Stock Market Performance and Corporate Board Member Profiles

Analyzing the Influences of Leadership on Stock Market Performance

By Jon Loyens [source]

About this dataset

This powerful dataset brings together publically-available information from leading stock markets with extensive details about corporate board members. For each company, discover not only their board composition and background, but also current market dynamics, trends and rule changes affecting them. Whether you're a teacher looking to add more detail to a class presentation or an investor seeking a competitive edge in the market - this dataset provides comprehensive insights into the world of stocks and those that play an influential role on its direction. Unprecedented access awaits as you explore hypothetical investments and strategies or actual risks associated with established entities today

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

Using this dataset, you can gain a better understanding of the relationship between corporate board members and stock market performance. You can analyze the data to determine the average performance of board members at different companies and compare it to the overall performance of other stocks. In addition, you can look into correlations between individual stocks, various industries, and different groups of companies with similar board membership profiles. This dataset provides an overview of all major stocks across multiple industries with detailed insights on each stock's current and past market performance as well as corporate boards

Research Ideas

• Analyzing the performance of individual board members in relation to their company’s stock market performance.
• Determining if certain board members are better at making decisions that benefit the company’s stock market position across all companies they have a stake in.
• Identifying correlations between trends in different companies' stocks and external factors such as the influence of particular board members or other events associated with that company's sectors or markets

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: boardmembers.csv | Column name | Description | |:--------------------|:-----------------------------------| | BoardMemberName | Name of the board member. (String) | | CompanyName | Name of the company. (String) | | Source | Source of the data. (String) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Jon Loyens.

📦 Global Financial and Macroeconomic Market Dataset

This dataset contains structured historical financial market data across various countries and time intervals. It is sourced from publicly available metastock-formatted and ASCII-formatted files used in economic and trading research.

📁 Directory Overview

financial_dataset/ ├── 5 min/ │ ├── hk/ # Hong Kong 5-min market data │ ├── hu/ # Hungary 5-min market data │ ├── pl/ # Poland 5-min market data │ ├── uk/ # United Kingdom 5-min market data │ ├── us/ # United States 5-min market data │ └── world/ # Global economic/market data (5-min) │ ├── daily/ │ ├── hk/ # Hong Kong daily data │ ├── hu/ # Hungary daily data │ └── jp/ # Japan daily market data

ℹ️ Note: Some hidden macOS metadata files (like ._filename) may appear; they can be safely ignored or removed before analysis.

⸻

🌍 Country & Region Codes

Code Country/Region hk Hong Kong hu Hungary pl Poland uk United Kingdom us United States jp Japan world Global aggregation (macroeconomic indices)

⸻

🕒 Timeframes

Folder Name Frequency Description daily/ Daily End-of-day market summaries (open, high, low, close, volume) 5 min/ 5-minute Intraday data for high-frequency market modeling or backtesting

⸻

📊 File Format & Content • All files are in ASCII format. • Columns typically include: • date • open • high • low • close • volume • There may be slight variations based on country/source.

⸻

📌 Source

Data retrieved from Sqoot/Metastock economic archives as of May 7, 2025.

⸻

🔍 Use Cases • Backtesting trading algorithms on intraday data • Macroeconomic trend modeling • Comparative analysis between developed and emerging markets • Visualization of international market movement patterns

China's main stock market index, the SHANGHAI, rose to 3826 points on August 22, 2025, gaining 1.45% from the previous session. Over the past month, the index has climbed 6.80% and is up 34.03% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from China. China Shanghai Composite Stock Market Index - values, historical data, forecasts and news - updated on August of 2025.

Analysis of ‘Time Series Forecasting with Yahoo Stock Price ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/arashnic/time-series-forecasting-with-yahoo-stock-price on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

Stocks and financial instrument trading is a lucrative proposition. Stock markets across the world facilitate such trades and thus wealth exchanges hands. Stock prices move up and down all the time and having ability to predict its movement has immense potential to make one rich. Stock price prediction has kept people interested from a long time. There are hypothesis like the Efficient Market Hypothesis, which says that it is almost impossible to beat the market consistently and there are others which disagree with it.

