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.

Can we predict stock market using machine learning? (WY Stock 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

Israel's main stock market index, the TA-125, fell to 3080 points on July 31, 2025, losing 0.21% from the previous session. Over the past month, the index has climbed 1.83% and is up 56.58% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Israel. Israel Stock Market (TA-125) - values, historical data, forecasts and news - updated on August of 2025.

France's main stock market index, the FR40, fell to 7546 points on August 1, 2025, losing 2.91% from the previous session. Over the past month, the index has declined 2.48%, though it remains 4.06% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from France. France Stock Market Index (FR40) - values, historical data, forecasts and news - updated on August 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.

FTSE 100: Where to Next?

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 study investigates the application of machine learning (ML) models in stock market forecasting, with a focus on their integration using PineScript, a domain-specific language for algorithmic trading. Leveraging diverse datasets, including historical stock prices and market sentiment data, we developed and tested various ML models such as neural networks, decision trees, and linear regression. Rigorous backtesting over multiple timeframes and market conditions allowed us to evaluate their predictive accuracy and financial performance. The neural network model demonstrated the highest accuracy, achieving a 75% success rate, significantly outperforming traditional models. Additionally, trading strategies derived from these ML models yielded a return on investment (ROI) of up to 12%, compared to an 8% benchmark index ROI. These findings underscore the transformative potential of ML in refining trading strategies, providing critical insights for financial analysts, investors, and developers. The study draws on insights from 15 peer-reviewed articles, financial datasets, and industry reports, establishing a robust foundation for future exploration of ML-driven financial forecasting. Tools and Technologies Used †PineScript PineScript, a scripting language integrated within the TradingView platform, was the primary tool used to develop and implement the machine learning models. Its robust features allowed for custom indicator creation, strategy backtesting, and real-time market data analysis. †Python Python was utilized for data preprocessing, model training, and performance evaluation. Key libraries included: Pandas

Spain's main stock market index, the ES35, fell to 14127 points on August 1, 2025, losing 1.88% from the previous session. Over the past month, the index has climbed 0.58% and is up 32.36% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Spain. Spain Stock Market Index (ES35) - values, historical data, forecasts and news - updated on August 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.

Can we predict stock market using machine learning? (Bovespa Index Stock 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 Rolling Stock Market Report is Segmented by Type (Locomotives, Metros and Light Rail Vehicles, Passenger Coaches, and More), Propulsion Type (Diesel, Electric, and More), Application (Passenger Rail and Freight Rail), End-User (National Rail Operators and More), Technology (Conventional and More) and Geography. The Market Forecasts are Provided in Terms of Value (USD) and Volume (Units).

Stock market forecasting is one of the most challenging problems in today’s financial markets. According to the efficient market hypothesis, it is almost impossible to predict the stock market with 100% accuracy. However, Machine Learning (ML) methods can improve stock market predictions to some extent. In this paper, a novel strategy is proposed to improve the prediction efficiency of ML models for financial markets. Nine ML models are used to predict the direction of the stock market. First, these models are trained and validated using the traditional methodology on a historic data captured over a 1-day time frame. Then, the models are trained using the proposed methodology. Following the traditional methodology, Logistic Regression achieved the highest accuracy of 85.51% followed by XG Boost and Random Forest. With the proposed strategy, the Random Forest model achieved the highest accuracy of 91.27% followed by XG Boost, ADA Boost and ANN. In the later part of the paper, it is shown that only classification report is not sufficient to validate the performance of ML model for stock market prediction. A simulation model of the financial market is used in order to evaluate the risk, maximum draw down and returns associate with each ML model. The overall results demonstrated that the proposed strategy not only improves the stock market returns but also reduces the risks associated with each ML model.

The global stock analysis software market is experiencing robust growth, driven by increasing adoption of algorithmic trading, rising retail investor participation, and the expanding use of advanced analytical tools. The market, currently valued at approximately $2.5 billion in 2025 (estimated based on typical market sizes for similar software segments and a logical extrapolation considering the provided CAGR), is projected to witness a Compound Annual Growth Rate (CAGR) of 12% over the forecast period (2025-2033). Key segments driving this expansion include the banking, financial services, and insurance (BFSI) sector, alongside the rapidly growing healthcare, telecom, and IT industries. The preference for sophisticated fundamental and technical analysis tools is fueling demand, with evolutionary analysis gaining traction as a promising emerging segment. Regional dominance is currently held by North America, attributable to a mature financial market and high technology adoption. However, Asia Pacific is anticipated to exhibit the highest growth rate, fueled by increasing market awareness and expanding internet penetration. The market's expansion is further propelled by the rising availability of user-friendly, cloud-based stock analysis platforms. However, challenges remain. These include the high initial investment costs for advanced software and the potential for complexities in data interpretation for less experienced users. Nonetheless, innovative features such as AI-powered predictive analytics and integration with brokerage accounts are expected to mitigate these barriers and enhance market adoption. The competitive landscape is marked by both established players and emerging startups, leading to innovation and further driving market growth. Competitive differentiation is achieved through advanced features, user experience, and robust customer support. The consistent need for accurate, timely, and actionable insights ensures the continued importance of this sector in navigating global financial markets.

