In economic studies and popular media, interest rates are routinely cited as a major factor behind commodity price fluctuations. At the same time, the transmission channels are far from transparent, leading to long-running debates on the sign and magnitude of interest rate effects. Purely empirical studies struggle to address these issues because of the complex interactions between interest rates, prices, supply changes, and aggregate demand. To move this debate to a solid footing, we extend the competitive storage model to include stochastically evolving interest rates. We establish general conditions for existence and uniqueness of solutions and provide a systematic theoretical and quantitative analysis of the interactions between interest rates and prices.

This paper constructs daily measures of the real interest rate and expected inflation using commodity futures prices and the term structure of Treasury yields. We find that commodity futures markets respond to surprise increases in the federal funds rate target by raising the inflation rate expected over the next three to nine months. There is no evidence that the real interest rate responds to surprises in the federal funds target. The data from the commodity futures markets are highly volatile. We show that one can substantially reduce the noise using limited information estimators such as the median change. Nevertheless, the basket of commodities actually traded daily is quite narrow and we do not know whether our observable rates are closely connected to the unobservable inflation and real rates that affect economy-wide consumption and investment decisions.

The balanced annual panel data for 32 sub-Saharan countries from 2000 to 2020 was used for this study. The countries and period of study was informed by availability of data of interest. Specifically, 11 agricultural commodity dependent countries, 7 energy commodity dependent countries and 14 mineral and metal ore dependent countries were selected (Appendix 1). The annual data comprised of agricultural commodity prices, global oil prices (GOP) and mineral and metal ore prices, export value of the dependent commodity, total export value of the country, real GDP (RGDP) and terms of trade (TOT). The data for export value of the dependent commodity, total export value of the country, real GDP and terms of trade was sourced from world bank database (World Development Indicators). Data for agricultural commodity prices, global oil prices (GOP) and mineral and metal ore prices are obtained from World Bank commodity price data portal. This study used data from global commodity prices from the World Bank's commodity price data site since the error term (endogenous) is connected with each country's commodity export price index. The pricing information covered agricultural products, world oil, minerals, and metal ores. One benefit of adopting international commodity prices, according to Deaton and Miller (1995), is that they are frequently unaffected by national activities. The utilization of studies on global commodity prices is an example (Tahar et al., 2021). The commodity dependency index of country i at time i was computed as the as the ratio of export value of the dependent commodity to the total export value of the country. The commodity price volatility is estimated using standard deviation from monthly commodity price index to incorporate monthly price variation (Aghion et al., 2009). This approach addresses challenges of within the year volatility inherent in the annual data. In footstep of Arezki et al. (2014) and Mondal & Khanam (2018), standard deviation is used in this study as a proxy of commodity price volatility. The standard deviation is used because of its simplicity and it is not conditioned on the unit of measurement.

Gold fell to 3,336.24 USD/t.oz on July 17, 2025, down 0.32% from the previous day. Over the past month, Gold's price has fallen 0.96%, but it is still 36.66% higher than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Gold - values, historical data, forecasts and news - updated on July of 2025.

CRB Index fell to 372.51 Index Points on July 15, 2025, down 0.22% from the previous day. Over the past month, CRB Index's price has fallen 2.07%, but it is still 10.80% higher than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. CRB Commodity Index - values, historical data, forecasts and news - updated on July of 2025.

Financial Derivatives Market Outlook

The global financial derivatives market size was valued at approximately USD 25 trillion in 2023 and is projected to reach USD 40 trillion by 2032, growing at a CAGR of 5.6% during the forecast period. The primary growth factor driving this market is the increasing demand for risk management tools and hedging strategies, particularly in volatile economic conditions. As businesses seek to protect themselves from fluctuations in interest rates, currency exchange rates, and commodity prices, the utilization of financial derivatives becomes increasingly critical. This growing need for financial stability and predictability is propelling the adoption of financial derivatives globally.

One of the significant growth factors for the financial derivatives market is the rising globalization of trade and investment. The interconnectedness of the global economy has heightened the exposure of firms to various financial risks, such as currency and interest rate risks. Consequently, there is a growing demand for derivatives as effective tools for managing these exposures. Additionally, advancements in financial markets infrastructure and technology have facilitated easier access to derivative products, further supporting market growth. These advancements include electronic trading platforms, sophisticated risk management software, and improved regulatory frameworks, all of which have streamlined the trading and utilization of derivatives.

