The main stock market index of United States, the US500, rose to 6271 points on July 14, 2025, gaining 0.19% from the previous session. Over the past month, the index has climbed 3.94% and is up 11.36% 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 July of 2025.

Prices for United States Stock Market Index (USVIX) including live quotes, historical charts and news. United States Stock Market Index (USVIX) was last updated by Trading Economics this July 14 of 2025.

Taiwan TWSE: Equity Market Index: Trading & Consumers' Goods data was reported at 265.650 31Dec1994=100 in Oct 2018. This records a decrease from the previous number of 276.610 31Dec1994=100 for Sep 2018. Taiwan TWSE: Equity Market Index: Trading & Consumers' Goods data is updated monthly, averaging 115.695 31Dec1994=100 from Jan 1995 (Median) to Oct 2018, with 286 observations. The data reached an all-time high of 276.680 31Dec1994=100 in Jun 2018 and a record low of 49.070 31Dec1994=100 in Apr 2003. Taiwan TWSE: Equity Market Index: Trading & Consumers' Goods data remains active status in CEIC and is reported by Taiwan Stock Exchange Corporation. The data is categorized under Global Database’s Taiwan – Table TW.Z001: Taiwan Stock Exchange (TWSE): Indices.

Sri Lanka CSE: Index: Trading data was reported at 12,056.720 NA in Nov 2018. This records a decrease from the previous number of 12,206.220 NA for Oct 2018. Sri Lanka CSE: Index: Trading data is updated monthly, averaging 1,312.570 NA from Jan 1987 (Median) to Nov 2018, with 383 observations. The data reached an all-time high of 33,276.330 NA in May 2011 and a record low of 155.760 NA in Nov 1988. Sri Lanka CSE: Index: Trading data remains active status in CEIC and is reported by Colombo Stock Exchange. The data is categorized under Global Database’s Sri Lanka – Table LK.Z001: Colombo Stock Exchange: Index.

In general, the stock prices of the same industry have a similar trend, but those of different industries do not. When investing in stocks of different industries, one should select the optimal model from lots of trading models for each industry because any model may not be suitable for capturing the stock trends of all industries. However, the study has not been carried out at present. In this paper, firstly we select 424 S&P 500 index component stocks (SPICS) and 185 CSI 300 index component stocks (CSICS) as the research objects from 2010 to 2017, divide them into 9 industries such as finance and energy respectively. Secondly, we apply 12 widely used machine learning algorithms to generate stock trading signals in different industries and execute the back-testing based on the trading signals. Thirdly, we use a non-parametric statistical test to evaluate whether there are significant differences among the trading performance evaluation indicators (PEI) of different models in the same industry. Finally, we propose a series of rules to select the optimal models for stock investment of every industry. The analytical results on SPICS and CSICS show that we can find the optimal trading models for each industry based on the statistical tests and the rules. Most importantly, the PEI of the best algorithms can be significantly better than that of the benchmark index and “Buy and Hold” strategy. Therefore, the algorithms can be used for making profits from industry stock trading.

Graph and download economic data for Gross domestic product: Trading gains index (W368RG3Q066SBEA) from Q1 1947 to Q1 2025 about trade, GDP, indexes, and USA.

Graph and download economic data for Trade Weighted U.S. Dollar Index: Other Important Trading Partners, Goods (DISCONTINUED) (TWEXO) from 1995-01-04 to 2020-01-01 about trade-weighted, trade, exchange rate, currency, goods, rate, indexes, and USA.

Baltic Dry rose to 1,783 Index Points on July 14, 2025, up 7.22% from the previous day. Over the past month, Baltic Dry's price has fallen 9.72%, and is down 10.54% 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.

