Predictions indicate a sustained upward trend for the Nikkei 225 index. Positive global economic conditions, strong corporate earnings, and government stimulus measures are expected to support growth. However, potential risks include geopolitical tensions, interest rate hikes, and supply chain disruptions, which could lead to volatility and a slowdown in growth.

Political Stability and Absence of Violence/Terrorism: Estimate in Belgium was reported at 0.40385 in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. Belgium - Political Stability and Absence of Violence/Terrorism: Estimate - actual values, historical data, forecasts and projections were sourced from the World Bank on September 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.

U.S. Government Closes in on Debt Ceiling Deadline

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.

Easterly Government's (DEA) Spring Forward or Summer Slide?

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.

Easterly Government Properties (EGP) Stock: A Springtime Revival?

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

FRBOP Forecast: 5Yr Forward: Ann Ave CPI Infla: sa: Mean: Next 5 Yrs data was reported at 2.201 % in Dec 2018. This records a decrease from the previous number of 2.239 % for Sep 2018. FRBOP Forecast: 5Yr Forward: Ann Ave CPI Infla: sa: Mean: Next 5 Yrs data is updated quarterly, averaging 2.426 % from Sep 2005 (Median) to Dec 2018, with 54 observations. The data reached an all-time high of 2.826 % in Sep 2009 and a record low of 2.201 % in Dec 2018. FRBOP Forecast: 5Yr Forward: Ann Ave CPI Infla: sa: Mean: Next 5 Yrs data remains active status in CEIC and is reported by Federal Reserve Bank of Philadelphia. The data is categorized under Global Database’s United States – Table US.I008: Consumer Price Index: Urban: sa: Forecast: Federal Reserve Bank of Philadelphia.

FRBOP Forecast: Annual Core CPI Infl: sa Current: Median: Plus 1 Yr data was reported at 2.367 % in Dec 2018. This records a decrease from the previous number of 2.400 % for Sep 2018. FRBOP Forecast: Annual Core CPI Infl: sa Current: Median: Plus 1 Yr data is updated quarterly, averaging 2.040 % from Mar 2007 (Median) to Dec 2018, with 48 observations. The data reached an all-time high of 2.400 % in Sep 2018 and a record low of 1.300 % in Dec 2010. FRBOP Forecast: Annual Core CPI Infl: sa Current: Median: Plus 1 Yr data remains active status in CEIC and is reported by Federal Reserve Bank of Philadelphia. The data is categorized under Global Database’s United States – Table US.I008: Consumer Price Index: Urban: sa: Forecast: Federal Reserve Bank of Philadelphia.

CNGOV Forecast: Consumer Price Index data was reported at 3.000 % in 2018. This stayed constant from the previous number of 3.000 % for 2017. CNGOV Forecast: Consumer Price Index data is updated yearly, averaging 3.150 % from Dec 2005 (Median) to 2018, with 14 observations. The data reached an all-time high of 4.800 % in 2008 and a record low of 3.000 % in 2018. CNGOV Forecast: Consumer Price Index data remains active status in CEIC and is reported by The Central People's Government. The data is categorized under Global Database’s China – Table CN.I003: Consumer Price Index: Forecast: The Central People's Government (Annual).

Political Stability and Absence of Violence/Terrorism: Estimate in United States was reported at 0.02942 in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. United States - Political Stability and Absence of Violence/Terrorism: Estimate - actual values, historical data, forecasts and projections were sourced from the World Bank on September of 2025.

Political Stability and Absence of Violence/Terrorism: Estimate in Iran was reported at --1.6941 in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. Iran - Political Stability and Absence of Violence/Terrorism: Estimate - actual values, historical data, forecasts and projections were sourced from the World Bank on September of 2025.

We investigated drivers of sub-field spatial variability in yield for 3 crops (hard red winter wheat, Triticum aestivum L. variety Langin; corn, Zea mays L.; and proso millet, Panicum milaceum L.) usings this multi-year dataset from a dryland research farm in northeastern Colorado, USA. The dataset spanned 18 2.6-4.3 ha management units collected over 4 years (2019-2022). The data includes high resolution topographic data collected via real-time kinematic GPS, densely sampled soil texture and chemical properties, and meteorological data from an on-site weather station.

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.

Should You Buy, Sell, or Hold? (Karachi 100 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.

SMI Index Poised for Moderate Growth Amidst Global Uncertainty

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 2023, the U.S. Consumer Price Index was 309.42, and is projected to increase to 352.27 by 2029. The base period was 1982-84. The monthly CPI for all urban consumers in the U.S. can be accessed here. After a time of high inflation, the U.S. inflation rateis projected fall to two percent by 2027. United States Consumer Price Index ForecastIt is projected that the CPI will continue to rise year over year, reaching 325.6 in 2027. The Consumer Price Index of all urban consumers in previous years was lower, and has risen every year since 1992, except in 2009, when the CPI went from 215.30 in 2008 to 214.54 in 2009. The monthly unadjusted Consumer Price Index was 296.17 for the month of August in 2022. The U.S. CPI measures changes in the price of consumer goods and services purchased by households and is thought to reflect inflation in the U.S. as well as the health of the economy. The U.S. Bureau of Labor Statistics calculates the CPI and defines it as, "a measure of the average change over time in the prices paid by urban consumers for a market basket of consumer goods and services." The BLS records the price of thousands of goods and services month by month. They consider goods and services within eight main categories: food and beverage, housing, apparel, transportation, medical care, recreation, education, and other goods and services. They aggregate the data collected in order to compare how much it would cost a consumer to buy the same market basket of goods and services within one month or one year compared with the previous month or year. Given that the CPI is used to calculate U.S. inflation, the CPI influences the annual adjustments of many financial institutions in the United States, both private and public. Wages, social security payments, and pensions are all affected by the CPI.

