Lebanon Shares Traded: BSE: Volume: Monthly Average data was reported at 848,190.000 Unit in Dec 2016. This records an increase from the previous number of 303,660.000 Unit for Nov 2016. Lebanon Shares Traded: BSE: Volume: Monthly Average data is updated monthly, averaging 227,090.500 Unit from Jan 2003 (Median) to Dec 2016, with 168 observations. The data reached an all-time high of 9,494,611.000 Unit in Jul 2012 and a record low of 28,117.000 Unit in Jul 2003. Lebanon Shares Traded: BSE: Volume: Monthly Average data remains active status in CEIC and is reported by Beirut Stock Exchange. The data is categorized under Global Database’s Lebanon – Table LB.Z003: Beirut Stock Exchange: Shares Traded.

This statistic illustrates the market volume of shares traded in the Bombay Stock Exchange across India from fiscal year 2012 to fiscal year 2017. During the fiscal year 2016, the Bombay Stock Exchange traded shares worth over 75 billion Indian rupees.

Lebanon Shares Traded: BSE: Volume data was reported at 1,888,985.000 Unit in Apr 2025. This records an increase from the previous number of 1,626,857.000 Unit for Mar 2025. Lebanon Shares Traded: BSE: Volume data is updated monthly, averaging 3,007,782.000 Unit from Jan 1996 (Median) to Apr 2025, with 352 observations. The data reached an all-time high of 121,955,414.000 Unit in Feb 2019 and a record low of 5,600.000 Unit in May 1996. Lebanon Shares Traded: BSE: Volume data remains active status in CEIC and is reported by Beirut Stock Exchange. The data is categorized under Global Database’s Lebanon – Table LB.Z003: Beirut Stock Exchange: Shares Traded.

Lebanon Shares Traded: BSE: Volume: Official Market: Trading data was reported at 0.000 Unit in Jun 2018. This stayed constant from the previous number of 0.000 Unit for May 2018. Lebanon Shares Traded: BSE: Volume: Official Market: Trading data is updated monthly, averaging 0.000 Unit from Jan 2000 (Median) to Jun 2018, with 222 observations. The data reached an all-time high of 2,006,301.000 Unit in May 2012 and a record low of 0.000 Unit in Jun 2018. Lebanon Shares Traded: BSE: Volume: Official Market: Trading data remains active status in CEIC and is reported by Beirut Stock Exchange. The data is categorized under Global Database’s Lebanon – Table LB.Z003: Beirut Stock Exchange: Shares Traded.

The dataset contains All India and Daily Turnover, Market Capitalisation and Traded Volume at BSE

Lebanon Shares Traded: BSE: Volume: Junior Market: Industrial data was reported at 0.000 Unit in Jun 2018. This stayed constant from the previous number of 0.000 Unit for May 2018. Lebanon Shares Traded: BSE: Volume: Junior Market: Industrial data is updated monthly, averaging 0.000 Unit from Feb 2004 (Median) to Jun 2018, with 173 observations. The data reached an all-time high of 756,445.000 Unit in Mar 2005 and a record low of 0.000 Unit in Jun 2018. Lebanon Shares Traded: BSE: Volume: Junior Market: Industrial data remains active status in CEIC and is reported by Beirut Stock Exchange. The data is categorized under Global Database’s Lebanon – Table LB.Z003: Beirut Stock Exchange: Shares Traded.

Lebanon Shares Traded: BSE: Volume: Daily Average data was reported at 848.000 Unit th in Dec 2016. This records an increase from the previous number of 304.000 Unit th for Nov 2016. Lebanon Shares Traded: BSE: Volume: Daily Average data is updated monthly, averaging 221.000 Unit th from Jan 2003 (Median) to Dec 2016, with 168 observations. The data reached an all-time high of 5,502.000 Unit th in Jan 2010 and a record low of 28.000 Unit th in Aug 2003. Lebanon Shares Traded: BSE: Volume: Daily Average data remains active status in CEIC and is reported by Beirut Stock Exchange. The data is categorized under Global Database’s Lebanon – Table LB.Z003: Beirut Stock Exchange: Shares Traded.

In fiscal year 2018, the share of options in average daily trading volume accounted for over 84 percent, up from around 80 percent in the previous fiscal year. Derivatives trading in India is done mainly through the National stock exchange (NSE) and Bombay stock exchange (BSE).

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.

