The main stock market index of United States, the US500, rose to 6008 points on June 9, 2025, gaining 0.13% from the previous session. Over the past month, the index has climbed 2.80% and is up 12.07% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from United States. United States Stock Market Index - values, historical data, forecasts and news - updated on June of 2025.

This graph shows a forecast of the gross domestic product of the United States of America for fiscal years 2024 to 2034. GDP refers to the market value of all final goods and services produced within a country in a given period. According to the CBO, the United States GDP will increase steadily over the next decade from 28.18 trillion U.S. dollars in 2023 to 41.65 trillion U.S. dollars in 2034. The annual GDP of the United States for recent years can be found here. Also, view the monthly inflation rate for the country.

This 6MB download is a zip file containing 5 pdf documents and 2 xlsx spreadsheets. Presentation on COVID-19 and the potential impacts on employment

May 2020Waka Kotahi wants to better understand the potential implications of the COVID-19 downturn on the land transport system, particularly the potential impacts on regional economies and communities.

To do this, in May 2020 Waka Kotahi commissioned Martin Jenkins and Infometrics to consider the potential impacts of COVID-19 on New Zealand’s economy and demographics, as these are two key drivers of transport demand. In addition to providing a scan of national and international COVID-19 trends, the research involved modelling the economic impacts of three of the Treasury’s COVID-19 scenarios, to a regional scale, to help us understand where the impacts might be greatest.

Waka Kotahi studied this modelling by comparing the percentage difference in employment forecasts from the Treasury’s three COVID-19 scenarios compared to the business as usual scenario.

The source tables from the modelling (Tables 1-40), and the percentage difference in employment forecasts (Tables 41-43), are available as spreadsheets.

Arataki - potential impacts of COVID-19 Final Report

Employment modelling - interactive dashboard

The modelling produced employment forecasts for each region and district over three time periods – 2021, 2025 and 2031. In May 2020, the forecasts for 2021 carried greater certainty as they reflected the impacts of current events, such as border restrictions, reduction in international visitors and students etc. The 2025 and 2031 forecasts were less certain because of the potential for significant shifts in the socio-economic situation over the intervening years. While these later forecasts were useful in helping to understand the relative scale and duration of potential COVID-19 related impacts around the country, they needed to be treated with care recognising the higher levels of uncertainty.

The May 2020 research suggested that the ‘slow recovery scenario’ (Treasury’s scenario 5) was the most likely due to continuing high levels of uncertainty regarding global efforts to manage the pandemic (and the duration and scale of the resulting economic downturn).

The updates to Arataki V2 were framed around the ‘Slower Recovery Scenario’, as that scenario remained the most closely aligned with the unfolding impacts of COVID-19 in New Zealand and globally at that time.

Find out more about Arataki, our 10-year plan for the land transport system

May 2021The May 2021 update to employment modelling used to inform Arataki Version 2 is now available. Employment modelling dashboard - updated 2021Arataki used the May 2020 information to compare how various regions and industries might be impacted by COVID-19. Almost a year later, it is clear that New Zealand fared better than forecast in May 2020.Waka Kotahi therefore commissioned an update to the projections through a high-level review of:the original projections for 2020/21 against performancethe implications of the most recent global (eg International monetary fund world economic Outlook) and national economic forecasts (eg Treasury half year economic and fiscal update)The treasury updated its scenarios in its December half year fiscal and economic update (HYEFU) and these new scenarios have been used for the revised projections.Considerable uncertainty remains about the potential scale and duration of the COVID-19 downturn, for example with regards to the duration of border restrictions, update of immunisation programmes. The updated analysis provides us with additional information regarding which sectors and parts of the country are likely to be most impacted. We continue to monitor the situation and keep up to date with other cross-Government scenario development and COVID-19 related work. The updated modelling has produced employment forecasts for each region and district over three time periods - 2022, 2025, 2031.The 2022 forecasts carry greater certainty as they reflect the impacts of current events. The 2025 and 2031 forecasts are less certain because of the potential for significant shifts over that time.

Data reuse caveats: as per license.

Additionally, please read / use this data in conjunction with the Infometrics and Martin Jenkins reports, to understand the uncertainties and assumptions involved in modelling the potential impacts of COVID-19.

COVID-19’s effect on industry and regional economic outcomes for NZ Transport Agency [PDF 620 KB]

Data quality statement: while the modelling undertaken is high quality, it represents two point-in-time analyses undertaken during a period of considerable uncertainty. This uncertainty comes from several factors relating to the COVID-19 pandemic, including:

a lack of clarity about the size of the global downturn and how quickly the international economy might recover differing views about the ability of the New Zealand economy to bounce back from the significant job losses that are occurring and how much of a structural change in the economy is required the possibility of a further wave of COVID-19 cases within New Zealand that might require a return to Alert Levels 3 or 4.

