Stay informed with our comprehensive Financial News Dataset, designed for investors, analysts, and businesses to track market trends, monitor financial events, and make data-driven decisions.

Dataset Features

Financial News Articles: Access structured financial news data, including headlines, summaries, full articles, publication dates, and source details. Market & Economic Indicators: Track financial reports, stock market updates, economic forecasts, and corporate earnings announcements. Sentiment & Trend Analysis: Analyze news sentiment, categorize articles by financial topics, and monitor emerging trends in global markets. Historical & Real-Time Data: Retrieve historical financial news archives or access continuously updated feeds for real-time insights.

Customizable Subsets for Specific Needs Our Financial News Dataset is fully customizable, allowing you to filter data based on publication date, region, financial topics, sentiment, or specific news sources. Whether you need broad coverage for market research or focused data for investment analysis, we tailor the dataset to your needs.

Popular Use Cases

Investment Strategy & Risk Management: Monitor financial news to assess market risks, identify investment opportunities, and optimize trading strategies. Market & Competitive Intelligence: Track industry trends, competitor financial performance, and economic developments. AI & Machine Learning Training: Use structured financial news data to train AI models for sentiment analysis, stock prediction, and automated trading. Regulatory & Compliance Monitoring: Stay updated on financial regulations, policy changes, and corporate governance news. Economic Research & Forecasting: Analyze financial news trends to predict economic shifts and market movements.

Whether you're tracking stock market trends, analyzing financial sentiment, or training AI models, our Financial News Dataset provides the structured data you need. Get started today and customize your dataset to fit your business objectives.

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In 2025, most of the Polish population got their information about finance and the economy via blogs and websites on the internet. Only eleven percent have learned about it through books.

France - Direct investment in the reporting economy: Financial account; Equity was MIO_NAC6317.00 Million in March of 2025, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for France - Direct investment in the reporting economy: Financial account; Equity - last updated from the EUROSTAT on July of 2025. Historically, France - Direct investment in the reporting economy: Financial account; Equity reached a record high of MIO_NAC31567.00 Million in December of 2018 and a record low of MIO_NAC-2620.00 Million in December of 2021.

The table 04 April: Economy and Finance - 2025 is part of the dataset Gallup Poll Social Series (GPSS), available at https://stanford.redivis.com/datasets/fsrz-3x73xfevf. It contains 26379 rows across 326 variables.

This STATA DTA file contains an unbalanced panel of maximun 73 countries observed over a maximum of 20 years used for the analysis conducted in the paper "How Family Ties Affect Underground Economy Tax Morale and Trust" by Mauro Marè, Antonello Motroni e Francesco Porcelli.

This dictionary gathers different disciplines and topics such as: finance, economy, trade, business, stock-exchange, banking, firms, negotiation, mailing, telephone conversation, values, etc. It also includes many phrases relevant for business, impersonal expressions, conjugated sentences, relevant sentences, standard sentences, synonyms, abbreviations. The DISCIPLINE field gives a subdivision into sectors : stock exchange, trade, export, business, values, economy, banking, etc. Single words are associated with the meaning or event which they apply to.Languages : French - English (GB, US), English (GB, US) - FrenchNumber of entries: 91,300. Number of terms per language: about -10% with respect to the number of entries (i.e. ca. 82,000 terms)Disciplines: about 105Format: .DBF files, sorted alphabetically in French and EnglishA viewer is also available upon demand. This software enables a spontaneous search French => English and English => French in the database according to different criteria:- by beginning of term, - by included word,- by discipline,- by abbreviation.Terms, phrases and conjugated sentences are sorted alphabetically.Examples : phrases beginning with "à" : à terme, à titre gracieux, à titre onéreux, à vue...; "en" : en compte, en vigueur..., "prix" : prix abordable, prix choc, prix exorbitant...Viewing format: .FIC (Windev)Please note that the prices indicated here are dependent from the number of entries available which is growing constantly. Please contact us for further details.