BSE Sensex 30 Index Target Price 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

The New York Stock Exchange (NYSE) is the largest stock exchange in the world, with an equity market capitalization of almost ** trillion U.S. dollars as of June 2025. The following three exchanges were the NASDAQ, PINK Exchange, and the Frankfurt Exchange. What is a stock exchange? A stock exchange is a marketplace where stockbrokers, traders, buyers, and sellers can trade in equities products. The largest exchanges have thousands of listed companies. These companies sell shares of their business, giving the general public the opportunity to invest in them. The oldest stock exchange worldwide is the Frankfurt Stock Exchange, founded in the late sixteenth century. Other functions of a stock exchange Since these are publicly traded companies, every firm listed on a stock exchange has had an initial public offering (IPO). The largest IPOs can raise billions of dollars in equity for the firm involved. Related to stock exchanges are derivatives exchanges, where stock options, futures contracts, and other derivatives can be traded.

Lebanon Shares Traded: BSE: Volume: Over The Counter Market: Banking data was reported at 535.000 Unit in Jun 2018. This records an increase from the previous number of 133.000 Unit for May 2018. Lebanon Shares Traded: BSE: Volume: Over The Counter Market: Banking data is updated monthly, averaging 44.000 Unit from Jun 2009 (Median) to Jun 2018, with 109 observations. The data reached an all-time high of 9,863,451.000 Unit in May 2010 and a record low of 0.000 Unit in Feb 2018. Lebanon Shares Traded: BSE: Volume: Over The Counter Market: Banking data remains active status in CEIC and is reported by Beirut Stock Exchange. The data is categorized under Global Database’s Lebanon – Table LB.Z003: Beirut Stock Exchange: Shares Traded.

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.

The BSE Sensex: A Hold for Now, But Long-Term Gains Are Possible

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 November 2020, the stock market turnover of both National Stock Exchange (NSE) and Bombay Stock Exchange (BSE) in India reached 14.08 trillion Indian rupees. Over the previous year, the turnover raised significantly from 8.89 trillion Indian rupees in November 2019. Additionally, the stock exchange turnover in India did not face major loss during lockdown and the COVID-19 pandemic, but remained stable.

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.

BSE Sensex: Heading for Another Record Run?

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.

BSE Sensex 30 Index assigned short-term B1 & long-term Ba1 forecasted stock rating.

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

Among the leading 20 exchanges worldwide for exchange-traded derivatives (ETDs), the BSE (Bombay Stock Exchange) saw the largest increase in terms of contracts traded, increasing by 265 percent compared to the previous year. Second in the ranking in terms of growth was the National Stock Exchange of India, which experienced an increase of 122 percent compared to 2022 and cemented its dominance as the leading exchange worldwide for ETDs.

Lebanon Shares Traded: BSE: Volume: Official Market: Development and Reconstruction data was reported at 1,023,671.000 Unit in Jun 2018. This records an increase from the previous number of 859,532.000 Unit for May 2018. Lebanon Shares Traded: BSE: Volume: Official Market: Development and Reconstruction data is updated monthly, averaging 929,569.000 Unit from Jan 2000 (Median) to Jun 2018, with 222 observations. The data reached an all-time high of 13,097,988.000 Unit in Sep 2008 and a record low of 50,552.000 Unit in Jul 2000. Lebanon Shares Traded: BSE: Volume: Official Market: Development and Reconstruction data remains active status in CEIC and is reported by Beirut Stock Exchange. The data is categorized under Global Database’s Lebanon – Table LB.Z003: Beirut Stock Exchange: Shares Traded.

Lebanon Shares Traded: BSE: Volume: Official Market: Banking data was reported at 4,453,851.000 Unit in Jun 2018. This records an increase from the previous number of 2,733,241.000 Unit for May 2018. Lebanon Shares Traded: BSE: Volume: Official Market: Banking data is updated monthly, averaging 1,726,757.500 Unit from Jan 2000 (Median) to Jun 2018, with 222 observations. The data reached an all-time high of 54,182,849.000 Unit in Feb 2010 and a record low of 29,837.000 Unit in Dec 2001. Lebanon Shares Traded: BSE: Volume: Official Market: Banking data remains active status in CEIC and is reported by Beirut Stock Exchange. The data is categorized under Global Database’s Lebanon – Table LB.Z003: Beirut Stock Exchange: Shares Traded.

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.

(BSE) Base Resources: Navigating the Shifting Sands of the Mineral Market

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

Lebanon Shares Traded: BSE: Volume: Junior Market: Funds data was reported at 0.000 Unit in Jun 2018. This stayed constant from the previous number of 0.000 Unit for May 2018. Lebanon Shares Traded: BSE: Volume: Junior Market: Funds data is updated monthly, averaging 575.000 Unit from Jan 2000 (Median) to Jun 2018, with 212 observations. The data reached an all-time high of 285,000.000 Unit in Jul 2000 and a record low of 0.000 Unit in Jun 2018. Lebanon Shares Traded: BSE: Volume: Junior Market: Funds data remains active status in CEIC and is reported by Beirut Stock Exchange. The data is categorized under Global Database’s Lebanon – Table LB.Z003: Beirut Stock Exchange: Shares Traded.