13 datasets found
  1. INR to dollar currency monthly (01-12-03:30-04-24)

    • kaggle.com
    Updated Apr 30, 2024
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    Siddharth.Jain468 (2024). INR to dollar currency monthly (01-12-03:30-04-24) [Dataset]. https://www.kaggle.com/datasets/siddharthjain468/inr-to-dollar-currency-monthly-01-12-0330-04-24
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Apr 30, 2024
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Siddharth.Jain468
    License

    ODC Public Domain Dedication and Licence (PDDL) v1.0http://www.opendatacommons.org/licenses/pddl/1.0/
    License information was derived automatically

    Description

    The dataset of INR to Dollar exchange rates from 2003 to 2024 downloaded from Yahoo Finance likely contains historical exchange rate data for the Indian Rupee (INR) against the US Dollar (USD) over the specified time period. Here's a general description of what you might find in such a dataset:

    1. Date: Each entry in the dataset likely includes a date or timestamp indicating when the exchange rate was recorded.

    2. Exchange Rate: The dataset should include the exchange rate value, representing the number of Indian Rupees equivalent to one US Dollar on the corresponding date.

    3. Time Period: The dataset should cover exchange rate data for each trading day or a specified frequency (e.g., weekly, monthly) from 2003 to 2024.

    4. Additional Information: Depending on the source and format of the dataset, it may include additional information such as opening, high, low, and closing exchange rates for each day, as well as volume and adjusted closing prices.

    5. Currency Pair: The dataset focuses specifically on the exchange rate between the Indian Rupee (INR) and the US Dollar (USD), allowing users to analyze trends and fluctuations in the value of the Indian Rupee relative to the US Dollar over time.

    6. The dataset of INR to Dollar exchange rates from 2003 to 2024 downloaded from Yahoo Finance likely contains historical exchange rate data for the Indian Rupee (INR) against the US Dollar (USD) over the specified time period. Here's a general description of what you might find in such a dataset:

    7. Date: Each entry in the dataset likely includes a date or timestamp indicating when the exchange rate was recorded.

    8. Exchange Rate: The dataset should include the exchange rate value, representing the number of Indian Rupees equivalent to one US Dollar on the corresponding date.

    9. Time Period: The dataset should cover exchange rate data for each trading day or a specified frequency (e.g., weekly, monthly) from 2003 to 2024.

    10. Additional Information: Depending on the source and format of the dataset, it may include additional information such as opening, high, low, and closing exchange rates for each day, as well as volume and adjusted closing prices.

    11. Currency Pair: The dataset focuses specifically on the exchange rate between the Indian Rupee (INR) and the US Dollar (USD), allowing users to analyze trends and fluctuations in the value of the Indian Rupee relative to the US Dollar over time.

    12. Data Quality: It's important to consider the reliability and accuracy of the data. Ensure that the dataset is sourced from a reputable financial data provider like Yahoo Finance and that any missing or erroneous data points are appropriately handled.

    Overall, this dataset can be used for various analytical purposes, including trend analysis, forecasting, and risk management in the context of currency exchange markets and international finance.: It's important to consider the reliability and accuracy of the data. Ensure that the dataset is sourced from a reputable financial data provider like Yahoo Finance and that any missing or erroneous data points are appropriately handled.

    Overall, this dataset can be used for various analytical purposes, including trend analysis, forecasting, and risk management in the context of currency exchange markets and international finance.

  2. X/Twitter valuation 2013-2025

    • statista.com
    Updated Apr 4, 2025
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    Statista (2025). X/Twitter valuation 2013-2025 [Dataset]. https://www.statista.com/statistics/1608082/valuation-x-formerly-twitter/
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    Dataset updated
    Apr 4, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    Worldwide
    Description

    X, formerly known as Twitter, has experienced significant valuation fluctuations since Elon Musk's acquisition in October 2022 for 44 billion U.S. dollars. Despite a sharp decline to 5.3 billion U.S. dollars in November 2023, recent estimates suggest a potential rebound. The Financial Times projects X's value could returned to 44 billion U.S. dollars in March 2025, while Bloomberg offers a more conservative estimate of 32 billion U.S. dollars. At that time the sale of X to xAI was announced with a value of 33 billion U.S. dollars. Platform growth and user engagement Despite valuation challenges, X continues to expand its user base globally. Forecasts indicate the platform could reach 503.42 million users by 2028, representing a 17.32 percent increase over four years. However, user engagement metrics show a decline, with the average number of posts per account dropping from 5.73 to 3.55 times per week between 2023 and 2024. Similarly, average likes per post decreased from 37.82 to 31.46 during the same period.

