The seasonally-adjusted national unemployment rate is measured on a monthly basis in the United States. In February 2025, the national unemployment rate was at 4.1 percent. Seasonal adjustment is a statistical method of removing the seasonal component of a time series that is used when analyzing non-seasonal trends. U.S. monthly unemployment rate According to the Bureau of Labor Statistics - the principle fact-finding agency for the U.S. Federal Government in labor economics and statistics - unemployment decreased dramatically between 2010 and 2019. This trend of decreasing unemployment followed after a high in 2010 resulting from the 2008 financial crisis. However, after a smaller financial crisis due to the COVID-19 pandemic, unemployment reached 8.1 percent in 2020. As the economy recovered, the unemployment rate fell to 5.3 in 2021, and fell even further in 2022. Additional statistics from the BLS paint an interesting picture of unemployment in the United States. In November 2023, the states with the highest (seasonally adjusted) unemployment rate were the Nevada and the District of Columbia. Unemployment was the lowest in Maryland, at 1.8 percent. Workers in the agricultural and related industries suffered the highest unemployment rate of any industry at seven percent in December 2023.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Will the U.S. Financial Sector Index Conquer the Market?

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 table shows the indicators of the macroeconomic scoreboard. Furthermore, some additional indicators are shown. To identify in a timely manner existing and potential imbalances and possible macroeconomic risks within the countries of the European Union in an early stage, the European Commission has drawn up a scoreboard with fourteen indicators. This scoreboard is part of the Macroeconomic Imbalance Procedure (MIP). This table contains quarterly and annual figures for both these fourteen indicators and nine additional indicators for the Netherlands.

The fourteen indicators in the macroeconomic scoreboard are: - Current account balance as % of GDP, 3 year moving average - Net international investment position, % of GDP - Real effective exchange rate, % change on three years previously - Share of world exports, % change on five years previously - Nominal unit labour costs, % change on three years previously - Deflated house prices, % change on one year previously - Private sector credit flow as % of GDP - Private sector debt as % of GDP - Government debt as % of GDP - Unemployment rate, three year moving average - Total financial sector liabilities, % change on one year previously - Activity rate, % of total population aged 15-64, change in percentage points on three years previously - Long-term unemployment rate, % of active population aged 15-74, change in percentage points on three years previously - Youth unemployment rate, % of active population aged 15-24, change in percentage points on three years previously

The additional indicators are: - Real effective exchange rate, index - Share of world exports, % - Nominal unit labour costs, index - Households credit flow as % of GDP - Non-financial corporations credit flow as % of GDP - Household debt as % of GDP - Non-financial corporations debt as % of GDP - Activity rate, % of total population aged 15-64 - Youth unemployment rate, % of active population aged 15-24

Data available from: first quarter of 2006.

Status of the figures: Annual and quarterly data are provisional.

Changes as of April 16th, 2025: The figures for the fourth quarter of 2025 have been added for all indicators. In addition, due to revisions in the sources, several indicators have also been updated for past periods.

Adjustment as of July 17th 2024: Data of the private sector’s credit flow and debt were not correct. They have been adjusted in this version.

When will new figures be published? New data are published within 120 days after the end of each quarter. The first quarter may be revised in October, the second quarter in January. Quarterly data for the previous three quarters are adjusted along when the fourth quarter figures are published in April. This corresponds with the first estimate of the annual data for the previous year. The annual and quarterly data for the last three years are revised together with the publication of the first quarter in July.

In April 2025, the agriculture and related private wage and salary workers industry had the highest unemployment rate in the United States, at eight percent. In comparison, government workers had the lowest unemployment rate, at 1.8 percent. The average for all industries was 3.9 percent. U.S. unemployment There are several factors that impact unemployment, as it fluctuates with the state of the economy. Unfortunately, the forecasted unemployment rate in the United States is expected to increase as we head into the latter half of the decade. Those with a bachelor’s degree or higher saw the lowest unemployment rate from 1992 to 2022 in the United States, which is attributed to the fact that higher levels of education are seen as more desirable in the workforce. Nevada unemployment Nevada is one of the states with the highest unemployment rates in the country and Vermont typically has one of the lowest unemployment rates. These are seasonally adjusted rates, which means that seasonal factors such as holiday periods and weather events that influence employment periods are removed. Nevada's economy consists of industries that are currently suffering high unemployment rates such as tourism. As of May 2023, about 5.4 percent of Nevada's population was unemployed, possibly due to the lingering impact of the coronavirus pandemic.