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.

Barr (BAG) Stock: Political Storms Brewing?

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.

(EGP) Easterly Government Properties: A Solid Foundation for Growth

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.

Government Income: MGF's Steady Returns in a Shifting Economy?

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

MEKR Forecast: Real GDP: PY=100: Final Consumption: Government data was reported at 100.700 Prev Year=100 in 2019. This records a decrease from the previous number of 101.000 Prev Year=100 for 2018. MEKR Forecast: Real GDP: PY=100: Final Consumption: Government data is updated yearly, averaging 100.700 Prev Year=100 from Dec 2017 (Median) to 2019, with 3 observations. The data reached an all-time high of 101.000 Prev Year=100 in 2018 and a record low of 100.200 Prev Year=100 in 2017. MEKR Forecast: Real GDP: PY=100: Final Consumption: Government data remains active status in CEIC and is reported by Ministry of Economy of the Kyrgyz Republic. The data is categorized under Global Database’s Kyrgyzstan – Table KG.A016: GDP Index: by Expenditure: Previous Year=100: Forecast: Ministry of Economy of the Kyrgyz Republic.

Data about World Development Indicators measured from 1960 to 2022, extracted from the World Bank database. It includes macro-economical, social, political and environmental data from all the countries and regions the world bank has data about.

It contains information about 268 countries and regions, including 48 features, all numerical. Several entries are missing for different reasons, so you may want to extract only the columns you are interested in.

The columns included in this dataset are:

• country: The country or geographic region.
• date: Date of the measurement. This column along with country can be used as index.
• agricultural_land%: Agricultural land as a % of land area of the country/region.
• forest_land%: Forest area as the % of land area of the country/region.
• land_area: Land area, measured in km^2.
• avg_precipitation: Average precipitation in depth, measured in mm per year.
• trade_in_services%: Trade in services as a % of GDP.
• control_of_corruption_estimate: Index that makes an estimate of the control of corruption.
• control_of_corruption_std: Standard error of the estimate of control of corruption.
• access_to_electricity%: Percentage of the population that has access to electricity.
• renewvable_energy_consumption%: Renewable energy consumption as a % of total final energy consumption.
• electric_power_consumption: Electric power consumption, measured in kWh per capita.
• CO2_emisions: CO2 emisions measured in kt.
• other_greenhouse_emisions: Total greenhouse gas emissions, measured in kt of CO2 equivalent.
• population_density: Population density, measured in people per km^2 of land area.
• inflation_annual%: Inflation, consumer prices, as annual %.
• real_interest_rate: Real interest rate (%).
• risk_premium_on_lending: Risk premium on lending (lending rate minus treasury bill rate, %).
• research_and_development_expenditure%: Research and development expenditure, as a percentage of GDP.
• central_goverment_debt%: Central government debt, total , as a % of GDP.
• tax_revenue%: Tax revenue as a % of GDP.
• expense%: Expense as a % of GDP.
• goverment_effectiveness_estimate: Index that makes an estimate of the Government Effectiveness.
• goverment_effectiveness_std: Standard error of the estimate of Government Effectiveness.
• human_capital_index: Human Capital Index (HCI) (scale 0-1).
• doing_business: Ease of doing business score (0 = lowest performance to 100 = best performance).
• time_to_get_operation_license: Days required to obtain an operating license.
• statistical_performance_indicators: Statistical performance indicators (SPI): Overall score (scale 0-100).
• individuals_using_internet%: Percentage of population using the internet.
• logistic_performance_index: Logistics performance index: Overall (1=low to 5=high).
• military_expenditure%: Military expenditure as a % of GDP.
• GDP_current_US: GDP (current US$).
• political_stability_estimate: Index that makes an estimate of the Political Stability and Absence of Violence/Terrorism.
• political_stability_std: Standard error of the estimate of Political Stability and Absence of Violence/Terrorism.
• rule_of_law_estimate: Index that makes an estimate of the Rule of Law.
• rule_of_law_std: Standard error of the estimate of Rule of Law.
• regulatory_quality_estimate: Index that makes an estimate of Regulatory Quality.
• regulatory_quality_std: Standard error of the estimate of Regulatory Quality.
• government_expenditure_on_education%: Government expenditure on education, total, as a % of GDP.
• government_health_expenditure%: Domestic general government health expenditure as a % of GDP.
• multidimensional_poverty_headcount_ratio%: Multidimensional poverty headcount ratio (% of total population).
• gini_index: Gini index.
• birth_rate: Birth rate, crude (per 1,000 people).
• death_rate: Death rate, crude (per 1,000 people).
• life_expectancy_at_birth: Life expectancy at birth, total (years).
• population: Total population.
• rural_population: Rural population.
• voice_and_accountability_estimate: Index that makes an estimate of Voice and Accountability.
• voice_and_accountability_std: Standard error of the estimate of Voice and Accountability.
• intentional_homicides: Intentional homicides (per 100,000 people).

The accompanying data is made available under the terms of the Non-Commercial Government Licence (http://www.nationalarchives.gov.uk/doc/non-commercial-government-licence/version/2/)

Met Office Space Weather Operations Centre (MOSWOC) Kp and Kuk forecasts are produced by a human forecaster every 3 hours, out to 3 days ahead. A value for Kp and Kuk is provided for each 3hourly interval. A 3 day forecast text summary is at the end of each file following the Kp and Kuk forecast values.

MOSWOC Kp forecast files (.json) provided here are for the dates 1st March through 30th September 2019.