The main stock market index of United States, the US500, rose to 6464 points on September 1, 2025, gaining 0.06% from the previous session. Over the past month, the index has climbed 2.13% and is up 16.92% compared to the same time last year, 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 September 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.

Dow Jones U.S. Select Insurance Index: Poised for a Rebound?

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.

U.S. Physical Therapy (USPH): Rebound or Reset?

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

Wholesale Inventories in the United States increased 0.20 percent in July of 2025 over the previous month. This dataset provides - United States Wholesale Inventories - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Stocks of crude oil in the United States decreased by 2.39million barrels in the week ending August 22 of 2025. This dataset provides the latest reported value for - United States Crude Oil Stocks Change - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

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.

American Petroleum Adventuring (APA): Ready to Rebound?

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

Chip stocks rise in premarket trading, buoyed by Nvidia's surge and a global market rebound, despite ongoing U.S.-China tariff tensions.

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.

Skechers USA (SKX) Future: Bounce Back or Remain Flat?

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

Credit bureaus and rating agencies in the US have experienced notable growth in recent years due to heightened demand for information. The reliance on data analytics has driven increased interest in these services, which provide vital information on creditworthiness for both individuals and businesses. This has been particularly significant as businesses and individuals seek to make well-informed financial decisions. Despite challenges related to the pandemic, inflation and high interest rates, the industry has thrived and profit has soared, indicating its resilience and the critical nature of the services it offers in a data-driven economy. While long-term demand for information has buoyed the industry, providers’ trajectory has been influenced by broader economic conditions, notably equity market fluctuations. The industry weathered initial pandemic-related disruptions, which precipitated a sharp fall in stock prices and corporate profit. Nonetheless, rapid fiscal and monetary responses bolstered investor confidence and led to a robust rebound in equity markets, contributing to massive revenue growth in 2020 and 2021. Soaring interest rates in 2022 and 2023 boosted recessionary fears among investors, hindering demand for equities, reducing stock prices and thus contributing to a major drop in revenue in 2022. These effects have percolated into the real economy as consumer and business borrowing has slowed, constraining aggregate household debt and corporate debt. These effects have negatively impacted the industry in 2023 and 2024, though a rebound in the stock market has prevented a major collapse in revenue. Overall, revenue for credit bureaus and rating agencies in the US is anticipated to soar at a CAGR of 4.3% over the past five years, reaching $16.4 billion in 2024. This includes a 1.3% drop in revenue in that year. Looking ahead, credit bureaus and rating agencies will face a more tempered growth trajectory over the next five years. The broad adoption of online services and data analytics has led to market saturation, reducing opportunities for exponential revenue growth. Nonetheless, stable economic growth and business formation should sustain a steady demand for credit reporting and rating services. The predicted slower growth in equity prices will moderate financial institutions' borrowing capacity, which will also contribute to the slowdown in revenue growth. Overall, revenue for credit bureaus and rating agencies in the United States is forecast to inch upward at a CAGR of 1.1% over the next five years, reaching $17.4 billion in 2029.

Over the five years through 2024-25, revenue has rocketed at a compound annual rate of 14.5% to £2.3 billion. The Online Stock Brokerages industry has gained users quickly, as many investors left their brokers and started trading online. The online space offered a quick and easy way for less knowledgeable people to start investing and experienced traders to get real-time updates. Recovered incomes, volatile stock markets, an increasing number of mobile connections and a growing appetite for online stock trading have fuelled revenue growth. The online stock brokerage industry experienced a rapid upward shift in revenue during the 2020-21 market volatility caused by the pandemic, rewarding commission-free platforms like Trading212. The sector managed to capitalise on surging and declining phases. Innovations became critical, with brokerages like Trading212, FreeTrade and eToro introducing attractive features to win over customers, like replicating other trade moves. Despite the sector's vulnerability during the sharp sink of Bitcoin in 2022, its subsequent rebound in 2024-25 brought renewed prospects. Offering stocks and shares ISAs and SIPPs helped certain brokerages attract more tax-savvy customers. Simultaneously, intense price competition saw various platforms reduce their commissions to lure new users, leading to a climb in revenue of 7.7% in 2024-25. Over the five years through 2029-30, revenue is set to push up at a compound annual rate of 7.9% to £3.3 billion. Investor uncertainty will weaken as macro-headwinds subside and stock markets worldwide stabilise. The value of UK and US stock markets is forecast to strengthen, enticing traders to online platforms. As UK business profits recover due to stability, businesses can manage costs efficiently, leading to increased returns and more trade commissions for online stock brokers. The brokerage industry faces fierce price competition, with companies reducing commissions to attract and retain users alongside developing novel product offerings, like AI insights and advice, ISAs, extended trading hour products and tight cybersecurity. The average profit margin is expected to improve as industry entrants, including eToro (UK) Ltd, become profitable after years of significant losses resulting from investing heavily in R&D and marketing to attract users.

United Kingdom's main stock market index, the GB100, fell to 9117 points on September 2, 2025, losing 0.87% from the previous session. Over the past month, the index has declined 0.13%, though it remains 9.86% 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 September of 2025.

Euro Area's main stock market index, the EU50, rose to 5377 points on September 2, 2025, gaining 0.16% from the previous session. Over the past month, the index has climbed 2.57% and is up 9.46% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Euro Area. Euro Area Stock Market Index (EU50) - values, historical data, forecasts and news - updated on September of 2025.

Russia's main stock market index, the MOEX, rose to 2911 points on September 1, 2025, gaining 0.40% from the previous session. Over the past month, the index has climbed 4.94% and is up 14.29% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Russia. Russia Stock Market Index MOEX CFD - values, historical data, forecasts and news - updated on September of 2025.

Canada's main stock market index, the TSX, fell to 28549 points on September 2, 2025, losing 0.06% from the previous session. Over the past month, the index has climbed 3.55% and is up 23.90% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Canada. Canada Stock Market Index (TSX) - values, historical data, forecasts and news - updated on September of 2025.

Japan's main stock market index, the JP225, rose to 42268 points on September 2, 2025, gaining 0.19% from the previous session. Over the past month, the index has climbed 4.91% and is up 9.26% compared to the same time last year, 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 September of 2025.

Brazil's main stock market index, the IBOVESPA, rose to 141422 points on August 29, 2025, gaining 0.26% from the previous session. Over the past month, the index has climbed 5.55% and is up 3.98% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Brazil. Brazil Stock Market (BOVESPA) - values, historical data, forecasts and news - updated on September of 2025.

South Korea's main stock market index, the KOSPI, fell to 3143 points on September 1, 2025, losing 1.35% from the previous session. Over the past month, the index has declined 0.15%, though it remains 17.23% 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 September of 2025.

Prices for DXY Dollar Index including live quotes, historical charts and news. DXY Dollar Index was last updated by Trading Economics this September 1 of 2025.

Australia's main stock market index, the ASX200, fell to 8878 points on September 1, 2025, losing 1.06% from the previous session. Over the past month, the index has climbed 2.47% and is up 9.47% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Australia. Australia Stock Market Index - values, historical data, forecasts and news - updated on September of 2025.

New Zealand's main stock market index, the NZX 50, rose to 13070 points on September 1, 2025, gaining 1.08% from the previous session. Over the past month, the index has climbed 3.05% and is up 4.10% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from New Zealand. New Zealand Stock Market (NZX 50) - values, historical data, forecasts and news - updated on September of 2025.