View data of the S&P 500, an index of the stocks of 500 leading companies in the US economy, which provides a gauge of the U.S. equity market.

China's main stock market index, the SHANGHAI, rose to 3385 points on June 6, 2025, gaining 0.04% from the previous session. Over the past month, the index has climbed 1.28% and is up 10.95% 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 June of 2025.

Graph and download economic data for Index of Common Stock Prices, New York Stock Exchange for United States (M11007USM322NNBR) from Jan 1902 to May 1923 about New York, stock market, indexes, and USA.

Israel's main stock market index, the TA-125, fell to 2746 points on June 9, 2025, losing 0.21% from the previous session. Over the past month, the index has climbed 2.86% and is up 40.16% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Israel. Israel Stock Market (TA-125) - values, historical data, forecasts and news - updated on June of 2025.

Euro Area's main stock market index, the EU50, rose to 5428 points on June 6, 2025, gaining 0.39% from the previous session. Over the past month, the index has climbed 3.78% and is up 7.45% 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 June of 2025.

While the global coronavirus (COVID-19) pandemic caused all major stock market indices to fall sharply in March 2020, both the extent of the decline at this time, and the shape of the subsequent recovery, have varied greatly. For example, on March 15, 2020, major European markets and traditional stocks in the United States had shed around 40 percent of their value compared to January 5, 2020. However, Asian markets and the NASDAQ Composite Index only shed around 20 to 25 percent of their value. A similar story can be seen with the post-coronavirus recovery. As of November 14, 2021 the NASDAQ composite index value was around 65 percent higher than in January 2020, while most other markets were only between 20 and 40 percent higher.

Why did the NASDAQ recover the quickest?

Based in New York City, the NASDAQ is famously considered a proxy for the technology industry as many of the world’s largest technology industries choose to list there. And it just so happens that technology was the sector to perform the best during the coronavirus pandemic. Accordingly, many of the largest companies who benefitted the most from the pandemic such as Amazon, PayPal and Netflix, are listed on the NADSAQ, helping it to recover the fastest of the major stock exchanges worldwide.

Which markets suffered the most?

The energy sector was the worst hit by the global COVID-19 pandemic. In particular, oil companies share prices suffered large declines over 2020 as demand for oil plummeted while workers found themselves no longer needing to commute, and the tourism industry ground to a halt. In addition, overall share prices in two major stock exchanges – the London Stock Exchange (as represented by the FTSE 100 index) and Hong Kong (as represented by the Hang Seng index) – have notably recovered slower than other major exchanges. However, in both these, the underlying issue behind the slower recovery likely has more to do with political events unrelated to the coronavirus than it does with the pandemic – namely Brexit and general political unrest, respectively.

Stock market index in Mexico, March, 2025 The most recent value is 119.81 points as of March 2025, a decline compared to the previous value of 121.28 points. Historically, the average for Mexico from January 1970 to March 2025 is 35.56 points. The minimum of 0 points was recorded in January 1970, while the maximum of 130.33 points was reached in February 2024. | TheGlobalEconomy.com

This dataset contains the essential files for conducting a dynamic stock market analysis using Power BI. The data is sourced from Yahoo Finance and includes historical stock prices, which can be dynamically updated by adding new stock codes to the provided Excel sheet.

Files Included: Power BI Report (.pbix): The interactive Power BI report that includes various visualizations such as Candle Charts, Line Charts for Support and Resistance, and Technical Indicators like SMA, EMA, Bollinger Bands, and RSI. The report is designed to provide a comprehensive analysis of stock performance over time.

Stock Data Excel Sheet (.xlsx): This Excel sheet is connected to the Power BI report and allows for dynamic data loading. By adding new stock codes to this sheet, the Power BI report automatically refreshes to include the new data, enabling continuous updates without manual intervention.

Overview and Chart Pages Snapshots for better understanding about the Report.