Data and codes used in the paper "Drivers of the Global Financial Cycle"

Economy and finance - cities and greater cities

Fichier de l'organisation Ministère de l'Economie et des Finances du Bénin

This dataset is about books. It has 1 row and is filtered where the book is Money and finance in the transition to a market economy. It features 7 columns including author, publication date, language, and book publisher.

This dataset comprises a hand collected market overview of the FinTech market in Germany as of December 2021. It includes various verified properties of 978 unique firms, which can be attributed to the financial technology sector and are operating in Germany. Each observation represents one company with 24 variables, including name, address, legal form, founders with corresponding LinkedIn accounts, register number or company-ID, attribution to FinTech segments and subsegments, bank cooperation, URL address, local court, former name, operating status. The dataset contains established companies as well as start-ups. Since the market in Germany and the nature of FinTech companies itself are dynamic as well as changing there is no complete overview of the market. Furthermore, the total number, the operating status as well as specific properties of FinTechs cannot be found in one accumulated data base. The dataset contains valuable information for researchers, practitioners as well as for supervising authorities. We provide the description of variables as well as a taxonomy for categorizing FinTechs. The nature of the dataset enables further cross-sectional and the possibility of longitudinal analyses of the complete market. The aim of the collection procedure was to find and identify all relevant FinTechs operating in Germany with a structured approach. Different databases and websites (see below) were used to obtain an overview of the market. The dataset was repeatedly updated and verified throughout the years within this process. An association to the segment of operations was conducted. Through structured Google searches the operating status was checked.

The corresponding paper with a detailed description of the variables and volume estimates can be downloaded here:
https://elibrary.duncker-humblot.com/article/72485/german-fintech-companies-a-market-overview-and-volume-estimates

Data Set Information:

The dataset is compiled from the National Accounts Main Aggregates Database that presents a series of analytical national accounts tables from 1970 onwards for more than 200 countries and areas of the world. It is the product of a global cooperation effort between the Economic Statistics Branch of the United Nations Statistics Division, international statistical agencies, and the national statistical services of these countries and is developed in accordance with the recommendation of the Statistical Commission at its first session in 1947 that the Statistics Division should publish regularly the most recent available data on national accounts for as many countries and areas as possible.

This dataset can be used to perform clustering, regression, and time series tasks.

Abstract

The fourth edition of the Global Findex offers a lens into how people accessed and used financial services during the COVID-19 pandemic, when mobility restrictions and health policies drove increased demand for digital services of all kinds.

The Global Findex is the world’s most comprehensive database on financial inclusion. It is also the only global demand-side data source allowing for global and regional cross-country analysis to provide a rigorous and multidimensional picture of how adults save, borrow, make payments, and manage financial risks. Global Findex 2021 data were collected from national representative surveys of almost 145,000 people in 139 economies, representing 97 percent of the world’s population. The latest edition follows the 2011, 2014, and 2017 editions, and it includes a number of new series measuring financial health and resilience and contains more granular data on digital payment adoption, including merchant and government payments.

The Global Findex is an indispensable resource for financial service practitioners, policy makers, researchers, and development professionals.

Geographic coverage

National coverage

Kind of data

Observation data/ratings [obs]

Sampling procedure

In most developing economies, Global Findex data have traditionally been collected through face-to-face interviews. Surveys are conducted face-to-face in economies where telephone coverage represents less than 80 percent of the population or where in-person surveying is the customary methodology. However, because of ongoing COVID-19–related mobility restrictions, face-to-face interviewing was not possible in some of these economies in 2021. Phone-based surveys were therefore conducted in 67 economies that had been surveyed face-to-face in 2017. These 67 economies were selected for inclusion based on population size, phone penetration rate, COVID-19 infection rates, and the feasibility of executing phone-based methods where Gallup would otherwise conduct face-to-face data collection, while complying with all government-issued guidance throughout the interviewing process. Gallup takes both mobile phone and landline ownership into consideration. According to Gallup World Poll 2019 data, when face-to-face surveys were last carried out in these economies, at least 80 percent of adults in almost all of them reported mobile phone ownership. All samples are probability-based and nationally representative of the resident adult population. Additionally, phone surveys were not a viable option in 16 economies in 2021, which were then surveyed in 2022.