There are a number of known approaches and new research going on to find the magic formula to make you rich. One of the traditional methods is the time series forecasting. Fundamental analysis is another method where numerous performance ratios are analyzed to assess a given stock. On the emerging front, there are neural networks, genetic algorithms, and ensembling techniques.

Another challenging problem in stock price prediction is Black Swan Event, unpredictable events that cause stock market turbulence. These are events that occur from time to time, are unpredictable and often come with little or no warning.

A black swan event is an event that is completely unexpected and cannot be predicted. Unexpected events are generally referred to as black swans when they have significant consequences, though an event with few consequences might also be a black swan event. It may or may not be possible to provide explanations for the occurrence after the fact – but not before. In complex systems, like economies, markets and weather systems, there are often several causes. After such an event, many of the explanations for its occurrence will be overly simplistic.

#
#

https://www.visualcapitalist.com/wp-content/uploads/2020/03/mm3_black_swan_events_shareable.jpg"> #
#
New bleeding age state-of-the-art deep learning models stock predictions is overcoming such obstacles e.g. "Transformer and Time Embeddings". An objectives are to apply these novel models to forecast stock price.

Content

Stock price prediction is the task of forecasting the future value of a given stock. Given the historical daily close price for S&P 500 Index, prepare and compare forecasting solutions. S&P 500 or Standard and Poor's 500 index is an index comprising of 500 stocks from different sectors of US economy and is an indicator of US equities. Other such indices are the Dow 30, NIFTY 50, Nikkei 225, etc. For the purpose of understanding, we are utilizing S&P500 index, concepts, and knowledge can be applied to other stocks as well.

Dataset

The historical stock price information is also publicly available. For our current use case, we will utilize the pandas_datareader library to get the required S&P 500 index history using Yahoo Finance databases. We utilize the closing price information from the dataset available though other information such as opening price, adjusted closing price, etc., are also available. We prepare a utility function get_raw_data() to extract required information in a pandas dataframe. The function takes index ticker name as input. For S&P 500 index, the ticker name is ^GSPC. The following snippet uses the utility function to get the required data.(See Simple LSTM Regression)

Features and Terminology: In stock trading, the high and low refer to the maximum and minimum prices in a given time period. Open and close are the prices at which a stock began and ended trading in the same period. Volume is the total amount of trading activity. Adjusted values factor in corporate actions such as dividends, stock splits, and new share issuance.

Starter Kernel(s)

• Simple LSTM Regression

Acknowledgements

Mining and updating of this dateset will depend upon Yahoo Finance .

Inspiration

Sort of variation of sequence modeling and bleeding age e.g. attention can be applied for research and forecasting

Some Readings

• Applications of deep learning in stock market prediction: recent progress
• Stock predictions with state-of-the-art Transformer and Time Embeddings
• https://arxiv.org/pdf/1712.02136.pdf
• https://catanacapital.com/blog/unpredictable-black-swan-event-stock-market/
• https://par.nsf.gov/servlets/purl/10149715
• https://arxiv.org/pdf/1712.02136.pdf

*If you download and find the data useful your upvote is an explicit feedback for future works*

--- Original source retains full ownership of the source dataset ---

Overview: Welcome to my Kaggle profile! In this dataset, you will find a comprehensive collection of intraday activity data for USA's stocks, covering a single day of trading. As an avid enthusiast of the stock market and data analysis, I have meticulously curated this dataset to provide valuable insights and opportunities for further research and analysis.

Content: The dataset contains a wealth of information on various USA's stocks, each represented as individual data points. The key features of the dataset include:

Timestamp: The exact time when the data was recorded during the trading session. Stock Symbol: The unique identifier for each stock listed on the USA stock exchanges. Open Price: The opening price of the stock at the given timestamp. High Price: The highest price reached by the stock during the timestamp. Low Price: The lowest price reached by the stock during the timestamp. Close Price: The closing price of the stock at the given timestamp. Volume: The total trading volume of the stock at the given timestamp. Potential Insights: With this dataset, you can uncover various insights and trends related to intraday trading of USA's stocks. Some potential analysis opportunities include:

Stock Price Movement: Analyzing the price movement of individual stocks throughout the trading day. Volume Analysis: Investigating the relationship between trading volume and price fluctuations. Stock Correlations: Identifying correlations between different stocks during the day. Identifying Market Patterns: Discovering intraday market patterns or trends. Market Sentiment Analysis: Exploring the sentiment of investors during specific time intervals. Applications: The dataset can be beneficial for a wide range of applications, including:

