April 9, 2025, saw the largest one-day gain in the history of the Dow Jones Industrial Average (DJIA), follwing Trump's announcement of 90-day delay in the introduction of tariffs imposed on imports from all countries. The second-largest one-day gain occurred on March 24, 2020, with the index increasing ******** points. This occurred approximately two weeks after the largest one-day point loss occurred on March 9, 2020, which was triggered by the growing panic about the coronavirus outbreak worldwide. Index fluctuations The DJIA is an index of ** large companies traded on the New York Stock Exchange. It is one of the numbers that financial analysts watch closely, using it as a bellwether for the United States economy. Seeing when these large gains occur, as well as the largest one-day point losses, gives insight to why these fluctuations may occur. The gains in 2009 are likely adjustments after major losses during the Financial Crisis, but those in 2018 are probably signs of high market volatility. Other leading financial indicators While the DJIA is closely watched, it only gives insight on the performance of thirty leading U.S. companies. An index like the S&P 500, tracking *** companies, can give a more comprehensive overview of the United States economy. Even so, this only reflects investment. Other parts of the economy, such as consumer spending or unemployment rate are not well reflected in stock market indices.

According to Cognitive Market Research, the Global Pre-owned Luxury Watches market size is $26,832.60 Million in 2024 and it is forecasted to reach $43,653.90 Million by 2031. Pre-owned Luxury Watches Industry's Compound Annual Growth Rate will be 7.20% from 2024 to 2031. Market Dynamics of the

Pre- owned Luxury Watches Market

Market Drivers of the

Pre owned Luxury Watches Market

Growing recognition of high-end timepieces as both status symbols and enduring investments is boosting the demand for pre-owned luxury watches

The concept of luxury has been changing dramatically across time and culture. Earlier, luxury was connected with things like wines, champagne, designer clothes and sports cars. These days, people have become richer and luxury is a blurred generation that is no longer the preserve of the elite. People are having much more disposable income in comparison to earlier generations, resulting in a tendency brands like apple mobile, boat watches. Luxury watches have gained popularity over the years with Swiss watches continuing to be the heart of the industry.

From August 2018 to January 2023, average prices in the second-hand market for top models from the three largest luxury brands— Rolex, Patek Philippe, and Audemars Piguet—rose at an annual rate of 20%, despite broader market downturns during the pandemic, compared with an annual rate of 8% for the S&P 500 index. Wealthy investors increasingly seek alternative investments to diversify their portfolios and to hedge against inflation. For these and other investors, luxury watches stand out as a class of alternative assets because of the strong demand for them and because they have generally delivered strong price performance in the market over the past five to ten years. Buyers regard the category as a stable investment built on reputable brands and supported by a consumer base of high-net-worth individuals. In the ten-year period from 2013 to 2022, watches outperformed collectible assets such as jewellery, handbags, wine, art, and furniture, growing in value at an average annual rate of 7%—and by 27% from 2020 to 2022—according to indices that track these categories. Classic buyers typically invest in traditional financial assets and appreciate durable, credible products. They purchase across price ranges and seek classic or timeless watches with a strong brand heritage or a distinctive design. Whereas, there are customers which can be categorized into two different segments which include, luxury watch hobbyist and collector/investor. Hobbyist buyers prefer technically complex watches, with a strong brand heritage in the super-luxury category, where watch value is generally expected to increase over time. Moderately frequent buyers, hobbyists (77% of whom are male) tend to be status-conscious and successful. Much of the pleasure they find in purchasing a second-hand watch is in the hunt for a special item. On the other hand, members of this buyer segment are the most active buyer group, on average, favouring ultra-luxury watches at a higher price point than other segments prefer. They represent 44% of watch buyers and claim a 58% share of the market by value. This segment is highly engaged with the secondary market, with nearly three-quarters having bought a second-hand piece in the past 24 months.

Therefore, one major reason that the secondary market has grown is clearly that consumers seeking investment opportunities are gravitating to it. Gen Z and younger millennial buyers said that they had increased their spending on luxury watches during the previous 24 months, citing increased ease of buying and selling and more investment opportunities as their top reasons.

Rising second-hand luxury watches consumption is gaining popularity, thereby, driving the market growth

Global sales of second-hand luxury products are steadily increasing. While there are more people than ever interested in owning a watch, luxury brands, which include the big four: Patek Philippe, Rolex, Audemars Piguet, and Richard Mille continue to produce limited inventory every year to ensure exclusivity and quality. Then there is the general growth in the second-hand luxury market. Since the pandemic took hold, consumers have begun investing in long-lasting, quality items, with luxury sales set to beat pre-COVID numbers this year. On Rebag, most watches sell within a few...