Another key driver for the financial derivatives market is the increasing sophistication of institutional investors. Entities such as pension funds, mutual funds, and hedge funds are employing complex strategies involving derivatives to enhance returns and manage portfolio risks. The growing presence of hedge funds in particular, which are known for their aggressive derivative strategies, has notably contributed to market expansion. Moreover, the continuous development of new derivative products tailored to meet the specific needs of these sophisticated investors has led to a more dynamic and diverse market landscape.

The regulatory environment also plays a crucial role in shaping the financial derivatives market. Post-2008 financial crisis reforms, such as the Dodd-Frank Act and the European Market Infrastructure Regulation (EMIR), have mandated greater transparency and reduced counterparty risks in derivatives trading. While these regulations have initially posed challenges, they have ultimately fostered a more robust and trustworthy market. Improved regulatory oversight has instilled confidence among market participants, leading to increased participation and growth. Moreover, ongoing regulatory advancements continue to evolve, ensuring the market adapts to new financial realities and risks.

Type Analysis

The financial derivatives market is segmented by type into futures, options, swaps, and forwards. Futures contracts, which are standardized agreements to buy or sell an asset at a predetermined price at a specified future date, constitute a substantial portion of the market due to their widespread use in hedging against price volatility in various underlying assets, such as commodities, currencies, and indices. The growing volume of trade in commodities and the need for price stability among producers and consumers have significantly boosted the demand for futures contracts. Additionally, the advent of electronic trading platforms has made trading futures more accessible and efficient, contributing to the segment's growth.

Options, which grant the holder the right but not the obligation to buy or sell an asset at a predetermined price before or at the expiration date, are another crucial segment of the financial derivatives market. The flexibility they offer, combined with the potential for high returns, makes options particularly attractive to both individual and institutional investors. The use of options in speculative strategies, as well as in risk management to hedge against unfavorable price movements, has seen steady growth. The development of exchange-traded options has further enhanced transparency and liquidity in this segment, attracting more participants.

Swaps, which involve the exchange of cash flows or liabilities between parties, have gained prominence, especially interest rate swaps and currency swaps. Interest rate swaps allow entities to manage exposure to fluctuations in interest rates, which is particularly relevant in enviro

High price volatility among various commodities and the recent lowering of interest rates has fueled strong growth among commodity contracts intermediation brokers. While the national economy has continued to recover following a period of high inflationary pressures, recent rate cuts by the Federal Reserve and continued price volatility of oil and agricultural products strengthened commodity contracts’ popularity. Short-term contracts and future continue to facilitate interest among brokers, with revenue growing at a CAGR of 4.6% to an estimated $21.8 billion through the end of 2024, including an estimated 2.3% boost in 2024 alone. Profit continues to remain steady, as higher price volatility and lower interest rates continue to facilitate favorable market conditions for commodity traders. Banks, once outsized players in the industry, have significantly downsized or completely ended their commodity trading activities. This has put significant downward pressure on revenue as these institutions have been forced to limit proprietary trading due to the Volcker rule, enacted prior to the current period. The decreased presence of banks in the industry has allowed smaller players to enter the industry, exacerbating fragmentation among various service groups. The inflationary spike played a key role in buoying growth, with recent geopolitical conflicts in the Middle East and Europe strengthening commodity price volatility. Moving forward, commodity contract intermediaries face a less certain landscape, as anticipated declines in global oil prices and the agricultural price index will dampen the popularity of long-term commodity trades. Increased demand for metal and energy products and the low inventories of metal commodities are expected to sustain a significant revenue stream for brokers. However, further uncertainty surrounding rising tensions in the Middle East will impact the types of trades made by commodity traders. Greater automation and adoption of new technologies such as blockchain will offer a workflow enhancement in the longer term. Nonetheless, an expected decline in global oil prices is poised to cause revenue to fall at a CAGR of 1.0% to an estimated $20.8 billion through the end of 2029.