In 1990, the unemployment rate of the United States stood at 5.6 percent. Since then there have been many significant fluctuations to this number - the 2008 financial crisis left millions of people without work, as did the COVID-19 pandemic. By the end of 2022 and throughout 2023, the unemployment rate came to 3.6 percent, the lowest rate seen for decades. However, 2024 saw an increase up to four percent. For monthly updates on unemployment in the United States visit either the monthly national unemployment rate here, or the monthly state unemployment rate here. Both are seasonally adjusted. UnemploymentUnemployment is defined as a situation when an employed person is laid off, fired or quits his work and is still actively looking for a job. Unemployment can be found even in the healthiest economies, and many economists consider an unemployment rate at or below five percent to mean there is 'full employment' within an economy. If former employed persons go back to school or leave the job to take care of children they are no longer part of the active labor force and therefore not counted among the unemployed. Unemployment can also be the effect of events that are not part of the normal dynamics of an economy. Layoffs can be the result of technological progress, for example when robots replace workers in automobile production. Sometimes unemployment is caused by job outsourcing, due to the fact that employers often search for cheap labor around the globe and not only domestically. In 2022, the tech sector in the U.S. experienced significant lay-offs amid growing economic uncertainty. In the fourth quarter of 2022, more than 70,000 workers were laid off, despite low unemployment nationwide. The unemployment rate in the United States varies from state to state. In 2021, California had the highest number of unemployed persons with 1.38 million out of work.

In 2023, it was estimated that over 161 million Americans were in some form of employment, while 3.64 percent of the total workforce was unemployed. This was the lowest unemployment rate since the 1950s, although these figures are expected to rise in 2023 and beyond. 1980s-2010s Since the 1980s, the total United States labor force has generally risen as the population has grown, however, the annual average unemployment rate has fluctuated significantly, usually increasing in times of crisis, before falling more slowly during periods of recovery and economic stability. For example, unemployment peaked at 9.7 percent during the early 1980s recession, which was largely caused by the ripple effects of the Iranian Revolution on global oil prices and inflation. Other notable spikes came during the early 1990s; again, largely due to inflation caused by another oil shock, and during the early 2000s recession. The Great Recession then saw the U.S. unemployment rate soar to 9.6 percent, following the collapse of the U.S. housing market and its impact on the banking sector, and it was not until 2016 that unemployment returned to pre-recession levels. 2020s 2019 had marked a decade-long low in unemployment, before the economic impact of the Covid-19 pandemic saw the sharpest year-on-year increase in unemployment since the Great Depression, and the total number of workers fell by almost 10 million people. Despite the continuation of the pandemic in the years that followed, alongside the associated supply-chain issues and onset of the inflation crisis, unemployment reached just 3.67 percent in 2022 - current projections are for this figure to rise in 2023 and the years that follow, although these forecasts are subject to change if recent years are anything to go by.

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.

Financial Services Sector Anticipates Moderate Growth, Dow Jones U.S. Financial Services index Shows.

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 table contains the indicators of the Macroeconomic Imbalance Procedure (MIP) scoreboard. In addition, some additional indicators have been included. In order to identify existing and potential imbalances and macroeconomic risks within the countries of the European Union in a timely manner, the European Commission has drawn up a scoreboard with fourteen indicators. The scoreboard is part of the Macroeconomic Imbalance Procedure (MIP). This table contains the quarterly and annual figures for the Netherlands for these fourteen indicators. The 14 indicators of the macroeconomic scoreboard are: - Current account balance as a percentage of GDP, three-year average - Net external assets as a percentage of GDP - Real effective exchange rate, percentage change compared with three years earlier - Export market share in world trade, percentage change compared to five years earlier - Nominal unit labour costs, percentage change compared to three years earlier - Real house price index, percentage change compared with the previous year - Private sector credit flow as a percentage of GDP - Private sector debt as a percentage of GDP - Public debt as a percentage of GDP - Unemployment rate, three-year average - Total liabilities of the financial sector, percentage change compared with the previous year - Gross employment rate as a percentage of the total population aged 15-64, change in percentage points compared to three years earlier - Long-term unemployment, percentage of the labour force aged 15-74, percentage point change compared to three years earlier - Youth unemployment, percentage of the labour force aged 15-24, change in percentage points compared to three years earlier.

