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

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.

The USD/CAD exchange rate rose to 1.3686 on July 11, 2025, up 0.22% from the previous session. Over the past month, the Canadian Dollar has weakened 0.61%, and is down by 0.38% over the last 12 months. Canadian Dollar - values, historical data, forecasts and news - updated on July of 2025.

This dataset contains news headlines relevant to key forex pairs: AUDUSD, EURCHF, EURUSD, GBPUSD, and USDJPY. The data was extracted from reputable platforms Forex Live and FXstreet over a period of 86 days, from January to May 2023. The dataset comprises 2,291 unique news headlines. Each headline includes an associated forex pair, timestamp, source, author, URL, and the corresponding article text. Data was collected using web scraping techniques executed via a custom service on a virtual machine. This service periodically retrieves the latest news for a specified forex pair (ticker) from each platform, parsing all available information. The collected data is then processed to extract details such as the article's timestamp, author, and URL. The URL is further used to retrieve the full text of each article. This data acquisition process repeats approximately every 15 minutes.

To ensure the reliability of the dataset, we manually annotated each headline for sentiment. Instead of solely focusing on the textual content, we ascertained sentiment based on the potential short-term impact of the headline on its corresponding forex pair. This method recognizes the currency market's acute sensitivity to economic news, which significantly influences many trading strategies. As such, this dataset could serve as an invaluable resource for fine-tuning sentiment analysis models in the financial realm.

We used three categories for annotation: 'positive', 'negative', and 'neutral', which correspond to bullish, bearish, and hold sentiments, respectively, for the forex pair linked to each headline. The following Table provides examples of annotated headlines along with brief explanations of the assigned sentiment.

Examples of Annotated Headlines Forex Pair Headline Sentiment Explanation GBPUSD Diminishing bets for a move to 12400 Neutral Lack of strong sentiment in either direction GBPUSD No reasons to dislike Cable in the very near term as long as the Dollar momentum remains soft Positive Positive sentiment towards GBPUSD (Cable) in the near term GBPUSD When are the UK jobs and how could they affect GBPUSD Neutral Poses a question and does not express a clear sentiment JPYUSD Appropriate to continue monetary easing to achieve 2% inflation target with wage growth Positive Monetary easing from Bank of Japan (BoJ) could lead to a weaker JPY in the short term due to increased money supply USDJPY Dollar rebounds despite US data. Yen gains amid lower yields Neutral Since both the USD and JPY are gaining, the effects on the USDJPY forex pair might offset each other USDJPY USDJPY to reach 124 by Q4 as the likelihood of a BoJ policy shift should accelerate Yen gains Negative USDJPY is expected to reach a lower value, with the USD losing value against the JPY AUDUSD RBA Governor Lowe’s Testimony High inflation is damaging and corrosive

Positive Reserve Bank of Australia (RBA) expresses concerns about inflation. Typically, central banks combat high inflation with higher interest rates, which could strengthen AUD. Moreover, the dataset includes two columns with the predicted sentiment class and score as predicted by the FinBERT model. Specifically, the FinBERT model outputs a set of probabilities for each sentiment class (positive, negative, and neutral), representing the model's confidence in associating the input headline with each sentiment category. These probabilities are used to determine the predicted class and a sentiment score for each headline. The sentiment score is computed by subtracting the negative class probability from the positive one.

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.

Dollar Index: Ascending or Descending?

Financial data:

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

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

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

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

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

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

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

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

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

• Data cleaning and preprocessing are essential before model training

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

The AUD/USD exchange rate fell to 0.6574 on July 11, 2025, down 0.29% from the previous session. Over the past month, the Australian Dollar has strengthened 0.63%, but it's down by 3.09% over the last 12 months. Australian Dollar - values, historical data, forecasts and news - updated on July of 2025.

The foreign exchange (Forex) market is a global decentralized market for the trading of currencies. It is the largest financial market in the world, with an average daily trading volume of over $5 trillion. The market size is expected to reach $84 million by 2033, growing at a CAGR of 5.83% during the forecast period 2025-2033. Key drivers of the Forex market growth include increasing international trade, rising foreign direct investment, and growing demand for hedging and speculation. The market is also being driven by the increasing use of online trading platforms and the growing popularity of cryptocurrencies. The major players in the Forex market include Deutsche Bank, UBS, JP Morgan, State Street, XTX Markets, Jump Trading, Citi, Bank of New York Mellon, Bank America, and Goldman Sachs. The market is segmented by type (spot Forex, currency swap, outright forward, Forex swaps, Forex options, other types), counterparty (reporting dealers, other financial institutions, non-financial customers), and region (North America, South America, Europe, Middle East & Africa, Asia Pacific). Recent developments include: In November 2023, JP Morgan revealed the introduction of novel FX Warrants denominated in Hong Kong dollars in the Hong Kong market, marking its status as the inaugural issuer in Asia to present FX Warrants featuring CNH/HKD (Chinese Renminbi traded outside Mainland China/Hong Kong dollar) and JPY/HKD (Japanese Yen/Hong Kong dollar) as underlying currency pairs. These fresh FX Warrants are set to commence trading on the Hong Kong Stock Exchange., In October 2023, Deutsche Bank AG finalized its purchase of Numis Corporation Plc. The integration of both brands under the name 'Deutsche Numis' underscores their collective influence and standing in the UK and global markets. 'Deutsche Numis' emerges as a prominent entity in UK investment banking and the preferred advisor for UK-listed companies. This acquisition aligns with Deutsche Bank's Global Hausbank strategy, aiming to become the primary partner for clients in financial services and fostering stronger relationships with corporations throughout the United Kingdom., In June 2023, UBS successfully finalized the acquisition of Credit Suisse, marking a significant achievement. Credit Suisse Group AG has merged into UBS Group AG, forming a unified banking entity.. Key drivers for this market are: International Transactions Driven by Growing Tourism Driving Market Demand, Market Liquidity Impacting the Foreign Exchange Market. Potential restraints include: International Transactions Driven by Growing Tourism Driving Market Demand, Market Liquidity Impacting the Foreign Exchange Market. Notable trends are: FX Swaps is leading the market.

