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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.
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)
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)
Stock price prediction
Portfolio optimization
Algorithmic trading
Market sentiment analysis
Risk management
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
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
Portugal, Italy, Ireland, Greece, and Spain were widely considered the Eurozone's weakest economies during the Great Recession and subsequent Eurozone debt crisis. These countries were grouped together due to the similarities in their economic crises, with much of them driven by house price bubbles which had inflated over the early 2000s, before bursting in 2007 due to the Global Financial Crisis. Entry into the Euro currency by 2002 had meant that banks could lend to house buyers in these countries at greatly reduced rates of interest.
This reduction in the cost of financing contributed to creating housing bubbles, which were further boosted by pro-cyclical housing policies among many of the countries' governments. In spite of these economies experiencing similar economic problems during the crisis, Italy and Portugal did not experience housing bubbles in the same way in which Greece, Ireland, and Spain did. In the latter countries, their real housing prices (which are adjusted for inflation) peaked in 2007, before quickly declining during the recession. In particular, house prices in Ireland dropped by over 40 percent from their peak in 2007 to 2011.
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This paper studies the role of the Federal Reserve's policy in the recent boom and bust of the housing market, and in the ensuing recession. By estimating a structural dynamic factor model on a panel of 109 US quarterly variables from 1982 to 2010, we find that, although the Federal Reserve's policy between 2002 and 2004 was slightly expansionary, its contribution to the recent housing cycle was negligible. We also show that a more restrictive policy would have smoothed the cycle but not prevented the recession. We thus find no role for the Federal Reserve in causing the recession.
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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.
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)
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)
Stock price prediction
Portfolio optimization
Algorithmic trading
Market sentiment analysis
Risk management
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
The dataset may include different levels of granularity (e.g., daily, hourly)
Data cleaning and preprocessing are essential before model training
Regular updates are recommended to maintain the accuracy and relevance of the data
In a 2019 analysis, Riverside, California was the most at risk of a housing downturn in a recession out of the ** largest metro areas in the United States. The Californian metro area received an overall score of **** percent, which was compiled after factors such as home price volatility and average home loan-to-value ratio were examined.
This paper studies the impact of unemployment insurance (UI) on the housing market. Exploiting heterogeneity in UI generosity across US states and over time, we find that UI helps the unemployed avoid mortgage default. We estimate that UI expansions during the Great Recession prevented more than 1.3 million foreclosures and insulated home values from labor market shocks. The results suggest that policies that make mortgages more affordable can reduce foreclosures even when borrowers are severely underwater. An optimal UI policy during housing downturns would weigh, among other benefits and costs, the deadweight losses avoided from preventing mortgage defaults.
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Graph and download economic data for Real Residential Property Prices for United States (QUSR628BIS) from Q1 1970 to Q1 2025 about residential, HPI, housing, real, price index, indexes, price, and USA.
The Global Financial Crisis of 2008-09 was a period of severe macroeconomic instability for the United States and the global economy more generally. The crisis was precipitated by the collapse of a number of financial institutions who were deeply involved in the U.S. mortgage market and associated credit markets. Beginning in the Summer of 2007, a number of banks began to report issues with increasing mortgage delinquencies and the problem of not being able to accurately price derivatives contracts which were based on bundles of these U.S. residential mortgages. By the end of 2008, U.S. financial institutions had begun to fail due to their exposure to the housing market, leading to one of the deepest recessions in the history of the United States and to extensive government bailouts of the financial sector.
Subprime and the collapse of the U.S. mortgage market
The early 2000s had seen explosive growth in the U.S. mortgage market, as credit became cheaper due to the Federal Reserve's decision to lower interest rates in the aftermath of the 2001 'Dot Com' Crash, as well as because of the increasing globalization of financial flows which directed funds into U.S. financial markets. Lower mortgage rates gave incentive to financial institutions to begin lending to riskier borrowers, using so-called 'subprime' loans. These were loans to borrowers with poor credit scores, who would not have met the requirements for a conventional mortgage loan. In order to hedge against the risk of these riskier loans, financial institutions began to use complex financial instruments known as derivatives, which bundled mortgage loans together and allowed the risk of default to be sold on to willing investors. This practice was supposed to remove the risk from these loans, by effectively allowing credit institutions to buy insurance against delinquencies. Due to the fraudulent practices of credit ratings agencies, however, the price of these contacts did not reflect the real risk of the loans involved. As the reality of the inability of the borrowers to repay began to kick in during 2007, the financial markets which traded these derivatives came under increasing stress and eventually led to a 'sudden stop' in trading and credit intermediation during 2008.