While high levels of uncertainty remain around the scale of impacts from the pandemic, particularly in coming years, the modelling is useful in indicating the direction of travel and the relative scale of impacts in different parts of the country.

Data quality caveats: as noted above, there is considerable uncertainty about the potential scale and duration of the COVID-19 downturn. Please treat the specific results of the modelling carefully, particularly in the forecasts to later years (2025, 2031), given the potential for significant shifts in New Zealand's socio-economic situation before then.

As such, please use the modelling results as a guide to the potential scale of the impacts of the downturn in different locations, rather than as a precise assessment of impacts over the coming decade.

Liabilities: Flow: PDF: RW: Held by: Household data was reported at 1.528 USD bn in Mar 2018. This records an increase from the previous number of 1.310 USD bn for Dec 2017. Liabilities: Flow: PDF: RW: Held by: Household data is updated quarterly, averaging 0.000 USD bn from Dec 1951 (Median) to Mar 2018, with 266 observations. The data reached an all-time high of 6.395 USD bn in Jun 2007 and a record low of -7.982 USD bn in Sep 2008. Liabilities: Flow: PDF: RW: Held by: Household data remains active status in CEIC and is reported by Federal Reserve Board. The data is categorized under Global Database’s USA – Table US.AB037: Funds by Instruments: Flows and Outstanding: US Deposits in Foreign Countries.

United States Liabilities: Flow: US Private Deposit in Foreign Countries (PDF) data was reported at 28.993 USD bn in Mar 2018. This records an increase from the previous number of 25.879 USD bn for Dec 2017. United States Liabilities: Flow: US Private Deposit in Foreign Countries (PDF) data is updated quarterly, averaging 0.050 USD bn from Dec 1951 (Median) to Mar 2018, with 266 observations. The data reached an all-time high of 134.844 USD bn in Jun 2007 and a record low of -133.529 USD bn in Sep 2008. United States Liabilities: Flow: US Private Deposit in Foreign Countries (PDF) data remains active status in CEIC and is reported by Federal Reserve Board. The data is categorized under Global Database’s USA – Table US.AB037: Funds by Instruments: Flows and Outstanding: US Deposits in Foreign Countries.

CR: Gross Domestic Product (GDP): Volume: 2015 Exchange Rates: USD: Single Hit Scenario data was reported at 61.213 USD bn in 2021. This records an increase from the previous number of 59.609 USD bn for 2020. CR: Gross Domestic Product (GDP): Volume: 2015 Exchange Rates: USD: Single Hit Scenario data is updated yearly, averaging 38.821 USD bn from Dec 1991 (Median) to 2021, with 31 observations. The data reached an all-time high of 62.149 USD bn in 2019 and a record low of 19.096 USD bn in 1991. CR: Gross Domestic Product (GDP): Volume: 2015 Exchange Rates: USD: Single Hit Scenario data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Costa Rica – Table CR.OECD.EO: GDP by Expenditure: Volume: Forecast: OECD Member: Annual. GDPV_USD-Gross domestic product, US $, volume, constant exchange rates, EO base yearExpenditure approach OECD calculation, see OECD Economic Outlook, Database Inventory OECD Economic Outlook, Database Inventory:https://www.oecd.org/eco/outlook/Database_Inventory.pdf

Liabilities: Outs: PDF: RW: Held by: Household data was reported at 38.027 USD bn in Mar 2018. This records an increase from the previous number of 36.499 USD bn for Dec 2017. Liabilities: Outs: PDF: RW: Held by: Household data is updated quarterly, averaging 7.108 USD bn from Dec 1951 (Median) to Mar 2018, with 266 observations. The data reached an all-time high of 87.722 USD bn in Dec 2007 and a record low of 0.000 USD bn in Sep 1982. Liabilities: Outs: PDF: RW: Held by: Household data remains active status in CEIC and is reported by Federal Reserve Board. The data is categorized under Global Database’s USA – Table US.AB037: Funds by Instruments: Flows and Outstanding: US Deposits in Foreign Countries.

