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.

TISI Stock: On the Rise or a Bumpy Road 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 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.

Ninja Sees Moderate Growth Ahead, (SN) Stock Predicted to Rise.

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

Stay informed on the latest coffee and tea market trends. U.S. Coffee and Tea prices have slightly increased to $8,767 per ton. Read more to stay ahead of the game.

Throughout Q1 2025, the nitrogen (fertilizer) market in North America showed a fluctuating but generally resilient trend. In January, prices rose slightly, supported by restocking ahead of the spring planting season and supply tightness caused by winter-related logistical disruptions. Strong agricultural demand, especially from corn and wheat growers, further fueled price strength as buyers secured inputs early. However, February saw a mild price correction, driven by high inventory levels that had built up from aggressive January procurement.

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.

Volatility or Stability Ahead for Volato (SOAR)?

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 June 2025, 59 percent of households in Great Britain reported that their cost of living had increased in the previous month, compared with 72 percent in April. Although the share of people reporting a cost of living increase has generally been falling since August 2022, when 91 percent of households reported an increase, the most recent figures indicate that the Cost of Living Crisis is still ongoing for many households in the UK. Crisis ligers even as inflation falls Although various factors have been driving the Cost of Living Crisis in Britain, high inflation has undoubtedly been one of the main factors. After several years of relatively low inflation, the CPI inflation rate shot up from 2021 onwards, hitting a high of 11.1 percent in October 2022. In the months since that peak, inflation has fallen to more usual levels, and was 2.5 percent in December 2024, slightly up from 1.7 percent in September. Since June 2023, wages have also started to grow at a faster rate than inflation, albeit after a long period where average wages were falling relative to overall price increases. Economy continues to be the main issue for voters Ahead of the last UK general election, the economy was consistently selected as the main issue for voters for several months. Although the Conservative Party was seen by voters as the best party for handling the economy before October 2022, this perception collapsed following the market's reaction to Liz Truss' mini-budget. Even after changing their leader from Truss to Rishi Sunak, the Conservatives continued to fall in the polls, and would go onto lose the election decisively. Since the election, the economy remains the most important issue in the UK, although it was only slightly ahead of immigration and health as of January 2025.

Brazil Market Expectation: Price Indices: General Price Index - Market (IGP-M): 2 Years Ahead: Average data was reported at 4.010 % in 28 Jun 2019. This records a decrease from the previous number of 4.020 % for 27 Jun 2019. Brazil Market Expectation: Price Indices: General Price Index - Market (IGP-M): 2 Years Ahead: Average data is updated daily, averaging 4.630 % from Jan 2001 (Median) to 28 Jun 2019, with 4428 observations. The data reached an all-time high of 7.130 % in 26 Dec 2002 and a record low of 3.190 % in 03 Jan 2001. Brazil Market Expectation: Price Indices: General Price Index - Market (IGP-M): 2 Years Ahead: Average data remains active status in CEIC and is reported by Central Bank of Brazil. The data is categorized under Brazil Premium Database’s Business and Economic Survey – Table BR.SA014: Market Expectation: Price Indices: General Price Index - Market (IGP-M). Market Expectations System was implemented in November 2001, previous projections were collected from incipient through telephone contacts, transcribed into spreadsheets and consolidated manually. Some empty time points occurred because the Market didn´t have the expectation for those days. Reflects the price changes from the 21st to the 20th of following month. It is made up of the IPA (Wholesale Price Index), Consumer Price Index (IPC) and INCC (National Construction Cost Index), with weights of 60%, 30% and 10%, respectively. The indicator is prepared for financial market contracts.

UK Gas fell to 78.09 GBp/thm on July 24, 2025, down 0.60% from the previous day. Over the past month, UK Gas's price has fallen 5.55%, but it is still 4.83% higher than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. UK Natural Gas - values, historical data, forecasts and news - updated on July of 2025.

Brazil Market Expectation: Price Indices: General Price Index - Market (IGP-M): 1 Year Ahead: Median data was reported at 4.000 % in 28 Jun 2019. This stayed constant from the previous number of 4.000 % for 27 Jun 2019. Brazil Market Expectation: Price Indices: General Price Index - Market (IGP-M): 1 Year Ahead: Median data is updated daily, averaging 4.500 % from Nov 2000 (Median) to 28 Jun 2019, with 4665 observations. The data reached an all-time high of 8.460 % in 20 Dec 2002 and a record low of 3.000 % in 11 Jan 2001. Brazil Market Expectation: Price Indices: General Price Index - Market (IGP-M): 1 Year Ahead: Median data remains active status in CEIC and is reported by Central Bank of Brazil. The data is categorized under Brazil Premium Database’s Business and Economic Survey – Table BR.SA014: Market Expectation: Price Indices: General Price Index - Market (IGP-M). Market Expectations System was implemented in November 2001, previous projections were collected from incipient through telephone contacts, transcribed into spreadsheets and consolidated manually. Some empty time points occurred because the Market didn´t have the expectation for those days. Reflects the price changes from the 21st to the 20th of following month. It is made up of the IPA (Wholesale Price Index), Consumer Price Index (IPC) and INCC (National Construction Cost Index), with weights of 60%, 30% and 10%, respectively. The indicator is prepared for financial market contracts.