    Geographic reach and content focus X maintains a strong presence in key markets, with the United States leading at 106.23 million users as of April 2024, followed by Japan and India. The platform continues to be a significant source for news and political discourse, with 53 percent of users reporting attention to mainstream news outlets and 43 percent following politicians and political activists. This underscores X's enduring role in shaping public opinion and facilitating global conversations, despite recent changes in ownership and valuation.

  3. f

    Data from: APPLYING SINGULAR SPECTRUM ANALYSIS AND ARIMA-GARCH FOR...

    • scielo.figshare.com
    jpeg
    Updated Jun 1, 2023
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    RAFAEL J. ABREU; RAFAEL M. SOUZA; JOICE G. OLIVEIRA (2023). APPLYING SINGULAR SPECTRUM ANALYSIS AND ARIMA-GARCH FOR FORECASTING EUR/USD EXCHANGE RATE [Dataset]. http://doi.org/10.6084/m9.figshare.9598889.v1
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    jpegAvailable download formats
    Dataset updated
    Jun 1, 2023
    Dataset provided by
    SciELO journals
    Authors
    RAFAEL J. ABREU; RAFAEL M. SOUZA; JOICE G. OLIVEIRA
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    ABSTRACT Purpose: The objective of this article is to model a minute series of exchange rates for the EUR/USD pair using the singular spectrum analysis (SSA) and ARIMA-GARCH methods and evaluate which one offers better forecasts for a five-minute horizon. Originality/value: Despite being a successful technique in other branches of science, the application of SSA in finance is quite new. Furthermore, exchange rate modeling is a complex problem, comprising statistical concepts and properties. However, despite the complexity, the analysis of this series is extremely important for several agents playing, directly or indirectly, a role in the economy and the financial market. Design/methodology/approach: Time series models were estimated using the ARIMA-GARCH and SSA techniques, taking into account three samples of the ask exchange rate (closing): uptrend, downtrend, and no well-defined trend. Findings: The forecasts carried out by the SSA were the ones closest to the original observations for the three cases. Regarding the quality measurements, SSA obtained the best results for both uptrend and downtrend samples; for the sample with no well-defined trend, the findings indicated that the ARIMA-GARCH technique attained better results. However, it was concluded that the SSA forecasts, regarding exchange rates during the studied period, are more appropriate than the ones obtained by the ARIMA-GARCH model, regardless of the market movement.

  4. w

    Gold Prices in London 1950-2008 (Monthly)

    • data.wu.ac.at
    csv +2
    Updated Jan 25, 2014
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    Core Datasets (2014). Gold Prices in London 1950-2008 (Monthly) [Dataset]. https://data.wu.ac.at/odso/datahub_io/YTFmZjNhYmEtMWQ3MC00ODRjLWFjZjktZWI2ODNjYWRhMTgx
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    json / csv / html / rss(8025.0), csv(206636.0), txt(14502.0)Available download formats
    Dataset updated
    Jan 25, 2014
    Dataset provided by
    Core Datasets
    License

    ODC Public Domain Dedication and Licence (PDDL) v1.0http://www.opendatacommons.org/licenses/pddl/1.0/
    License information was derived automatically

    Area covered
    London
    Description

    Monthly gold prices (USD) in London from Bundesbank.