The International Financial Statistics database covers about 200 countries and areas, with some aggregates calculated for selected regions, plus some world totals. Topics covered include balance of payments, commodity prices, exchange rates, fund position, government finance, industrial production, interest rates, international investment position, international liquidity, international transactions, labor statistics, money and banking, national accounts, population, prices, and real effective exchange rates.

Silver fell to 38.14 USD/t.oz on July 18, 2025, down 0.02% from the previous day. Over the past month, Silver's price has risen 4.79%, and is up 30.54% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Silver - values, historical data, forecasts and news - updated on July of 2025.

ABSTRACT The paper discusses the determination of inflation in Brazil, especially after the great recession of 2015-2016, to assess the adequacy of manipulating interest rates to control the rise in prices due to permanent cost pressure. The burden of using the interest rate to fight cost inflation is to create a highly conventional level of the real interest rate, which benefits the rentier class in a financialized economy. In the light of the post-Keynesian macroeconomics, a high-interest rate convention keeps the economy with a low growth rate and a low investment rate, which in the case of the Brazilian economy has resulted in a regression in the productive matrix and productivity stagnation, and both contribute to perpetuating cost pressures on prices. The empirical analysis corroborates the discussion about recent inflation having its origin in cost pressures over which the interest rate impact for its control is limited. We complement the empirical analysis by testing the response to the SELIC interest rate of the variables used to explain the fluctuation of market prices and administered prices: commodity price index, exchange rate and activity level. As expected, the impact of an increase in the interest rate appreciates the exchange rate, favouring inflation control and reducing the level of activity but has no impact on the commodity price index.

Agricultural price shocks strongly affect farmers' income and food security. It is therefore important to understand and anticipate their origins and occurrence, particularly for the world's main agricultural commodities. In this study, we assess the impacts of yearly variations in regional maize productions and yields on global maize prices using several statistical and machine-learning (ML) methods. Our results show that, of all regions considered, Northern America is by far the most influential. More specifically, our models reveal that a yearly yield gain of +8% in Northern America negatively impacts the global maize price by about –7%, while a decrease of –0.1% is expected to increase global maize price by more than +7%. Our classification models show that a small decrease in the maize yield in Northern America can inflate the probability of maize price increase on the global scale. The maize productions in the other regions have a much lower influence on the global price. Among the tested methods, random forest and gradient boosting perform better than linear models. Our results highlight the interest of ML in analyzing global prices of major commodities and reveal the strong sensitivity of maize prices to small variations of maize production in Northern America.

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 Price Outlook: DJ Commodity Nickel Index Faces Volatility

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

Discover the latest shifts in corn futures as volume and open interest see significant changes, affecting global commodity trends.

This annual report is a compendium of historical statistics covering the agriculture, fisheries, food and forestry sectors. It provides a set of comprehensive statistical tables on Australian and world prices, production, consumption, stocks and trade for 19 rural commodities. The commodities covered include grains and oilseeds, livestock, livestock products, food, wool, horticulture, forestry products and fisheries products. This report also contains statistics on agricultural water use and macroeconomic indicators such as economic growth, employment, balance of trade, exchange rates and interest rates.

Predictions for the S&P/TSX index indicate a potential for positive returns, driven by improving economic conditions, corporate earnings growth, and a favorable interest rate environment. However, risks include geopolitical uncertainty, volatility in commodity prices, and potential interest rate increases. Investors should exercise caution and diversify their portfolios to mitigate these risks.

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 DJ Commodity?

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

Futures Trading Service Market Outlook

The global futures trading service market size was valued at USD 5.2 billion in 2023 and is projected to reach USD 10.8 billion by 2032, growing at a CAGR of 8.5% during the forecast period. The significant growth in market size can be attributed to increased trading activities, technological advancements in trading platforms, and rising interest from individual and institutional investors alike.

A major growth factor for the futures trading service market is the rising prevalence of advanced trading platforms and technologies. Technological advancements have made futures trading more accessible and efficient, enabling traders to execute complex strategies with greater ease. The integration of artificial intelligence and machine learning into trading algorithms has also enhanced decision-making processes, resulting in improved trading outcomes and increased market participation.