Market Panic and The Great Recession
As borrowers failed to make repayments, this had a knock-on effect among financial institutions who were highly leveraged with financial instruments based on the mortgage market. Lehman Brothers, one of the world's largest investment banks, failed on September 15th 2008, causing widespread panic in financial markets. Due to the fear of an unprecedented collapse in the financial sector which would have untold consequences for the wider economy, the U.S. government and central bank, The Fed, intervened the following day to bailout the United States' largest insurance company, AIG, and to backstop financial markets. The crisis prompted a deep recession, known colloquially as The Great Recession, drawing parallels between this period and The Great Depression. The collapse of credit intermediation in the economy lead to further issues in the real economy, as business were increasingly unable to pay back loans and were forced to lay off staff, driving unemployment to a high of almost 10 percent in 2010. While there has been criticism of the U.S. government's actions to bailout the financial institutions involved, the actions of the government and the Fed are seen by many as having prevented the crisis from spiraling into a depression of the magnitude of The Great Depression.
The homeownership rate in the United States declined slightly in 2023 and remained stable in 2024. The U.S. homeownership rate was the highest in 2004 before the 2007-2009 recession hit and decimated the housing market. In 2024, the proportion of households occupied by owners stood at **** percent in 2024, *** percentage points below 2004 levels. Homeownership since the recession The rate of homeownership in the U.S. fell in the lead up to the recession and continued to do so until 2016. Despite this trend, the share of Americans who perceived homeownership as part of their personal American dream remained relatively stable. This suggests that the financial hardship caused by the recession led to the fall in homeownership, rather than a change in opinion about the importance of homeownership itself. What the future holds for homeownership Homeownership trends vary from generation to generation. Homeownership among Americans over 65 years old is declining, whereas most Millennial renters plan to buy a home in the near future. This suggests that homeownership will remain important in the future, as Millennials are forecast to head most households over the next two decades.
US Residential Construction Market Size 2025-2029
The US residential construction market size is forecast to increase by USD 242.9 million at a CAGR of 4.5% between 2024 and 2029.
The Residential Construction Market in the US is experiencing significant growth driven by increasing household formation rates and a rising focus on sustainability in new projects. According to the latest data, household formation is projected to continue growing at a steady pace, fueling the demand for new residential units. This trend is particularly evident in urban areas, where population growth and limited space for new development are driving up demand. Meanwhile, the emphasis on sustainability in residential construction is transforming the market landscape. With consumers increasingly prioritizing energy efficiency and eco-friendly features in their homes, builders and developers are responding by incorporating green technologies and sustainable materials into their projects.
This shift not only appeals to environmentally-conscious consumers but also offers long-term cost savings and regulatory compliance benefits. However, the market is not without challenges. Skilled labor shortages continue to pose a significant hurdle for large-scale residential real estate projects. The ongoing shortage of skilled laborers, including carpenters, electricians, and plumbers, is driving up labor costs and delaying project timelines. To mitigate this challenge, some builders are exploring alternative solutions, such as modular construction and automation, to streamline their operations and reduce their reliance on traditional labor sources. The Residential Construction Market in the US presents significant opportunities for companies seeking to capitalize on the growing demand for new housing units and the shift towards sustainability.
However, navigating the challenges of labor shortages and rising costs will require innovative solutions and strategic planning. By staying informed of market trends and adapting to evolving consumer preferences, companies can effectively position themselves for success in this dynamic market.
What will be the size of the US Residential Construction Market during the forecast period?
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The residential construction market in the United States continues to exhibit dynamic activity, driven by various economic factors. Housing supply remains a key focus, with ongoing discussions surrounding the affordable housing trend and efforts to increase inventory, particularly for single-family homes and new constructions. Mortgage and federal funds rates have an impact on residential investment, with fluctuations influencing buyer decisions and construction costs. The labor market plays a crucial role, as workforce availability and wages affect both housing starts and cancellation rates. Inflation and interest rates, monitored closely by the Federal Reserve, also shape the market's direction. Recession risks and economic conditions influence construction spending across various sectors, including multifamily and single-family homes.