Key Features: Dynamic Data Loading: Easily update the dataset by adding new stock codes to the Excel sheet. The Power BI report will automatically pull the corresponding data from Yahoo Finance. Comprehensive Visualizations: Analyze stock trends using Candle Charts, identify key price levels with Support and Resistance lines, and explore market behavior through various technical indicators. Interactive Analysis: The Power BI report includes slicers and navigation buttons to switch between different time periods and visualizations, providing a tailored analysis experience. Use Cases: Ideal for financial analysts, traders, or anyone interested in conducting a detailed stock market analysis. Can be used to monitor the performance of individual stocks or compare trends across multiple stocks over time. Tags: Stock Market Power BI Financial Analysis Yahoo Finance Data Visualization

Spain's main stock market index, the ES35, rose to 14251 points on June 9, 2025, gaining 0.03% from the previous session. Over the past month, the index has climbed 4.36% and is up 25.48% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Spain. Spain Stock Market Index (ES35) - values, historical data, forecasts and news - updated on June of 2025.

India's main stock market index, the SENSEX, rose to 82445 points on June 9, 2025, gaining 0.31% from the previous session. Over the past month, the index has climbed 0.02% and is up 7.79% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from India. BSE SENSEX Stock Market Index - values, historical data, forecasts and news - updated on June of 2025.

Snapshot img

Stockbroking Market Size 2024-2028

The stockbroking market size is estimated to increase by USD 739.6 billion, at a CAGR of 9.58% between 2023 and 2028. Market expansion hinges on various factors such as market surveillance, supportive government regulations, and improved cash flow fostering business expansion. However, challenges persist, including repercussions from trade conflicts, limited attention to stock broking for small and medium enterprises (SMEs), and inadequate risk assessment capabilities. Effective market surveillance is crucial for identifying emerging trends and mitigating risks. Moreover, government regulations that facilitate market growth and innovation are imperative for sustained expansion. Enhanced cash flow empowers businesses to invest in research, development, and expansion initiatives for security brokerage and stock exchange services, driving overall market growth. Despite these opportunities, challenges like navigating trade tensions, addressing the neglect of SMEs in stock broking, and bolstering risk evaluation capacities necessitate proactive measures to sustain market momentum and ensure long-term viability. Additionally, users of online trading platforms can easily monitor the performance of their assets thanks to real-time stock data.

What will be the Size of the Stockbroking Market During the Forecast Period?

Market Forecast 2024-2028

To learn more about this stockbroking market report, Download Report Sample

Stockbroking Market Segmentation

The stockbroking market forecasting report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD Billion' for the period 2024 to 2028, as well as historical data from 2018 to 2022 for the following segments.

Mode Of Booking Outlook 

  Offline
  Online





Type Outlook

  Long-term trading
  Short-term trading





Region Outlook 

  North America

    The U.S.
    Canada





  Europe

    U.K.
    Germany
    France
    Rest of Europe





  APAC

    China
    India


  South America

    Chile 
    Brazil
    Argentina


  Middle East & Africa

    Saudi Arabia
    South Africa
    Rest of the Middle East & Africa

By Mode Of Booking

The offline segment will account for a major share of the market's growth during the forecast period. Without the use of online platforms or electronic systems, offline stockbroking is the traditional method of engaging in stock trading activities. Investors work with stockbroker who act as an intermediate between traders and the stock exchange. Offline includes communication, paper-based documentation, and personalized investment advicor.

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The offline segment was valued at USD 760.60 billion in 2018.?Offline is still dominating the market due to the ease of use due to factors such as personalized services, extensive research, complex investment strategies, trust, and relationship building by the investors over time, also in the offline segment they can access initial public offerings or other restricted offerings which may not be readily available on an online brokerage platform or with e-brokerage. Due to the above-mentioned factors, the market is expected to grow during the forecast period.

Regional Analysis

For more insights on the market share of various regions, View PDF Sample now!

North America is estimated to contribute 34% to global stockbroking market growth by 2028. Technavio's analysts have elaborately explained the regional trends, drivers, and challenges that are expected to shape the market during the forecast period. The market in North America refers to the financial industry. Which are involved in buying and selling securities, such as stocks, bonds, and derivatives, on behalf of investors. North America has the dominant and the most developed stock markets in the world, centered primarily in the US and Canada. In North America, the market includes the stock exchange, brokerage firms, online trading, regulatory bodies, investment products, and high-frequency trading. Hence, such factors are driving the market in North America during the forecast period.

