In December 2022 and 2023, the median income of discount/dollar store visitors in the United States increased to over ** thousand U.S. dollars. In other words, wealthier consumers visit discount and dollar stores during the peak holiday season.

Prices for DXY Dollar Index including live quotes, historical charts and news. DXY Dollar Index was last updated by Trading Economics this September 15 of 2025.

The dollar and variety store industry has been on a steady upward trajectory, with revenue expanding at a CAGR of 0.8% over the past five years. In 2025, industry revenue will reach $119.2 billion, a notable 2.0% gain from the previous year. This growth has been fueled by strategic product diversification and an appealing value proposition that attracts a broader consumer base. Notably, the introduction of higher-priced items by chains like Dollar Tree has driven up the average ticket size, boosting sales per store. Additionally, these stores have effectively managed inflationary pressures by maintaining competitive pricing while expanding their product offerings, holding profit steady over the period. Over the past five years, the industry has significantly shifted its image and customer base. By expanding product selections to include top-brand and private-label goods, dollar stores have attracted higher-income shoppers seeking value without compromising quality. Strategic store locations in urban areas have made these businesses more accessible and appealing to affluent consumers. Though there was a 1.1% decline in foot traffic, the average transaction amount rose by 2.3%, indicating a shift toward larger purchases per visit. Enhanced e-commerce capabilities through partnerships with platforms like Instacart and DoorDash have bolstered revenue by providing convenient shopping options, allowing dollar stores to compete more effectively with retail giants like Walmart and Amazon. Looking ahead, the industry is poised for continued growth at a CAGR of 1.2% over the next five years. Revenue will climb to $126.4 billion through 2030, driven by private label expansion and strategic forays into underserved areas. By increasing private label penetration, major chains like Dollar Tree and Dollar General are taking advantage of the products’ higher returns. Meanwhile, targeting rural and low-income regions offers new revenue streams with less competition. However, the industry's growth won't be without challenges. Increasing competition from warehouse clubs and supermarkets and potential tariff-related cost pressures will challenge profit. Yet, dollar stores can sustain their momentum by innovating store concepts and expanding product lines to continue capturing a diverse consumer base.

Graph and download economic data for Real Emerging Market Economies Dollar Index (RTWEXEMEGS) from Jan 2006 to Aug 2025 about trade-weighted, emerging markets, goods, services, real, indexes, and USA.

Key information about United States Market Capitalization

• United States Market Capitalization accounted for 40,297.980 USD bn in Dec 2022, compared with a percentage of 48,548.538 USD bn in the previous year
• US Market Capitalization is updated yearly, available from Dec 1975 to Dec 2022
• The data reached an all-time high of 48,548.538 USD bn in Dec 2021 and a record low of 703.800 USD bn in Dec 1975

The DXY exchange rate fell to 97.3000 on September 15, 2025, down 0.26% from the previous session. Over the past month, the United States Dollar has weakened 0.88%, and is down by 3.39% over the last 12 months. United States Dollar - values, historical data, forecasts and news - updated on September of 2025.

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.

Dollars and Sense: The Correlation Between US Total Reserves and the Dollar Index

Financial data:

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

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

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

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

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

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

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

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

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

• Data cleaning and preprocessing are essential before model training

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

Average daily turnover of the U.S. dollar on global foreign exchange (forex) markets increased more than **** fold from 2001 to 2022. In total - covering both spot transactions and forex derivatives like swaps, forwards and options - the average daily turnover of the U.S. dollar as of ********** amounted to *** trillion U.S. dollars. The forex - or foreign exchange market - turnover per day is a figure that is not often measured, only once every three years. No figures are available for 2020, for instance.

Dollar General reported $22.96B in Market Capitalization this September of 2025, considering the latest stock price and the number of outstanding shares.Data for Dollar General | DG - Market Capitalization including historical, tables and charts were last updated by Trading Economics this last September in 2025.

This dataset is about books. It has 31 rows and is filtered where the book subjects is Euro-dollar market. It features 9 columns including author, publication date, language, and book publisher.

In 2025 there were almost over 39,000 dollar stores in the United States. This was an increase of approximately 570 in comparison to the previous year, and more than 4,700 since 2021. Profitable years for dollar stores While many industries struggled in 2020 and 2021 due to the impact of the coronavirus (COVID-19) pandemic and difficult financial circumstances, dollar stores in the United States experienced significant growth. Discount department stores experienced falling sales as an industry from 2007 to 2020, but saw a massive rebound since 2021 when sales reached the highest levels since 2016. Dollar stores have fared better; revenues are estimated to have steadily grown since 2016. Dollar General sales The sales of Dollar General, one of the market leaders, reflect this pattern. In the fiscal year 2024, Dollar General's net sales amounted to approximately 40.6 billion U.S. dollars. The company’s sales have consistently grown since 2007, seeing the most significant year-on-year growth in 2020. This growth is understandable, considering that in 2023, Dollar General was the retailer with the most U.S. stores, followed by rival Dollar Tree.

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.

Dollar Index (Live)

Financial data:

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

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

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

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

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

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

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

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

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

• Data cleaning and preprocessing are essential before model training

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

Graph and download economic data for Nominal Emerging Market Economies U.S. Dollar Index (DTWEXEMEGS) from 2006-01-02 to 2025-07-25 about trade-weighted, emerging markets, exchange rate, currency, goods, services, rate, indexes, and USA.

