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.

Inflation: Friend or Foe to the Stock Market?

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 CNBC Economy Articles Dataset is an invaluable collection of data extracted from CNBC’s economy section, offering deep insights into global and U.S. economic trends, market dynamics, financial policies, and industry developments.

This dataset encompasses a diverse array of economic articles on critical topics like GDP growth, inflation rates, employment statistics, central bank policies, and major global events influencing the market. Designed for researchers, analysts, and businesses, it serves as an essential resource for understanding economic patterns, conducting sentiment analysis, and developing financial forecasting models.

Dataset Highlights

Each record in the dataset is meticulously structured and includes:

• Article Titles
• Publication Dates
• Author Names
• Content Summaries
• URLs to Original Articles

This rich combination of fields ensures seamless integration into data science projects, research papers, and market analyses.

Key Features

• Number of Articles: Hundreds of articles sourced directly from CNBC.
• Data Fields: Includes title, publication date, author, article content, summary, URL, and relevant keywords.
• Topics Covered: U.S. and global economy, GDP trends, inflation, employment, financial markets, and monetary policies.
• Format: Delivered in CSV format for easy integration with research tools and analytical platforms.
• Source: Extracted directly from CNBC’s economy news section, ensuring accuracy and relevance.

Use Cases

• Economic Research: Gain insights into U.S. and global economic policies, market trends, and industry developments.
• Sentiment Analysis: Assess the sentiment of economic articles to gauge market perspectives and investor confidence.
• Financial Modeling: Build forecasting models leveraging key economic indicators discussed in the dataset.
• Content Creation: Develop research-backed reports, articles, and presentations on economic topics.

Explore More News Datasets

Interested in additional structured news datasets for your research or analytics needs? Check out our news dataset collection to find datasets tailored for diverse analytical applications.

This dataset contains 4,987 daily record behavior of financial markets. It includes stock price metrics, macroeconomic indicators, sentiment scores, and event flags.

Key highlights:

Time span: 4,987 days

Financial indicators: Open, High, Low, Close, Adjusted Close, Volume

Macroeconomic variables: GDP, Inflation, Unemployment, Interest Rate, CPI

Sentiment analysis: News and Social Sentiment scores

Event tagging: Binary event flag (e.g., market shocks)

Target label: Market condition — Stable, Volatile, or Crash

SENSEX Index (Annual Closing Value) – Benchmark index of the Bombay Stock Exchange (BSE)

GDP Growth (%) – Annual real GDP growth rates (constant prices)

Inflation Rate (%) – Annual consumer price index (CPI)-based inflation

Exchange Rate (INR/USD) – End-of-year nominal exchange rate

Market Capitalization (INR billion) – Total BSE market value

Trading Volume (Million Shares) – Aggregate trading activity per year

All data have been sourced from official publications including the Reserve Bank of India (RBI), BSE archives, International Monetary Fund (IMF), and World Bank.

The dataset is structured in wide format, with each row representing a calendar year from 1980 to 2024 and each column representing one variable.

Japan's main stock market index, the JP225, rose to 50413 points on October 27, 2025, gaining 2.26% from the previous session. Over the past month, the index has climbed 11.92% and is up 30.58% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Japan. Japan Stock Market Index (JP225) - values, historical data, forecasts and news - updated on October of 2025.

View data of the S&P 500, an index of the stocks of 500 leading companies in the US economy, which provides a gauge of the U.S. equity market.

The Index of Common Inflation Expectations (CIE) pulls together a variety of measures that look at the inflation expectations of economic agents. Data dimensions include the type of economic agent, the horizon of the expectation, the source of data (survey versus market-based measures), and the associated inflation concept. CIE is constructed using 21 inflation expectation indicators derived from households, firms, professional forecasters, and financial market participants. Both 'short horizon' (forecasts for the year ahead) and 'long horizon' (forecasts for a period over the next 5-10 years) inflation expectations are included. The quarterly index began in September 2020 and includes data from 1999 to present.