    General: 1 ounce of fine gold = 31.1034768g. Method of calculation:

    • Since 1 April 1968, calculated from the daily morning fixing;
    • From January 1950 to 21 March 1954, calculated using the Bank of England's gold purchasing price (1 ounce of fine = pound 12.40) in connection with the average exchange rate for the pound in New York (up to the end of 1952; source: Federal Reserve Bulletin) and, from January 1953, midpoint exchange rates for the US dollar in London (source: Financial Times (FT)).
    • From 22 March 1954 to December 1959, calculated using the fixing price for gold bars of approx. 12 1/2 kg and 995/1000 fineness and over (so-called standard bars) according to data from Metallgesellschaft AG, Frankfurt am Main, in connection with the average midpoint exchange rates for the US dollar in London (source: FT).
    • From January 1960 to 14 March 1968, average fixing price for standard bars as specified in the Bank of England's Quarterly Bulletin.
    • On 15 March 1968, fixing price suspended. Gold market split into an official (reserved for central banks) and a free market as a result of the Washington Communique of 17 March 1968. Gold trading suspended from 18 to 29 March 1968.
    • Sources for daily prices: April 1968 - March 1974: FT; April 1974 - December 1980: Samuel Montagu & Co. Ltd.; January 1981 - December 2005: FT; January 2006 - present: Reuters.
    • Comment on 1968-03: Average from 1 to 14 March 1968.

    License: PDDL (Source indicates no restrictions on data).

  5. Quarterly USD exchange rate against the 10 most traded currencies worldwide...

    • statista.com
    Updated Jun 30, 2025
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    Statista (2025). Quarterly USD exchange rate against the 10 most traded currencies worldwide 2001-2025 [Dataset]. https://www.statista.com/statistics/655224/conversion-rate-of-major-currencies-to-the-us-dollar/
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    Dataset updated
    Jun 30, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    Hong Kong, Worldwide, Canada, China, Europe, Switzerland, Japan, South Korea, Australia, United Kingdom
    Description

    A graphic that displays the dollar performance against other currencies reveals that economic developments had mixed results on currency exchanges. The third quarter of 2023 marked a period of disinflation in the euro area, while China's projected growth was projected to go up. The United States economy was said to have a relatively strong performance in Q3 2023, although growing capital market interest rate and the resumption of student loan repayments might dampen this growth at the end of 2023. A relatively weak Japanese yen Q3 2023 saw pressure from investors towards Japanese authorities on how they would respond to the situation surrounding the Japanese yen. The USD/JPY rate was close to ***, whereas analysts suspected it should be around ** given the country's purchase power parity. The main reason for this disparity is said to be the differences in central bank interest rates between the United States, the euro area, and Japan. Any future aggressive changes from, especially the U.S. Fed might lower those differences. Financial markets responded somewhat disappoint when Japan did not announce major plans to tackle the situation. Potential rent decreases in 2024 Central bank rates peak in 2023, although it is expected that some of these will decline in early 2024. That said, analysts expect overall policies will remain restrictive. For example, the Bank of England's interest rate remained unchanged at **** percent in Q3 2023. It is believed the United Kingdom's central bank will ease its interest rate in 2024 but less than either the U.S. Fed or the European Central Bank. This should be a positive development for the pound compared to either the euro or the dollar.

  6. Currency exchange rate Chinese yuan to U.S. dollar by month 2014-2025

    • statista.com
    Updated Jun 6, 2025
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    Statista (2025). Currency exchange rate Chinese yuan to U.S. dollar by month 2014-2025 [Dataset]. https://www.statista.com/statistics/456227/monthly-exchange-rate-chinese-yuan-to-us-dollar/
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    Dataset updated
    Jun 6, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Jun 2014 - Apr 2025
    Area covered
    China, United States
    Description

    In April 2025, the exchange rate of yuan to U.S. dollar was ****. In the past decades, the yuan has undergone a slow liberalization, being increasingly exposed to the international money market. FOREX history of the Renminbi After the Communist Party took control over China, it introduced a unified currency which has since then undergone many changes. During the planned economy, the yuan had a fixed exchange rate. At the time, the currency’s exchange rate was deliberately set high to support the industrial development, which relied on imports. After the country committed to opening its economy, the Renminbi was gradually exposed to the supply and demand of the global FOREX markets. Until 2005, the yuan remained pegged to the U.S. dollar. Currency manipulator, or not? As China manifested its role in the global economy, the country was repeatedly accused of manipulating the value of its currency. Especially, voices from the United States claimed that Beijing would intentionally keep the value of the yuan low. A cheap Renminbi would make products from China more attractive for foreign buyers which in turn would support the country’s export-driven economy. However, currency manipulation is difficult to make out and even harder to prove, which is why no significant actions have been taken.

