Nickel fell to 14,879.88 USD/T on December 3, 2025, down 0.20% from the previous day. Over the past month, Nickel's price has fallen 1.20%, and is down 7.47% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Nickel - values, historical data, forecasts and news - updated on December of 2025.

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

The price of nickel stock can be highly volatile and is influenced by various factors such as global demand, supply issues, economic stability, geopolitical events, and market speculation. This article explores how these factors impact the price of nickel stock and provides insights for investors in making informed investment decisions.

The article provides an overview of the nickel stock market price, highlighting the factors that influence it, such as supply and demand dynamics, government policies, and global economic conditions. It also discusses the recent volatility in nickel prices due to the electric vehicle industry, the COVID-19 pandemic, and geopolitical events. Investors and traders interested in nickel stocks can use this information to make informed investment decisions.

View monthly updates and historical trends for Nickel Price. Source: World Bank. Track economic data with YCharts analytics.

China Settlement Price: Shanghai Future Exchange: Nickel: 1st Month data was reported at 117,380.000 RMB/Ton in 02 Dec 2025. This records an increase from the previous number of 117,230.000 RMB/Ton for 01 Dec 2025. China Settlement Price: Shanghai Future Exchange: Nickel: 1st Month data is updated daily, averaging 107,630.000 RMB/Ton from Mar 2015 (Median) to 02 Dec 2025, with 2599 observations. The data reached an all-time high of 263,290.000 RMB/Ton in 25 Mar 2022 and a record low of 62,400.000 RMB/Ton in 15 Feb 2016. China Settlement Price: Shanghai Future Exchange: Nickel: 1st Month 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: Settlement Price: Daily.

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

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.

Nickel Price Outlook: TR/CC CRB Nickel index Faces Volatility Amidst Shifting Supply Dynamics.

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.

TR/CC CRB Nickel Index: A Reliable Gauge of Nickel Market Performance?

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, the Indian nickel market increased by X% to $X for the first time since 2018, thus ending a two-year declining trend. Overall, the total consumption indicated a tangible expansion from 2012 to 2021: its value increased at an average annual rate of +X% over the last nine years. The trend pattern, however, indicated some noticeable fluctuations being recorded throughout the analyzed period. As a result, consumption attained the peak level and is likely to continue growth in the immediate term.

The Nickel Marketsize was valued at USD 36.27 USD Billion in 2023 and is projected to reach USD 51.38 USD Billion by 2032, exhibiting a CAGR of 5.1 % during the forecast period. Key drivers for this market are: Rising Demand for Stainless Steel Fueled Ni Metal Consumption. Potential restraints include: Fluctuating Prices to Hamper Market Growth.

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.

Nickel index faces uncertain future amid shifting market dynamics.

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

Norilsk Nickel reported RUB4.8B in Stock for its fiscal semester ending in December of 2022. Data for Norilsk Nickel | GMKN - Stock including historical, tables and charts were last updated by Trading Economics this last December in 2025.

The article explores the various factors that influence the nickel price in the stock market, including demand and supply dynamics, global economic conditions, and market speculation. It highlights the importance of nickel in various industries and how its price is closely tied to the growth and performance of these sectors. The article also discusses the impact of supply disruptions, changes in government policies, and demand from emerging economies on the nickel market. Additionally, it emphasizes the rol

The Philippines was also the world's largest nickel exporting nation in 2023 based on value, having accounted for nearly ** percent of global exports that year. New Caledonia came second, having accounted for almost **** percent of global nickel exports. The total value of nickel exports worldwide in 2023 was **** billion U.S. dollars.

Nickel Mines reported $139.82M in Stock for its fiscal semester ending in December of 2024. Data for Nickel Mines | NIC - Stock including historical, tables and charts were last updated by Trading Economics this last November in 2025.

Norilsk Nickel reported RUB19.72B in Market Capitalization this December of 2025, considering the latest stock price and the number of outstanding shares.Data for Norilsk Nickel | GMKN - Market Capitalization including historical, tables and charts were last updated by Trading Economics this last December 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.

Nickel Commodity DJ Forecast: Volatility Expected for the Nickel 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

China Settlement Price: Shanghai Future Exchange: Nickel: 2nd Month data was reported at 117,740.000 RMB/Ton in 02 Dec 2025. This records an increase from the previous number of 117,480.000 RMB/Ton for 01 Dec 2025. China Settlement Price: Shanghai Future Exchange: Nickel: 2nd Month data is updated daily, averaging 107,770.000 RMB/Ton from Mar 2015 (Median) to 02 Dec 2025, with 2599 observations. The data reached an all-time high of 267,700.000 RMB/Ton in 10 Mar 2022 and a record low of 64,390.000 RMB/Ton in 24 Nov 2015. China Settlement Price: Shanghai Future Exchange: Nickel: 2nd Month 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: Settlement Price: Daily.

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.

Nickel Index: A Leading Indicator?

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