Under the dollar-dominated international monetary system, the cross-border capital flows of emerging economies reverse sharply following policy shifts by the Fed. To investigate the sensitivity of cross-border capital inflows to dollar shocks, we analyze 33 emerging economies from 2006Q1 to 2021Q4 and use the panel quantile model to explore the dynamic evolution of dollar appreciation shocks at different stages of capital inflows, especially the tail effects. We find that dollar appreciation shocks reduce the total cross-border capital inflows of emerging economies. This impact is mainly through internal and external financial cycle difference channels. Dollar shock impacts differ significantly across different quantiles of capital inflows. Specifically, dollar appreciation shifts the capital inflow to the left and increases the severity of the left-tail risk of capital flows. More flexible exchange rate regimes exacerbate the negative effects of dollar shocks across the distribution of capital inflows. The moderating effect of the fixed exchange rate and intermediate exchange rate systems on external shocks are effective in low quantiles of capital inflows. The sensitivity of “capital flows at risk” to dollar shocks depends on national structural characteristics. As a key risk factor for emerging economies, US dollar appreciation can predict the trend of cross-border capital inflows. Countries should adopt policy measures to curb the adverse effects of US dollar fluctuations.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

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 faces uncertain future amid economic headwinds

Financial data:

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

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

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

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

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

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

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

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

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

• Data cleaning and preprocessing are essential before model training

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

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

[Video] US Dollar Index: Charting a New Course?

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

Postwar trade politics in the US have exhibited intermittent periods of rising industry demands for protection from imports. At present, however, we don’t fully understand why industry demands for protection rise and fall over time. We argue that intermittent protectionism in postwar US has been driven by changes in the real exchange rate. To do so, we incorporate the real exchange rate into a basic model of sectoral trade policy preferences to show how the number of sectors that expect to benefit from protection grows as the real exchange rate appreciates. We test two hypotheses generated from this model: that the number of antidumping and escape clause petitions rises as the dollar strengthens and falls as the dollar weakens. Second, that competitive sectors are more sensitive to exchange rate movements than comparatively disadvantaged and comparatively advantaged sectors. We evaluate these expectations with a Bayesian statistical analysis of data on antidumping and escape clause petitions in the United States between 1974 and 2012. The empirical models provide robust support for the study’s principal hypotheses.

The USD/SGD exchange rate rose to 1.2802 on July 11, 2025, up 0.07% from the previous session. Over the past month, the Singapore Dollar has weakened 0.13%, but it's up by 4.52% over the last 12 months. Singapore Dollar - values, historical data, forecasts and news - updated on July of 2025.

📈 Daily Historical Stock Price Data for Dollar General Corporation (2009–2025)

A clean, ready-to-use dataset containing daily stock prices for Dollar General Corporation from 2009-11-13 to 2025-05-28. This dataset is ideal for use in financial analysis, algorithmic trading, machine learning, and academic research.

  🗂️ Dataset Overview

Company: Dollar General Corporation Ticker Symbol: DG Date Range: 2009-11-13 to 2025-05-28 Frequency: Daily Total Records: 3907 rows… See the full description on the dataset page: https://huggingface.co/datasets/khaledxbenali/daily-historical-stock-price-data-for-dollar-general-corporation-20092025.

Traffic analytics, rankings, and competitive metrics for dollar.com as of May 2025

📈 Daily Historical Stock Price Data for Dollar Industries Limited (2017–2025)

A clean, ready-to-use dataset containing daily stock prices for Dollar Industries Limited from 2017-04-21 to 2025-05-28. This dataset is ideal for use in financial analysis, algorithmic trading, machine learning, and academic research.

  🗂️ Dataset Overview

Company: Dollar Industries Limited Ticker Symbol: DOLLAR.NS Date Range: 2017-04-21 to 2025-05-28 Frequency: Daily Total Records: 2001 rows… See the full description on the dataset page: https://huggingface.co/datasets/khaledxbenali/daily-historical-stock-price-data-for-dollar-industries-limited-20172025.

Comprehensive dataset of 14 Dollar stores in State of Bahia, Brazil as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Comprehensive performance analytics and metrics for Dollar Streak by Ainsworth.