United States New York Stock Exchange: Index: US 100 Index data was reported at 16,491.357 NA in Apr 2025. This records a decrease from the previous number of 16,852.600 NA for Mar 2025. United States New York Stock Exchange: Index: US 100 Index data is updated monthly, averaging 10,169.600 NA from Jan 2012 (Median) to Apr 2025, with 160 observations. The data reached an all-time high of 17,440.815 NA in Feb 2025 and a record low of 5,695.000 NA in May 2012. United States New York Stock Exchange: Index: US 100 Index data remains active status in CEIC and is reported by Exchange Data International Limited. The data is categorized under Global Database’s United States – Table US.EDI.SE: New York Stock Exchange: Monthly.

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.

Flexing the Future?: (FLEX)

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 Rolling Stock Market is segmented into various product categories, including locomotives, rapid transit, and wagons. Locomotives account for the largest market share due to their central role in providing motive power to trains. Rapid transit systems, such as metros and light rail vehicles, are also gaining popularity in urban areas, driven by the need for efficient and environmentally friendly transportation options. Wagons, which serve as the primary means of transporting freight, are expected to witness steady growth due to the increasing demand for efficient cargo transportation. Recent developments include: In February 2023, Stadler Rail AG joined forces with ASPIRE Engineering Research Centre and Utah State University in order to build a battery-driven passenger train around the concept of FLIRT Akku. The project encompasses the development, manufacture, and testing of a two-car multi-unit that runs on batteries under the brand name FLIRT Akku., Stadler Rail AG announced it had acquired BBR Verkehrstechnik GmbH (BBR), a rail company, and its group businesses in February 2023 to strengthen its internal know-how in the fields of digitization and signaling technology., Alstom SA is setting up a new factory for railway electrification in Valmadrera (Lecco, Italy) by March 2023. It will produce power transmission components for rail, metro, or tram lines., Siemens Mobility has entered into an agreement with Indian Railways under which Siemens India received an order for 9,000-hp locomotives from Indian Railways totaling 1,200 units – the largest ever single order placed with Siemens Mobility and Siemens India in their history. Siemens Mobility will design, develop, assemble, and test these trains for over a period of thirty-five years from delivery, and they shall also render full-service maintenance., In November 2022, Siemens Mobility unveiled plans to build a train bogies factory in Aurangabad, India, capable of delivering more than one export consignment exceeding two hundred bogies at once. These rail bogies were made by Siemens using the SF30 Combino Plus global design idea., In December 2022, HS2 awarded contracts to a joint venture comprising Hitachi Rail and Alstom (HAH-S) for the development, manufacture, and maintenance of high-speed rolling stock for Phase 1.. Notable trends are: Data analytics and advanced maintenance usage are fueling market growth.

Card Stock Market Outlook

The global card stock market size was valued at approximately USD 2.8 billion in 2023 and is projected to grow to USD 4.2 billion by 2032, at a compound annual growth rate (CAGR) of 4.6% during the forecast period. This robust growth is driven by increasing demand in the packaging and printing industries, along with a burgeoning interest in crafting and DIY activities globally.

One of the primary growth factors fueling the card stock market is the rising demand for sustainable and eco-friendly packaging solutions. As consumers and businesses alike become more environmentally conscious, the demand for recyclable and biodegradable card stock has surged. This trend is particularly evident in the packaging sector, where companies are increasingly opting for card stock over plastic to meet consumer preferences and regulatory requirements aimed at reducing plastic waste.

The growth of the e-commerce industry is another significant driver for the card stock market. With the rapid expansion of online retailing, the need for secure and appealing packaging solutions has increased. Card stock is often used in packaging for its durability and printability, which helps in creating visually attractive and sturdy packaging. Moreover, the rise in personalized and custom packaging trends among e-commerce platforms has further amplified the demand for high-quality card stock.

Additionally, the increasing popularity of crafting and DIY activities has spurred the demand for various types of card stock. With more people engaging in hobbies such as scrapbooking, card-making, and other creative projects, the market for card stock has expanded significantly. This trend is further bolstered by the proliferation of social media platforms, where users share their crafting ideas and projects, thereby inspiring others and driving demand for crafting materials, including card stock.

From a regional perspective, North America and Europe hold significant shares in the card stock market, driven by high levels of consumer awareness and stringent environmental regulations. Asia Pacific, however, is expected to witness the fastest growth during the forecast period due to increasing industrialization, rising disposable income, and the growing e-commerce sector. Latin America and the Middle East & Africa are also anticipated to exhibit moderate growth, supported by expanding packaging and printing industries in these regions.