Algorithmic Trading: Developing and testing intraday trading strategies using historical data. Predictive Modeling: Building models to predict stock price movements based on intraday activity. Financial Research: Conducting in-depth studies on specific stocks or sectors. Market Analysis: Gaining insights into broader market behavior and trends. Acknowledgment: I would like to express my gratitude to the financial community and Kaggle for providing an incredible platform to share and explore data. This dataset is a product of my passion for the stock market and data analytics. I hope it sparks curiosity and serves as a valuable resource for fellow data enthusiasts, traders, and researchers.

Happy exploring and may this dataset lead you to new discoveries and successful endeavors in the exciting world of stock trading!

Note: Please keep in mind that stock market data can be volatile and subject to fluctuations. Always exercise caution and perform thorough analysis before making any financial decisions based on this dataset.

The popularization of financial literacy has become a global trend, with governments across the world expressing commitment to continuously enhancing the financial literacy of their citizens to improve the country’s overall financial well-being. However, there is a lack of research evaluating the actual effects of financial literacy on Chinese households. This study first investigated the micro impact of financial literacy on the household stock profit level using data from the 2019 China Household Finance Survey. As most existing studies use factor analysis to measure financial literacy from a single dimension of financial knowledge, our study additionally used the entropy method to construct a composite evaluation system of financial literacy from four dimensions: financial skills, knowledge, attitudes, and behaviors. The ordinary least squares model was utilized as the primary regression model to estimate the correlation, and the average financial literacy of other households in the same community was selected as an instrumental variable. Further instrumental variable regression analysis was conducted using the two-stage least squares method. Three robustness tests were performed to ensure the reliability of the research findings. The results demonstrate that financial literacy significantly enhances household stock profit levels. The mediation effect analysis indicates that financial literacy affects stock profit levels through financial information attention. Moreover, financial literacy has a more substantial promoting effect on stock profit levels for households with members working for state-owned enterprises and those living in first-tier cities. This study confirms the value of financial literacy; identifies important channels for residents to increase their property income; and provides important guidance for the government, educational organizations, and financial institutions. This also injects more vigor into market participation to improve the persistently sluggish Chinese stock market.

Pakistan's main stock market index, the KSE 100, rose to 149564 points on August 22, 2025, gaining 0.22% from the previous session. Over the past month, the index has climbed 7.40% and is up 89.80% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Pakistan. Pakistan Stock Market (KSE100) - values, historical data, forecasts and news - updated on August of 2025.

The “richness index” represents the level of economical wellbeing a country certain area in 2010. Regions with higher income per capita and low poverty rate and more access to market are wealthier and are therefore better able to prepare for and respond to adversity. The index results from the second cluster of the Principal Component Analysis preformed among 9 potential variables. The analysis identifies four dominant variables, namely “GDPppp per capita”, “agriculture share GDP per agriculture sector worker”, “poverty rate” and “market accessibility”, assigning weights of 0.33, 0.26, 0.25 and 0.16, respectively. Before to perform the analysis all variables were log transformed (except the “agriculture share GDP per agriculture sector worker”) to shorten the extreme variation and then were score-standardized (converted to distribution with average of 0 and standard deviation of 1; inverse method was applied for the “poverty rate” and “market accessibility”) in order to be comparable. The 0.5 arc-minute grid total GDPppp is based on the night time light satellite imagery of NOAA (see Ghosh, T., Powell, R., Elvidge, C. D., Baugh, K. E., Sutton, P. C., & Anderson, S. (2010).Shedding light on the global distribution of economic activity. The Open Geography Journal (3), 148-161) and adjusted to national total as recorded by International Monetary Fund for 2010. The “GDPppp per capita” was calculated dividing the total GDPppp by the population in each pixel. Further, a focal statistic ran to determine mean values within 10 km. This had a smoothing effect and represents some of the extended influence of intense economic activity for the local people. Country based data for “agriculture share GDP per agriculture sector worker” were calculated from GDPppp (data from International Monetary Fund) fraction from agriculture activity (measured by World Bank) divided by the number of worker in the agriculture sector (data from World Bank). The tabular data represents the average of the period 2008-2012 and were linked by country unit to the national boundaries shapefile (FAO/GAUL) and then converted into raster format (resolution 0.5 arc-minute). The first administrative level data for the “poverty rate” were estimated by NOAA for 2003 using nighttime lights satellite imagery. Tabular data were linked by first administrative unit to the first administrative boundaries shapefile (FAO/GAUL) and then converted into raster format (resolution 0.5 arc-minute). The 0.5 arc-minute grid “market accessibility” measures the travel distance in minutes to large cities (with population greater than 50,000 people). This dataset was developed by the European Commission and the World Bank to represent access to markets, schools, hospitals, etc.. The dataset capture the connectivity and the concentration of economic activity (in 2000). Markets may be important for a variety of reasons, including their abilities to spread risk and increase incomes. Markets are a means of linking people both spatially and over time. That is, they allow shocks (and risks) to be spread over wider areas. In particular, markets should make households less vulnerable to (localized) covariate shocks. This dataset has been produced in the framework of the “Climate change predictions in Sub-Saharan Africa: impacts and adaptations (ClimAfrica)” project, Work Package 4 (WP4). More information on ClimAfrica project is provided in the Supplemental Information section of this metadata.