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.

VIX Futures Signal Increased Market Volatility 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

In Q1 2025, Cefaclor prices in the USA experienced notable fluctuations. January saw moderate price increases driven by preemptive purchasing ahead of a 10% tariff on Chinese goods and the Chinese Lunar New Year, which created short-term supply pressures. Rising energy costs and congestion at Los Angeles ports added to the strain, elevating operational expenses and contributing to price hikes. February recorded a slight price increase as U.S. buyers anticipated additional tariffs and continued to accelerate procurement, maintaining moderate supply-side pressures.

South Korea's main stock market index, the KOSPI, rose to 3190 points on July 24, 2025, gaining 0.21% from the previous session. Over the past month, the index has climbed 2.64% and is up 17.70% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from South Korea. South Korea Stock Market - values, historical data, forecasts and news - updated on July of 2025.

TTF Gas fell to 32.37 EUR/MWh on July 24, 2025, down 1.76% from the previous day. Over the past month, TTF Gas's price has fallen 8.56%, but it is still 1.12% higher than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. EU Natural Gas TTF - values, historical data, forecasts and news - updated on July of 2025.

The retail price for electricity in the United States stood at an average of ***** U.S. dollar cents per kilowatt-hour in 2024. This is the highest figure reported in the indicated period. Nevertheless, the U.S. still has one of the lowest electricity prices worldwide. As a major producer of primary energy, energy prices are lower than in countries that are more reliant on imports or impose higher taxes. Regional variations and sector disparities The impact of rising electricity costs across U.S. states is not uniform. Hawaii stands out with the highest household electricity price, reaching a staggering ***** U.S. cents per kilowatt-hour in September 2024. This stark contrast is primarily due to Hawaii's heavy reliance on imported oil for power generation. On the other hand, states like Utah benefit from lower rates, with prices around **** U.S. cents per kilowatt-hour. Regarding U.S. prices by sector, residential customers have borne the brunt of price increases, paying an average of ***** U.S. cents per kilowatt-hour in 2023, significantly more than commercial and industrial sectors. Factors driving price increases Several factors contribute to the upward trend in electricity prices. The integration of renewable energy sources, investments in smart grid technologies, and rising peak demand all play a role. Additionally, the global energy crisis of 2022 and natural disasters affecting power infrastructure have put pressure on the electric utility industry. The close connection between U.S. electricity prices and natural gas markets also influences rates, as domestic prices are affected by higher-paying international markets. Looking ahead, projections suggest a continued increase in electricity prices, with residential rates expected to grow by *** percent in 2024, driven by factors such as increased demand and the ongoing effects of climate change.

The Gas Station industry has seen a rollercoaster of fluctuations in recent years, directly tied to the volatile nature of oil and natural gas prices. Driven by global disruptions from the pandemic and the Ukraine conflict, these price swings have heavily influenced the industry’s performance. In 2025, the industry’s revenue slightly declined by 0.9%, reaching $121.0 billion. Profit stability, despite these challenges, highlights the industry’s resilience to oil price dynamics. As stations grapple with intense market competition and shifting consumer preferences, they’ve innovated rapidly to keep pace. Gas stations have faced increasing pressure to adapt. Despite revenue stagnating at a CAGR of 0.0% over the past five years, shifts in consumer behavior have spurred changes. With consumers opting for premium fuels and diesel, stations have capitalized on the surge in demand for higher-grade products. Diesel sales have benefited from elevated freight activities. At the same time, the climb of gas stations with convenience stores has reshaped the landscape. These multipurpose locations cater to modern consumer needs, blending retail with fuel sales to buffer against fluctuating oil prices and rising credit card fees. Looking ahead, the industry is poised for modest growth, at a projected CAGR of 0.4% over the next five years, reaching $123.6 billion in 2030. As per capita disposable income is expected to grow, more consumers will likely opt for premium fuels. However, revenue per gallon could face pressure from anticipated drops in crude oil prices. Stations must diversify offerings and integrate new technologies to stay competitive in the face of rising electric vehicle adoption. By evolving into multi-service hubs, offering everything from fresh food to electric charging stations, gas stations can better navigate the changing market landscape. This approach will be crucial as they face heightened competition from supermarkets and warehouse clubs entering the fuel retail game, further intensifying the industry’s transformation.

AI Price Optimisation Software Market Outlook

The global AI Price Optimisation Software Market size was valued at approximately USD 1.2 billion in 2023 and is projected to reach around USD 4.1 billion by 2032, growing at a compound annual growth rate (CAGR) of 14.6% during the forecast period. The primary growth factors driving this market include the increasing adoption of AI solutions across various industries, the growing need for competitive pricing strategies, and the rising significance of data-driven decision-making in business operations.

