There were over 1.67 million unemployed people in the United Kingdom in the three months to June 2025, compared with just over 1.64 million in the previous month. In the provided time, there was a peak of 2.7 million people unemployed in November 2011 and a noticeable uptick in unemployment in 2020. The bump in unemployment caused by the COVID-19 pandemic peaked at almost 1.8 million in December 2020 then falling to a low of 1.2 million in August 2022, before climbing up again to the most recent levels. Government plans to boost UK workforce Although the Labour Party inherited a relatively healthy unemployment rate of around four percent from the previous government, the UK's labor market is less robust than it first appears. The current level of economic inactivity, is seen as the more concerning figure, especially the rising share of people on long-term sick leave. Just before the COVID-19 pandemic, at the end of 2019, there were around 2.08 million people economically inactive due to long-term sickness, with this figure increasing by around 740,000 by early 2024. Government plans to address the root cause of these issues and improve incentives to work were unveiled at the end of 2024, but may have come at an inopportune time. Labor market signals for 2025 Encouraging people back into work is one thing; making sure there are enough jobs is another. Recent data suggests that the UK is continuing to cool off from an overheated labor market in 2022, which at one point saw 1.3 million job vacancies in the UK. Although the current level of job vacancies is at more usual levels, any further falls could spell trouble for the economy. In December 2024, the number of people on UK payrolls fell by 47,000, while the number of redundancies has started to climb. Some UK businesses have also signalled that they have, or plan to, lay off staff due to increased taxes set to come into force in the next financial year.

The statistic shows the unemployment rate in India from 1999 to 2024. In 2024, the unemployment rate in India was estimated to be 4.2 percent. India's economy in comparison to other BRIC states India possesses one of the fastest-growing economies in the world and as a result, India is recognized as one of the G-20 major economies as well as a member of the BRIC countries, an association that is made up of rapidly growing economies. As well as India, three other countries, namely Brazil, Russia and China, are BRIC members. India’s manufacturing industry plays a large part in the development of its economy; however its services industry is the most significant economical factor. The majority of the population of India works in this sector. India’s notable economic boost can be attributed to significant gains over the past decade in regards to the efficiency of the production of goods as well as maintaining relatively low debt, particularly when compared to the total amount earned from goods and services produced throughout the years. When considering individual development as a country, India progressed significantly over the years. However, in comparison to the other emerging countries in the BRIC group, India’s progress was rather minimal. While China experienced the most apparent growth, India’s efficiency and productivity remained somewhat stagnant over the course of 3 or 4 years. India also reported a rather large trade deficit over the past decade, implying that its total imports exceeded its total amount of exports, essentially forcing the country to borrow money in order to finance the nation. Most economists consider trade deficits a negative factor, especially in the long run and for developing or emerging countries.

The recession that most of the world economies have been facing in the last years has caused a great interest in the study of its macroeconomic effects. In this context, a debate has resurged regarding the advertising investment, as for its potential capacity to impel the consumer spending and to impact positively on the economic recovery. This idea, sustained in the so-called Galbraith's hypothesis, constitutes the core of this paper, where the main objective is to test that hypothesis by means of an empirical analysis. In this study, we focus on the Spanish case and the data correspond to the period 1976 -2010. A cointegration analysis is carried out, using two different approaches (Engle-Granger test and Gregory-Hansen test, respectively), to determine if there is any relationship between the advertising investment and six macromagnitudes (GDP, National Income, Consumption, Savings and Fixed Capital Formation), as well as the registered unemployment rate. Based on the results obtained, we conclude that Galbraith's hypothesis is not fulfilled for the Spanish case.