Japan TSE: Turnover: Value: For Members' Accounts data was reported at 21,785.000 JPY bn in Jun 2018. This records a decrease from the previous number of 22,841.000 JPY bn for May 2018. Japan TSE: Turnover: Value: For Members' Accounts data is updated monthly, averaging 14,082.000 JPY bn from Mar 1988 (Median) to Jun 2018, with 364 observations. The data reached an all-time high of 50,286.000 JPY bn in Aug 2007 and a record low of 1,751.000 JPY bn in Jan 1993. Japan TSE: Turnover: Value: For Members' Accounts data remains active status in CEIC and is reported by Japan Exchange Group. The data is categorized under Global Database’s Japan – Table JP.Z008: Tokyo Stock Exchange: Turnover: By Type of Investors and Accounts.

Crude Oil rose to 63.73 USD/Bbl on August 22, 2025, up 0.34% from the previous day. Over the past month, Crude Oil's price has fallen 2.32%, and is down 14.83% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Crude Oil - values, historical data, forecasts and news - updated on August of 2025.

Number of Listed Companies: Nigeria Stock Exchange data was reported at 164.000 Unit in Sep 2018. This stayed constant from the previous number of 164.000 Unit for Jun 2018. Number of Listed Companies: Nigeria Stock Exchange data is updated quarterly, averaging 184.000 Unit from Jun 2013 (Median) to Sep 2018, with 21 observations. The data reached an all-time high of 192.000 Unit in Sep 2014 and a record low of 164.000 Unit in Sep 2018. Number of Listed Companies: Nigeria Stock Exchange data remains active status in CEIC and is reported by The Nigerian Stock Exchange. The data is categorized under Global Database’s Nigeria – Table NG.Z005: Nigeria Stock Exchange: Number of Listed Companies.

The global big data market is forecasted to grow to 103 billion U.S. dollars by 2027, more than double its expected market size in 2018. With a share of 45 percent, the software segment would become the large big data market segment by 2027.

What is Big data?

Big data is a term that refers to the kind of data sets that are too large or too complex for traditional data processing applications. It is defined as having one or some of the following characteristics: high volume, high velocity or high variety. Fast-growing mobile data traffic, cloud computing traffic, as well as the rapid development of technologies such as artificial intelligence (AI) and the Internet of Things (IoT) all contribute to the increasing volume and complexity of data sets.

Big data analytics

Advanced analytics tools, such as predictive analytics and data mining, help to extract value from the data and generate new business insights. The global big data and business analytics market was valued at 169 billion U.S. dollars in 2018 and is expected to grow to 274 billion U.S. dollars in 2022. As of November 2018, 45 percent of professionals in the market research industry reportedly used big data analytics as a research method.

South Korea's main stock market index, the KOSPI, rose to 3169 points on August 22, 2025, gaining 0.86% from the previous session. Over the past month, the index has declined 0.47%, though it remains 17.29% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from South Korea. South Korea Stock Market - values, historical data, forecasts and news - updated on August of 2025.