Another key driver is the increased participation of institutional investors. As financial markets become more interconnected, institutional investors are increasingly turning to futures trading to hedge against market volatility and optimize their portfolios. The availability of diverse asset classes within futures trading, including commodities, financials, and indices, provides these investors with a wide range of options to manage their risk exposure effectively.

Moreover, the growing interest among individual investors is fueling market expansion. The democratization of trading platforms has lowered entry barriers, allowing retail traders to participate in futures markets. Educational resources and advisory services provided by brokerage firms further support individual investors in navigating the complexities of futures trading, thereby contributing to market growth.

Commodity Services play a pivotal role in the futures trading market, offering a wide range of opportunities for both hedgers and speculators. These services encompass the trading of various commodities such as agricultural products, energy resources, and precious metals. The inherent volatility in commodity prices makes futures contracts an attractive tool for managing risk and securing price stability. As global demand for commodities continues to rise, driven by factors like population growth and industrialization, the importance of robust commodity services in futures trading becomes increasingly evident. These services not only facilitate efficient price discovery but also provide a platform for market participants to capitalize on price movements and achieve their financial objectives.

In terms of regional outlook, North America holds the largest market share due to the presence of major financial institutions and advanced trading infrastructure. The Asia Pacific region is expected to witness the highest growth rate, driven by increasing economic development, rising disposable incomes, and the expansion of financial markets in countries like China and India. Europe also shows significant potential, with well-established financial hubs such as London and Frankfurt contributing to market growth.

Service Type Analysis

The futures trading service market can be segmented by service type into brokerage services, trading platforms, advisory services, and others. Brokerage services dominate the market, providing essential intermediary functions that facilitate trading activities. These services are crucial for both individual and institutional investors, offering benefits such as access to diverse markets, real-time data, and personalized customer support. The competitive landscape among brokerage firms is intense, with key players continuously enhancing their offerings to attract and retain clients.

Trading platforms are another significant segment within the futures trading service market. These platforms offer a suite of tools and features that enable traders to execute trades, monitor market conditions, and analyze trends. The evolution of trading platforms from desktop-based applications to web-based and mobile solutions has made it easier for traders to engage with the market anytime and anywhere. Features such as automated trading, advanced charting, and customizable interfaces are driving the adoption of these platforms among traders.

Advisory services play a critical role in guiding investors through the complexities of futures trading. These services provide expert anal

Derivatives Market size is growing at a moderate pace with substantial growth rates over the last few years and is estimated that the market will grow significantly in the forecasted period i.e. 2024 to 2031.

Global Derivatives Market Drivers

The market drivers for the Derivatives Market can be influenced by various factors. These may include:

Hedging and Risk Management: Through the use of derivatives, investors and companies can guard against the risks associated with price volatility in underlying assets such as interest rates, equities, commodities, and currencies. This need for risk management is what essentially drives the derivatives market. Speculation and arbitrage: Speculators use derivatives to bet on how market prices will move in the future, whilst arbitrageurs exploit price differences between markets. These two activities play a major role in the growth and liquidity of the derivatives market. Market Efficiency: Derivatives increase market efficiency by allowing participants to quickly adjust how exposed they are to various financial risks. Because of their effectiveness, traders and investors find derivatives to be an attractive instrument. Financial Innovation: The constant development of new derivative products and trading strategies drives market expansion. Novelties that cater to a variety of financial needs and attract a greater number of players include futures, swaps, options, and highly constructed products. Globalization: As the world's financial markets become more interdependent, so does the need for derivatives. Businesses engaged in international trade and investment utilize derivatives as a tool to control cross-border financial exposures, such as exchange rate risk. Modifications to Regulations: The objectives of regulatory frameworks such as the Dodd-Frank Act in the United States and the European Market Infrastructure Regulation (EMIR) in the European Union aim to reduce systemic risk and enhance transparency in the derivatives market. While these regulations may incur additional costs, they also enhance the stability and trust of the market, which may promote involvement. Technological Developments: Data analytics, algorithmic trading, and trading platforms have all advanced, enabling faster and more efficient trading of derivatives. Technology also makes better risk management and compliance possible, which attracts new competitors to the market. Interest Rate Environment: The present interest rate environment has an impact on the derivatives market, particularly on interest rate derivatives. Interest rate changes have an impact on the demand for and price of some financial assets. Institutional Participation: An rise in insurance firms, hedge funds, and pension funds among other institutional investors is driving market growth. These companies regularly employ derivatives to manage their portfolios and achieve certain financial objectives. Economic and Geopolitical Factors: Due to geopolitical developments and economic conditions, the financial markets are unstable and uncertain, which raises demand for derivatives as tools for risk management and speculation. Growing Knowledge and Awareness: As market participants become more knowledgeable about the benefits and uses of derivatives, there is an increasing demand for these financial instruments. Educational initiatives and professional training programs help to create this increased awareness.