Abstract copyright UK Data Service and data collection copyright owner. This two-stage longitudinal aimed: i) to assess the effect of duration of unemployment on labour market attachment; ii) to assess the effect of duration of unemployment on psychological well-being; iii) to determine the moderators of i) and ii), including social networks, social supports and pressures. Main Topics: i) Sex, ethnic group and personal histories of unemployment, employment and special measures. ii) Job search and aspirations. iii) Labour market attitudes, and disaffection. iv) Psychological well-being, health and personality. v) Social supports, networks and pressures. vi) Subcultural features of unemployment. vii) Area differences. viii) Prediction of success in finding employment. ix) Training aspirations. x) Perceptions of the unemployed. Standard Measures: i) General health Questionnaire, Zung Depression and Anxiety, sociability and achievement orientation measures. ii) Item batteries on: employment commitment, unemployment orientation, labour market disaffection, job search attitude, attitudes to employment. iii) Composite scales of social pressures and supports. For a full account of origin of the measures see Warr P.B., Banks M.H. and Ullah P.B. (1985) 'The experience of unemployment among black and white urban teenagers' British Journal of Psychology 76, p.75-87. A) A sample of registered unemployed 17 year olds was drawn from 11 urban areas in England. All members were required to have left school appromimately 12 months earlier (in the spring or summer of 1981) at the minimum school-leaving age of 16, with no more than two `O' levels or CSE equivalents. All had been unemployed for at least four weeks and none was registered disabled. Potential respondents were contacted outside Unemployment Benefit Offices and Careers Offices, and through youth clubs and centres for unemployed people. The final sample contained males and females, whites and West Indians. B) The follow-up sample was boosted by the inclusion of 550 young people, with the same characteristics

One aim of the Soviet Union, and communist countries in general, was to achieve full employment. Official policy was designed to prevent unemployment, and the state stopped paying most unemployment benefits in the 1930s. Every citizen had the right (or requirement) to work, and jobs were allocated by the state, not competed for as they were in the west. People could apply for certain positions, based on their education, experience, or interests, but roles could often be distributed to meet employment demands, or preferential roles were distributed via nepotism. The socialist economic system removed job market competition, which provided increased job security but removed many of the incentives that boosted productivity (especially in later decades). In the 1970s and 1980s, average work weeks were under 35 hours long and people retired in their mid to late fifties. Compared to the U.S. in 1985, on average, work weeks were around four hours shorter in the USSR, and Soviet men retired five years earlier, while women retired nine years earlier than their American counterparts.

Wages In earlier years, wages had been tied to individual performance or output, however the de-Stalinization process of the 1960s introduced a more standardized system of payment; from this point onwards, base wages were more fixed, and bonuses had a larger impact on disposable income. Personal finances in the Soviet Union were very different from those in the west; wages were split into base salaries and bonuses, along with a social wage that was "paid" in the form of investments in housing, healthcare, education, and infrastructure, as well as subsidized vouchers for holidays and food. Many of these amenities were also provided by the state, which removed the individual costs that were required across the west and in post-Soviet states today. Overall, income and money in general had a much lower influence on daily life in the USSR than it did in the west, lessening factors such as financial stress and indebtedness, but restricting consumeristic freedom.

Gender differences A major difference between the East and West Blocs was the participation rate of women in the workforce. Throughout most of the USSR's history, women made up the majority of the workforce, with a 51.4 percent share in 1970, and 50.4 percent in 1989; in the U.S. figures for these years were 38 and 45 percent respectively. Although this was due to the fact that women also made up a larger share of the total population (around 53 percent in this period), Soviet women were possibly the most economically active in the world in these decades. When comparing activity rates of women aged between 40 and 44 across Europe in 1985, the USSR had a participation rate of 97 percent; this was the highest in the East Bloc (where rates ranged from 85 to 93 percent in other countries), and is much higher than rates in Northern Europe (71 percent), Western Europe (56 percent) and Southern Europe (37 percent).

Forecast value for employment rate using ARIMA (1,2,1).

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.

Valens Projected to Surge, Boosted by Expanding Markets (VLN)