Context

Dow Jones 30 is a price-weighted measurement stock market index of 30 prominent companies listed on stock exchanges in the United States. It is one of the most concise Indices as compared to other comprehensive ones such as NASDAQ or S&P 500.

The value index can be sum of the stock prices of the companies included in the index, divided by a factor which is currently approximately 0.152. The factor is changed whenever a constituent company undergoes a stock split so that the value of the index is unaffected by the stock split.

Content

In the dataset, You'll find stock prices of 16 major companies which are listed in DOW JONES 30 Index before Apr 1, 1999 (Obviously there are 14 others which they've replaced others over time and were added in the index after the said date)

Date: Simply the data in m/d/yyyy format (which works in the US)

MMM to Walt Disney: Average stock price of the day of the corresponding companies

DJIA: "Dow Jones Industrial Average" which can be said the target column of the dataset. It is the pool in which all the 30 stocks have their influence. Target can be seen as how much impact each and every stock lays on the pool.

Acknowledgement

The data is obtained from the Moneycontrol website.

Inspiration

Predicting the influence of major stocks on one of the most weight carrying indices of the world.

The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly, reaching *** zettabytes in 2024. Over the next five years up to 2028, global data creation is projected to grow to more than *** zettabytes. In 2020, the amount of data created and replicated reached a new high. The growth was higher than previously expected, caused by the increased demand due to the COVID-19 pandemic, as more people worked and learned from home and used home entertainment options more often. Storage capacity also growing Only a small percentage of this newly created data is kept though, as just * percent of the data produced and consumed in 2020 was saved and retained into 2021. In line with the strong growth of the data volume, the installed base of storage capacity is forecast to increase, growing at a compound annual growth rate of **** percent over the forecast period from 2020 to 2025. In 2020, the installed base of storage capacity reached *** zettabytes.

The population share with mobile internet access in North America was forecast to increase between 2024 and 2029 by in total 2.9 percentage points. This overall increase does not happen continuously, notably not in 2028 and 2029. The mobile internet penetration is estimated to amount to 84.21 percent in 2029. Notably, the population share with mobile internet access of was continuously increasing over the past years.The penetration rate refers to the share of the total population having access to the internet via a mobile broadband connection.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the population share with mobile internet access in countries like Caribbean and Europe.

In 2022, the global total corporate investment in artificial intelligence (AI) reached almost ** billion U.S. dollars, a slight decrease from the previous year. In 2018, the yearly investment in AI saw a slight downturn, but that was only temporary. Private investments account for a bulk of total AI corporate investment. AI investment has increased more than ******* since 2016, a staggering growth in any market. It is a testament to the importance of the development of AI around the world. What is Artificial Intelligence (AI)? Artificial intelligence, once the subject of people’s imaginations and the main plot of science fiction movies for decades, is no longer a piece of fiction, but rather commonplace in people’s daily lives whether they realize it or not. AI refers to the ability of a computer or machine to imitate the capacities of the human brain, which often learns from previous experiences to understand and respond to language, decisions, and problems. These AI capabilities, such as computer vision and conversational interfaces, have become embedded throughout various industries’ standard business processes. AI investment and startups The global AI market, valued at ***** billion U.S. dollars as of 2023, continues to grow driven by the influx of investments it receives. This is a rapidly growing market, looking to expand from billions to trillions of U.S. dollars in market size in the coming years. From 2020 to 2022, investment in startups globally, and in particular AI startups, increased by **** billion U.S. dollars, nearly double its previous investments, with much of it coming from private capital from U.S. companies. The most recent top-funded AI businesses are all machine learning and chatbot companies, focusing on human interface with machines.

This dataset offers a comprehensive historical record of Netflix’s stock price movements, capturing the company’s financial journey from its early days to its position as a global streaming giant.

From its IPO in May 2002, Netflix (Ticker: NFLX) has transformed from a DVD rental service to a powerhouse in on-demand digital content. With its disruptive innovation, strategic shifts, and global expansion, Netflix has seen dramatic shifts in stock prices, reflecting not just market trends but also cultural impact. This dataset provides a window into that evolution.

What’s Included?

