Lead rose to 2,003.78 USD/T on December 2, 2025, up 0.11% from the previous day. Over the past month, Lead's price has fallen 1.32%, and is down 3.99% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Lead - values, historical data, forecasts and news - updated on December of 2025.

In 2024, the average price for lead stood at 2,069 nominal U.S. dollars per metric ton. It is forecast that in 2026 the price of one metric ton of lead will amount to 2,000 nominal U.S. dollars.

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

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 Lead index forecasts mixed signals 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 dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.

Comprehensive daily stock market data for 21 major S&P 500 companies across diverse sectors from 2020 to 2024.

This dataset contains daily stock data for 21 prominent companies in the S&P 500 index from January 1, 2020, to December 31, 2024. Covering a range of sectors including Technology, Healthcare, Energy, Financials, Consumer, Industrials, and Cloud/Software, this dataset offers a diverse view of market trends and performance over a five-year period.

Features Include:

Date: The trading day. Open: Opening price of the stock. High: Highest price during the trading day. Low: Lowest price during the trading day. Close: Closing price of the stock. Volume: Number of shares traded. Ticker: Stock symbol representing the company.

Sectors Covered:

Technology & AI: Apple (AAPL), Microsoft (MSFT), Alphabet (GOOGL), Amazon (AMZN), NVIDIA (NVDA), Taiwan Semiconductor (TSM). Healthcare: Johnson & Johnson (JNJ), UnitedHealth Group (UNH), Eli Lilly (LLY). Energy: ExxonMobil (XOM), NextEra Energy (NEE). Financial: JPMorgan Chase (JPM), Visa (V), BlackRock (BLK). Consumer: Walmart (WMT), Costco (COST), Procter & Gamble (PG). Industrial: Caterpillar (CAT), Honeywell (HON). Software/Cloud: Salesforce (CRM), ASML Holding (ASML).

This dataset is ideal for financial analysts, data scientists, and machine learning enthusiasts interested in exploring stock market trends, building predictive models, or conducting sector-based analysis over a significant time span.

Data Source: Retrieved using Yahoo Finance API, ensuring accuracy and reliability.

Usage: This dataset can be used for time-series analysis, machine learning predictions, financial modeling, and comparative studies across different sectors.

Feel free to download and explore the data, and share your findings with the community!

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 Lead Index Projected to Experience Moderate Growth

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

LME Index rose to 4,700 Index Points on October 29, 2025, up 0.79% from the previous day. Over the past month, LME Index's price has risen 7.33%, and is up 13.22% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. LME Index - values, historical data, forecasts and news - updated on December of 2025.

In 2024, the residential brokerage company Real was the best-performing U.S. proptech, with a stock price increase of *** percent. During that year, residential real estate The commercial brokerage segment experienced the best overall share price performance in the proptech sector.

The prediction of stock prices has long been a captivating subject in academic research. This study aims to forecast the prices of prominent stocks in five key industries of the Chinese A-share market by leveraging the synergistic power of deep learning techniques and investor sentiment analysis. To achieve this, a sentiment multi-classification dataset is for the first time constructed for China’s stock market, based on four types of sentiments in modern psychology. The significant heterogeneity of sentiment changes in the sectors’ leading stock markets is trained and mined using the Bi-LSTM-ATT model. The impact of multi-classification investor sentiment on stock price prediction was analyzed using the CNN-Bi-LSTM-ATT model. It finds that integrating sentiment indicators into the prediction of industry leading stock prices can enhance the accuracy of the model. Drawing upon four fundamental sentiment types derived from modern psychology, our dataset provides a comprehensive framework for analyzing investor sentiment and its impact on forecasting the stock prices of China’s A-share market.

Golden Bria Holdings was the leading PSE equity stock in the Philippines in 2018, with a price gain rate of ***** percent. Golden Bria Holdings Inc. engages in the development of memorial parks in the Philippines and had a total net income of *** billion Philippine pesos in 2018.

Value of closing stock net price I (positive return to produced capital), closing stock net price II (zero return to produced capital), closing stock present value (based on net price I) and the reconciliation values (opening stock, additions, depletions, revaluation and closing stock) of proven and probable zinc, lead and silver reserves, annual (dollars x 1,000,000).

Stock Price Time Series for Xometry Inc. Xometry, Inc. operates an artificial intelligence (AI) powered online manufacturing marketplace in the United States and internationally. The company's marketplace uses AI to assist buyers to source custom-manufactured parts and assemblies and attain instant pricing and lead times. It operates Xometry marketplace, an AI powered online marketplace that connects buyers with suppliers of manufacturing services; Xometry Instant Quoting Engine, which prices transactions based on volume, manufacturing process, material, and location; and Thomasnet, an industrial sourcing platform that features an online directory of industrial suppliers, products, and services, as well as digital marketing services and insights to manufacturers and industrial services providers. The company also provides cloud-based systems, such as Workcenter, a financial service product that facilitates payments and a cloud-based manufacturing execution system; and Teamspace, a collaborative workspace that provides engineers, project managers, and procurement personnel access to quotes, order placements, part statuses, and tracking information. In addition, it offers computer numerical control manufacturing, sheet metal forming, sheet cutting, 3D printing, die casting, stamping, injection molding, urethane casting, and tube cutting and bending, as well as finishing, rapid prototyping, and production services. It serves the aerospace, industrial, medical devices, automotive, consumer goods, defense, government, energy, education, and robotics industries. The company was formerly known as NextLine Manufacturing Corp. and changed its name to Xometry, Inc. in June 2015. Xometry, Inc. was incorporated in 2013 and is headquartered in North Bethesda, Maryland.