The additional indicators in this table are: - Real effective exchange rate, index - Export market share of world trade, percentage - Nominal unit labour costs, index - Households' credit flow (incl. non-profit institutions serving households) as a percentage of GDP - Non-financial corporations' credit flow as a percentage of GDP - Debt of households (including non-profit institutions serving households) as a percentage of GDP - Debt of non-financial corporations as a percentage of GDP - Gross employment rate as a percentage of the total population aged 15-64 - Youth unemployment, percentage of the labour force aged 15-24.

Data available from: 1st quarter 2006.

Status of figures: All annual and quarterly figures are provisional.

Changes as of 8 July 2024 None, this table has been discontinued. The Central Bureau of Statistics recently revised the national accounts. New sources, methods and concepts are introduced into the national accounts, so that the picture of the Dutch economy is optimally aligned with all underlying statistics, sources and international guidelines for compiling the national accounts. For more information see section 3.

When will there be new figures? No longer applicable.

The unemployment rate of the United Kingdom was 4.7 percent in May 2025, an increase from the previous month. Before the arrival of the COVID-19 pandemic, the UK had relatively low levels of unemployment, comparable with the mid-1970s. Between January 2000 and the most recent month, unemployment was highest in November 2011, when the unemployment rate hit 8.5 percent. Will unemployment continue to rise in 2025? Although low by historic standards, there has been a noticeable uptick in the UK's unemployment rate, with other labor market indicators also pointing to further loosening. In December 2024, the number of job vacancies in the UK fell to its lowest level since May 2021, while payrolled employment declined by 47,000 compared with November. Whether this is a continuation of a broader cooling of the labor market since 2022 or a reaction to more recent economic developments, such as upcoming tax rises for employers, remains to be seen. Forecasts made in late 2024 suggest that the unemployment rate will remain relatively stable in 2025, averaging out at 4.1 percent and falling again to four percent in 2026.
Demographics of the unemployed As of the third quarter of 2024, the unemployment rate for men was slightly higher than that of women, at 4.4 percent, compared to 4.1 percent. During the financial crisis at the end of the 2000s, the unemployment rate for women peaked at a quarterly rate of 7.7 percent, whereas for men, the rate was 9.1 percent. Unemployment is also heavily associated with age, and young people in general are far more vulnerable to unemployment than older age groups. In late 2011, for example, the unemployment rate for those aged between 16 and 24 reached 22.3 percent, compared with 8.2 percent for people aged 25 to 34, while older age groups had even lower peaks during this time.

The unemployment rate in the Republic of Ireland was four percent in June 2025, unchanged from the previous month. Between 2000 and 2007, Ireland's unemployment rate was broadly stable, fluctuating between 3.9 and 5.4 percent. Following the global financial crisis, however, Ireland's unemployment rate increased dramatically, eventually peaking at 16.1 percent in early 2012. For the next eight years, unemployment gradually fell, eventually reaching pre-crisis levels in the late 2010s. This was, however, followed by an uptick in unemployment due to the COVID-19 pandemic, which peaked at 7.6 percent in March 2021, before falling to pre-pandemic levels by February 2022. Risk and rewards of the Irish economic model After being quite hard hit by the global financial crisis of 2008, Ireland staged a strong recovery in the mid-2010s, and was frequently the EU's fastest growing economy between 2014 and 2022. This growth, was however, fueled in part by multinational companies, such as Apple, basing their European operations in the country. As of 2022, an adjusted measure of gross national income valued Ireland's economy at around 273 billion Euros, rather than the 506 billion Euros GDP figure. Ireland's close economic relationship with American tech companies also leaves it vulnerable to the political weather in the United States. It is currently unclear, for example, what the recent return to power of Donald Trump as President in early 2025 could mean for the Irish economy going forward. Ireland's labor market As of the third quarter of 2024, there were approximately 2.79 million people employed in the Republic of Ireland. Of these workers, 379,200 people worked in Ireland's human health and social work sector, the most of any industry at that time. Other sectors with high employment levels include wholesale and retail trade, at 323,500 people, and education, at 228,200 people. While unemployment still remains quite low, some indicators suggest a moderate loosening of the labor market. Job vacancies, are slightly down from their peak of 35,300 in Q2 2022, amounting to 28,900 in Q3 2024, while youth unemployment has begun to tick upwards, and was 11.9 percent in January 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.

Surging Services: Will Dow Jones CPI Signal Continued Consumer Strength?

