The Quarterly Census of Employment and Wages (QCEW) Program is a Federal-State cooperative program between the U.S. Department of Labor’s Bureau of Labor Statistics (BLS) and the California EDD’s Labor Market Information Division (LMID). The QCEW program produces a comprehensive tabulation of employment and wage information for workers covered by California Unemployment Insurance (UI) laws and Federal workers covered by the Unemployment Compensation for Federal Employees (UCFE) program.

The QCEW program serves as a near census of monthly employment and quarterly wage information by 6-digit industry codes from the North American Industry Classification System (NAICS) at the national, state, and county levels. At the national level, the QCEW program publishes employment and wage data for nearly every NAICS industry. At the state and local area level, the QCEW program publishes employment and wage data down to the 6-digit NAICS industry level, if disclosure restrictions are met. In accordance with the BLS policy, data provided to the Bureau in confidence are used only for specified statistical purposes and are not published. The BLS withholds publication of Unemployment Insurance law-covered employment and wage data for any industry level when necessary to protect the identity of cooperating employers.

Data from the QCEW program serve as an important input to many BLS programs. The Current Employment Statistics and the Occupational Employment Statistics programs use the QCEW data as the benchmark source for employment. The UI administrative records collected under the QCEW program serve as a sampling frame for the BLS establishment surveys.

In addition, the data serve as an input to other federal and state programs. The Bureau of Economic Analysis (BEA) of the Department of Commerce uses the QCEW data as the base for developing the wage and salary component of personal income.

The U.S. Department of Labor’s Employment and Training Administration (ETA) and California's EDD use the QCEW data to administer the Unemployment Insurance program. The QCEW data accurately reflect the extent of coverage of California’s UI laws and are used to measure UI revenues; national, state and local area employment; and total and UI taxable wage trends.

The U.S. Department of Labor’s Bureau of Labor Statistics publishes new QCEW data in its County Employment and Wages news release on a quarterly basis. The BLS also publishes a subset of its quarterly data through the Create Customized Tables system, and full quarterly industry detail data at all geographic levels.

The monthly summary report is intended to provide the user with a quick overview of the status of the UI system at the national and state levels. This summary report contains monthly information on claims activities and on the number and amount of payments under State unemployment insurance laws. This data is used in budgetary and administrative planning, program evaluation, and reports to Congress and the public.

Analysis of ‘Benefits Paid By County (All Programs)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/78f3805c-806f-4717-a8c5-cd22ce9d006e on 26 January 2022.

--- Dataset description provided by original source is as follows ---

∙ The data provided is the amount of Unemployment Insurance (UI) benefits paid to claimants.
∙ The data by county represents the mailing address given by the claimant at the time of filing for UI. It is possible that an individual can reside in a different county than their mailing address. Also, this information does not represent the county where the individual worked. It is also possible that a claimant could have moved or changed their mailing address after filing for UI which would not be reflected here. Data for claimants residing outside of California but collecting benefits are not included in these figures nor are invalid addresses in California where a county cannot be determined.
∙ Data includes the regular UI program and the federal extended benefit programs. The Federal extended benefit programs are:
∙ Emergency Unemployment Compensation (EUC) Tier 1 - California began paying benefits in July 2008.
∙ Emergency Unemployment Compensation (EUC) Tier 2 - California began paying benefits in January 2009, payments retroactive to November 2008.
∙ Emergency Unemployment Compensation (EUC) Tier 3 - California began paying benefits in December 2009, payments retroactive to November 2009.
∙ Emergency Unemployment Compensation (EUC) Tier 4 - California began paying benefits in January 2010, payments retroactive to December 2009.
∙ FED-ED - California began paying benefits May 2009, payments retroactive to February 2009.