Federal programs, such as housing choice vouchers and fair housing initiatives, continue to support home buyers and promote equitable housing opportunities. Building permits and housing starts serve as essential indicators of market health and future growth, with some sectors experiencing double-digit growth. Overall, the residential construction market in the US remains a significant economic driver, shaped by a complex interplay of economic, demographic, and policy factors.
How is this market segmented?
The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.
Product
Apartments and condominiums
Luxury Homes
Other types
Type
New construction
Renovation
Application
Single family
Multi-family
Construction Material
Wood-framed
Concrete
Steel
Modular/Prefabricated
Geography
US
By Product Insights
The apartments and condominiums segment is estimated to witness significant growth during the forecast period.
The residential construction market in the US is experiencing growth in both the apartment and condominium sectors, driven by the increasing trend toward urbanization and changing lifestyle preferences. Apartments, typically owned by property management companies, and condominiums, with individually owned units within a larger complex, contribute significantly to the market. The Federal Reserve's influence on the economy through the federal funds rate and mortgage rates impacts borrowing rates and home construction activity. The affordability of housing, particularly for younger generations, is a concern due to factors such as inflation, labor market conditions, and savings
The Federal National Mortgage Association, commonly known as Fannie Mae, was created by the U.S. congress in 1938, in order to maintain liquidity and stability in the domestic mortgage market. The company is a government-sponsored enterprise (GSE), meaning that while it was a publicly traded company for most of its history, it was still supported by the federal government. While there is no legally binding guarantee of shares in GSEs or their securities, it is generally acknowledged that the U.S. government is highly unlikely to let these enterprises fail. Due to these implicit guarantees, GSEs are able to access financing at a reduced cost of interest. Fannie Mae's main activity is the purchasing of mortgage loans from their originators (banks, mortgage brokers etc.) and packaging them into mortgage-backed securities (MBS) in order to ease the access of U.S. homebuyers to housing credit. The early 2000s U.S. mortgage finance boom During the early 2000s, Fannie Mae was swept up in the U.S. housing boom which eventually led to the financial crisis of 2007-2008. The association's stated goal of increasing access of lower income families to housing finance coalesced with the interests of private mortgage lenders and Wall Street investment banks, who had become heavily reliant on the housing market to drive profits. Private lenders had begun to offer riskier mortgage loans in the early 2000s due to low interest rates in the wake of the "Dot Com" crash and their need to maintain profits through increasing the volume of loans on their books. The securitized products created by these private lenders did not maintain the standards which had traditionally been upheld by GSEs. Due to their market share being eaten into by private firms, however, the GSEs involved in the mortgage markets began to also lower their standards, resulting in a 'race to the bottom'. The fall of Fannie Mae The lowering of lending standards was a key factor in creating the housing bubble, as mortgages were now being offered to borrowers with little or no ability to repay the loans. Combined with fraudulent practices from credit ratings agencies, who rated the junk securities created from these mortgage loans as being of the highest standard, this led directly to the financial panic that erupted on Wall Street beginning in 2007. As the U.S. economy slowed down in 2006, mortgage delinquency rates began to spike. Fannie Mae's losses in the mortgage security market in 2006 and 2007, along with the losses of the related GSE 'Freddie Mac', had caused its share value to plummet, stoking fears that it may collapse. On September 7th 2008, Fannie Mae was taken into government conservatorship along with Freddie Mac, with their stocks being delisted from stock exchanges in 2010. This act was seen as an unprecedented direct intervention into the economy by the U.S. government, and a symbol of how far the U.S. housing market had fallen.
The year-end value of the S&P Case Shiller National Home Price Index amounted to 321.45 in 2024. The index value was equal to 100 as of January 2000, so if the index value is equal to 130 in a given year, for example, it means that the house prices increased by 30 percent since 2000. S&P/Case Shiller U.S. home indices – additional informationThe S&P Case Shiller National Home Price Index is calculated on a monthly basis and is based on the prices of single-family homes in nine U.S. Census divisions: New England, Middle Atlantic, East North Central, West North Central, South Atlantic, East South Central, West South Central, Mountain and Pacific. The index is the leading indicator of the American housing market and one of the indicators of the state of the broader economy. The index illustrates the trend of home prices and can be helpful during house purchase decisions. When house prices are rising, a house buyer might want to speed up the house purchase decision as the transaction costs can be much higher in the future. The S&P Case Shiller National Home Price Index has been on the rise since 2011.The S&P Case Shiller National Home Price Index is one of the indices included in the S&P/Case-Shiller Home Price Index Series. Other indices are the S&P/Case Shiller 20-City Composite Home Price Index, the S&P/Case Shiller 10-City Composite Home Price Index and twenty city composite indices.