Israel Trading Volume: TASE: Avg Daily: Derivative: Option & Future: TA-35 Index data was reported at 104.000 Unit th in Oct 2018. This records an increase from the previous number of 94.000 Unit th for Sep 2018. Israel Trading Volume: TASE: Avg Daily: Derivative: Option & Future: TA-35 Index data is updated monthly, averaging 98.500 Unit th from May 2017 (Median) to Oct 2018, with 18 observations. The data reached an all-time high of 136.000 Unit th in Feb 2018 and a record low of 66.000 Unit th in Jul 2018. Israel Trading Volume: TASE: Avg Daily: Derivative: Option & Future: TA-35 Index data remains active status in CEIC and is reported by Tel Aviv Stock Exchange. The data is categorized under Global Database’s Israel – Table IL.Z005: Tel Aviv Stock Exchange: Trading Value and Trading Volume. The index was released under the name TA-25 Monthly Options and was broadened to TA-35 Monthly Options in May 2017.

Nepal Stock Exchange: Index: Trading Index data was reported at 4,531.250 NA in Apr 2025. This records an increase from the previous number of 4,293.210 NA for Mar 2025. Nepal Stock Exchange: Index: Trading Index data is updated monthly, averaging 292.510 NA from Jun 2013 (Median) to Apr 2025, with 133 observations. The data reached an all-time high of 4,531.250 NA in Apr 2025 and a record low of 170.860 NA in Nov 2013. Nepal Stock Exchange: Index: Trading Index data remains active status in CEIC and is reported by Exchange Data International Limited. The data is categorized under Global Database’s Nepal – Table NP.EDI.SE: Nepal Stock Exchange: Monthly.

China's main stock market index, the SHANGHAI, fell to 3505 points on July 15, 2025, losing 0.42% from the previous session. Over the past month, the index has climbed 3.43% and is up 17.76% 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 July of 2025.

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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.

Trading Signals (NASDAQ Composite Index Stock Forecast)

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

United States USD Trade Weighted Index: Nominal: Other Important Trading Partner data was reported at 168.237 Jan1997=100 in Nov 2018. This records an increase from the previous number of 166.528 Jan1997=100 for Oct 2018. United States USD Trade Weighted Index: Nominal: Other Important Trading Partner data is updated monthly, averaging 96.825 Jan1997=100 from Jan 1973 (Median) to Nov 2018, with 551 observations. The data reached an all-time high of 168.237 Jan1997=100 in Nov 2018 and a record low of 1.998 Jan1997=100 in Jul 1973. United States USD Trade Weighted Index: Nominal: Other Important Trading Partner data remains active status in CEIC and is reported by Federal Reserve Board. The data is categorized under Global Database’s United States – Table US.M016: US Dollar Trade Weighted Index.

Argentina Trading Value: BCBA: ARS: Index Futures data was reported at 0.000 ARS mn in Feb 2021. This stayed constant from the previous number of 0.000 ARS mn for Jan 2021. Argentina Trading Value: BCBA: ARS: Index Futures data is updated monthly, averaging 0.000 ARS mn from Jan 2000 (Median) to Feb 2021, with 254 observations. The data reached an all-time high of 101.342 ARS mn in May 2003 and a record low of 0.000 ARS mn in Feb 2021. Argentina Trading Value: BCBA: ARS: Index Futures data remains active status in CEIC and is reported by Buenos Aires Stock Exchange. The data is categorized under Global Database’s Argentina – Table AR.Z003: Buenos Aires Stock Exchange: Trading Value (Discontinued).

At the end of February 2025, the DAX index reached ********* points, marking its highest level since January 2015. Moreover, this also reflected a strong recovery from the global coronavirus (COVID-19) pandemic, having risen from ******** points at the end of March 2020 and surpassing its pre-pandemic level of approximately ********* points at the end of December 2019. Origin and composition of the DAX Index The DAX (Deutscher Aktienindex) is the most important German stock index, showing the value trends of the 40 largest companies by market capitalization listed on the Frankfurt stock exchange. The DAX index was introduced on July 1, 1988 and is a continuation of the Börsen-Zeitung Index, established in 1959. The count among their number some of the most recognizable companies in the world, such as carmakers Volkswagen and Daimler, sportswear brand adidas, and industrial giants Siemens and BASF. After the DAX, the 50 next-largest German companies are included in the midcap MDAX index, while the 70 next-largest small and medium-sized German companies (ranked from 91 to 160) are included in the SDAX index. The Frankfurt Stock Exchange All the companies included in the DAX family of indices are traded on the Frankfurt Stock Exchange. Dating back to 1585, the Frankfurt Stock Exchange is considered to be the oldest exchange in the world. It is the twelfth largest stock exchange in the world in terms of market capitalization, and accounts for around ** percent of all equity trading in Germany. Two main trading venues comprise the Frankfurt Stock Exchange: the Börse Frankfurt is a traditional trading floor; while the Xetra is an electronic trading system which accounts for the vast majority of trading volume on Frankfurt Stock Exchange. As of December 2023, the total market capitalization of all companies listed on the Frankfurt Stock Exchange was around *** trillion euros.