Czech Republic CZ: Gross Domestic Product (GDP): Volume: 2015 Exchange Rates: USD: Single Hit Scenario data was reported at 208.000 USD bn in Dec 2021. This records an increase from the previous number of 206.000 USD bn for Sep 2021. Czech Republic CZ: Gross Domestic Product (GDP): Volume: 2015 Exchange Rates: USD: Single Hit Scenario data is updated quarterly, averaging 170.500 USD bn from Mar 1995 (Median) to Dec 2021, with 108 observations. The data reached an all-time high of 212.000 USD bn in Dec 2019 and a record low of 113.000 USD bn in Mar 1995. Czech Republic CZ: Gross Domestic Product (GDP): Volume: 2015 Exchange Rates: USD: Single Hit Scenario data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Czech Republic – Table CZ.OECD.EO: GDP by Expenditure: Volume: Forecast: OECD Member: Quarterly. GDPV_USD-Gross domestic product, US $, volume, constant exchange rates, EO base yearExpenditure approach OECD calculation, see OECD Economic Outlook, Database Inventory OECD Economic Outlook, Database Inventory:https://www.oecd.org/eco/outlook/Database_Inventory.pdf

Argentina AR: Gross Domestic Product (GDP): Volume: 2015 Exchange Rates: USD: Double Hit Scenario data was reported at 566.000 USD bn in 2021. This records an increase from the previous number of 557.000 USD bn for 2020. Argentina AR: Gross Domestic Product (GDP): Volume: 2015 Exchange Rates: USD: Double Hit Scenario data is updated yearly, averaging 545.000 USD bn from Dec 1993 (Median) to 2021, with 29 observations. The data reached an all-time high of 649.000 USD bn in 2017 and a record low of 366.000 USD bn in 2002. Argentina AR: Gross Domestic Product (GDP): Volume: 2015 Exchange Rates: USD: Double Hit Scenario data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Argentina – Table AR.OECD.EO: GDP by Expenditure: Volume: Forecast: Non OECD Member: Annual. GDPV_USD-Gross domestic product, US $, volume, constant exchange rates, EO base yearExpenditure approach OECD calculation, see OECD Economic Outlook, Database Inventory OECD Economic Outlook, Database Inventory:https://www.oecd.org/eco/outlook/Database_Inventory.pdf

Norway NO: Gross Domestic Product (GDP): Volume: 2015 Exchange Rates: USD: Single Hit Scenario data was reported at 403.000 USD bn in 2021. This records an increase from the previous number of 385.000 USD bn for 2020. Norway NO: Gross Domestic Product (GDP): Volume: 2015 Exchange Rates: USD: Single Hit Scenario data is updated yearly, averaging 214.500 USD bn from Dec 1960 (Median) to 2021, with 62 observations. The data reached an all-time high of 409.000 USD bn in 2019 and a record low of 68.990 USD bn in 1960. Norway NO: Gross Domestic Product (GDP): Volume: 2015 Exchange Rates: USD: Single Hit Scenario data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Norway – Table NO.OECD.EO: GDP by Expenditure: Volume: Forecast: OECD Member: Annual. GDPV_USD-Gross domestic product, US $, volume, constant exchange rates, EO base yearExpenditure approach OECD calculation, see OECD Economic Outlook, Database Inventory OECD Economic Outlook, Database Inventory:https://www.oecd.org/eco/outlook/Database_Inventory.pdf

Non Farm Payrolls in the United States increased by 139 thousand in May of 2025. This dataset provides the latest reported value for - United States Non Farm Payrolls - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Analysis of ‘Time Series Forecasting with Yahoo Stock Price ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/arashnic/time-series-forecasting-with-yahoo-stock-price on 28 January 2022.

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

Context

Stocks and financial instrument trading is a lucrative proposition. Stock markets across the world facilitate such trades and thus wealth exchanges hands. Stock prices move up and down all the time and having ability to predict its movement has immense potential to make one rich. Stock price prediction has kept people interested from a long time. There are hypothesis like the Efficient Market Hypothesis, which says that it is almost impossible to beat the market consistently and there are others which disagree with it.

There are a number of known approaches and new research going on to find the magic formula to make you rich. One of the traditional methods is the time series forecasting. Fundamental analysis is another method where numerous performance ratios are analyzed to assess a given stock. On the emerging front, there are neural networks, genetic algorithms, and ensembling techniques.

Another challenging problem in stock price prediction is Black Swan Event, unpredictable events that cause stock market turbulence. These are events that occur from time to time, are unpredictable and often come with little or no warning.

A black swan event is an event that is completely unexpected and cannot be predicted. Unexpected events are generally referred to as black swans when they have significant consequences, though an event with few consequences might also be a black swan event. It may or may not be possible to provide explanations for the occurrence after the fact – but not before. In complex systems, like economies, markets and weather systems, there are often several causes. After such an event, many of the explanations for its occurrence will be overly simplistic.