Stockbroking Market Dynamics

The market encompasses various entities, including stockbrokers, investment advisors, and registered representatives, who facilitate trading activities for investors. Brokerage firms provide the infrastructure for these professionals to execute orders on behalf of their clients. The integration of artificial intelligence (AI) and algorithms into trading platforms has led to cloud-based solutions, enabling active and passive portfolio management. Investment advisory and financial planning services are crucial components of the market. Advisors help investors make informed investment decisions based on their financial health and profitability goals. Asset allocation is a significant factor in investment decisions, with

This study investigates the application of machine learning (ML) models in stock market forecasting, with a focus on their integration using PineScript, a domain-specific language for algorithmic trading. Leveraging diverse datasets, including historical stock prices and market sentiment data, we developed and tested various ML models such as neural networks, decision trees, and linear regression. Rigorous backtesting over multiple timeframes and market conditions allowed us to evaluate their predictive accuracy and financial performance. The neural network model demonstrated the highest accuracy, achieving a 75% success rate, significantly outperforming traditional models. Additionally, trading strategies derived from these ML models yielded a return on investment (ROI) of up to 12%, compared to an 8% benchmark index ROI. These findings underscore the transformative potential of ML in refining trading strategies, providing critical insights for financial analysts, investors, and developers. The study draws on insights from 15 peer-reviewed articles, financial datasets, and industry reports, establishing a robust foundation for future exploration of ML-driven financial forecasting. Tools and Technologies Used †PineScript PineScript, a scripting language integrated within the TradingView platform, was the primary tool used to develop and implement the machine learning models. Its robust features allowed for custom indicator creation, strategy backtesting, and real-time market data analysis. †Python Python was utilized for data preprocessing, model training, and performance evaluation. Key libraries included: Pandas

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.

DJ US Healthcare: Poised for Recovery?

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 stock market today saw mixed results with major U.S. stock indexes closing higher, while crude oil prices experienced a decline. Factors including a tech sell-off, economic recovery concerns, stimulus package delays, demand outlook, OPEC+ production increase, and U.S. inventory buildup influenced the market and oil prices.

Stock market forecasting is one of the most challenging problems in today’s financial markets. According to the efficient market hypothesis, it is almost impossible to predict the stock market with 100% accuracy. However, Machine Learning (ML) methods can improve stock market predictions to some extent. In this paper, a novel strategy is proposed to improve the prediction efficiency of ML models for financial markets. Nine ML models are used to predict the direction of the stock market. First, these models are trained and validated using the traditional methodology on a historic data captured over a 1-day time frame. Then, the models are trained using the proposed methodology. Following the traditional methodology, Logistic Regression achieved the highest accuracy of 85.51% followed by XG Boost and Random Forest. With the proposed strategy, the Random Forest model achieved the highest accuracy of 91.27% followed by XG Boost, ADA Boost and ANN. In the later part of the paper, it is shown that only classification report is not sufficient to validate the performance of ML model for stock market prediction. A simulation model of the financial market is used in order to evaluate the risk, maximum draw down and returns associate with each ML model. The overall results demonstrated that the proposed strategy not only improves the stock market returns but also reduces the risks associated with each ML model.

Iran Market Capitalization: Tehran Stock Exchange (TSE) data was reported at 1,743.023 USD bn in Sep 2023. This records a decrease from the previous number of 1,761.129 USD bn for Aug 2023. Iran Market Capitalization: Tehran Stock Exchange (TSE) data is updated monthly, averaging 105.978 USD bn from Dec 2005 (Median) to Sep 2023, with 199 observations. The data reached an all-time high of 1,991.152 USD bn in May 2023 and a record low of 33.814 USD bn in Jun 2007. Iran Market Capitalization: Tehran Stock Exchange (TSE) data remains active status in CEIC and is reported by Tehran Stock Exchange. The data is categorized under Global Database’s Iran – Table IR.Z002: Tehran Stock Exchange: Market Capitalization. [COVID-19-IMPACT]

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. Financial Services Index: Strength or Weakness Ahead?

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.

Can we predict stock market using machine learning? (WY 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

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.

Cotton Index: The Future of Textile Trade?

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

Oman's main stock market index, the MSM 30, rose to 4579 points on June 4, 2025, gaining 0.56% from the previous session. Over the past month, the index has climbed 5.52%, though it remains 4.18% lower than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from Oman. Oman Stock Market (MSM 30) - values, historical data, forecasts and news - updated on June of 2025.