Not all IPOs result in long-term gains for the respective company; however, the average year-end gains for all companies that went public in the United States have been mostly positive each year over the past decade. In 2023, however, the average returns amounted to a negative ** percent in the first year after their IPO.

Initial IPO returns in the United States fluctuated between 2005 and 2023. Throughout the period considered, 2020 was the best year for first-day gains, amounting to ** percent. In 2023, the average first-day gain after an IPO in the U.S. was **** percent, as IPOs maintained their offering prices on their first day of trading.

2021 was quite a year for initial public offers (IPOs) in the United States, which was largely influenced by the significant rise in the number of special purpose acquisition companies (SPACs) who went public. In 2021, there were ***** initial public offerings (IPOs) in the United States. In 2022 and 2023, however, the number of IPOs dropped to *** and *** respectively. 2024 saw a rise in the number of IPOs, reaching *** by the end of the year. What does it mean to go public? The management of a private company has a lot of control over its operation, but raising funds from investors is more difficult. To access funds from regular investors, that is the general public, firms go public by offering stock shares at a certain price. As a result, these firms often have more capital to work with. An IPO can, and often does, raise ******** of dollars for a firm. However, publicly traded companies also face increased regulation and disclosure requirements. Staying private Some firms delay going public for a longer time, in spite of their increasing value. If their valuation goes above *********** U.S. dollars, these firms are called unicorns, and the highest valued unicorns are mostly based in the U.S. and China. Some firms, such as SpaceX, are still heavily investing in research and development projects, which shareholders often dislike due to low short-run dividends. At the moment, most unicorns are found in the technology sector, which is also the leading sector for IPOs in the United States. This indicates that investors consider this to be the industry most likely to see growth, and thus most worth investing in when companies go public.

The latest closing stock price for Exxon as of June 27, 2025 is 109.38. An investor who bought $1,000 worth of Exxon stock at the IPO in 1984 would have $41,833 today, roughly 42 times their original investment - a 9.60% compound annual growth rate over 41 years. The all-time high Exxon stock closing price was 122.12 on October 07, 2024. The Exxon 52-week high stock price is 126.34, which is 15.5% above the current share price. The Exxon 52-week low stock price is 97.80, which is 10.6% below the current share price. The average Exxon stock price for the last 52 weeks is 112.58. For more information on how our historical price data is adjusted see the Stock Price Adjustment Guide.

At nearly ** billion U.S. dollars, the 2014 initial public offering (IPO) of Alibaba Group Holding Limited remains the largest IPO in the United States ever. Trailing by almost **** billion U.S. dollars, Visa takes second place, followed by ENEL SpA, an energy company based in Italy. What is an IPO? An IPO is when a private company offers shares to the public for the first time through a stock exchange. Companies do this to raise money, as seen with Alibaba. However, public companies are subject to more scrutiny, such as publishing quarterly reports for investors. Also, not all IPOs are profitable. A bad IPO can result in significant losses. Companies that could go public Unicorns are private companies valued over a billion U.S. dollars. Any of these could go public, raising significant funds. However, most IPOs are valued in the ********, not ********. The median deal size of these offerings in the United States tends to be a little more than *** million U.S. dollars. Investors keep a watch for the next IPO, since a strong offering means high returns for those who buy the stock early.

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.

2020 and 2021 were a record year for SPAC IPO filings, even though they had been steadily growing in popularity over the last decade. In 2021, SPACs had raised capital in *** IPOs in that year alone. A special purpose acquisition company (SPAC) is a company with no business operations which is set up for the sole purpose of raising capital through an initial public offering with the goal of buying an existing company. The U.S. ranked second globally in terms of traditional IPO numbers, with the highest number of traditional IPOs occurring in mainland China. In comparison, there were ** SPAC IPOs in 2023, and ** in 2024. How have SPAC IPOs historically performed in the U.S.? From 2003 to 2019, the funds raised by SPAC IPOs remained somewhat consistent, with the value of funds never exceeding ** billion U.S. dollars except in 2003 and 2019. SPAC IPOs raised the largest amount of funds between January and December 2021, with the value of funds raised surpassing *** billion U.S. dollars. In the previous year, SPAC IPOs raised more funds than all preceding years combined. The U.S. vs Europe While SPAC IPOs in the U.S. have been slowly increasing over the past six years, numbers have remained significantly lower in Europe. Europe has still not seen annual SPAC IPO numbers exceed nine per year, while those in the U.S. have increased more each year, reaching a significant high-point in 2020 that is expected to be further surpassed by the end of 2021. During the first three months of 2021, less than **** percent of SPAC IPOs completed globally came from Europe.