One of the key growth factors in the AI Price Optimisation Software Market is the escalating demand for personalized pricing strategies. Businesses are increasingly leveraging AI to analyze consumer behavior, preferences, and purchasing patterns to set optimal prices. This enables companies to maximize their revenues by offering the right prices to the right customers at the right time. The growing e-commerce and retail sectors are particularly benefitting from these AI-driven pricing strategies, as they can adapt prices dynamically based on real-time data.

Another significant factor propelling market growth is the advancements in machine learning algorithms and data analytics. AI price optimization software utilizes complex algorithms to process vast amounts of data from various sources, including historical sales data, market trends, and competitor pricing. This enables businesses to make informed pricing decisions that enhance their competitive edge. Additionally, the integration of AI with other technologies like the Internet of Things (IoT) and Big Data further enhances the software's capabilities, providing more accurate and comprehensive pricing strategies.

Furthermore, the increasing competition among businesses is driving the adoption of AI price optimization solutions. Companies are under constant pressure to stay ahead of their competitors by offering attractive prices without compromising on their profit margins. AI price optimization software helps businesses achieve this by providing insights into market dynamics, customer segmentation, and price elasticity. This allows companies to implement dynamic pricing strategies that respond quickly to market changes, ensuring they remain competitive.

From a regional perspective, North America currently holds the largest share in the AI Price Optimisation Software Market, driven by the presence of major technology companies and high adoption rates of advanced technologies. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, owing to the rapid digital transformation across various industries and the increasing investments in AI technologies. Europe also represents a significant market due to the growing awareness of AI's benefits and its application in pricing strategies across various sectors.

Component Analysis

The AI Price Optimisation Software Market is segmented by components into software and services. The software segment encompasses the core AI applications and tools used for price optimization, including machine learning algorithms, data analytics platforms, and integration interfaces with other business systems. This segment is expected to witness substantial growth due to the increasing adoption of AI-driven tools in pricing strategies. Businesses are investing heavily in software solutions to automate and enhance their pricing decisions, thereby maximizing revenue and improving operational efficiency.

The services segment includes consulting, implementation, training, and support services provided by vendors to help businesses effectively deploy and utilize AI price optimization software. These services are crucial for ensuring the successful adoption and integration of AI solutions within existing business processes. As the demand for AI price optimization grows, the need for specialized services to assist with software deployment, customization, and ongoing support also increases. This segment plays a vital role in addressing the challenges businesses face in implementing and managing AI solutions.

Within the software segment, the sub-segment of machine learning algorithms is particularly noteworthy. These algorithms are the backbone of AI price optimization software, enabling it to analyze large datasets and generate actionable insights. The continuous advancements in mac

Graph and download economic data for 5-Year Breakeven Inflation Rate (T5YIE) from 2003-01-02 to 2025-07-22 about spread, interest rate, interest, 5-year, inflation, rate, and USA.

During the first quarter of 2025, the North American Polyetheramine market experienced a price decline of 8% compared to the previous quarter, driven primarily by weakening demand and improved supply conditions. At the start of the quarter, prices in the U.S. rose slightly due to supply constraints from exporting regions, elevated feedstock costs, and global logistics disruptions ahead of the Lunar New Year.

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.

Bodycote (BOY) Heating Up: Will This Stock Forge 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

Brazil Market Expectation: Price Indices: General Price Index - Internal Availability (IGP-DI): 2 Years Ahead: Standard Deviation data was reported at 0.420 % in 28 Jun 2019. This records an increase from the previous number of 0.410 % for 27 Jun 2019. Brazil Market Expectation: Price Indices: General Price Index - Internal Availability (IGP-DI): 2 Years Ahead: Standard Deviation data is updated daily, averaging 0.530 % from Nov 2001 (Median) to 28 Jun 2019, with 4426 observations. The data reached an all-time high of 3.580 % in 12 Dec 2002 and a record low of 0.220 % in 17 Apr 2019. Brazil Market Expectation: Price Indices: General Price Index - Internal Availability (IGP-DI): 2 Years Ahead: Standard Deviation data remains active status in CEIC and is reported by Central Bank of Brazil. The data is categorized under Brazil Premium Database’s Business and Economic Survey – Table BR.SA013: Market Expectation: Price Indices: General Price Index - Internal Availability (IGP-DI). Market Expectations System was implemented in November 2001, previous projections were collected from incipient through telephone contacts, transcribed into spreadsheets and consolidated manually. Some empty time points occurred because the Market didn´t have the expectation for those days. Reflects the price changes of the entire reference month. That is, from the 1st to the 30th of each month. It is made up of the IPA (Wholesale Price Index), Consumer Price Index (IPC) and INCC (National Construction Cost Index), with weights of 60%, 30% and 10%, respectively. The indicator analyzes the price changes of agricultural and industrial raw materials in wholesale and of final goods and services in consumption.