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 table shows the indicators of the macroeconomic scoreboard. Furthermore, some additional indicators are shown. To identify in a timely manner existing and potential imbalances and possible macroeconomic risks within the countries of the European Union in an early stage, the European Commission has drawn up a scoreboard with fourteen indicators. This scoreboard is part of the Macroeconomic Imbalance Procedure (MIP). This table contains quarterly and annual figures for both these fourteen indicators and nine additional indicators for the Netherlands. The fourteen indicators in the macroeconomic scoreboard are: - Current account balance as % of GDP, 3 year moving average - Net international investment position, % of GDP - Real effective exchange rate, % change on three years previously - Share of world exports, % change on five years previously - Nominal unit labour costs, % change on three years previously - Deflated house prices, % change on one year previously - Private sector credit flow as % of GDP - Private sector debt as % of GDP - Government debt as % of GDP - Unemployment rate, three year moving average - Total financial sector liabilities, % change on one year previously - Activity rate, % of total population aged 15-64, change in percentage points on three years previously - Long-term unemployment rate, % of active population aged 15-74, change in percentage points on three years previously - Youth unemployment rate, % of active population aged 15-24, change in percentage points on three years previously The additional indicators are: - Real effective exchange rate, index - Share of world exports, % - Nominal unit labour costs, index - Households credit flow as % of GDP - Non-financial corporations credit flow as % of GDP - Household debt as % of GDP - Non-financial corporations debt as % of GDP - Activity rate, % of total population aged 15-64 - Youth unemployment rate, % of active population aged 15-24 Data available from: first quarter of 2006. Status of the figures: Annual and quarterly data are provisional. Changes as of July 8th 2024: None. This table has been discontinued. Statistics Netherlands has carried out a revision of the national accounts. The Dutch national accounts are recently revised. New statistical sources, methods and concepts are implemented in the national accounts, in order to align the picture of the Dutch economy with all underlying source data and international guidelines for the compilation of the national accounts. For further information see section 3. When will new figures be published? Not applicable anymore.

Kenya’s unemployment rate was 5.43 percent in 2024. This represents a steady decline from the increase after the financial crisis. What is unemployment? The unemployment rate of a country refers to the share of people who want to work but cannot find jobs. This includes workers who have lost jobs and are searching for new ones, workers whose jobs ended due to an economic downturn, and workers for whom there are no jobs because the labor supply in their industry is larger than the number of jobs available. Different statistics suggest which factors contribute to the overall unemployment rate. The Kenyan context The first type, so-called “search unemployment”, is hardest to see in the data. The closest proxy is Kenya’s inflation rate. As workers take new jobs faster, employers are forced to increase wages, leading to higher employment. Jobs lost due to economic downturns, called “cyclical unemployment”, can be seen by decreases in the GDP growth rate, which are not significant in Kenya. Finally, “structural unemployment” refers to workers changing the industry, or even economic sector, in which they are working. In Kenya, more and more workers switch to the services sector. This is often a result of urbanization, but any structural shift in the economy’s composition can lead to this unemployment.

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.

Banking Sector: A Strong Investment for the Next 3 Months

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

Workers’ compensation and other insurance funds businesses have experienced significant changes in recent years, largely driven by economic fluctuations and shifts in investment income. The crash of the US economy in 2020 due to pandemic-related restrictions placed immense pressure on the industry. Business formation plunged and unemployment soared, resulting in a diminished customer base for insurance funds and a steep drop in revenue. Regardless, the Federal Reserve's injection of liquidity into the financial system propelled stock prices upward, boosting investment income for insurance providers. This increase in investment income provided some relief for providers, enabling them to cover expenses and sustain profits despite revenue losses. The relaxation of COVID-19 restrictions spurred economic recovery in 2021, driving unemployment down and corporate profit up. This positive economic climate increased demand for insurance services and enhanced investment income due to robust stock market conditions. However, since 2022, inflation has wreaked havoc, causing businesses and organizations to slash investments in insurance funds amid soaring prices. More recently, rising interest rates have reduced downstream demand due to the emergence of recessionary fears, but revenue and profit have expanded because of growing returns on fixed-income products. Overall, revenue for workers’ compensation and other insurance funds has inched downward at a CAGR of 0.2% over the past five years, reaching $56.6 billion in 2025. This includes a 0.5% rise in revenue in that year. Looking ahead, providers are poised for moderate growth over the next five years. As the US economy stabilizes, with solid GDP growth and potential increases in business formation and employment, the customer base for insurance funds is likely to expand. These favorable economic conditions should bolster consumer confidence and investment in the stock market, leading to greater investment income for the industry. Nonetheless, larger players are expected to dominate, given their ability to invest in cutting-edge technologies like AI for predicting claim risks and optimizing business operations. Smaller providers may face intensified internal competition, prompting some to exit the market, while others could focus on niche offerings or invest in technological advancements to remain viable and competitive. Overall, revenue for workers’ compensation and other insurance funds is expected to expand at a CAGR of 1.3% over the next five years, reaching $60.3 billion in 2030.