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.

(IPO) IP Group: Innovation Engine or Value Trap?

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 latest closing stock price for Equus Total Return as of June 12, 2025 is 1.31. An investor who bought $1,000 worth of Equus Total Return stock at the IPO in 1992 would have $-490 today, roughly 0 times their original investment - a -2.02% compound annual growth rate over 33 years. The all-time high Equus Total Return stock closing price was 7.79 on March 31, 1998. The Equus Total Return 52-week high stock price is 1.52, which is 16% above the current share price. The Equus Total Return 52-week low stock price is 0.74, which is 43.5% below the current share price. The average Equus Total Return stock price for the last 52 weeks is 1.22. For more information on how our historical price data is adjusted see the Stock Price Adjustment Guide.

In total, the number of initial public offerings (IPOs) on the London Stock Exchange (LSE) in 2024 was **, less than the number of IPOs of 2023. Among these, more than half of the IPOs took place on the alternative investment market (AIM).

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.

LON:IPO IP GROUP PLC

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.

Opera IPO: Can OPRA Outperform the Competition?

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.

ADNOC Logistics & Services' IPO a Success

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 latest closing stock price for Cohen & Steers Total Return Realty Fund as of July 09, 2025 is 11.88. An investor who bought $1,000 worth of Cohen & Steers Total Return Realty Fund stock at the IPO in 1993 would have $15,179 today, roughly 15 times their original investment - a 9.09% compound annual growth rate over 32 years. The all-time high Cohen & Steers Total Return Realty Fund stock closing price was 12.87 on January 04, 2022. The Cohen & Steers Total Return Realty Fund 52-week high stock price is 13.44, which is 13.1% above the current share price. The Cohen & Steers Total Return Realty Fund 52-week low stock price is 10.43, which is 12.2% below the current share price. The average Cohen & Steers Total Return Realty Fund stock price for the last 52 weeks is 12.25. For more information on how our historical price data is adjusted see the Stock Price Adjustment Guide.

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.

Olaplex Holdings Inc. (OLPX) Stock: Can It Replicate Its Post-IPO Success?

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 latest closing stock price for Six Flags Entertainment as of June 11, 2025 is 32.03. An investor who bought $1,000 worth of Six Flags Entertainment stock at the IPO in 1987 would have $66,932 today, roughly 67 times their original investment - a 11.74% compound annual growth rate over 38 years. The all-time high Six Flags Entertainment stock closing price was 57.63 on July 05, 2024. The Six Flags Entertainment 52-week high stock price is 58.70, which is 83.3% above the current share price. The Six Flags Entertainment 52-week low stock price is 28.02, which is 12.5% below the current share price. The average Six Flags Entertainment stock price for the last 52 weeks is 42.37. For more information on how our historical price data is adjusted see the Stock Price Adjustment Guide.

The global Capital Markets Advisory Services market is experiencing robust growth, driven by increasing complexities in financial markets, the rise of private equity and venture capital investments, and a growing need for strategic guidance in mergers and acquisitions (M&A). The market, segmented by application (private and public companies) and service type (accounting advisory, IPO services, debt advisory, and others), shows significant potential across various regions. While North America currently holds the largest market share due to a mature financial ecosystem and a high concentration of large multinational corporations, Asia-Pacific is projected to witness the fastest growth rate fueled by rapid economic expansion and increasing cross-border investments. The strong performance of leading firms such as Deloitte, PwC, and KPMG highlights the market's consolidation trend and the premium placed on experienced advisors with a proven track record of successful transactions. The increasing demand for specialized services, including ESG (environmental, social, and governance) advisory within capital market transactions, further fuels the market's expansion. The forecast period of 2025-2033 anticipates continued expansion, albeit at a potentially moderating CAGR compared to the historical period, as the market matures. However, emerging economies and regulatory changes in specific regions will continue to present significant opportunities for growth. Restrictive regulations and economic downturns can act as temporary restraints, impacting transaction volumes and subsequently, the demand for advisory services. Nevertheless, the long-term outlook remains positive, supported by the ongoing need for sophisticated financial guidance and strategic planning in a dynamic global marketplace. The market's segmentation allows for niche players to thrive alongside established giants, resulting in a competitive yet innovative landscape.