USD index is expected to strengthen in the near term due to persistent safe-haven demand amid global economic uncertainties. The risk associated with this prediction is the potential for a correction if risk appetite improves or the Federal Reserve signals a dovish pivot.

The Dollar and Variety Store industry in Canada has been resilient and adaptable in the face of economic shifts and inflationary pressures. Industry revenue has risen over the past five years at a CAGR of 2.8%. With an anticipated growth of 1.0% this year, it will reach $8.3 billion in 2025. These stores have evolved beyond their basic, impulse-buy roots, gaining traction with middle-income consumers by diversifying product lines and enhancing customer experiences. With over 85% of Canadians living within 10 kilometres of a Dollarama, according to the company’s estimates, their accessibility and broadened offerings have made them essential in the retail landscape. Despite tight and fluctuating profit driven by volatile input costs, the temperance of inflation promises more predictability, setting the stage for future growth. Over the past five years, these stores have skillfully leveraged economic volatility to their advantage. The spike in 2020 revenue by 11.8% highlights their ability to capture budget-conscious shoppers during the pandemic downturn. They’ve navigated through shifts in per capita disposable income and spikes in the consumer price index by emphasizing their affordable essentials. Dollar stores’ adaptability and emphasis on in-store innovations, like strategic product placements and optimized queue lines, have kept them afloat against external competition. By revamping aesthetics and introducing national brands, dollar stores have shattered negative stereotypes and set new standards for chic, budget-friendly shopping. The next five years hold promising potential, tempered with challenges. Revenue is projected to climb at a CAGR of 1.1%, hitting $8.8 billion in 2030. Major chains like Dollarama and Dollar Tree are expected to drive this growth through aggressive expansion and tech investments. These strategies will ensure they remain competitive amid increased pressure from major retailers like Amazon and Temu. As consumers continue tightening their wallets with an expected 0.7% annual decline in disposable income, dollar stores are poised to capture this bargain-hunting demographic. By embracing advancements in AI and inventory management, they aim to enhance shopping experiences and extend their market reach. With anticipated interest rate cuts potentially lowering operational costs, these stores are set to consolidate their position in Canada’s evolving retail scene.

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.

US Dollar Index: Bullish Break or Bearish Trap?

Financial data:

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

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

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

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

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

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

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

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

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

• Data cleaning and preprocessing are essential before model training

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

United States USD Trade Weighted Index: Nominal: Emerging Market Economies data was reported at 121.368 2006=100 in Jan 2019. This records a decrease from the previous number of 123.885 2006=100 for Dec 2018. United States USD Trade Weighted Index: Nominal: Emerging Market Economies data is updated monthly, averaging 98.829 2006=100 from Jan 2006 (Median) to Jan 2019, with 157 observations. The data reached an all-time high of 124.362 2006=100 in Nov 2018 and a record low of 89.858 2006=100 in Jul 2008. United States USD Trade Weighted Index: Nominal: Emerging Market Economies data remains active status in CEIC and is reported by Federal Reserve Board. The data is categorized under Global Database’s United States – Table US.M016: US Dollar Trade Weighted Index.

Russia US Dollar Denominated Indices: Market Capitalization: RTS Index data was reported at 79.691 USD bn in Apr 2025. This records an increase from the previous number of 78.429 USD bn for Mar 2025. Russia US Dollar Denominated Indices: Market Capitalization: RTS Index data is updated monthly, averaging 139.656 USD bn from Apr 1997 (Median) to Apr 2025, with 337 observations. The data reached an all-time high of 294.008 USD bn in Oct 2021 and a record low of 7.925 USD bn in Sep 1998. Russia US Dollar Denominated Indices: Market Capitalization: RTS Index data remains active status in CEIC and is reported by Moscow Exchange. The data is categorized under Global Database’s Russian Federation – Table RU.ZA003: Moscow Exchange: Indices Denominated in USD: Market Capitalization.

This statistic depicts the dollar market share of the leading cider brands in the United States in 2015. According to the report, Johnny Appleseed accounted for a *** percent share of the U.S. cider market that year, based on dollar sales.

The U.S. dollar was the most common currency in foreign exchange reserves in 2025, comprising more than three times the amount of the euro in global reserves that year. This total peaked in 2015, partly due to the strength of the dollar during the Eurozone crisis. The share of the U.S. dollar has lost since to the Japanese yen and euro, as well as other currencies. Why do foreign exchange reserves matter? When countries with different currencies export goods, they must agree on a currency for payment. As a result, countries hold currency reserves worth trillions of U.S. dollars. After World War II, the U.S. dollar itself became the international currency in the Bretton Woods Agreement and is thus the most common currency for international payments. The United States Treasury is also seen by most as risk-free, giving the country a low-risk premium. For this reason, countries hold U.S. dollars in reserve because the currency holds value relatively well eventually. China and currency reserves Since 2016, the International Monetary Fund has included the Chinese renminbi (yuan) as part of the Special Drawing Rights (SDR) basket. This decision recognized the influence of the renminbi as a reserve currency, particularly in several Asian countries. China also holds significant foreign exchange reserves itself, funded by its large positive trade balance.