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To improve estimates of household consumption behavior, we extend a widely-used model by allowing for dynamic consumption elasticities with respect to transitory income shocks. Applying our model to biennial household survey data, we find a significant structural break in marginal propensities to consume from before to after the housing market boom and bust just prior to the Great Recession, with the average level for all households estimated to have increased by more than 40%. There is important heterogeneity across households grouped by different balance sheet characteristics and our results suggest the increase for all households was driven by higher short-run consumption elasticities for homeowners with low liquid wealth. The change appears to be related to tighter borrowing constraints for homeowners more than a shift in wealth distributions.
Home prices fell by **** percent during the Great Recession of 2007 to 2009 in the United States. However, such a significant decrease in prices did not happen in the other four recessions which have occurred since 1980.
This project will explore the impact of the economic recession on cities and households through a systematic comparison of the experiences of two English cities, Bristol and Liverpool.The research will use both quantitative and qualitative approaches. Interviews will be held in both cities with stakeholders from across the public, private and voluntary and community sectors. A social survey of 1000 households will also be conducted in the two cities covering 10 specific household types. A series of in-depth qualitative interviews will then be held with households drawn from the survey and chosen to illustrate the spectrum of experience.In the context of globalisation and the rescaling of cities and states, the research aims to develop our understanding of the relationship between economic crisis, global connectivity and the transnational processes shaping cities and the everyday lives of residents. It will explore the 'capillary-like' impact of the crisis and austerity measures on local economic development, and local labour and housing markets, as well as highlight the intersecting realities of everyday life for households across the life course.The research will document the responses and coping strategies developed across different household types and evaluate the impact and effectiveness of 'anti-recession' strategies and policies.
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Graph and download economic data for All-Transactions House Price Index for Reno, NV (MSA) (ATNHPIUS39900Q) from Q2 1978 to Q1 2025 about Reno, NV, appraisers, HPI, housing, price index, indexes, price, and USA.
The Long Depression was, by a large margin, the longest-lasting recession in U.S. history. It began in the U.S. with the Panic of 1873, and lasted for over five years. This depression was the largest in a series of recessions at the turn of the 20th century, which proved to be a period of overall stagnation as the U.S. financial markets failed to keep pace with industrialization and changes in monetary policy. Great Depression The Great Depression, however, is widely considered to have been the most severe recession in U.S. history. Following the Wall Street Crash in 1929, the country's economy collapsed, wages fell and a quarter of the workforce was unemployed. It would take almost four years for recovery to begin. Additionally, U.S. expansion and integration in international markets allowed the depression to become a global event, which became a major catalyst in the build up to the Second World War. Decreasing severity When comparing recessions before and after the Great Depression, they have generally become shorter and less frequent over time. Only three recessions in the latter period have lasted more than one year. Additionally, while there were 12 recessions between 1880 and 1920, there were only six recessions between 1980 and 2020. The most severe recession in recent years was the financial crisis of 2007 (known as the Great Recession), where irresponsible lending policies and lack of government regulation allowed for a property bubble to develop and become detached from the economy over time, this eventually became untenable and the bubble burst. Although the causes of both the Great Depression and Great Recession were similar in many aspects, economists have been able to use historical evidence to try and predict, prevent, or limit the impact of future recessions.
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We estimate across-county spending flows between firms and consumers for every county in the United States, providing a new consumption link that has not been studied previously. We highlight the importance of this link by estimating the effect of changes in local housing wealth on consumption and employment from 2001 to 2019. We generally find that the effect from changes in housing wealth crosses borders to affect consumption and employment in a pattern consistent with our spending flows. However, we find potential consumers who reside outside the local commuting zone disproportionately affect local spending and employment during the Great Recession.