  7. Size of Federal Reserve's balance sheet 2007-2025

    • statista.com
    Updated Jul 2, 2025
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    Statista (2025). Size of Federal Reserve's balance sheet 2007-2025 [Dataset]. https://www.statista.com/statistics/1121448/fed-balance-sheet-timeline/
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    Dataset updated
    Jul 2, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Aug 1, 2007 - Jun 25, 2025
    Area covered
    United States
    Description

    The Federal Reserve's balance sheet has undergone significant changes since 2007, reflecting its response to major economic crises. From a modest *** trillion U.S. dollars at the end of 2007, it ballooned to approximately **** trillion U.S. dollars by June 2025. This dramatic expansion, particularly during the 2008 financial crisis and the COVID-19 pandemic - both of which resulted in negative annual GDP growth in the U.S. - showcases the Fed's crucial role in stabilizing the economy through expansionary monetary policies. Impact on inflation and interest rates The Fed's expansionary measures, while aimed at stimulating economic growth, have had notable effects on inflation and interest rates. Following the quantitative easing in 2020, inflation in the United States reached ***** percent in 2022, the highest since 1991. However, by *************, inflation had declined to *** percent. Concurrently, the Federal Reserve implemented a series of interest rate hikes, with the rate peaking at **** percent in ***********, before the first rate cut since ************** occurred in **************. Financial implications for the Federal Reserve The expansion of the Fed's balance sheet and subsequent interest rate hikes have had significant financial implications. In 2023, the Fed reported a negative net income of ***** billion U.S. dollars, a stark contrast to the ***** billion U.S. dollars profit in 2022. This unprecedented shift was primarily due to rapidly rising interest rates, which caused the Fed's interest expenses to soar to over *** billion U.S. dollars in 2023. Despite this, the Fed's net interest income on securities acquired through open market operations reached a record high of ****** billion U.S. dollars in the same year.