Product Type Analysis

The card stock market can be segmented by product type into coated card stock, uncoated card stock, textured card stock, recycled card stock, and others. Coated card stock holds a significant share due to its smooth surface and excellent printability, which makes it ideal for high-quality printing applications. It is widely used in business cards, brochures, and luxury packaging, where visual appeal is paramount. The coating enhances the card's durability and resistance to moisture, making it suitable for various commercial uses.

Uncoated card stock, on the other hand, is preferred for applications that require a more natural and tactile feel. It is often used in stationery, greeting cards, and certain types of packaging where a rustic or minimalist aesthetic is desired. The lack of coating allows for better ink absorption, which can be advantageous for certain printing techniques and crafting projects.

Textured card stock offers a unique advantage with its distinct surface patterns, adding a tactile dimension to printed materials. This type of card stock is popular in high-end invitations, business cards, and special event stationery. The textured surface can range from subtle linen-like patterns to more pronounced embossing, catering to diverse design needs.

Recycled card stock is gaining traction due to the growing emphasis on sustainability. Made from post-consumer waste, this type of card stock appeals to eco-conscious consumers and businesses. It is used in a variety of applications, including packaging, printing, and crafting, and offers a viable alternative to traditional paper products with a lower environmental footprint.

Other types of card stock include specialty variants tailored for specific applications, such as metallic finishes, which are used for luxury packaging and special occasions. These niche products, while not as widely used as the more common types, play an important role in meeting the diverse needs of the market and offering unique solutions for specific projects.

The foreign exchange (Forex) market is a global decentralized market for the trading of currencies. It is the largest financial market in the world, with an average daily trading volume of over $5 trillion. The market size is expected to reach $84 million by 2033, growing at a CAGR of 5.83% during the forecast period 2025-2033. Key drivers of the Forex market growth include increasing international trade, rising foreign direct investment, and growing demand for hedging and speculation. The market is also being driven by the increasing use of online trading platforms and the growing popularity of cryptocurrencies. The major players in the Forex market include Deutsche Bank, UBS, JP Morgan, State Street, XTX Markets, Jump Trading, Citi, Bank of New York Mellon, Bank America, and Goldman Sachs. The market is segmented by type (spot Forex, currency swap, outright forward, Forex swaps, Forex options, other types), counterparty (reporting dealers, other financial institutions, non-financial customers), and region (North America, South America, Europe, Middle East & Africa, Asia Pacific). Recent developments include: In November 2023, JP Morgan revealed the introduction of novel FX Warrants denominated in Hong Kong dollars in the Hong Kong market, marking its status as the inaugural issuer in Asia to present FX Warrants featuring CNH/HKD (Chinese Renminbi traded outside Mainland China/Hong Kong dollar) and JPY/HKD (Japanese Yen/Hong Kong dollar) as underlying currency pairs. These fresh FX Warrants are set to commence trading on the Hong Kong Stock Exchange., In October 2023, Deutsche Bank AG finalized its purchase of Numis Corporation Plc. The integration of both brands under the name 'Deutsche Numis' underscores their collective influence and standing in the UK and global markets. 'Deutsche Numis' emerges as a prominent entity in UK investment banking and the preferred advisor for UK-listed companies. This acquisition aligns with Deutsche Bank's Global Hausbank strategy, aiming to become the primary partner for clients in financial services and fostering stronger relationships with corporations throughout the United Kingdom., In June 2023, UBS successfully finalized the acquisition of Credit Suisse, marking a significant achievement. Credit Suisse Group AG has merged into UBS Group AG, forming a unified banking entity.. Key drivers for this market are: International Transactions Driven by Growing Tourism Driving Market Demand, Market Liquidity Impacting the Foreign Exchange Market. Potential restraints include: International Transactions Driven by Growing Tourism Driving Market Demand, Market Liquidity Impacting the Foreign Exchange Market. Notable trends are: FX Swaps is leading the market.

Euro Area's main stock market index, the EU50, fell to 5174 points on August 1, 2025, losing 2.80% from the previous session. Over the past month, the index has declined 2.72%, though it remains 11.54% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from Euro Area. Euro Area Stock Market Index (EU50) - values, historical data, forecasts and news - updated on August 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.

How do you determine buy or sell? (TA 35 Index Stock 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

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.

Can neural networks predict stock market? (LON:HANA Stock 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

China's main stock market index, the SHANGHAI, fell to 3560 points on August 1, 2025, losing 0.37% from the previous session. Over the past month, the index has climbed 3.04% and is up 22.53% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from China. China Shanghai Composite Stock Market Index - values, historical data, forecasts and news - updated on August of 2025.

In 2024, the Croatian market for toilet paper, napkins, towels and tissue stock decreased by -14.8% to $212M for the first time since 2021, thus ending a two-year rising trend. Overall, the total consumption indicated a measured expansion from 2012 to 2024: its value increased at an average annual rate of +4.9% over the last twelve years. The trend pattern, however, indicated some noticeable fluctuations being recorded throughout the analyzed period.