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https://www.visualcapitalist.com/wp-content/uploads/2020/03/mm3_black_swan_events_shareable.jpg"> #
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New bleeding age state-of-the-art deep learning models stock predictions is overcoming such obstacles e.g. "Transformer and Time Embeddings". An objectives are to apply these novel models to forecast stock price.

Content

Stock price prediction is the task of forecasting the future value of a given stock. Given the historical daily close price for S&P 500 Index, prepare and compare forecasting solutions. S&P 500 or Standard and Poor's 500 index is an index comprising of 500 stocks from different sectors of US economy and is an indicator of US equities. Other such indices are the Dow 30, NIFTY 50, Nikkei 225, etc. For the purpose of understanding, we are utilizing S&P500 index, concepts, and knowledge can be applied to other stocks as well.

Dataset

The historical stock price information is also publicly available. For our current use case, we will utilize the pandas_datareader library to get the required S&P 500 index history using Yahoo Finance databases. We utilize the closing price information from the dataset available though other information such as opening price, adjusted closing price, etc., are also available. We prepare a utility function get_raw_data() to extract required information in a pandas dataframe. The function takes index ticker name as input. For S&P 500 index, the ticker name is ^GSPC. The following snippet uses the utility function to get the required data.(See Simple LSTM Regression)

Features and Terminology: In stock trading, the high and low refer to the maximum and minimum prices in a given time period. Open and close are the prices at which a stock began and ended trading in the same period. Volume is the total amount of trading activity. Adjusted values factor in corporate actions such as dividends, stock splits, and new share issuance.

Starter Kernel(s)

• Simple LSTM Regression

Acknowledgements

Mining and updating of this dateset will depend upon Yahoo Finance .

Inspiration

Sort of variation of sequence modeling and bleeding age e.g. attention can be applied for research and forecasting

Some Readings

• Applications of deep learning in stock market prediction: recent progress
• Stock predictions with state-of-the-art Transformer and Time Embeddings
• https://arxiv.org/pdf/1712.02136.pdf
• https://catanacapital.com/blog/unpredictable-black-swan-event-stock-market/
• https://par.nsf.gov/servlets/purl/10149715
• https://arxiv.org/pdf/1712.02136.pdf

*If you download and find the data useful your upvote is an explicit feedback for future works*

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

US Dialysis Service Market size was valued at USD 26.2 billion in 2021 and is poised to grow from USD 27.03 billion in 2022 to USD 34.5 billion by 2030, growing at a CAGR of 3.11% in the forecast period (2023-2030).

Revenue from the global financial analytics market is expected to reach US$ 12.19 billion in 2024. Latest projections are that the market will increase at a CAGR of 8.8% to ascend to a value of US$ 28.34 billion by 2034.

Report Attribute	Detail
Financial Analytics Market Size (2024E)	US$ 12.19 Billion
Forecasted Market Value (2034F)	US$ 28.34 Billion
Global Market Growth Rate (2024 to 2034)	8.8% CAGR
Canada Market Growth Rate (2024 to 2034)	8.5% CAGR
China Market Value (2034F)	US$ 3.5 Billion
North America Market Share (2024E)	25%
East Asia Market Share (2034F)	26%
Key Companies Profiled	Oracle Corporation; IBM Corporation; SAP SE; Teradata Corporation; TIBCO Software; SAS Institute; Alteryx; Qlik; FICO; Infor Birst; Google LLC; Information Builders; Zoho Corporation; Domo Inc.

Country-wise Analysis

Attribute	United States
Market Value (2024E)	US$ 1.31 Billion
Growth Rate (2024 to 2034)	8.1% CAGR
Projected Value (2034F)	US$ 2.86 Billion

Attribute	China
Market Value (2024E)	US$ 1.35 Billion
Growth Rate (2024 to 2034)	10% CAGR
Projected Value (2034F)	US$ 3.5 Billion

Category-wise Evaluation

Attribute	On-cloud
Segment Value (2024E)	US$ 8.5 Billion
Growth Rate (2024 to 2034)	9.6% CAGR
Projected Value (2034F)	US$ 21.3 Billion

Attribute	BFSI
Segment Value (2024E)	US$ 3 Billion
Growth Rate (2024 to 2034)	10% CAGR
Projected Value (2034F)	US$ 7.9 Billion

U.S. Genetic Testing Market size was valued at USD 5.27 Billion in 2022 and is poised to grow from USD 5.6 billion in 2023 to USD 10.45 billion by 2031, growing at a CAGR of 7.9% in the forecast period (2024-2031).