  8. t

    Ashtead financial dataset - Vdataset - LDM

    • service.tib.eu
    Updated May 16, 2025
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    (2025). Ashtead financial dataset - Vdataset - LDM [Dataset]. https://service.tib.eu/ldmservice/dataset/goe-doi-10-25625-xbzczz
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    Dataset updated
    May 16, 2025
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    Description Ashtead (“Sunbelt”) is the second largest equipment rental company in the States, and cyclical fears plus a few minor operational missteps have created an attractive entry point into a secular winner. I also believe Sunbelt is under-earning to a larger degree than peers because of the organic nature of recent growth. Business Overview I'll keep this short because this and other equipment rental companies have been covered on VIC. Sunbelt buys and maintains a fleet of equipment including aerial work platforms (30% of fleet), forklifts (20%), earthmoving (14%), power and HVAC (11%) and more. Equipment is depreciated over 10 years (chosen to make equipment disposals breakeven at the low point of a cycle) and Sunbelt typically keeps it around for 7 years, getting more than 50% of original cost ("OEC" or original equipment cost) in rental revenue per year. After 7 years, equipment is disposed of at 40 cents on the dollar. Non-resi construction end markets are less than half of the business, and the rest is industrial, MRO and more. Renting equipment lets you get the exact right piece of equipment for a job. As an example, you used to find backhoes on jobsites much more, because a backhoe is the swiss army knife of earthmoving. That user might now prefer to rent either an excavator or a bucket loader, each of which peform half the function of the backhoe but in a more efficient manner. Rental also conserves capital, reduces the need for equipment yards/storage, solves logistics/ eliminates the need for vehicles that can move equipment, and solves the difficulty of maintaining owned equipment. Secular Trends The secular tailwinds come from both increased rental penetration as well as market share gains by the largest players. The use of rented equipment accounts for about 55% of the equipment market today and I expect it to hit at least 65% over time. Penetration is up from the low 40% range pre-GFC and single-digits in the 1990s. The top two players URI and Sunbelt have 15% and 11% share, respectively, and players smaller than the top 100 have 44% of the market. The top 10 players have grown market share from 20% in 2010 to about 45% today. The largest rental company businesses have improved over time. Scale gives purchasing economies with OEM suppliers, efficiencies in logistics and maintenance, and higher equipment utilization. URI and Sunbelt purchase equipment 15-20% cheaper than mom & pop operators. Moving heavy equipment to and from job sites requires a large fleet of dedicated vehicles. Equipment maintenance benefits from having expertise by equipment type, mechanic sharing and better utilization of parts and spares. In a typical branch, 6 out of 20 total employees might be mechanics. Utilization is measured both by time/physical utilization, which is just the amount of time the equipment is on rent, or by dollar utilization, which is measured by the rental revenue divided by the cost of the equipment (basically, asset turns). Dollar utilization is perhaps the most important metric, because it combines the time on rent and the rental rate. Dollar utilization is higher at the scale players for a large variety of reasons. More locations give larger players density and a higher likelihood that a given piece of equipment is needed by someone in that geography. It also lowers transportation costs and time and most importantly allows locations to share equipment. A better repair function means machines are on rent for longer and means that there is more equipment available to rent. A wider variety of equipment on rent also leads to higher rates. Sunbelt frequently mentions that they are not the lowest price, but they win business because of breadth, availability and service. The factors I’ve outlined above have led to stable dollar utilization, rising margins and thus rising returns on capital over time: Specialty rental equipment has become a larger part of Sunbelt’s mix over time. Specialty is a catch-all for equipment that can have more of a service component or more of a temporary, emergency, or one-off use case. When looking at historical results, note that specialty carries lower physical utilization but higher margins. Specialty equipment also depreciates more slowly and is generally less cyclical than general tool (i.e. non-specialty). Cyclical Factors Equipment rental is a cyclical business. Sunbelt will tell you that because equipment rental is now an essential part of customer’s businesses, rather than used as a top-up, future cycles will be more muted than the past. I mostly believe this for a few reasons. First, the large players are larger and more sophisticated. CEO Brendan Horgan likes to say that in the GFC they almost blindly lowered prices by 20% across the board without any pricing tools or great reason to do so. Second, the top 10 players are less leveraged. In the GFC, you not only had more leveraged companies, but some companies actually had covenants tied to...

  9. F

    Money Market Funds; Total Financial Assets, Level

    • fred.stlouisfed.org
    json
    Updated Jun 12, 2025
    + more versions
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    (2025). Money Market Funds; Total Financial Assets, Level [Dataset]. https://fred.stlouisfed.org/series/MMMFFAQ027S
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    jsonAvailable download formats
    Dataset updated
    Jun 12, 2025
    License

    https://fred.stlouisfed.org/legal/#copyright-public-domainhttps://fred.stlouisfed.org/legal/#copyright-public-domain

    Description

    Graph and download economic data for Money Market Funds; Total Financial Assets, Level (MMMFFAQ027S) from Q4 1945 to Q1 2025 about MMMF, IMA, financial, assets, and USA.

  10. F

    Federal Debt: Total Public Debt

    • fred.stlouisfed.org
    json
    Updated Jun 3, 2025
    + more versions
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    (2025). Federal Debt: Total Public Debt [Dataset]. https://fred.stlouisfed.org/series/GFDEBTN
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    jsonAvailable download formats
    Dataset updated
    Jun 3, 2025
    License

    https://fred.stlouisfed.org/legal/#copyright-public-domainhttps://fred.stlouisfed.org/legal/#copyright-public-domain

    Description

    Graph and download economic data for Federal Debt: Total Public Debt (GFDEBTN) from Q1 1966 to Q1 2025 about public, debt, federal, government, and USA.