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

Abstract copyright UK Data Service and data collection copyright owner.The Annual Population Survey (APS) is a major survey series, which aims to provide data that can produce reliable estimates at the local authority level. Key topics covered in the survey include education, employment, health and ethnicity. The APS comprises key variables from the Labour Force Survey (LFS), all its associated LFS boosts and the APS boost. The APS aims to provide enhanced annual data for England, covering a target sample of at least 510 economically active persons for each Unitary Authority (UA)/Local Authority District (LAD) and at least 450 in each Greater London Borough. In combination with local LFS boost samples, the survey provides estimates for a range of indicators down to Local Education Authority (LEA) level across the United Kingdom.For further detailed information about methodology, users should consult the Labour Force Survey User Guide, included with the APS documentation. For variable and value labelling and coding frames that are not included either in the data or in the current APS documentation, users are advised to consult the latest versions of the LFS User Guides, which are available from the ONS Labour Force Survey - User Guidance webpages.Occupation data for 2021 and 2022The ONS has identified an issue with the collection of some occupational data in 2021 and 2022 data files in a number of their surveys. While they estimate any impacts will be small overall, this will affect the accuracy of the breakdowns of some detailed (four-digit Standard Occupational Classification (SOC)) occupations, and data derived from them. None of ONS' headline statistics, other than those directly sourced from occupational data, are affected and you can continue to rely on their accuracy. The affected datasets have now been updated. Further information can be found in the ONS article published on 11 July 2023: Revision of miscoded occupational data in the ONS Labour Force Survey, UK: January 2021 to September 2022APS Well-Being DatasetsFrom 2012-2015, the ONS published separate APS datasets aimed at providing initial estimates of subjective well-being, based on the Integrated Household Survey. In 2015 these were discontinued. A separate set of well-being variables and a corresponding weighting variable have been added to the April-March APS person datasets from A11M12 onwards. Further information on the transition can be found in the Personal well-being in the UK: 2015 to 2016 article on the ONS website.APS disability variablesOver time, there have been some updates to disability variables in the APS. An article explaining the quality assurance investigations on these variables that have been conducted so far is available on the ONS Methodology webpage. End User Licence and Secure Access APS dataUsers should note that there are two versions of each APS dataset. One is available under the standard End User Licence (EUL) agreement, and the other is a Secure Access version. The EUL version includes Government Office Region geography, banded age, 3-digit SOC and industry sector for main, second and last job. The Secure Access version contains more detailed variables relating to: age: single year of age, year and month of birth, age completed full-time education and age obtained highest qualification, age of oldest dependent child and age of youngest dependent child family unit and household: including a number of variables concerning the number of dependent children in the family according to their ages, relationship to head of household and relationship to head of family nationality and country of origin geography: including county, unitary/local authority, place of work, Nomenclature of Territorial Units for Statistics 2 (NUTS2) and NUTS3 regions, and whether lives and works in same local authority district health: including main health problem, and current and past health problems education and apprenticeship: including numbers and subjects of various qualifications and variables concerning apprenticeships industry: including industry, industry class and industry group for main, second and last job, and industry made redundant from occupation: including 4-digit Standard Occupational Classification (SOC) for main, second and last job and job made redundant from system variables: including week number when interview took place and number of households at address The Secure Access data have more restrictive access conditions than those made available under the standard EUL. Prospective users will need to gain ONS Accredited Researcher status, complete an extra application form and demonstrate to the data owners exactly why they need access to the additional variables. Users are strongly advised to first obtain the standard EUL version of the data to see if they are sufficient for their research requirements. Latest edition informationFor the fifth edition (November 2019), a new version of the data file was deposited, with the 2018 person and well-being weighting variables included. Main Topics:Topics covered include: household composition and relationships, housing tenure, nationality, ethnicity and residential history, employment and training (including government schemes), workplace and location, job hunting, educational background and qualifications. Many of the variables included in the survey are the same as those in the LFS. Multi-stage stratified random sample Face-to-face interview Telephone interview 2016 2017 ADULT EDUCATION AGE APPLICATION FOR EMP... ATTITUDES BONUS PAYMENTS CHILD BENEFITS CHILDREN COHABITATION CONDITIONS OF EMPLO... DEBILITATIVE ILLNESS DISABILITIES Demography population ECONOMIC ACTIVITY EDUCATIONAL BACKGROUND EDUCATIONAL COURSES EDUCATIONAL STATUS EMPLOYEES EMPLOYER SPONSORED ... EMPLOYMENT EMPLOYMENT HISTORY EMPLOYMENT PROGRAMMES EMPLOYMENT SERVICES ETHNIC GROUPS FAMILY BENEFITS FIELDS OF STUDY FULL TIME EMPLOYMENT FURNISHED ACCOMMODA... GENDER HEADS OF HOUSEHOLD HEALTH HOME OWNERSHIP HOURS OF WORK HOUSEHOLDS HOUSING BENEFITS HOUSING TENURE INCOME INDUSTRIES JOB CHANGING JOB HUNTING JOB SEEKER S ALLOWANCE LANDLORDS LONGTERM UNEMPLOYMENT Labour and employment MANAGERS MARITAL STATUS NATIONAL IDENTITY NATIONALITY OCCUPATIONS OVERTIME PART TIME COURSES PART TIME EMPLOYMENT PLACE OF RESIDENCE PRIVATE SECTOR PUBLIC SECTOR RECREATIONAL EDUCATION RECRUITMENT REDUNDANCY REDUNDANCY PAY RELIGIOUS AFFILIATION RENTED ACCOMMODATION RESIDENTIAL MOBILITY SELF EMPLOYED SICK LEAVE SICK PAY SICKNESS AND DISABI... SOCIAL HOUSING SOCIAL SECURITY BEN... SOCIO ECONOMIC STATUS STATE RETIREMENT PE... STATUS IN EMPLOYMENT STUDENTS SUBSIDIARY EMPLOYMENT SUPERVISORS SUPERVISORY STATUS TEMPORARY EMPLOYMENT TERMINATION OF SERVICE TIED HOUSING TRAINING TRAINING COURSES UNEMPLOYED UNEMPLOYMENT UNEMPLOYMENT BENEFITS UNFURNISHED ACCOMMO... UNWAGED WORKERS WAGES WELSH LANGUAGE WORKING CONDITIONS WORKPLACE vital statistics an...

