the stock price will drop over 5% in a day?

b) What is the probability that the stock price will drop over 10% in a day?

c) What is the probability that the stock price will drop over 50% in a year?

d) What is the probability that the stock price will drop over 25% in a year?

e) What is the 50th percentile of the yearly stock price?

Dataset:

MSFT.csv: It contains information about the stock price of Microsoft.

Date: Date of the stock price

Open: The average value of opened price on a particular day

Close: The average value of closed price on a particular day

Low: The lowest price reached on a particular day

High: The highest price reached on a particular day

ESG data is used as a basis for sound investment and financial decisions. It includes metrics related to Environmental-Social-Governance topics. The data is used to measure the progress of companies (and governments) towards sustainability goals such as greenhouse gas emissions, human rights, and board ethics to name just a few.

**The most successful companies use ESG as a key component of their business strategies. **Why? It can increase access to capital, help with efficiencies and innovation, improve talent acquisition and retention,and ensure compliance with regulations.

What is the relation of ESG to stock market data? ESG performance is used by analysts, financial institutions, investors, and more to identify how risky an investment might be. Companies with low ESG scores compared to their industry peers are increasingly considered to be riskier investments.

This dataset includes ESG scores from 3 well-known providers: MSCI, S&P Global, and Sustainalytics. It also includes scores from a company called ESGAnalytics.io that uses AI to detect ESG "signals" from press releases, media, etc. and then produces a real-time ESG score based on that "sentiment analysis." The ESG scores from the other 3 providers are generally updated annually.

The datasets also include key ratios used to analyze a stock's value: Price-to-book (P/B), price-to-earnings (P/E), Price-to-earnings-growth (PEG), and debt-to-equity. The stock market data was extracted from Finazon.io and Yahoo Finance the last week of June 2024.

**Similar datasets, including datasets for S&P 500 companies and for all 11 GICS (Global Industry Classification Standard) sectors are available at esgdatashop.io. **

View data of the S&P 500, an index of the stocks of 500 leading companies in the US economy, which provides a gauge of the U.S. equity market.

United Kingdom's main stock market index, the GB100, fell to 9690 points on December 2, 2025, losing 0.13% from the previous session. Over the past month, the index has declined 0.12%, though it remains 15.91% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from United Kingdom. United Kingdom Stock Market Index (GB100) - values, historical data, forecasts and news - updated on December of 2025.

1) name - The full name of the company or stock listed in the dataset.Example: NVIDIA Corporation. dtype -- object

2) symbol - The stock ticker symbol, which is a unique identifier for the company in the stock exchange. Example: NVDA (NVIDIA). dtype -- object

3) price - The current trading price of the stock in USD.Example: 131.29. dtype -- float64

4) change - The net change in the stock price during the last trading session, expressed in USD. Positive values indicate an increase, while negative values indicate a decrease in price. Example: -1.54. dtype -- flaot64

5) volume - The total number of shares traded for the stock during the trading session.Represented in millions (e.g., 197.102M = 197,102,000 shares). Example: 197.102M. dtype -- object

6) market_cap - The market capitalization of the company, calculated as the total number of outstanding shares multiplied by the stock's price.Represented in trillions (T), billions (B), or other notations.Example: 3.202T. dtype -- object

7) pe_ratio - The Price-to-Earnings ratio, a financial metric to evaluate a company's profitability relative to its stock price.A value of -- indicates that the P/E ratio is unavailable, often because the company is not profitable.Example: 44.66. dtype -- float

This dataset was generated using yfinance (yahoo finance python api), and contain the historical information from 2016-02-17 to 2024-05-07.

The purpose of this dataset is for testing an Offline Deep Reinforcement Learning algorithm.

Columns description:

• episode_id (string): The column representing the episode identifier.
• seq (integer): The column representing the order of the data.
• Close (float): The close price in each day.
• change_ewm (floats): Represent Exponential Moving Average in 3, 7, 15 and 30 days.
• action (integer): The column representing the action variable made by an expert.
• reward (float): The column representing the reward variable, which represent del percentage of how much you increase your portfolio after 30 days of trading.

Hong Kong's main stock market index, the HK50, rose to 26095 points on December 2, 2025, gaining 0.24% from the previous session. Over the past month, the index has declined 0.24%, though it remains 32.15% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from Hong Kong. Hong Kong Stock Market Index (HK50) - values, historical data, forecasts and news - updated on December of 2025.

Indonesia's main stock market index, the JCI, rose to 8617 points on December 2, 2025, gaining 0.80% from the previous session. Over the past month, the index has climbed 4.13% and is up 19.75% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Indonesia. Indonesia Stock Market (JCI) - values, historical data, forecasts and news - updated on December of 2025.

Stock Market Prediction by Taking Random Values Allying Two Deep Learning Models

The stock market is a place to lay investments in companies to boost their growth. The stock market can play an important role in a nation's future. A good stock market of a country always produces a decent mindset for entrepreneurs in those countries. But the stock market is a very volatile place. The price fluctuates rapidly in a short moment. There is also some common misconception among small shareholders that big companies always have a good price. The stock price can be changed due to the company's profit or loss at that moment, but it is not only bound to that. The weather forecast, festivals, and international relations of countries also play an important role. However, this project is for general purposes, to predict stock in normal situations. Anyone can use the data to grasp the whole situation of a company for predicting the near future. By stock prediction, govt. may also find irregular and suspicious stock fluctuation. To sell and buy stocks only help of stock prediction will be a very risky idea. But to find out some trends, prediction can help. Here, we have used time-series data to predict the next values. Normal deep learning models perform very well by learning complex time-shifted correlations between stepwise trends of a large number of noisy time series, using only the preceding time steps’ gradients as inputs. Thus, different models predict different results. Such correlations are present in stock prices, and these models can be used to predict changes in a price’s trend based on other stocks’ trend gradients of the previous time step. In more narrowly defined terms, this applied part is situated at the intersection of computational finance and financial econometrics. Combining and comparing two or more models can give us a good result. And combining it with random values may increase the fixed trends of a specific model. Thus, an average value and randomness can give us a better insight.