--- Original source retains full ownership of the source dataset ---

The Quarterly Census of Employment and Wages (QCEW) Program is a Federal-State cooperative program between the U.S. Department of Labor’s Bureau of Labor Statistics (BLS) and the California EDD’s Labor Market Information Division (LMID). The QCEW program produces a comprehensive tabulation of employment and wage information for workers covered by California Unemployment Insurance (UI) laws and Federal workers covered by the Unemployment Compensation for Federal Employees (UCFE) program. The QCEW program serves as a near census of monthly employment and quarterly wage information by 6-digit industry codes from the North American Industry Classification System (NAICS) at the national, state, and county levels. At the national level, the QCEW program publishes employment and wage data for nearly every NAICS industry. At the state and local area level, the QCEW program publishes employment and wage data down to the 6-digit NAICS industry level, if disclosure restrictions are met. In accordance with the BLS policy, data provided to the Bureau in confidence are used only for specified statistical purposes and are not published. The BLS withholds publication of Unemployment Insurance law-covered employment and wage data for any industry level when necessary to protect the identity of cooperating employers. Data from the QCEW program serve as an important input to many BLS programs. The Current Employment Statistics and the Occupational Employment Statistics programs use the QCEW data as the benchmark source for employment. The UI administrative records collected under the QCEW program serve as a sampling frame for the BLS establishment surveys. In addition, the data serve as an input to other federal and state programs. The Bureau of Economic Analysis (BEA) of the Department of Commerce uses the QCEW data as the base for developing the wage and salary component of personal income. The U.S. Department of Labor’s Employment and Training Administration (ETA) and California's EDD use the QCEW data to administer the Unemployment Insurance program. The QCEW data accurately reflect the extent of coverage of California’s UI laws and are used to measure UI revenues; national, state and local area employment; and total and UI taxable wage trends. The U.S. Department of Labor’s Bureau of Labor Statistics publishes new QCEW data in its County Employment and Wages news release on a quarterly basis. The BLS also publishes a subset of its quarterly data through the Create Customized Tables system, and full quarterly industry detail data at all geographic levels.

Analysis of ‘Initial Claims By County (All Programs)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/bc31fe5c-49c7-4a1b-9190-78fa17248dca on 26 January 2022.

--- Dataset description provided by original source is as follows ---

Initial Claims by County (all programs) - The number of claims submitted for all UI programs. Initial claims totals are not representative of the number of individuals filing as a claimant can have multiple initial claims. ∙ Initial Claims by County - The data provided is the number of Unemployment Insurance (UI) initial claim counts, which includes new claims, additional claims, and transitional claims.
• A "new claim" is the first claim for a benefit year period (e.g., for the regular UI program it is 52 weeks). An individual would only have one new claim during a benefit year period.
• An "additional claim" is when another claim is filed during the same benefit year and there is intervening work between the current claim and the previous claim. For example, an individual files a new claim, goes back to work, gets laid off and files another claim before the benefit year period of the first claim expires. An individual can have multiple additional claims during the same benefit year if the individual meets the eligibility requirements.
• A "transitional claim" is when a claimant is still collecting benefits at the end of their benefit year period and had sufficient wage earnings during that year to start up a new claim once the first benefit year period ends.
∙ The data by county represents the mailing address given by the claimant at the time of filing for UI. It is possible that an individual can reside in a different county than their mailing address. Also, this information does not represent the county where the individual worked. It is also possible that a claimant could have moved or changed their mailing address after filing for UI which would not be reflected here. Data for claimants residing outside of California but collecting benefits are not included in these figures nor are invalid addresses in California where a county cannot be determined.
"∙ Initial claims does not represent total individuals as an individual can have multiple claims. For example, someone may begin collecting UI benefits, then go off UI to return to work, then get laid off and go back on UI. In this example, the individual would have two claim counts. "
∙ Data includes the regular UI program and the federal extended benefit programs. The Federal extended benefit programs are:
∙ Emergency Unemployment Compensation (EUC) Tier 1 - California began paying benefits in July 2008.
∙ Emergency Unemployment Compensation (EUC) Tier 2 - California began paying benefits in January 2009, payments retroactive to November 2008.
∙ Emergency Unemployment Compensation (EUC) Tier 3 - California began paying benefits in December 2009, payments retroactive to November 2009.
∙ Emergency Unemployment Compensation (EUC) Tier 4 - California began paying benefits in January 2010, payments retroactive to December 2009.
∙ FED-ED - California began paying benefits May 2009, payments retroactive to February 2009.