In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households. Each eligible household member is listed, and the hand-held survey device randomly selects the household member to be interviewed. For paper surveys, the Kish grid method is used to select the respondent. In economies where cultural restrictions dictate gender matching, respondents are randomly selected from among all eligible adults of the interviewer's gender.

In traditionally phone-based economies, respondent selection follows the same procedure as in previous years, using random digit dialing or a nationally representative list of phone numbers. In most economies where mobile phone and landline penetration is high, a dual sampling frame is used.

The same respondent selection procedure is applied to the new phone-based economies. Dual frame (landline and mobile phone) random digital dialing is used where landline presence and use are 20 percent or higher based on historical Gallup estimates. Mobile phone random digital dialing is used in economies with limited to no landline presence (less than 20 percent).

For landline respondents in economies where mobile phone or landline penetration is 80 percent or higher, random selection of respondents is achieved by using either the latest birthday or household enumeration method. For mobile phone respondents in these economies or in economies where mobile phone or landline penetration is less than 80 percent, no further selection is performed. At least three attempts are made to reach a person in each household, spread over different days and times of day.

Sample size for Eswatini is 1000.

Mode of data collection

Face-to-face [f2f]

Research instrument

Questionnaires are available on the website.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar. 2022. The Global Findex Database 2021: Financial Inclusion, Digital Payments, and Resilience in the Age of COVID-19. Washington, DC: World Bank.

Abstract

The fourth edition of the Global Findex offers a lens into how people accessed and used financial services during the COVID-19 pandemic, when mobility restrictions and health policies drove increased demand for digital services of all kinds.

The Global Findex is the world's most comprehensive database on financial inclusion. It is also the only global demand-side data source allowing for global and regional cross-country analysis to provide a rigorous and multidimensional picture of how adults save, borrow, make payments, and manage financial risks. Global Findex 2021 data were collected from national representative surveys of about 128,000 adults in more than 120 economies. The latest edition follows the 2011, 2014, and 2017 editions, and it includes a number of new series measuring financial health and resilience and contains more granular data on digital payment adoption, including merchant and government payments.

The Global Findex is an indispensable resource for financial service practitioners, policy makers, researchers, and development professionals.

Geographic coverage

National coverage

Analysis unit

Individual

Kind of data

Observation data/ratings [obs]

Sampling procedure

In most developing economies, Global Findex data have traditionally been collected through face-to-face interviews. Surveys are conducted face-to-face in economies where telephone coverage represents less than 80 percent of the population or where in-person surveying is the customary methodology. However, because of ongoing COVID-19 related mobility restrictions, face-to-face interviewing was not possible in some of these economies in 2021. Phone-based surveys were therefore conducted in 67 economies that had been surveyed face-to-face in 2017. These 67 economies were selected for inclusion based on population size, phone penetration rate, COVID-19 infection rates, and the feasibility of executing phone-based methods where Gallup would otherwise conduct face-to-face data collection, while complying with all government-issued guidance throughout the interviewing process. Gallup takes both mobile phone and landline ownership into consideration. According to Gallup World Poll 2019 data, when face-to-face surveys were last carried out in these economies, at least 80 percent of adults in almost all of them reported mobile phone ownership. All samples are probability-based and nationally representative of the resident adult population. Phone surveys were not a viable option in 17 economies that had been part of previous Global Findex surveys, however, because of low mobile phone ownership and surveying restrictions. Data for these economies will be collected in 2022 and released in 2023.

In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households. Each eligible household member is listed, and the hand-held survey device randomly selects the household member to be interviewed. For paper surveys, the Kish grid method is used to select the respondent. In economies where cultural restrictions dictate gender matching, respondents are randomly selected from among all eligible adults of the interviewer's gender.