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.

Citizens Financial Group (CFG) Stock Outlook Bullish Amid Favorable Sector Trends

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.

KFS Kingsway Financial Services Inc. Common Stock (DE)

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

Accounting service providers have enjoyed steady growth over the past five years, buoyed by economic growth early on in the period and demand for monetary advice through the financial turmoil of the early 2020s. As revenue has expanded, new accounting service providers have begun operating locally, heightening competition between smaller companies and focusing on local clients like individual households. Steady 4.1% growth in aggregate private investment over the past five years created a greater need for accountants and proliferated a higher need for financial statements and tax preparation, although the effects of high interest rates limited smaller businesses' demand. Accounting services revenue grew at a CAGR of 0.2% to an estimated $145.5 billion over the past five years, including an expected 0.2% boost in 2025 alone. Profit slipped in recent years due to higher interest rates pushing accountants’ labor costs upward. In recent years, increased demand for accounting services has drastically outpaced the number of new accountants that have begun operating, enabling companies to raise prices, garner more revenue per client and allocate funding toward capital investment. Software that analyzes large data sets ("big data") and other labor-saving technologies have helped boost market share and competition with companies focusing on digital revenue generation. In addition, growth in the number of businesses nationally positively impacted the availability of commercial clients, particularly within the manufacturing and financial spaces. Steady interest in corporate tax analysis and guidance bolstered demand across the Big Four accounting firms, such as Deloitte and PwC, as clients sought professional input on how to navigate a turbulent fiscal market. Moving forward, the outlook for accountants is mixed. Competition will intensify in the consumer market because of a steady decline in the national unemployment rate and the record penetration of lower-cost alternatives, like online tax preparation. Higher interest rates will constrain business activity in the short term, although dampening inflation is poised to reverse this trend in the long run. Accounting service providers will integrate with nearly every aspect of the US economy, with new technologies such as artificial intelligence (AI) offering improved workflow efficiency for larger accounting firms. Finally, potential new tax policies in response to expiring tax laws from the 2017 Tax Cuts and Jobs Act will force clients to procure professional accountants. Accounting services revenue is expected to grow at a CAGR of 1.1% to an estimated $154.0 billion over the next five years.

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.

Stewart Information Services (STC) Navigating the Real Estate Landscape: A 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