This panel dataset contains quarterly series on inflation targets, bands, and track records for 41 inflation targeting countries from 1990 to 2024. Data on inflation targets and bands are collected through each central bank’s historical documents and rules-based track record measures are calculated by the author to assess actual inflation outcomes with respect to the central banks’ stated policy objectives. The dataset supports research work in Zhang (2025), Zhang and Wang (2022), and Zhang (2021). Please cite the following paper when using the data: Z. Zhang, Inflation Targets, Bands, and Track Records: a Dataset of Inflation Targeting Countries, Data in Brief, Volume 61, 2025, 111753.

Other related papers: Z. Zhang, Does inflation targeting track record matter for asset prices? Evidence from stock, bond, and foreign exchange markets, Journal of International Financial Markets, Institutions and Money, Volume 101, 2025, 102141. Z. Zhang, S. Wang, Do actions speak louder than words? Assessing the effects of inflation targeting track records on macroeconomic performance, 2022, IMF Working Papers 2022/227.
Z. Zhang, Stock returns and inflation redux: An explanation from monetary policy in advanced and emerging markets, 2021, IMF Working Papers 2021/219.

The 2025 August online version has added two non-IT countries (Switzerland and China) for comparison purpose.

This dataset contains monthly time-series data used in the study "Volatility Spillovers Between Housing and Stock Markets: Evidence from Iran". The data covers April 2016 to October 2023 and includes the Tehran Stock Exchange index, average housing prices in Tehran, and relevant macroeconomic variables such as inflation rate and exchange rate. The dataset is provided in .xlsx format with variable descriptions in the accompanying README file. All values are collected from official sources, including the Central Bank of Iran and the Statistical Center of Iran. These data were used to estimate a DCC–GARCH model to analyze volatility spillovers between the two markets.

This dataset contains simulated financial forecasting data designed to predict business performance. It includes multiple features representing historical sales data, market indicators, and key macroeconomic variables that affect financial outcomes. The dataset is aimed at developing models for business forecasting and strategic management decisions.

The dataset includes the following key columns:

Sales: Historical sales data (normalized).

Market Indicator 1 & 2: Simulated market-related indicators reflecting market conditions.

GDP Growth: The GDP growth rate, which reflects the economic growth of the country.

Unemployment Rate: The unemployment rate, indicating the health of the labor market.

Inflation Rate: The inflation rate impacting economic stability.

Target Sales: The future sales prediction based on the aforementioned features, serving as the target column for model training.

This dataset is valuable for training machine learning models in predicting future sales and assessing business strategy based on financial performance, market conditions, and macroeconomic factors.

Inflation Rate in India decreased to 1.54 percent in September from 2.07 percent in August of 2025. This dataset provides - India Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Les attentes en matière d'inflation en République tchèque ont diminué à 2,10 % au deuxième trimestre 2025, contre 2,30 % au premier trimestre 2025. Cette dataset fournit - République tchèque Prévisions d'inflation - valeurs réelles, données historiques, prévisions, graphique, statistiques, calendrier économique et actualités.

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.

Surging Services: Will Dow Jones CPI Signal Continued Consumer Strength?

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 dataset contains monthly and quarterly time-series data from 2012 to 2024 for Indonesian sovereign credit risk (∆CDS), global volatility (VIX), international equity proxy (MSCI World Index), Indonesia Stock Exchange Composite Index (IHSG), exchange rate (USD/IDR), and inflation. The dataset supports the empirical analysis in the article titled “The Interaction Between Sovereign Risk, Global Volatility, and Domestic Stock Returns: An Indonesian Case Study.

France's main stock market index, the FR40, fell to 8226 points on October 24, 2025, losing 0.00% from the previous session. Over the past month, the index has climbed 5.52% and is up 9.71% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from France. France Stock Market Index (FR40) - values, historical data, forecasts and news - updated on October 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

    <p>RBA Governor Lowe’s Testimony High inflation is damaging and corrosive </p>

    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.

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.