--- Original source retains full ownership of the source dataset ---

The Current Employment Statistics (CES) program is a Federal-State cooperative effort in which monthly surveys are conducted to provide estimates of employment, hours, and earnings based on payroll records of business establishments. The CES survey is based on approximately 119,000 businesses and government agencies representing approximately 629,000 individual worksites throughout the United States.

CES data reflect the number of nonfarm, payroll jobs. It includes the total number of persons on establishment payrolls, employed full- or part-time, who received pay (whether they worked or not) for any part of the pay period that includes the 12th day of the month. Temporary and intermittent employees are included, as are any employees who are on paid sick leave or on paid holiday. Persons on the payroll of more than one establishment are counted in each establishment. CES data excludes proprietors, self-employed, unpaid family or volunteer workers, farm workers, and household workers. Government employment covers only civilian employees; it excludes uniformed members of the armed services.

The Bureau of Labor Statistics (BLS) of the U.S. Department of Labor is responsible for the concepts, definitions, technical procedures, validation, and publication of the estimates that State workforce agencies prepare under agreement with BLS.

Analysis of ‘Exhausted Claims By County (All Programs)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/5fbb6ae7-3159-4acc-bbc5-202af8d684f1 on 26 January 2022.

--- Dataset description provided by original source is as follows ---

"∙ The data provided is the number of Unemployment Insurance (UI) claims that have exhausted, regardless of the program. The data includes exhaustion counts for the regular UI and the federal extended programs. The data counts the number of individuals who ran out of benefits in a specific program who may or may not qualify for additional benefits. For example, individuals who have exhausted a regular UI claim may qualify for a federal extension. Individuals who have exhausted all available benefits are also included in the data. The data is representative of those claims that were processed during the month and does not necessarily represent the month the final payment was made in. For example, if a claimant is entitled to benefits for the week-ending January 30, 2010, the claim might not get processed until early February and that count would display in the February data. There are a small percentage of claimants that could go back onto a training extension even after exhausting their FED-ED claim. "
∙ The data by county represents the mailing address given by the claimant at the time of filing for UI. It is possible that an individual can reside in a different county than their mailing address. Also, this information does not represent the county where the individual worked. It is also possible that a claimant could have moved or changed their mailing address after filing for UI which would not be reflected here. Data for claimants residing outside of California but collecting benefits are not included in these figures nor are invalid addresses in California where a county cannot be determined.
∙ Data includes the regular UI program and the federal extended benefit programs. The Federal extended benefit programs are:
∙ Emergency Unemployment Compensation (EUC) Tier 1 - California began paying benefits in July 2008.
∙ Emergency Unemployment Compensation (EUC) Tier 2 - California began paying benefits in January 2009, payments retroactive to November 2008.
∙ Emergency Unemployment Compensation (EUC) Tier 3 - California began paying benefits in December 2009, payments retroactive to November 2009.
∙ Emergency Unemployment Compensation (EUC) Tier 4 - California began paying benefits in January 2010, payments retroactive to December 2009.
∙ FED-ED - California began paying benefits May 2009, payments retroactive to February 2009.
∙ Data may include multiple counts for the same individual. For example, a claimant could have exhausted their Regular UI claim in January and then exhausted their EUC Tier I claim in June.

--- Original source retains full ownership of the source dataset ---

VITAL SIGNS INDICATOR Jobs (LU2)

FULL MEASURE NAME Employment estimates by place of work

LAST UPDATED October 2019

DESCRIPTION Jobs refers to the number of employees in a given area by place of work. These estimates do not include self-employed and private household employees.