Main Topics: Attitudinal/Behavioural Questions Attention to newspapers and television, degree of political interest, attitude towards election, perceived differences between political parties. Opinion of Liberal and Scottish National Party campaigns, opinion on the various political parties. Knowledge, perception of party position/record on, and own opinion on: prices, strikes, unemployment, pensions, housing, North Sea Oil, Common Market, nationalisation, social services, wage controls. Party identification and strength of support, frequency of discussion about politics. Party preferences, opinion on best government (in general and in October 1974). Respondents were asked to give marks out of ten to political parties and personalities. Party membership, degree of political activity. Attitude to power held by unions/big business. Prediction for incomes, prices, unemployment and Britain's economy. Comparison of Britain's government and industry with that of Europe. Attitude to politicians, personal financial status, change/getting ahead, political parties, life in general, today's standards, local government, own occupation, the government's achievements. Likes and dislikes of the Conservative, Liberal, Labour and Scottish National parties. Whether respondents felt the following had 'gone too far': sex and race equality, police handling of demonstrations, law breakers, pornography, modern teaching methods, abortion, welfare benefits, military cuts. Whether respondents agree/disagree with the suggestion that government should: establish comprehensives, increase cash to health service, repatriate immigrants, control land, increase foreign aid, toughen on crime, control pollution, give workers more say, curb Communists, spend on poverty, redistribute wealth, decentralise power, preserve countryside. Most/least important government aims. Assessment of chances of Liberals, Nationalists. Opinion on best type of government (in general and in October 1974). Expected October 1974 result. Background Variables Age, sex, marital status, place of residence during childhood, subjective class, forced subjective class, family class. Tenure, type and length of residence. Employment status, degree of responsibility in and training for job (for respondent and spouse). Experience of unemployment in household, income. Trade union membership (respondent and spouse) socio-economic group. Multi-stage, self-weighting, stratified probability sample designed to represent the eligible British electorate in 1974 Face-to-face interview 1974 ABORTION ACHIEVEMENT AGE AID ATTITUDES AUTHORITY BRITISH POLITICAL P... BUSINESS MANAGEMENT BUSINESSES CENSORSHIP CHILDHOOD CHILDREN CIVIL AND POLITICAL... CLASS CONSCIOUSNESS COMMUNISM COMPREHENSIVE SCHOOLS CONSERVATIVE PARTY ... COST OF LIVING COUNTRYSIDE CONSERV... CRIME AND SECURITY DECENTRALIZED GOVER... DECISION MAKING ECONOMIC ACTIVITY ECONOMIC CONDITIONS EDUCATIONAL BACKGROUND ELDERLY ELECTION CAMPAIGNS ELECTIONS ELECTORAL ISSUES EMPLOYMENT ENVIRONMENTAL PLANN... EQUALITY BETWEEN TH... EUROPEAN ECONOMIC C... EUROPEAN UNION FAMILIES FATHER S OCCUPATION FATHER S OCCUPATION... FATHERS FINANCIAL EXPECTATIONS FINANCIAL RESOURCES FORECASTING GENDER GOVERNMENT Great Britain HEALTH SERVICES HIGHER EDUCATION HOME OWNERSHIP HOUSEHOLDS HOUSEWIVES HOUSING HOUSING TENURE HUMAN SETTLEMENT INCOME INCOME DISTRIBUTION INDUSTRIES INFLATION JOB SATISFACTION LABOUR DISPUTES LABOUR PARTY GREAT ... LAND USE LAW ENFORCEMENT LIBERAL PARTY GREAT... LOCAL GOVERNMENT MARITAL STATUS MEMBERSHIP MILITARY POWER MORTGAGES NATIONAL ECONOMY NATIONALIZATION NEWSPAPER READERSHIP NEWSPAPERS OCCUPATIONAL STATUS OCCUPATIONS OIL RESOURCES PERIODICALS READERSHIP PERSONAL EFFICACY PETROLEUM INDUSTRY PLAID CYMRU POLICING POLITICAL ACTION POLITICAL ALLEGIANCE POLITICAL ATTITUDES POLITICAL AWARENESS POLITICAL BEHAVIOUR POLITICAL COALITIONS POLITICAL INFLUENCE POLITICAL INTEREST POLITICAL LEADERS POLITICAL PARTICIPA... POLITICAL POWER POLITICIANS POLLUTION CONTROL POPULATION MIGRATION PORNOGRAPHY POVERTY PRESERVATION OF MON... PRICES PRIVATE SECTOR PRIVATIZATION PROFITS PUBLIC EXPENDITURE PUBLIC SECTOR QUALITY OF LIFE RACIAL DISCRIMINATION REGIONAL GOVERNMENT RELIGIOUS AFFILIATION RELIGIOUS BEHAVIOUR RENTED ACCOMMODATION REPATRIATION RESIDENTIAL MOBILITY SATISFACTION SCOTTISH NATIONAL P... SELF EMPLOYED SEX DISCRIMINATION SOCIAL CHANGE SOCIAL CLASS SOCIAL CONFORMITY SOCIAL HOUSING SOCIAL ORIGIN SOCIAL POLICY SOCIAL SECURITY BEN... SOCIAL SERVICES SOCIAL VALUES SOCIO ECONOMIC STATUS SPOUSE S ECONOMIC A... SPOUSE S OCCUPATION SPOUSE S OCCUPATION... SPOUSES STANDARD OF LIVING STATE CONTROL STATE RETIREMENT PE... STRIKES STUDENTS TAXATION TEACHING METHODS TELEVISION TELEVISION VIEWING TRADE UNION MEMBERSHIP TRADE UNIONS TRUST UNEMPLOYED UNEMPLOYMENT VOTING BEHAVIOUR VOTING INTENTION WAGE DETERMINATION WAGES WAGES POLICY WELFARE POLICY WORKERS PARTICIPATION