Probabilistic AI: A New Approach to Artificial Intelligence

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

A monthly and quarterly data set spanning July 1995 to December 2016 of the following macro-economic variables 1. South African stock market 2. South African GDP3. United States GDP 4. South African interest rate 5. US interest rate 6. South African inflation rate 7. US inflation rate 8. South African Money Supply 9. Rand/Dollar Exchange 10. FTSE

This dataset facilitates an analysis of the impact of the recent Israel-Hamas conflict on the stock market performance of U.S. defense companies, as measured by the returns of defense-sector Exchange-Traded Funds (ETFs). The conflict is quantified using variables such as a binary "attack" indicator, casualty counts, and the intensity of Google search activity related to the war. Additionally, the dataset incorporates a comprehensive set of control variables, including interest rates, exchange rates, oil prices, inflation rates, and factors related to the Ukraine conflict, ensuring a robust framework for evaluating the effects of this geopolitical event.

Techsalerator’s Location Sentiment Data for Trinidad and Tobago

Techsalerator’s Location Sentiment Data for Trinidad and Tobago provides an extensive collection of sentiment insights crucial for businesses, researchers, and policymakers. This dataset offers valuable data on public sentiment, consumer perceptions, and emotional trends across various locations in the country.

For access to the full dataset, contact us at info@techsalerator.com or visit Techsalerator Contact Us.

Techsalerator’s Location Sentiment Data for Trinidad and Tobago

Techsalerator’s Location Sentiment Data for Trinidad and Tobago delivers structured sentiment analysis across urban, suburban, and rural areas. This dataset is essential for market research, brand analysis, social sentiment tracking, and AI-driven insights.

Top 5 Key Data Fields

• Geographic Sentiment Mapping – Provides location-based sentiment trends to help businesses understand regional variations in public opinion.
• Emotional Tone Analysis – Detects emotions such as happiness, anger, sadness, and excitement in location-based conversations.
• Industry-Specific Sentiment – Analyzes consumer attitudes towards various industries, including retail, tourism, finance, and entertainment.
• Social Media Sentiment Trends – Tracks public sentiment through social media platforms to identify emerging trends and customer perceptions.
• Event-Based Sentiment Shifts – Captures sentiment changes before, during, and after key events, such as festivals, elections, and economic shifts.

Top 5 Sentiment Trends in Trinidad and Tobago

• Tourism Sentiment Fluctuations – Public sentiment towards travel and tourism shifts seasonally, influenced by events and visitor experiences.
• Consumer Confidence in Retail – Insights into shopping behavior, brand loyalty, and customer satisfaction across different retail sectors.
• Public Opinion on Government Policies – Monitoring changes in sentiment regarding local governance, economic policies, and social programs.
• Cultural and Festival Sentiments – Analyzing emotions surrounding major events such as Carnival and Independence Day.
• Economic Optimism and Concerns – Identifying public sentiment trends related to employment, inflation, and financial stability.

Top 5 Applications of Location Sentiment Data in Trinidad and Tobago

• Marketing and Brand Strategy – Businesses can leverage sentiment data to tailor marketing campaigns based on regional consumer emotions.
• Urban Planning and Development – City planners can assess public perception of infrastructure projects and community initiatives.
• Crisis Management and Public Relations – Organizations can monitor sentiment shifts to manage crises and improve public communication.
• AI and Machine Learning Insights – Enhancing AI models with localized sentiment data for more accurate natural language processing.
• Political and Social Research – Academics and analysts can study public sentiment towards policies and social issues in real-time.

Accessing Techsalerator’s Location Sentiment Data

To obtain Techsalerator’s Location Sentiment Data for Trinidad and Tobago, contact info@techsalerator.com with your specific requirements. Techsalerator provides customized datasets based on requested fields, with delivery available within 24 hours. Ongoing access options can also be discussed.

Included Data Fields

• Geographic Sentiment Mapping
• Emotional Tone Analysis
• Industry-Specific Sentiment
• Social Media Sentiment Trends
• Event-Based Sentiment Shifts
• Consumer Confidence Index
• Brand Perception Data
• Economic Sentiment Trends
• Political Sentiment Analysis
• Contact Information

For in-depth insights into location-based sentiment trends in Trinidad and Tobago, Techsalerator’s dataset is an essential tool for businesses, analysts, and policymakers.