Prices for United States Stock Market Index (US30) including live quotes, historical charts and news. United States Stock Market Index (US30) was last updated by Trading Economics this July 13 of 2025.

Taiwan's main stock market index, the Taiwan Stock Market Index, rose to 22751 points on July 11, 2025, gaining 0.25% from the previous session. Over the past month, the index has climbed 2.08%, though it remains 4.87% lower than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from Taiwan. Taiwan Stock Market Index (TWSE) - values, historical data, forecasts and news - updated on July of 2025.

For the sixth consecutive year, the SADC watch market recorded growth in sales value, which increased by 6.4% to $179M in 2024. The total consumption indicated notable growth from 2012 to 2024: its value increased at an average annual rate of +3.6% over the last twelve years. The trend pattern, however, indicated some noticeable fluctuations being recorded throughout the analyzed period. Based on 2024 figures, consumption increased by +140.2% against 2018 indices.

The CIS watch market surged to $650M in 2024, jumping by 42% against the previous year. The total consumption indicated a tangible increase from 2012 to 2024: its value increased at an average annual rate of +2.8% over the last twelve years. The trend pattern, however, indicated some noticeable fluctuations being recorded throughout the analyzed period. Based on 2024 figures, consumption increased by +142.4% against 2022 indices.

Vietnam's main stock market index, the VN, rose to 1458 points on July 11, 2025, gaining 0.84% from the previous session. Over the past month, the index has climbed 10.19% and is up 13.82% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Vietnam. Vietnam Ho Chi Minh Stock Index - values, historical data, forecasts and news - updated on July of 2025.

In 2024, the Spanish watch market increased by 17% to $710M, rising for the fourth year in a row after six years of decline. In general, the total consumption indicated modest growth from 2012 to 2024: its value increased at an average annual rate of +1.8% over the last twelve-year period. The trend pattern, however, indicated some noticeable fluctuations being recorded throughout the analyzed period. Based on 2024 figures, consumption increased by +126.1% against 2020 indices.

Since mid 2022, market prices of a selected group of most traded luxury watches have consistently declined. As of ***********, the average price of a luxury watch was worth ****** U.S. dollars.

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.

First Watch (FWRG) Ready for Breakfast?

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.

Intel: A Company to Watch

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 global watch and clock cases market revenue amounted to $X in 2017, going down by -X% against the previous year. In general, the total market indicated a remarkable growth from 2007 to 2017: its value increased at an average annual rate of +X% over the last decade. The trend pattern, however, indicated some noticeable fluctuations throughout the analyzed period. Based on 2017 figures, the watch and clock cases consumption decreased by -X% against 2015 indices.

Baltic Dry rose to 1,663 Index Points on July 11, 2025, up 13.52% from the previous day. Over the past month, Baltic Dry's price has fallen 15.50%, and is down 16.73% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Baltic Exchange Dry Index - values, historical data, forecasts and news - updated on July of 2025.

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

Guardant Health: A Stock to Watch? (GH)

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.

VinFast: Warrant Watch (VFSWW)

Financial data:

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

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

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

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

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

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

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

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

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

• Data cleaning and preprocessing are essential before model training

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

In 2024, the Europe watch market increased by 1.7% to $8.2B, rising for the fourth consecutive year after two years of decline. The total consumption indicated slight growth from 2012 to 2024: its value increased at an average annual rate of +1.2% over the last twelve-year period. The trend pattern, however, indicated some noticeable fluctuations being recorded throughout the analyzed period. Based on 2024 figures, consumption increased by +63.1% against 2020 indices.

This dataset is prepared for statistical factor pricing models and standardized across variables including country, region, currency, vendor, artist for seamless data filtering. It contains 20+ years of all items in the luxury watches both on auction and in the private markets. Brands include: A. Lange & Söhne, Audemars Piguet, Blancpain, Breguet, Breitling, Bremont, Bulgari, Cartier, Chopard, F.P. Journe, Hublot, IWC, Jaeger-LeCoultre, Omega, Panerai, Patek Philippe, Piaget, Richard Mille, Rolex, Seiko, TAG Heuer, Tudor, Ulysse Nardin, Vacheron Constantin, Zenith Vendors include: Christie's, Sotheby's, Phillips, Bonhams

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.

Nikkei 225: A Market to Watch, But Don't Be Fooled

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.

Palantir: The Stock to Watch in 2023

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.

Ranpak: A Packstock Ticker ( PACK ) To Watch

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