Abstract copyright UK Data Service and data collection copyright owner.The Annual Population Survey (APS) is a major survey series, which aims to provide data that can produce reliable estimates at the local authority level. Key topics covered in the survey include education, employment, health and ethnicity. The APS comprises key variables from the Labour Force Survey (LFS), all its associated LFS boosts and the APS boost. The APS aims to provide enhanced annual data for England, covering a target sample of at least 510 economically active persons for each Unitary Authority (UA)/Local Authority District (LAD) and at least 450 in each Greater London Borough. In combination with local LFS boost samples, the survey provides estimates for a range of indicators down to Local Education Authority (LEA) level across the United Kingdom.For further detailed information about methodology, users should consult the Labour Force Survey User Guide, included with the APS documentation. For variable and value labelling and coding frames that are not included either in the data or in the current APS documentation, users are advised to consult the latest versions of the LFS User Guides, which are available from the ONS Labour Force Survey - User Guidance webpages.Occupation data for 2021 and 2022The ONS has identified an issue with the collection of some occupational data in 2021 and 2022 data files in a number of their surveys. While they estimate any impacts will be small overall, this will affect the accuracy of the breakdowns of some detailed (four-digit Standard Occupational Classification (SOC)) occupations, and data derived from them. None of ONS' headline statistics, other than those directly sourced from occupational data, are affected and you can continue to rely on their accuracy. The affected datasets have now been updated. Further information can be found in the ONS article published on 11 July 2023: Revision of miscoded occupational data in the ONS Labour Force Survey, UK: January 2021 to September 2022APS Well-Being DatasetsFrom 2012-2015, the ONS published separate APS datasets aimed at providing initial estimates of subjective well-being, based on the Integrated Household Survey. In 2015 these were discontinued. A separate set of well-being variables and a corresponding weighting variable have been added to the April-March APS person datasets from A11M12 onwards. Further information on the transition can be found in the Personal well-being in the UK: 2015 to 2016 article on the ONS website.APS disability variablesOver time, there have been some updates to disability variables in the APS. An article explaining the quality assurance investigations on these variables that have been conducted so far is available on the ONS Methodology webpage. End User Licence and Secure Access APS dataUsers should note that there are two versions of each APS dataset. One is available under the standard End User Licence (EUL) agreement, and the other is a Secure Access version. The EUL version includes Government Office Region geography, banded age, 3-digit SOC and industry sector for main, second and last job. The Secure Access version contains more detailed variables relating to: age: single year of age, year and month of birth, age completed full-time education and age obtained highest qualification, age of oldest dependent child and age of youngest dependent child family unit and household: including a number of variables concerning the number