DATA SOURCE California Employment Development Department: Current Employment Statistics 1990-2018 http://www.labormarketinfo.edd.ca.gov/

U.S. Census Bureau: LODES Data Longitudinal Employer-Household Dynamics Program (2005-2010) http://lehd.ces.census.gov/

U.S. Census Bureau: American Community Survey 5-Year Estimates, Tables S0804 (2010) and B08604 (2010-2017) https://factfinder.census.gov/

Bureau of Labor Statistics: Current Employment Statistics Table D-3: Employees on nonfarm payrolls (1990-2018) http://www.bls.gov/data/

METHODOLOGY NOTES (across all datasets for this indicator) The California Employment Development Department (EDD) provides estimates of employment, by place of employment, for California counties. The Bureau of Labor Statistics (BLS) provides estimates of employment for metropolitan areas outside of the Bay Area. Annual employment data are derived from monthly estimates and thus reflect “annual average employment.” Employment estimates outside of the Bay Area do not include farm employment. For the metropolitan area comparison, farm employment was removed from Bay Area employment totals. Both EDD and BLS data report only wage and salary jobs, not the self-employed.

For measuring jobs below the county level, Vital Signs assigns collections of incorporated cities and towns to sub-county areas. For example, the cities of East Palo Alto, Menlo Park, Portola Valley, Redwood City and Woodside are considered South San Mateo County. Because Bay Area counties differ in footprint, the number of sub-county city groupings varies from one (San Francisco and San Jose counties) to four (Santa Clara County). Estimates for sub-county areas are the sums of city-level estimates from the U.S. Census Bureau: American Community Survey (ACS) 2010-2017.

The following incorporated cities and towns are included in each sub-county area: North Alameda County – Alameda, Albany, Berkeley, Emeryville, Oakland, Piedmont East Alameda County - Dublin, Livermore, Pleasanton South Alameda County - Fremont, Hayward, Newark, San Leandro, Union City Central Contra Costa County - Clayton, Concord, Danville, Lafayette, Martinez, Moraga, Orinda, Pleasant Hill, San Ramon, Walnut Creek East Contra Costa County - Antioch, Brentwood, Oakley, Pittsburg West Contra Costa County - El Cerrito, Hercules, Pinole, Richmond, San Pablo Marin – all incorporated cities and towns Napa – all incorporated cities and towns San Francisco – San Francisco North San Mateo - Brisbane, Colma, Daly City, Millbrae, Pacifica, San Bruno, South San Francisco Central San Mateo - Belmont, Burlingame, Foster City, Half Moon Bay, Hillsborough, San Carlos, San Mateo South San Mateo - East Palo Alto, Menlo Park, Portola Valley, Redwood City, Woodside North Santa Clara - Los Altos, Los Altos Hills, Milpitas, Mountain View, Palo Alto, Santa Clara, Sunnyvale San Jose – San Jose Southwest Santa Clara - Campbell, Cupertino, Los Gatos, Monte Sereno, Saratoga South Santa Clara - Gilroy, Morgan Hill East Solano - Dixon, Fairfield, Rio Vista, Suisun City, Vacaville South Solano - Benicia, Vallejo North Sonoma - Cloverdale, Healdsburg, Windsor South Sonoma - Cotati, Petaluma, Rohnert Park, Santa Rosa, Sebastopol, Sonoma

VITAL SIGNS INDICATOR Jobs (LU2)

FULL MEASURE NAME Employment estimates by place of work

LAST UPDATED October 2019

DESCRIPTION Jobs refers to the number of employees in a given area by place of work. These estimates do not include self-employed and private household employees.