  11. Largest bankruptcies in the U.S. as of January 2025, by assets

    • statista.com
    • ai-chatbox.pro
    Updated Mar 10, 2025
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    Statista (2025). Largest bankruptcies in the U.S. as of January 2025, by assets [Dataset]. https://www.statista.com/statistics/1096794/largest-bankruptcies-usa-by-assets/
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    Dataset updated
    Mar 10, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    United States
    Description

    As of January 2025, the largest all-time bankruptcy in the United States remained Lehman Brothers. The New York-based investment bank had assets worth 691 billion U.S. dollars when it filed for bankruptcy on September 15, 2008. This event was one of the major points in the timeline of the Great Recession, as it was the first time a bank of its size had failed and had a domino effect on the global banking sector, as well as wiping almost five percent of the S&P 500 in one day. Bank failures in the U.S. In March 2023, for the first time since 2021, two banks collapsed in the United States. Both bank failures made the list of largest bankruptcies in terms of total assets lost: The failure of Silicon Valley Bank amounted to roughly 209 billion U.S. dollars worth of assets lost, while Signature Bank had approximately 110.4 billion U.S. dollars when it collapsed. These failures mark the second- and the third-largest bank failures in the U.S. since 2001. Unprofitable banks in the U.S. The collapse of Silicon Valley Bank and Signature Bank painted an alarming picture of the U.S. banking industry. In reality, however, the state of the industry was much better in 2022 than in earlier periods of economic downturns. The share of unprofitable banks, for instance, was 3.4 percent in 2022, which was an increase compared to 2021, but remained well below the share of unprofitable banks in 2020, let alone during the global financial crisis in 2008. The share of unprofitable banks in the U.S. peaked in 2009, when almost 30 percent of all FDIC-insured commercial banks and savings institutions were unprofitable.

  12. Big Mac index worldwide 2025

    • statista.com
    • tiktok-play.menuridamusic.com
    • +1more
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    Statista, Big Mac index worldwide 2025 [Dataset]. https://www.statista.com/statistics/274326/big-mac-index-global-prices-for-a-big-mac/
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    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Jan 2025
    Area covered
    Worldwide
    Description

    At **** U.S. dollars, Switzerland has the most expensive Big Macs in the world, according to the January 2025 Big Mac index. Concurrently, the cost of a Big Mac was **** dollars in the U.S., and **** U.S. dollars in the Euro area. What is the Big Mac index? The Big Mac index, published by The Economist, is a novel way of measuring whether the market exchange rates for different countries’ currencies are overvalued or undervalued. It does this by measuring each currency against a common standard – the Big Mac hamburger sold by McDonald’s restaurants all over the world. Twice a year the Economist converts the average national price of a Big Mac into U.S. dollars using the exchange rate at that point in time. As a Big Mac is a completely standardized product across the world, the argument goes that it should have the same relative cost in every country. Differences in the cost of a Big Mac expressed as U.S. dollars therefore reflect differences in the purchasing power of each currency. Is the Big Mac index a good measure of purchasing power parity? Purchasing power parity (PPP) is the idea that items should cost the same in different countries, based on the exchange rate at that time. This relationship does not hold in practice. Factors like tax rates, wage regulations, whether components need to be imported, and the level of market competition all contribute to price variations between countries. The Big Mac index does measure this basic point – that one U.S. dollar can buy more in some countries than others. There are more accurate ways to measure differences in PPP though, which convert a larger range of products into their dollar price. Adjusting for PPP can have a massive effect on how we understand a country’s economy. The country with the largest GDP adjusted for PPP is China, but when looking at the unadjusted GDP of different countries, the U.S. has the largest economy.

  13. Largest companies on FTSE 100 index 2024, by market cap

    • statista.com
    Updated Jun 26, 2025
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    Statista (2025). Largest companies on FTSE 100 index 2024, by market cap [Dataset]. https://www.statista.com/statistics/1405426/largest-companies-on-ftse-100-index/
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    Dataset updated
    Jun 26, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Jun 12, 2024
    Area covered
    United Kingdom
    Description