Each row in this dataset represents daily trading activity on the stock market and includes the following columns:

• Date – The trading day (from 2002 onward)
• Open – Stock price when the market opened
• High – Highest trading price of the day
• Low – Lowest trading price of the day
• Close – Final price at market close
• Adj Close – Closing price adjusted for splits and dividends
• Volume – Number of shares traded that day

The data is structured in CSV format and is clean, easy to use, and ready for immediate analysis.

Why Use This Dataset?

Whether you're learning data science, building a financial model, or exploring machine learning in the real world, this dataset is a goldmine of insights. Netflix's market history includes:

• Periods of explosive growth during digital transformation
• Volatility during market crashes and global events (e.g., 2008, COVID-19)
• Strategic pivots such as the shift to original content
• Market reactions to earnings, acquisitions, and subscriber milestones

This makes the dataset ideal for:

• Time-series forecasting (ARIMA, Prophet, LSTM)
• Technical and trend analysis (moving averages, RSI, Bollinger Bands)
• Predictive modeling with machine learning
• Investment simulation projects
• Stock market visualization and storytelling
• Financial dashboards (Tableau, Power BI, Streamlit, etc.)

Who Can Use It?

This dataset is designed for:

• Aspiring data scientists practicing EDA and modeling
• Financial analysts and traders exploring trends
• AI researchers working on time-series models
• Students building ML projects
• Developers creating stock visualization tools
• Kaggle competitors seeking real-world datasets

Data Source & Credits

The dataset is derived from publicly available historical stock price data, such as Yahoo Finance, and has been cleaned and organized for educational and research purposes. It is continuously maintained to ensure accuracy.

Start Exploring

Netflix’s rise is more than just a business story — it’s a data-driven journey. With this dataset, you can analyze the company’s stock behavior, train models to predict future trends, or simply visualize how tech reshapes the market.

Money Supply M2 in the United States increased to 21942 USD Billion in May from 21862.40 USD Billion in April of 2025. This dataset provides - United States Money Supply M2 - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This dataset presents an extensive record of daily historical stock prices for Tesla, Inc. (TSLA), one of the world’s most innovative and closely watched electric vehicle and clean energy companies. The data was sourced from Yahoo Finance, a widely used and trusted provider of financial market data, and covers a significant period spanning from Tesla’s initial public offering (IPO) to the most recent date available at the time of extraction.

The dataset includes critical trading metrics for each market day, such as the opening price, highest and lowest prices of the day, closing price, adjusted closing price (accounting for dividends and splits), and total trading volume. This rich dataset supports a variety of use cases, including financial market analysis, investment research, time series forecasting, development and backtesting of trading algorithms, and educational projects in data science and finance.

Dataset Features

• Date: The calendar date for each trading session (in YYYY-MM-DD format)
• Open: The opening price of TSLA shares at the start of the trading day
• High: The highest price reached during the trading session
• Low: The lowest price reached during the trading session
• Close: The last price at which the stock traded during the day
• Adj Close: The closing price adjusted for corporate actions (splits, dividends, etc.)
• Volume: The total number of TSLA shares traded on that day

Source and Collection Details

• Source: Yahoo Finance - Tesla (TSLA) Historical Data
• Collection Method: Data was downloaded using Yahoo Finance's CSV export feature for accuracy and completeness.
• Time Range: Covers from Tesla’s IPO (June 2010) to the most recent available trading day.
• Data Integrity: Minimal cleaning was performed—dates were standardized, and any duplicate or empty rows were removed; all values remain as originally reported by Yahoo Finance.

Example Use Cases

• Stock Price Prediction: Train and test time series models (ARIMA, LSTM, Prophet, etc.) to forecast Tesla’s stock prices.
• Algorithmic Trading: Backtest and evaluate trading strategies using historical price and volume data.
• Market Trend Analysis: Analyze price trends, volatility, and return rates over different periods.
• Event Study: Investigate the impact of major announcements (e.g., product launches, earnings releases) on TSLA stock price.
• Educational Projects: Use as a hands-on resource for learning finance, statistics, or machine learning.

License & Acknowledgments

• Intended Use: This dataset is provided for academic, research, and personal projects. For commercial or investment use, please verify data accuracy and consult Yahoo Finance’s terms of use.
• Acknowledgment: Data sourced from Yahoo Finance. All trademarks and copyrights belong to their respective owners.