Stock Price Time Series for Warehouse REIT plc. Warehouse REIT is a UK Real Estate Investment Trust that invests in UK warehouses, focused on multi-let assets in industrial hubs across the UK. We provide a range of warehouse accommodation in key locations, which meets the needs of a broad range of occupiers. Our focus on multi-let assets means we provide occupiers with greater flexibility so we can continue to match their requirements as their businesses evolve, encouraging them to stay with us for longer. We invest in our business by selectively acquiring assets with potential and by delivering opportunities we have created. Through pro-active asset management we unlock the value inherent in our portfolio, helping to capture rising rents and driving an increase in capital values to deliver strong returns for our investors over the long term. Sustainability is embedded throughout our business, helping us meet the expectations of our stakeholders today and futureproofing our business for tomorrow. The Company is an alternative investment fund (AIF) for the purposes of the AIFM Directive and, as such, is required to have an investment manager who is duly authorised to undertake the role of an alternative investment fund manager (AIFM). The AIFM and the Investment Manager is currently G10 Capital Limited (Part of the IQEQ Group).

ABSTRACT Purpose: This paper analyses the viability of stock trading as a mechanism to promote corporate governance, addressing its effects on abnormal returns, information, and firm performance. Originality/value: The study indicates that competition among institutional investors is important to raise stock price efficiency. Policies that allow capital inflow, increase in liquidity, and a link between managers’ salaries and stock performance are beneficial to reinforce the stock market efficiency. Design/methodology/approach: Hypotheses testing using panel data regressions of 233 stocks between December 2009 to December 2017 from Thomson Eikon, Economatica and ComDinheiro. Findings: The results indicate that the number of institutional investors is not related to abnormal returns. On the other hand, the number of institutional investors increases the amount of firm-specific information into stock prices, rising stock market price efficiency. This relationship is stronger among the preferred stocks (PN), but this mechanism is still not valid to increase firms’ operational performance. Despite the possible increase in stock price efficiency, the investors cannot adopt such a mechanism to exercise governance if there is no remuneration linked to performance.

Context

Apple's transformation from "Apple Computer, Inc." to the world's first trillion-dollar company was driven by a masterful expansion of its product portfolio. While the iPhone was the star, its success was bolstered and complemented by the steady performance of the Mac, the creation of the tablet market with the iPad, and the explosive growth of the Wearables category with the Apple Watch and AirPods.

This synthetic dataset was created to provide a single, unified view of this incredible journey. It allows analysts, students, and enthusiasts to explore the entire Apple hardware ecosystem side-by-side on an annual basis. Since Apple stopped reporting official unit sales in 2018, this dataset uses a combination of historical reported data and realistic, revenue-based estimations to provide a continuous timeline from 2007 to a projection for 2025.

Content

The dataset consists of a single CSV file, apple_full_product_portfolio_2007_2025.csv. The columns are structured to provide a complete overview of Apple's performance:

Identifier Columns:

Year: The calendar year.

Average_Stock_Price_USD_Annual: The approximate average AAPL stock price for the year, adjusted for splits.

Model Release Columns:

iPhone_Model_Released: The flagship iPhone model(s) launched that year.

MacBook_Model_Released: The year's most significant MacBook releases (e.g., MacBook Air, Pro, key chip updates like M1).

iPad_Model_Released: The year's most significant iPad releases (e.g., iPad, Pro, Air, Mini).

Watch_Model_Released: The year's most significant Apple Watch releases (e.g., Series number, SE, Ultra).

Product Performance Metrics (pattern repeats for each product):

[Product]_Units_Sold_Millions: Estimated units sold for the product line.

[Product]_ASP_USD: Estimated Average Selling Price for the product line.

[Product]_Revenue_Billions: Estimated revenue in billions for the product line. (Products include: iPhone, MacBook, iPad, Watch, AirPods)

Consolidated Financials:

Services_Revenue_Billions: Revenue from services like the App Store, iCloud, Apple Music, etc.

Other_Products_Revenue_Billions: Revenue from all other minor products.

Total_Revenue_Billions: The comprehensive total annual revenue for Apple Inc.

Methodology

This dataset is a carefully constructed synthetic chronicle.

Data before 2018 is based on Apple's official (but now discontinued) unit sale reports and financial statements.

Data from 2018 onwards is estimated based on Apple's public quarterly financial reports, using reported category revenues to inform unit sales and ASP calculations.

Projections for 2024-2025 are conservative forecasts based on recent market trends.

Inspiration (Potential Project Ideas) This rich, multi-product dataset opens the door for deep strategic analysis:

The Rise of an Ecosystem: Create a stacked area chart of all revenue columns to visualize how Apple's revenue mix has evolved from being iPhone-centric to a balanced portfolio with massive growth in Wearables and Services.

Impact of Generational Leaps: Did the introduction of the M1 chip for MacBooks in 2020 have a more significant impact on sales and ASP than the Touch Bar in 2016? Pinpoint key model releases and measure their financial impact.

Cannibalization or Halo Effect?: Explore the relationship between product lines. Did the explosive growth of the iPad in its early years affect MacBook sales? Does a strong iPhone year correlate with a strong Apple Watch year?

Predictive Modeling: With over 18 years of comprehensive data, can you build a model that uses the performance of individual product lines to predict Apple's total revenue or future stock price?

The benchmark interest rate in India was last recorded at 5.50 percent. This dataset provides - India Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.