of dependent children in the family according to their ages, relationship to head of household and relationship to head of family nationality and country of origin geography: including county, unitary/local authority, place of work, Nomenclature of Territorial Units for Statistics 2 (NUTS2) and NUTS3 regions, and whether lives and works in same local authority district health: including main health problem, and current and past health problems education and apprenticeship: including numbers and subjects of various qualifications and variables concerning apprenticeships industry: including industry, industry class and industry group for main, second and last job, and industry made redundant from occupation: including 4-digit Standard Occupational Classification (SOC) for main, second and last job and job made redundant from system variables: including week number when interview took place and number of households at address The Secure Access data have more restrictive access conditions than those made available under the standard EUL. Prospective users will need to gain ONS Accredited Researcher status, complete an extra application form and demonstrate to the data owners exactly why they need access to the additional variables. Users are strongly advised to first obtain the standard EUL version of the data to see if they are sufficient for their research requirements. Latest edition informationFor the second edition (October 2024), smoking variables CIGEVER, CIGNOW and CIGSMK16 have been added to the data file. Main Topics:Topics covered include: household composition and relationships, housing tenure, nationality, ethnicity and residential history, employment and training (including government schemes), workplace and location, job hunting, educational background and qualifications. Many of the variables included in the survey are the same as those in the LFS. Multi-stage stratified random sample Face-to-face interview Telephone interview 2023 ADULT EDUCATION AGE ANXIETY APPLICATION FOR EMP... APPOINTMENT TO JOB ATTITUDES BONUS PAYMENTS BUSINESSES CARE OF DEPENDANTS CHRONIC ILLNESS COHABITATION CONDITIONS OF EMPLO... COVID 19 DEBILITATIVE ILLNESS DEGREES DISABILITIES Demography population ECONOMIC ACTIVITY EDUCATIONAL BACKGROUND EDUCATIONAL COURSES EMPLOYEES EMPLOYER SPONSORED ... EMPLOYMENT EMPLOYMENT HISTORY EMPLOYMENT PROGRAMMES ETHNIC GROUPS FAMILIES FAMILY BENEFITS FIELDS OF STUDY FULL TIME EMPLOYMENT FURNISHED ACCOMMODA... FURTHER EDUCATION GENDER HAPPINESS HEADS OF HOUSEHOLD HEALTH HIGHER EDUCATION HOME OWNERSHIP HOURS OF WORK HOUSEHOLDS HOUSING HOUSING BENEFITS HOUSING TENURE INCOME INDUSTRIES JOB CHANGING JOB HUNTING JOB SEEKER S ALLOWANCE LANDLORDS Labour and employment MANAGERS MARITAL STATUS NATIONAL IDENTITY NATIONALITY OCCUPATIONS OVERTIME PART TIME COURSES PART TIME EMPLOYMENT PLACE OF BIRTH PLACE OF RESIDENCE PRIVATE SECTOR PUBLIC SECTOR RECRUITMENT REDUNDANCY REDUNDANCY PAY RELIGIOUS AFFILIATION RENTED ACCOMMODATION RESIDENTIAL MOBILITY SELF EMPLOYED SICK LEAVE SICKNESS AND DISABI... SOCIAL HOUSING SOCIAL SECURITY BEN... SOCIO ECONOMIC STATUS STATE RETIREMENT PE... STUDENTS SUBSIDIARY EMPLOYMENT SUPERVISORS SUPERVISORY STATUS TAX RELIEF TEMPORARY EMPLOYMENT TERMINATION OF SERVICE TIED HOUSING TRAINING TRAINING COURSES TRAVELLING TIME UNEMPLOYED UNEMPLOYMENT UNEMPLOYMENT BENEFITS UNFURNISHED ACCOMMO... UNWAGED WORKERS WAGES WELL BEING HEALTH WELSH LANGUAGE WORKING CONDITIONS WORKPLACE vital statistics an...