The Hong Kong Capital Market Exchange ecosystem, boasting a market size of approximately $XX million in 2025 (assuming a logical extrapolation based on the provided CAGR of 8% and a known 2019-2024 historical period), exhibits robust growth potential. Driven by factors such as increasing foreign investment, a strengthening of the mainland China connection under the "Greater Bay Area" initiative, and the continued diversification of financial products offered (including debt and equity instruments catering to both retail and institutional investors), the market is poised for significant expansion. Key players like Tencent, Alibaba, and HSBC are pivotal in shaping this dynamic landscape, leveraging technological advancements and strategic partnerships to enhance market liquidity and attract international capital. Regulatory reforms aimed at improving market transparency and investor protection further contribute to the market's appeal. While potential restraints include geopolitical uncertainties and global economic fluctuations, the long-term outlook remains positive, particularly considering the strategic location of Hong Kong as a global financial hub. The segmentation of the Hong Kong Capital Market Exchange ecosystem reveals a complex interplay of market forces. The primary market, focused on initial public offerings (IPOs) and new listings, is expected to experience consistent growth driven by strong technology sector performance and continuing expansion of Chinese companies looking for international listings. Meanwhile, the secondary market, involving the trading of already-issued securities, benefits from high trading volumes and active participation from both retail and institutional investors. The balance between debt and equity financing is likely to shift according to prevailing economic conditions and investor risk appetite, with a potential increase in demand for fixed-income securities during periods of market volatility. Finally, the dominance of institutional investors is expected to persist, though the increasing financial literacy and participation of retail investors will gradually reshape the overall investor landscape. The forecast period (2025-2033) signals an exciting trajectory for this ecosystem, with continued growth projected across all segments. Recent developments include: In March 2023, In Hong Kong, Credit Suisse reopened as usual following UBS's US$3.25 billion takeover. Clients can continue trading stocks and derivatives at Credit Suisse's Hong Kong branch, as well as access their deposits. With assets of HK$100 billion (US$12.74 billion), or roughly 0.5 percent of the city's total banking assets, Credit Suisse operates just one branch in Hong Kong., In March 2022, The most prominent listed insurer in Asia, AIA Group, with headquarters in Hong Kong, declared after releasing better-than-expected 2021 earnings that it will repurchase USD 10 billion worth of its shares over the following three years.. Notable trends are: Investment and Holding, Real Estate, Professional and Business Services are Major FDIs in Hong Kong.

The value of SPAC IPOs completed in Europe fluctuated significantly between 2010 and 2020. In 2019, the value of European companies who went public via SPAC amounted to *** million U.S. dollars, a decrease of *** million U.S. dollars from the previous year. There were no reported SPAC IPOs completed in 2012, 2014, and 2020. Meanwhile, 2020 was a landmark year for SPAC IPOs in the United States with the total value exceeding ** billion U.S. dollars.

IPO: SPAC vs traditional Special purpose acquisition companies are public companies with neither a specific business plan, nor a product or service to sell. The specific purpose of SPACs is to raise capital and then merge with, or acquire a private company. This allows the private company to go public without going through the usual process of filing for an IPO, which typically takes longer and is more expensive. However, the equity returns of IPOs were almost double those of SPAC mergers in 2020.

Why are SPACs less common in Europe?
Despite the boom in SPACs across the pond, Europe has been slow to jump on the SPAC bandwagon. The U.S. has historically been home to more tech companies than Europe, which also means that fewer venture capital-backed IPO exits take place in Europe. This makes it harder for investors to analyze the potential returns a firm in the sector could generate.

The Star ** index value at the Shanghai Stock Exchange in China at the end of March 2025 was about ***** points. The Star ** index was dominated by the information technology industry and the pharmaceutical industry, including Beijing Kingsoft Office, Advanced Micro, and Haier Biometrical. A new milestone The Shanghai Stock Exchange introduced the Star ** index at the end of July 2020, which was a significant step in constructing the trading board. Created in July 2019, the Star Market targeted tech-startups by having a more lenient listing process compared to other markets in China. For instance, companies did not have to be profitable, and they did not require approval from government regulators. Instead, companies applied via a registration-style process, overseen by the stock exchange itself. Therefore, it could provide young companies with easier access to capital and facilitate their growth. Finally, the introduction of a stock index allowed investors to monitor and assess the performance of the board. The Star ** index tracked the performance of the ** biggest companies traded on the Star Market. Facilitating self-reliance Under the ongoing decoupling of the United States and China, policymakers in Beijing strived to have independent, domestic capital markets that were attractive enough to Chinese tech companies to list at home rather than overseas. Therefore, the Shanghai Stock Exchange copied the registration-style listing process from the NASDAQ as well as lowered the listing requirements. By mid-2020, the Star Market had the second most IPOs in the first half of the year, behind the NASDAQ.