According to Cognitive Market Research, the Global Energy Trading and Risk Management Market Size will be USD XX Billion in 2023 and is set to achieve a market size of USD XX Billion by the end of 2031 growing at a CAGR of XX% from 2024 to 2031.

• The global energy trading and risk management (ETRM). market will expand significantly by XX% CAGR between 2024 and 2031. • The software type segment accounts for the largest market share and is anticipated to a healthy growth over the approaching years. • The United States energy trading and risk management (ETRM). had a market share of about XX% in 2022. • The physical trading sector holds the largest share and is expected to grow in the coming years as well. • Power application is the market's largest contributor and is anticipated to expand at a CAGR of XX% during the projected period. • The North America region dominated the market and accounted for the highest revenue of XX% in 2022 and it is projected that it will grow at a CAGR of XX% in the future.

Market Dynamics: Energy Trading and Risk Management (ETRM)

Key Drivers

Market volatility and uncertainty will drive the market for energy trading and risk management-

Uncertainty has Various causes, including geopolitical events, supply-demand mismatches, weather patterns, and regulatory changes, these uncertainties in the market cause price fluctuation in the energy markets. For monitoring and reducing risks related to price swings and market uncertainty, ETRM systems are crucial. The rapid growth of commodities markets has drawn a wave of new entrants, such as tech-focused trading players, hedge funds, and banks, as well as players involved in mining and processing—creating a need for additional liquid and risk management offerings. Despite the decrease in market prices, commodity markets remain tight, and changes in demand and supply have become harder to predict. Further, uncertainty around the security of the energy supply contributes to price volatility, which is amplified by higher, shifting interest rates. For instance, Elections can also influence investor sentiment, impacting market dynamics. Proposals related to fiscal discipline, trade agreements, or monetary policy can sway investor sentiment and drive market movements. Investors often closely monitor candidates' economic agendas and assess the potential implications for sectors and industries (source:https://m.economictimes.com/markets/stocks/news/navigating-market-volatility-during-elections-insights-strategies-for-investors/articleshow/110384178.cms) Hence, ETRM systems are necessary to manage the complexities of multi-commodity trading, derivatives, complex contracts, and shifting market structures.

Restraint

Legacy systems and infrastructure will impede market expansion-

Many industrial processes and critical infrastructure rely on outdated legacy OT systems that lack modern security features. The high replacement costs and perceived prohibitive nature of upgrading these systems contribute to their continued use, despite their vulnerabilities. Challenges might also arise from the complexity of integrating energy trading and risk management ETRM systems into current IT infrastructure and procedures. It can be challenging for smaller energy firms to justify the costs of implementing ETRM. These legacy systems now stand resolutely in the way of progress. They’re big, rigid, change-resistant, costly to run, and lock-in-place practices that are long overdue for change. Any business process or system that has not been materially rethought with cloud computing, mobile-connected workers, and widespread telecoms networks is now an anchor, not an enabler. (source:https://molecule.io/blog/how-legacy-etrm-systems-impede-digital-transformation/) Hence, energy trading and risk management systems require precise and reliable data from various sources, such as market data feeds, trading platforms, and internal systems. Poor data quality or integration issues with data from several sources may impede the efficiency of energy trading and risk management systems.

Opportunity

Increasing Focus on Environmental Sustainability Initiatives is an opportunity for the market of energy trading and risk management.

The transition to environmental susta...

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.

SGI Commodities Optimix TR index Sees Moderate Growth 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