DATA SOURCE California Employment Development Department: Current Employment Statistics 1990-2018 http://www.labormarketinfo.edd.ca.gov/

U.S. Census Bureau: LODES Data Longitudinal Employer-Household Dynamics Program (2005-2010) http://lehd.ces.census.gov/

U.S. Census Bureau: American Community Survey 5-Year Estimates, Tables S0804 (2010) and B08604 (2010-2017) https://factfinder.census.gov/

Bureau of Labor Statistics: Current Employment Statistics Table D-3: Employees on nonfarm payrolls (1990-2018) http://www.bls.gov/data/

METHODOLOGY NOTES (across all datasets for this indicator) The California Employment Development Department (EDD) provides estimates of employment, by place of employment, for California counties. The Bureau of Labor Statistics (BLS) provides estimates of employment for metropolitan areas outside of the Bay Area. Annual employment data are derived from monthly estimates and thus reflect “annual average employment.” Employment estimates outside of the Bay Area do not include farm employment. For the metropolitan area comparison, farm employment was removed from Bay Area employment totals. Both EDD and BLS data report only wage and salary jobs, not the self-employed.

For measuring jobs below the county level, Vital Signs assigns collections of incorporated cities and towns to sub-county areas. For example, the cities of East Palo Alto, Menlo Park, Portola Valley, Redwood City and Woodside are considered South San Mateo County. Because Bay Area counties differ in footprint, the number of sub-county city groupings varies from one (San Francisco and San Jose counties) to four (Santa Clara County). Estimates for sub-county areas are the sums of city-level estimates from the U.S. Census Bureau: American Community Survey (ACS) 2010-2017.

The following incorporated cities and towns are included in each sub-county area: North Alameda County – Alameda, Albany, Berkeley, Emeryville, Oakland, Piedmont East Alameda County - Dublin, Livermore, Pleasanton South Alameda County - Fremont, Hayward, Newark, San Leandro, Union City Central Contra Costa County - Clayton, Concord, Danville, Lafayette, Martinez, Moraga, Orinda, Pleasant Hill, San Ramon, Walnut Creek East Contra Costa County - Antioch, Brentwood, Oakley, Pittsburg West Contra Costa County - El Cerrito, Hercules, Pinole, Richmond, San Pablo Marin – all incorporated cities and towns Napa – all incorporated cities and towns San Francisco – San Francisco North San Mateo - Brisbane, Colma, Daly City, Millbrae, Pacifica, San Bruno, South San Francisco Central San Mateo - Belmont, Burlingame, Foster City, Half Moon Bay, Hillsborough, San Carlos, San Mateo South San Mateo - East Palo Alto, Menlo Park, Portola Valley, Redwood City, Woodside North Santa Clara - Los Altos, Los Altos Hills, Milpitas, Mountain View, Palo Alto, Santa Clara, Sunnyvale San Jose – San Jose Southwest Santa Clara - Campbell, Cupertino, Los Gatos, Monte Sereno, Saratoga South Santa Clara - Gilroy, Morgan Hill East Solano - Dixon, Fairfield, Rio Vista, Suisun City, Vacaville South Solano - Benicia, Vallejo North Sonoma - Cloverdale, Healdsburg, Windsor South Sonoma - Cotati, Petaluma, Rohnert Park, Santa Rosa, Sebastopol, Sonoma

Analysis of ‘Exhausted Claims by County (Claimants that have exhausted all available benefits)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/2cbf281f-2296-4800-b2ee-bf908db860f0 on 26 January 2022.

--- Dataset description provided by original source is as follows ---

The data provided is the number of FED-ED (Federal Funding for Extended Unemployment) and EUC (Emergency Unemployment Compensation) Tier IV Unemployment Insurance (UI) claims that have exhausted. Based on the current legislation, claimants can have either an EUC Tier IV or FED-ED claim as their last available benefit option. The data is representative of those claims that were processed during the month and does not necessarily represent the month the final payment was made in. For example, if a claimant is entitled to benefits for the week-ending October 31, 2009, the claim might not get processed until early November and that count would display in the November data. There are a small percentage of claimants that could go back onto a training extension even after exhausting their FED-ED claim.