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According to Cognitive Market Research, the global Title Insurance market size is USD 57181.2 million in 2024 and will expand at a compound annual growth rate (CAGR) of 12.00% from 2024 to 2031.
North America held the major market of more than 40% of the global revenue with a market size of USD 22872.48 million in 2024 and will grow at a compound annual growth rate (CAGR) of 10.2% from 2024 to 2031.
Europe accounted for a share of over 30% of the global market size of USD 17154.36 million.
Asia Pacific held the market of around 23% of the global revenue with a market size of USD 13151.68 million in 2024 and will grow at a compound annual growth rate (CAGR) of 14.0%from 2024 to 2031.
Latin America market of more than 5% of the global revenue with a market size of USD 2859.06 million in 2024 and will grow at a compound annual growth rate (CAGR) of 11.4% from 2024 to 2031.
Middle East and Africa held the major market of around 2% of the global revenue with a market size of USD 1143.62 million in 2024 and will grow at a compound annual growth rate (CAGR) of 11.7% from 2024 to 2031.
The dominant end user category is the enterprise segment, which includes businesses and organizations that require title insurance for commercial properties and real estate transactions.
Market Dynamics of Title Insurance Market
Key Drivers for Title Insurance Market
Increasing Property Transactions to Increase the Demand Globally
One key driver propelling the Title Insurance market is the steady rise in property transactions. As the real estate industry continues to expand globally, fueled by urbanization, population growth, and economic development, the demand for title insurance has surged. Property buyers and lenders increasingly recognize the importance of safeguarding their investments against potential title defects, encumbrances, or legal disputes that may arise in the future. This heightened awareness has led to a greater adoption of title insurance policies, driving market growth. Additionally, regulatory mandates in many jurisdictions require title insurance as a prerequisite for property transactions, further boosting market demand. As property markets remain dynamic and resilient, the increasing volume of real estate transactions is expected to sustain the growth momentum of the Title Insurance market.
Evolving Regulatory Landscape to Propel Market Growth
Another crucial driver shaping the Title Insurance market is the evolving regulatory landscape governing real estate transactions. Regulatory changes, including updates to property laws, mortgage regulations, and consumer protection measures, have a significant impact on the demand for title insurance. Stricter regulations often necessitate comprehensive due diligence procedures and risk mitigation strategies, prompting property buyers and lenders to seek robust title insurance coverage. Moreover, regulatory reforms aimed at enhancing transparency and reducing fraud in property transactions have contributed to the growing adoption of title insurance as a risk management tool. Market players in the title insurance industry are continually adapting their products and services to align with evolving regulatory requirements, thereby driving market growth. As regulatory frameworks continue to evolve, the demand for title insurance is expected to remain strong, especially in regions undergoing significant legislative changes in the real estate sector.
Restraint Factor for the Title Insurance Market
Economic Downturns and Property Market Volatility to Limit the Sales
One key restraints affecting the Title Insurance market is its vulnerability to economic downturns and property market volatility. During periods of economic uncertainty or recession, property transactions tend to decline, leading to a reduction in demand for title insurance. Economic downturns also increase the risk of mortgage defaults and foreclosures, which can result in higher claims payouts for title insurers. Additionally, property market volatility, influenced by factors such as fluctuating interest rates, regulatory changes, and geopolitical events, can impact the stability of the Title Insurance market. Uncertain property valuations and shifting market dynamics can make it challenging for title insurers to accurately assess risks and set premiums, leading to potential revenue losses. As such, the Title Insurance market is sensitive to mac...
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United States Home Warranty Market was valued at USD 4,262.56 Million in 2024 and is projected to reach USD 5,682.72 Million by 2032, growing at a CAGR of 4.19% from 2026 to 2032.The aging housing stock in the United States is a significant driver of growth for the U.S. Home Warranty Market, as a significant portion of homes now require ongoing repairs, system upgrades, and appliance replacements. This aging trend has accelerated in recent years, mainly due to a slowdown in new housing construction after the Great Recession, combined with persistent economic barriers including rising material costs, labor shortages, and elevated interest rates that have hampered the supply of newer homes. As a result, millions of homeowners now live-in homes built in earlier decades, creating a vast market need for repair-oriented services like home warranties.
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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.
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)
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)
Stock price prediction
Portfolio optimization
Algorithmic trading
Market sentiment analysis
Risk management
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
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