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 Lead Index Outlook Signals Shifting Market Winds

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 2023, the stock of Kweichow Moutai had a market value of over ************ yuan. The stock exchange in Shanghai had the second-highest annual turnover in the Greater China region behind the bourse in Shenzhen.

The SSE trading boards

The Shanghai Stock Exchange has *** trading boards known as the Main Board and the Star A board. The Main Board lists some of China’s largest companies, such as the Industrial and Commercial Bank of China, and Pingan Insurance. While the main board is geared toward large companies that have a consolidated market position and stable profitability, the Star A market targets early-stage tech startups.

Kweichow Moutai

Kweichow Moutai is a Chinese Baijiu manufacturer from Guizhou province and the most valuable stock in China. Initially, after the company’s first public offering in 2001, its share price remained stable until it skyrocketed in 2016. By now, Moutai is not only the most valuable spirits company in China, but also worldwide. In China, Moutai’s Baijiu is seen as a status symbol and the official spirit of the Chinese government.

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.

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 Health Care Index Lead 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

Stock Price Time Series for Korea Zinc Inc. Korea Zinc Company, Ltd. operates as a general non-ferrous metal smelting company primarily in South Korea. The company offers zinc slab ingots, alloy jumbo blocks, anode ingots, and die cast ingots; and lead and lead alloy ingots; and copper cathodes. The company also provides gold and silver; and rare metals, including indium, bismuth, and antimony; and sulfuric acid, semi sulfuric acid, and oleum. In addition, it engages in non-ferrous metals import and export, and recycling; wholesale and product brokerage; provision of logistics warehousing services; shipping; construction equipment operation; waste lubricant refining; electricity, gas, and steam supply; concentrate export; and logistics businesses. Further, the company offers private equity fund services; media content production services; electrolytic copper foil for secondary batteries; and electronic waste collection, dismantling, shredding, and processing services. The company was incorporated in 1974 and is headquartered in Seoul, South Korea.

As of June 17, 2024, the most shorted stock was for, the American holographic technology services provider, MicroCloud Hologram Inc., with 66.64 percent of their total float having been shorted. This is a change from mid-January 2021, when video game retailed GameStop had an incredible 121.07 percent of their available shares in a short position. In effect this means that investors had 'borrowed' more shares (with a future promise to return them) than the total number of shares available for public trading. Owing to this behavior of professional investors, retail investors enacted a campaign to drive up the stock price of Gamestop, leading to losses of billions when investors had to repurchase the stock they had borrowed. At this time, a similar – but less effective – social media campaign was also carried out for the stock price of cinema operator AMC, and the price of silver. What is short selling? Short selling is essentially where an investor bets on a share price falling by: borrowing a number of shares selling these shares while the price is still high; purchasing the same number again once the price falls; then returning the borrowed shares at a profit. Of course, a profit will only be made if the share price does fall; should the share price rise the investor will then need to purchase the shares back at a higher price, and thus incur a loss. Short selling can lead to some very large profits in a short amount of time, with Tesla stock generating over one billion dollars in short sell profits during the first week of March 2020 alone, owing to the financial crash caused by the coronavirus (COVID-19) pandemic. However, owing to the short-term, opportunistic nature of short selling, these returns look less impressive when considered as net profits from short sell positions over the full year. The risks of short selling Short selling carries greater risks than traditional investments, and for this reason financial advisors often recommend against this strategy for ‘retail’ (i.e. non-professional) investors. The reason for this is that losses from short selling are potentially uncapped, whereas losses from traditional investments are limited to the initial cost. For example, if someone purchases 100 dollars of shares, the maximum they can lose is the 100 dollars the spent on those shares. However, say someone borrows 100 dollars of shares instead, betting on the price falling. If these shares are then sold for 100 dollars but the price subsequently rises, the losses could greatly exceed the initial investment should the price rise to, say, 500 dollars. The risks of short selling can be seen by looking again at Tesla, with the company causing the greatest losses over 2020 from short selling at over 40 billion U.S. dollars.

In 2024, stocks in the ********************************* sectors delivered the highest returns in the large-cap segment following the announcement of election dates in India. Hindustan Zinc Ltd. emerged as the top gainer in the large-cap category from the 2024 general elections in India, with its stock prices rising by over *** percent. Vedanta Ltd. followed closely with a nearly ** percent increase in stock prices.