    Astrazeneca was the leading pharmaceutical company in the United Kingdom as of March 7, 2024, with a market capitalization amounting to approximately ***** billion U.S. dollars. GlaxoSmithKline followed as the second largest pharma company in the country, with market capitalization of nearly **** billion U.S. dollars. Examining the development of the FTSE 100 Index, which was launched in January 1984 with a base level of 1,000, increased by more than sevenfold to date. What is the FTSE 100 index? The Financial Times Stock Exchange 100 Index, commonly known as the "Footsie", is the most widely recognized stock market index in the United Kingdom. It is made up of the 100 largest blue-chip companies on the London Stock Exchange. Companies from various sectors, such as healthcare, consumer goods, and energy, are included in the index, as are leading banks of the United Kingdom, such as HSBC, Lloyds Banking Group, and Barclays. Moreover, it can be seen as a reflection of the investment climate in the United Kingdom. What is not included in the FTSE 100 Index? Most notably, the FTSE 100 Index, like most indices, is not adjusted for inflation. While inflation in the United Kingdom has gone down dramatically since 2023, it might be useful to adjust the historic figures on the index when comparing historic data to current levels. This is especially important when the index seems to have increased by a few percentage points because inflation may have increased at a faster rate than stock prices.

  14. Not seeing a result you expected?
    Learn how you can add new datasets to our index.

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Siddharth.Jain468 (2024). INR to dollar currency monthly (01-12-03:30-04-24) [Dataset]. https://www.kaggle.com/datasets/siddharthjain468/inr-to-dollar-currency-monthly-01-12-0330-04-24
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INR to dollar currency monthly (01-12-03:30-04-24)

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CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
Dataset updated
Apr 30, 2024
Dataset provided by
Kagglehttp://kaggle.com/
Authors
Siddharth.Jain468
License

ODC Public Domain Dedication and Licence (PDDL) v1.0http://www.opendatacommons.org/licenses/pddl/1.0/
License information was derived automatically

Description

The dataset of INR to Dollar exchange rates from 2003 to 2024 downloaded from Yahoo Finance likely contains historical exchange rate data for the Indian Rupee (INR) against the US Dollar (USD) over the specified time period. Here's a general description of what you might find in such a dataset:

  1. Date: Each entry in the dataset likely includes a date or timestamp indicating when the exchange rate was recorded.

  2. Exchange Rate: The dataset should include the exchange rate value, representing the number of Indian Rupees equivalent to one US Dollar on the corresponding date.

  3. Time Period: The dataset should cover exchange rate data for each trading day or a specified frequency (e.g., weekly, monthly) from 2003 to 2024.

  4. Additional Information: Depending on the source and format of the dataset, it may include additional information such as opening, high, low, and closing exchange rates for each day, as well as volume and adjusted closing prices.

  5. Currency Pair: The dataset focuses specifically on the exchange rate between the Indian Rupee (INR) and the US Dollar (USD), allowing users to analyze trends and fluctuations in the value of the Indian Rupee relative to the US Dollar over time.

  6. The dataset of INR to Dollar exchange rates from 2003 to 2024 downloaded from Yahoo Finance likely contains historical exchange rate data for the Indian Rupee (INR) against the US Dollar (USD) over the specified time period. Here's a general description of what you might find in such a dataset:

  7. Date: Each entry in the dataset likely includes a date or timestamp indicating when the exchange rate was recorded.

  8. Exchange Rate: The dataset should include the exchange rate value, representing the number of Indian Rupees equivalent to one US Dollar on the corresponding date.

  9. Time Period: The dataset should cover exchange rate data for each trading day or a specified frequency (e.g., weekly, monthly) from 2003 to 2024.

  10. Additional Information: Depending on the source and format of the dataset, it may include additional information such as opening, high, low, and closing exchange rates for each day, as well as volume and adjusted closing prices.

  11. Currency Pair: The dataset focuses specifically on the exchange rate between the Indian Rupee (INR) and the US Dollar (USD), allowing users to analyze trends and fluctuations in the value of the Indian Rupee relative to the US Dollar over time.

  12. Data Quality: It's important to consider the reliability and accuracy of the data. Ensure that the dataset is sourced from a reputable financial data provider like Yahoo Finance and that any missing or erroneous data points are appropriately handled.

Overall, this dataset can be used for various analytical purposes, including trend analysis, forecasting, and risk management in the context of currency exchange markets and international finance.: It's important to consider the reliability and accuracy of the data. Ensure that the dataset is sourced from a reputable financial data provider like Yahoo Finance and that any missing or erroneous data points are appropriately handled.

Overall, this dataset can be used for various analytical purposes, including trend analysis, forecasting, and risk management in the context of currency exchange markets and international finance.

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