In traditionally phone-based economies, respondent selection follows the same procedure as in previous years, using random digit dialing or a nationally representative list of phone numbers. In most economies where mobile phone and landline penetration is high, a dual sampling frame is used.

The same respondent selection procedure is applied to the new phone-based economies. Dual frame (landline and mobile phone) random digital dialing is used where landline presence and use are 20 percent or higher based on historical Gallup estimates. Mobile phone random digital dialing is used in economies with limited to no landline presence (less than 20 percent).

For landline respondents in economies where mobile phone or landline penetration is 80 percent or higher, random selection of respondents is achieved by using either the latest birthday or household enumeration method. For mobile phone respondents in these economies or in economies where mobile phone or landline penetration is less than 80 percent, no further selection is performed. At least three attempts are made to reach a person in each household, spread over different days and times of day.

Sample size for Hong Kong SAR, China is 1003.

Mode of data collection

Landline and mobile telephone

Research instrument

Questionnaires are available on the website.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar. 2022. The Global Findex Database 2021: Financial Inclusion, Digital Payments, and Resilience in the Age of COVID-19. Washington, DC: World Bank.

Graph and download economic data for Amount Outstanding of International Debt Securities for Issuers in Other Financial Corporations, All Maturities, Residence of Issuer in Latin America and Caribbean (DISCONTINUED) (IDSOFAMRIAO4U) from Q1 1987 to Q2 2015 about Caribbean Economies, Latin America, finance companies, companies, finance, maturity, financial, debt, residents, and securities.

This dataset is about books. It has 1 row and is filtered where the book is The political economy of the Japanese financial big bang : institutional change in finance and public policymaking. It features 7 columns including author, publication date, language, and book publisher.

Why do some countries indulge in financial repression, harming economic development in the process, whilst others promote financial development? Three main explanations have been put forth. Market failures, due to information asymmetries, mean that credit is rationed even when lenders could potentially benefit from making loans readily available. Political failures, due to state capture, mean that credit will be rationed as a way of generating rents for politically powerful financial incumbents. The state might, however, have its own fiscal reasons for politicizing the supply and price of credit, since financial repression provides easy-to-collect revenues. I draw on the third approach to argue that the state’s fiscal imperative is usually the primary reason behind financial repression, and even when private actors benefit they are subordinate to this concern. A dynamic panel analysis that exploits instrumental variables and a case study of Mexico adduce strong empirical support for my fiscal transaction cost theory.

External Debt: Treasury: Service Payments: Interests: BC: Other Countries data was reported at 58.000 MAD mn in Mar 2018. This records an increase from the previous number of 15.000 MAD mn for Dec 2017. External Debt: Treasury: Service Payments: Interests: BC: Other Countries data is updated quarterly, averaging 26.000 MAD mn from Sep 2004 (Median) to Mar 2018, with 55 observations. The data reached an all-time high of 145.000 MAD mn in Sep 2012 and a record low of 5.000 MAD mn in Jun 2017. External Debt: Treasury: Service Payments: Interests: BC: Other Countries data remains active status in CEIC and is reported by Ministry of Economy and Finance. The data is categorized under Global Database’s Morocco – Table MA.JB009: External Debt: Ministry of Economy and Finance.

Le budget 2017 est un budget de la continuité, de la fiabilité et de la solidarité. Il s’agit d’un budget de la continuité et de la fiabilité dans la mesure où les chiffres confirment et montrent clairement que nous sommes sur la bonne voie. Les mesures du paquet d’avenir portent leurs fruits. Nous nous approchons à grands pas de notre objectif, à savoir l’assainissement des finances publiques. Il s’agit d’un budget de la solidarité dans la mesure où près de la moitié des dépenses sont destinées aux prestations sociales, aux aides, aux subventions et aux transferts. Répartition des dépenses de l'administration centrale suivant leur nature économique