The benchmark interest rate in Canada was last recorded at 2.75 percent. This dataset provides - Canada Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The benchmark interest rate in China was last recorded at 3 percent. This dataset provides the latest reported value for - China Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

In 2024, China’s monetary authority, the People’s Bank of China, issued more than ** trillion yuan which was the highest amount issued in one year so far. Over the past years, the value of printed money increased steadily. The issuing of currency was one function of a central bank. Maintaining price stability One of the main policy objectives of the People’s Bank of China was to maintain price stability. Typically, countries set the desired inflation target and the central bank implements the necessary policies to achieve the said target. Usually, China keeps its inflation target at around ***** percent, but in 2021, the inflation rate dropped to under *** percent. If the inflation rate is too low, central banks can issue more currency and decrease the interest rate. In the opposite scenario, if the inflation rate is too high central banks try to reduce the amount of money in circulation by increasing the interest rate or decreasing bond prices. Managing the economy In capitalist market economies, economies usually undergo a boom and bust cycle. Central banks attempt to counteract this cyclical development to soften the impact for its citizens. For instance, the Chinese government aims to maintain an unemployment rate of around **** percent. However, crises such as the 2008 financial crisis and the outbreak of COVID-19 have an unforeseen impact on the economy. To lower the employment rate, the People’s Bank engaged specific monetary policies to stimulate the economy with the aim of increasing job creation.

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.

Boston Omaha: A Sustainable Investment? (BOC)

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 Hong Kong credit card market, valued at approximately $113.41 million in 2025, is projected to experience robust growth, exhibiting a Compound Annual Growth Rate (CAGR) of 8.68% from 2025 to 2033. This expansion is fueled by several key drivers. Rising disposable incomes among Hong Kong residents, coupled with a burgeoning e-commerce sector and increasing preference for cashless transactions, are significantly boosting credit card adoption. Furthermore, attractive rewards programs, competitive interest rates offered by various banks (including HSBC, Bank of China, Standard Chartered Bank, and others), and the expanding acceptance of credit cards across diverse sectors like food & groceries, travel & tourism, and online shopping are contributing to market growth. However, challenges remain. Stringent regulatory oversight aimed at managing consumer debt and potential economic fluctuations could act as restraints on market expansion. The market segmentation reveals a significant presence of general-purpose credit cards, while the application-based segmentation indicates strong demand across food & groceries, travel & tourism, and consumer electronics. Visa and MasterCard dominate the provider landscape, although local banks like Hang Seng Bank and DBS Bank also hold significant market share. The competitive landscape is characterized by intense rivalry among both international and local banks, leading to innovative product offerings and aggressive marketing strategies. The forecast period (2025-2033) suggests a continued upward trajectory, driven by evolving consumer preferences and the sustained growth of the Hong Kong economy. While specific regional data for Hong Kong is not explicitly provided, the overall growth trend suggests a parallel increase in credit card usage within the region, reflecting broader global patterns of financial inclusion and digital payment adoption. Market players are likely to focus on enhancing customer loyalty programs, expanding digital payment capabilities, and developing tailored credit card solutions to maintain their competitive edge and capitalize on the market's growth potential. Risk management and compliance with regulatory standards will remain crucial for sustained success in this competitive environment. Recent developments include: April 2023: Hang Seng Bank delivered an innovative green receivables financing solution for its long-term customer, Leo Paper Group, with export credit insurance provided by Hong Kong Export Credit Insurance Corporation that supports greater supply chain sustainability., April 2023: Hang Seng Bank Limited and Chubb entered an exclusive 15-year distribution agreement. Chubb will provide Hang Seng banking customers with a comprehensive range of personal and commercial general insurance products and solutions in Hong Kong.. Key drivers for this market are: Usage of Credit Card Give the Bonus and Reward Points. Potential restraints include: Usage of Credit Card Give the Bonus and Reward Points. Notable trends are: Increasing Number of Credit Card Transaction in Hong Kong.

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.

(BOC) Boston Omaha: A Portfolio Diversification Play?

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 Canadian furniture store industry has faced a rollercoaster of economic shifts, influencing its performance. The industry's struggle is evident after revenue waned at a CAGR of -0.4% over the past five years. In 2025, revenue will drop by 1.7%, landing at $13.8 billion. Macroeconomic factors like fluctuating interest rates, inflationary pressures, and changes in disposable income have been the primary challenges to furniture stores' ability to maintain revenue. The pandemic initially boosted sales due to increased disposable income and low interest rates, but subsequent economic hurdles painted a more volatile picture. Now, as the industry’s profit relies heavily on broader economic health, furniture stores are poised for growth with opportunities in eco-friendly products and customization. Over the past five years, Canadian furniture stores benefited from an initial boom in residential construction, spurring consumer demand for new furniture. However, interest rate hikes and upstream material inflation in 2022 and 2023 put a damper on growth. This pressure pushed consumers toward cost-saving alternatives, with big-box stores like Walmart capturing budget-conscious shoppers through competitive pricing. Meanwhile, e-commerce boomed as consumers pivoted to online shopping, challenging smaller retailers lacking digital infrastructure. Big names like IKEA expanded their market share through strategic acquisitions and vertical integration. Despite these challenges, innovative solutions, like visualization tools and advanced inventory management, brought some stability to the diverse landscape. The industry is poised for a rebound, with an expected CAGR of 3.0% through 2030, reaching $16.0 billion. As macroeconomic conditions stabilize, interest rates lower and inflation cools, the residential sector’s recovery offers solid growth in consumer purchasing. Consumers will regain confidence and unlock their pent-up demand for furniture. Customization, sustainability and unique offerings will be the industry’s focal points, catering to evolving consumer tastes. Larger retailers will continue consolidation strategies, enhancing economies of scale and geographic reach. Meanwhile, the popularity of second-hand furniture, driven by eco-conscious consumers, will maintain its momentum and challenge traditional retailers. As e-commerce capabilities expand, price-based competition will intensify.

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.

Cake Box (CBOX): Baked to Perfection or Past Its Prime?

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 benchmark interest rate in Uganda was last recorded at 9.75 percent. This dataset provides the latest reported value for - Uganda Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

1 Year MLF Rate in China remained unchanged at 2 percent in January. This dataset includes a chart with historical data for China One-Year Medium-Term Lending Facility Rate.

The yield on Canada 10Y Bond Yield eased to 3.47% on July 29, 2025, marking a 0.06 percentage point decrease from the previous session. Over the past month, the yield has edged up by 0.20 points and is 0.24 points higher than a year ago, according to over-the-counter interbank yield quotes for this government bond maturity. Canada 10-Year Government Bond Yield - values, historical data, forecasts and news - updated on July of 2025.

Inflation Rate in Canada increased to 1.90 percent in June from 1.70 percent in May of 2025. This dataset provides - Canada Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Reverse Repo Rate in China remained unchanged at 1.40 percent in June. This dataset provides - China Reverse Repo Rate- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Unemployment Rate in Canada decreased to 6.90 percent in June from 7 percent in May of 2025. This dataset provides - Canada Unemployment Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.