Toho Zinc stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

Zinc rose to 2,948.80 USD/T on September 12, 2025, up 1.64% from the previous day. Over the past month, Zinc's price has risen 4.29%, and is up 1.53% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Zinc - 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.

Will the Zinc Index Dictate the Future of the 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

China Warehouse Stock: Shanghai Future Exchange: Zinc data was reported at 1,600.000 Ton in 13 May 2025. This records a decrease from the previous number of 1,903.000 Ton for 12 May 2025. China Warehouse Stock: Shanghai Future Exchange: Zinc data is updated daily, averaging 48,982.000 Ton from Oct 2008 (Median) to 13 May 2025, with 4034 observations. The data reached an all-time high of 363,267.000 Ton in 08 Aug 2011 and a record low of 25.000 Ton in 13 Dec 2022. China Warehouse Stock: Shanghai Future Exchange: Zinc data remains active status in CEIC and is reported by Shanghai Futures Exchange. The data is categorized under China Premium Database’s Financial Market – Table CN.ZB: Shanghai Futures Exchange: Commodity Futures: Stock.

SHFE Zinc Stocks data, recent 17 years (traceable to Oct 06,2008), the unit is t, latest value is 32791, updated at Aug 22,2025

Hindustan Zinc stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

Graph and download economic data for Slab Zinc Stocks at Refineries for United States (M0558BUSM601NNBR) from Jan 1929 to Dec 1942 about zinc, inventories, metals, and USA.

Explore the dynamics of LME zinc stocks and their impact on global market pricing, influenced by geopolitical tensions, industrial demand shifts, recycling efforts, and technological advancements in mining and smelting. Understand how these factors affect inventory levels in LME warehouses and what it means for market participants.

This dataset is about stocks. It has 2 rows and is filtered where the company is Toho Zinc. It features 8 columns including stock name, company, exchange, and exchange symbol.

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 Commodity Zinc Index 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

In 2021, Australia's national stock of zinc was **** thousand metric tons. This represented an increase from the previous year.

Toho Zinc reported JPY71.28B in Current Assets for its fiscal quarter ending in June of 2025. Data for Toho Zinc | 5707 - Current Assets including historical, tables and charts were last updated by Trading Economics this last September in 2025.

Graph and download economic data for Zinc Ore Stocks, Joplin District for Joplin, MO (M05089US27900M601NNBR) from Jan 1923 to Aug 1941 about Joplin, zinc, ore, MO, inventories, metals, and USA.

Forecast: Producer and Consumer Reported Stocks of Refined Zinc in the US 2024 - 2028 Discover more data with ReportLinker!

AI-powered price forecasts for ZINC.L stock across different timeframes including weekly, monthly, yearly, and multi-year predictions.

Preferred-Stock-and-Other-Adjustments Time Series for Huludao Zinc Industry Co Ltd. Huludao Zinc Industry Co.,Ltd. engages in the non-ferrous metal zinc and lead smelting products primarily in China. The company is involved in the zinc, copper, and lead smelting and deep processing; and cadmium, indium, sulfuric acid, and copper sulfate utilization processing activities. It also provides zinc ingot, electric lead, high purity, silver bullion, hot dip galvanized, zinc sulfate, gold products, etc. The company's products are used in the metallurgy, machinery, electronics, medicine, chemical, military, and other industries. It also exports its products to approximately 20 countries and regions. The company was founded in 1993 and is based in Huludao, China.

Hindustan Zinc reported INR21.46 in PE Price to Earnings for its fiscal quarter ending in March of 2025. Data for Hindustan Zinc | HZNC - PE Price to Earnings including historical, tables and charts were last updated by Trading Economics this last September in 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.

Zinc Price Volatility Expected to Impact DJ Commodity Zinc Index.

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.

Zinc Index: Analysts Predict Ongoing Volatility Amidst Supply Concerns.

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 Dataset contains month and district wise received, unusable and distributed stock data for all the indicators related to Family Planning, Maternal Health, and Immunization under HMIS for Madhya Pradesh