∙ The data by county represents the mailing address given by the claimant at the time of filing for UI. It is possible that an individual can reside in a different county than their mailing address. Also, this information does not represent the county where the individual worked. It is also possible that a claimant could have moved or changed their mailing address after filing for UI, which would not be reflected here.
∙ Data for claimants residing outside of California but collecting benefits are not included in these figures nor are invalid addresses in California where a county cannot be determined.
∙ California began paying FED-ED benefits in May 2009, with payments retroactive to February 2009 and began paying EUC Tier IV benefits in February 2010, with payments retroactive to December 2009. Currently, FED-ED or EUC Tier IV are the last extended benefit programs available to claimants.
∙ Claimants who had a FED-ED extension filed with an effective date of November 22, 2009, or before, will continue to be paid FED-ED extended benefits until they exhaust those FED-ED extensions, before filing for the additional EUC Tiers (which includes EUC Tier IV). Due to the complexity of the extended benefits legislation, a claimant could have exhausted an EUC Tier IV claim and still be eligible for FED-ED benefits, or vice versa. Therefore, to capture any claimants that have exhausted all available benefits a query is run to look for those claimants who have exhausted EUC Tier IV and FED-ED.
∙ Data for May 2010 through December 2010 have been revised on March 2011.
∙ Data for August 2013 through October 2013 have been revised on December 23, 2013.

--- Original source retains full ownership of the source dataset ---

Income from employment allows for the purchase of goods and services. Employment status may also affect esteem and well-being. A high unemployment rate inversely impacts quality of life, since it may influence personal self-esteem, dignity and security; as well, have an impact on the purchasing of more basic needs. The greater the proportion of people working part year, part time relative to those working full-year, full time inversely influences quality of life, since it may indicate the lack of full-time employment opportunities. High average employment income directly influences quality of life, since it can indicate the ability of individuals to purchase a wider range of goods and services.

Employment income (in 2019 and 2020) by detailed major field of study and highest certificate, diploma or degree, including work activity (full time full year, part time full year, or part year).

Get statistical data for rural and urban Ontario on key socioeconomic variables.

The data identifies:

• demographic information for rural and urban Ontario by census year (population change and age breakdown, immigrants, visible minorities, educational attainment, average household income)
• economic indicators for rural and urban Ontario (monthly and annual employment by industry, monthly and annual labour force characteristics, number of businesses by industries and employee number)
• rural and urban Ontario census profiles (for all census of population variables starting with 2001)

Find more resources with socioeconomic data and information about Rural Ontario

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.

PIMCO California Municipal Income: A Wise Investment for Tax-Conscious Investors? (PCQ)

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.

NAC Nuveen California Quality Municipal Income Fund

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

The median income indicates the income bracket separating the income earners into two halves of equal size.

The SCAG_ATDB_Demographics shapefile contains Census tract level population, race, employment, English speaking, income, and elderly data of the SCAG region. Race data includes the percentage of population that is white, black, Asian, Latino, Pacific Islander, Native American, multiple races, or other. Population data includes 2010 population 2015 population, and population density. Employment data includes 2015 employment, unemployment, and employment density. English speaking data includes the percentage of the population that speaks English well. This shapefile also includes median household income and percentage of the population that is 65 years or older. This data was sourced mostly from Census data as well as the Healthy Places Index (HPI). Original data sources are listed in the relevant fields.

Population is the sum of births plus in-migration, and it signifies the total market size possible in the area. This is an important metric for economic developers to measure their economic health and investment attraction. Businesses also use this as a metric for market size when evaluating startup, expansion or relocation decisions.

The median age indicates the age separating the population group into two halves of equal size.

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 Blackrock MuniHoldings California Quality Fund (MUC) Remain a Steady Stream of Income?

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