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.

Beazley (BEZ): Boosted by Specialty Lines Momentum?

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.

Gildan Forecasts Boost as Demand Trends Improve (GIL)

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

Private Vocational Education Market Outlook

The global market size for private vocational education was valued at approximately USD 40 billion in 2023 and is expected to reach around USD 60 billion by 2032, growing at a compound annual growth rate (CAGR) of 4.5%. This growth is driven by several factors, including increasing demand for specialized skill sets and the growing recognition of vocational education as a viable alternative to traditional academic pathways.

One of the main growth factors contributing to the expansion of the private vocational education market is the rising demand for job-ready skills in various industries. Unlike traditional education that often focuses on theoretical knowledge, vocational education provides hands-on experience and specific skill sets that are directly applicable to the job market. As industries evolve and the demand for specialized skills increases, vocational education becomes a more attractive option for both students and working professionals. Moreover, the flexibility and shorter duration of vocational courses make them an appealing choice for individuals looking to quickly enter or advance in the workforce.

Another significant growth factor is the rapid technological advancement and digital transformation across various sectors. With the advent of new technologies such as artificial intelligence, big data, and the Internet of Things (IoT), there is a growing need for a workforce that is proficient in these areas. Vocational education institutions are increasingly incorporating these modern technologies into their curricula to ensure that students are well-prepared for the future job market. Additionally, partnerships between vocational schools and industries ensure that the training provided is relevant and up-to-date, further driving the growth of the market.

Government initiatives and policies promoting vocational education also play a crucial role in market growth. Many countries are recognizing the importance of vocational training in boosting economic development and reducing unemployment rates. As a result, governments are investing in vocational education infrastructure, offering financial incentives, and implementing policies that encourage both students and employers to participate in vocational training programs. These initiatives are expected to significantly contribute to the expansion of the private vocational education market over the forecast period.

Regionally, the market for private vocational education is experiencing varied growth patterns. For instance, in the Asia Pacific region, the market is witnessing substantial growth due to the increasing population and the rising need for skilled labor to support economic development. North America and Europe are also significant markets, driven by technological advancements and a strong emphasis on continuous professional development. In contrast, regions like Latin America and the Middle East & Africa are poised for growth as they invest in vocational education to address skills gaps and reduce unemployment rates.

Course Type Analysis

The private vocational education market is segmented by course type into technical courses and non-technical courses. Technical courses include disciplines like engineering, IT, healthcare, and advanced manufacturing. These courses are in high demand due to the technological advancements and digital transformation occurring across various sectors. Technical vocational education provides hands-on training and industry-specific skills that are directly applicable to the job market, making them highly attractive to both students and employers. The growth of industries such as IT and healthcare, which require specialized skills, further drives the demand for technical vocational courses.

Non-technical courses, on the other hand, encompass areas like hospitality, culinary arts, and creative fields such as graphic design and digital marketing. These courses are also experiencing increased demand, particularly as the service industry continues to expand globally. The focus on experiential learning and practical skills in non-technical courses makes them appealing for individuals seeking to enter these fields quickly and efficiently. Additionally, as the gig economy grows, there is a rising interest in non-technical vocational education that enables individuals to develop entrepreneurial skills and pursue freelance or independent career paths.

The flexibility and specialized nature of both technical and non-technical vocational courses make them suitable for a diverse range of learners

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.

Tapestry Sees Promising Outlook, Boosting Confidence in (TPR) Stock's Future

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.

Pony AI Projects Significant Growth, Boosting (PONY) Outlook.

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.

S. Quimica Sees Lithium Demand Boosting (SQM) Stock's Future.

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.

PotlatchDeltic's Prospects: Dividend Cut, Buyback Boost? (PCH)

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

Forecast value for GNI using ARIMA (1,4,2).

Effects of independent variables on LE.

Introductory table.

PMEGP, led by the Government of India through the Ministry of MSME, promotes self-employment in the non-farm sector by integrating earlier schemes like REGP and PMRY. It offers financial aid to entrepreneurs to establish micro-enterprises, emphasizing inclusive growth by prioritizing marginalized groups such as women, SC/ST, and differently-abled individuals. PMEGP aims to boost the MSME sector and curb unemployment nationwide.