Sri Lanka CSE: Total Return Index: Trading data was reported at 14,435.500 NA in Nov 2018. This records a decrease from the previous number of 14,614.500 NA for Oct 2018. Sri Lanka CSE: Total Return Index: Trading data is updated monthly, averaging 16,387.375 NA from Dec 2004 (Median) to Nov 2018, with 168 observations. The data reached an all-time high of 35,887.650 NA in May 2011 and a record low of 1,235.950 NA in Dec 2008. Sri Lanka CSE: Total Return Index: Trading data remains active status in CEIC and is reported by Colombo Stock Exchange. The data is categorized under Global Database’s Sri Lanka – Table LK.Z002: Colombo Stock Exchange: Total Return Index.

This research presents a transfer learning approach for deep learning models to predict monthly average index of Standard and Poor's 500(S&P 500) and Taiwan Stock Exchange Capitalization Weighted Stock Index(TAIEX) and use it to simulate trading E-mini S&P 500 and Mini-TAIEX futures contracts for evaluation. It conducts three experiments to show that the approach can gain stable profits. The first experiment is to analyze the results of different types of data preprocessing and trading strategies and find a general one for the following experiments. Second, we compared the results between the original and transfer learning methods to prove that our techniques are able to get consistent earnings. Finally, we proposed some ensemble models and found that the ensemble methods were more effective and stable to make profits.

The global Trading Open-End Index Fund market exhibits robust growth, driven by increasing investor preference for passive investment strategies and the diversification benefits offered by index funds. The market's size in 2025 is estimated at $500 billion, reflecting a Compound Annual Growth Rate (CAGR) of approximately 12% from 2019. This growth is fueled by several key factors, including the rising popularity of Exchange-Traded Funds (ETFs) which are a type of open-end index fund, the increasing accessibility of investment platforms, and a growing awareness among retail and institutional investors about the advantages of cost-effective index fund investing compared to actively managed funds. Technological advancements, such as robo-advisors and algorithmic trading, further contribute to market expansion by lowering barriers to entry and enhancing efficiency. While regulatory changes and market volatility can pose challenges, the long-term outlook for the Trading Open-End Index Fund market remains positive, with significant potential for expansion in developing economies where investment awareness is rapidly growing. Leading players like BlackRock (iShares), Nomura, Nikko, and Daiwa, along with several other prominent asset management firms in Asia and beyond, are actively shaping the market landscape through product innovation and strategic partnerships. The forecast period from 2025 to 2033 projects a continued upward trajectory for the market, with a CAGR projection remaining above 10%. Regional variations are expected, with North America and Europe maintaining substantial market shares, while Asia-Pacific is anticipated to witness the fastest growth rate due to its expanding middle class and increasing participation in global capital markets. Competitive pressures are expected to intensify, with existing players focusing on enhancing their product offerings, expanding their distribution networks, and leveraging technological innovations to gain a competitive edge. The market will likely see further consolidation through mergers and acquisitions as firms seek to optimize their scale and reach. Segmentation within the market is likely to become more nuanced, with specialization emerging around specific index types (e.g., sector-specific, thematic, or sustainable indices).

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.

Trading Signals (Taiwan Weighted Index Stock Forecast)

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