Snapshot img

Alternative Finance Market Size 2024-2028

The alternative finance market size is estimated to increase by USD 64.3 billion at a CAGR of 7.44% between 2023 and 2028. The key factor driving the market forward is the potential for higher returns for investors. Alternative finance channels offer significantly greater returns compared to traditional investment options like fixed deposits (FDs) or government bonds from conventional financial institutions. Another important contributor to market growth is the rapid expansion in the APAC region and the increasing focus on structured finance. Alternative finance platforms, such as P2P lending, crowdfunding, and invoice trading, are gaining traction in APAC, driven by the presence of numerous small and medium-sized enterprises (SMEs).

What will be the Size of the Alternative Finance Market During the Forecast Period?

To learn more about this alternative finance market report, Download Report Sample

Alternative Finance Market Segmentation

The alternative finance market research report provides comprehensive data (region wise segment analysis), with forecasts and estimates in 'USD Billion' for the period 2024 to 2028, as well as historical data from 2018 to 2022 for the following segments.

Type Outlook 

  P2P lending
  Crowdfunding
  Invoice trading





End-User Outlook

  Individual
  Organization





Region Outlook

  North America

    The U.S.
    Canada





  Europe

    The U.K.
    Germany
    France
    Rest of Europe





  APAC

    China
    India


  South America

    Chile
    Argentina
    Brazil





  Middle East & Africa

    Saudi Arabia
    South Africa
    Rest of the Middle East & Africa

By Type

The alternative financing market share growth in the segment of P2P lending will be significant during the forecast period. The P2P consumer lending sub-segment holds a major share of the P2P lending segment due to the growth in the number of online consumer lending platforms and the increasing use of technology in financial transactions. Some popular P2P lending platforms include LendingClub, Zopa, Bondora Capital, Prosper Marketplace, and Upstart Network. However, P2P lending is associated with a high risk of defaults as the loans are unsecured. Therefore, large investors usually maintain a spread portfolio of their investments. P2P lending is also associated with challenges such as platform failures, the risk of fraud, hacking, and data theft. These factors are expected to augment the demand of the P2P lending segment hence driving the growth of the market in focus during the forecast period.

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The P2P lending segment was valued at USD 123.70 billion in 2018. In this segment, P2P lending is similar to credit obtained from financial institutions. However, the funds are raised from one or more independent investors. P2P borrowers must make weekly or monthly repayments of the principal amount with interest. P2P lending is usually carried out through online platforms. Investors directly select businesses to fund, or the lending platforms provide the terms of credit. Some variations in the model allow investors to bid on loan amounts and interest rates through an online auction. P2P lending is popular among individual borrowers and SMEs, as small to medium-scale loans can be obtained easily. Several individuals opt for P2P loans for debt consolidation, which allows them to pay debts accrued from credit cards or loans from financial institutions.

By Region

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North America is estimated to contribute 70% to the global alternative financing market during the forecast period. Technavio's analysts have elaborately explained the regional market growth and trends that shape the market during the forecast period. The growth of P2P lending and crowdfunding has increased significantly in North America. The increasing number of students, growing awareness about clearing personal debt, rising Internet penetration, technological advances, the rise of online trading platforms and finance platforms, and the presence of prominent companies are the major factors driving the market in North America. The number of SMEs has grown significantly in North America. Therefore, a growing number of SMEs in this region are boosting the growth in North America.

Alternative Finance Market Dynamics

The market is reshaping the landscape traditionally dominated by conventional big banks and regulated banks. Instead of relying solely on traditional finance systems, entrepreneurs and investors are increasingly turning to alternative lenders and innovative financial services solutions. Online lenders offer streamlined access to capital, while reward-based crowdfunding and equity-based crowdfunding present opp

U.S. Bidet Market is expected to grow at a CAGR of 5.2% during the forecast period 2024-2031

U.S. Frozen Pizza Market size was valued at USD 6.62 billion in 2022 and is poised to grow from USD 7.08 billion in 2023 to USD 12.07 billion by 2031, growing at a CAGR of 6.90% during the forecast period (2024-2031).

US Hair Brush Market size was valued at USD 1.25 billion in 2021 and is poised to grow from USD 1.25 billion in 2022 to USD 1.28 billion by 2030, growing at a CAGR of 0.25% in the forecast period (2023-2030).

U.S. Telehealth Market size was valued at USD 29.59 billion in 2022 and is poised to grow from USD 36.37 billion in 2023 to USD 189.28 billion by 2031, growing at a CAGR of 22.9% in the forecast period (2024-2031).