U.S. Economic and Financial Dataset

Dataset Description

This dataset combines historical U.S. economic and financial indicators, spanning the last 50 years, to facilitate time series analysis and uncover patterns in macroeconomic trends. It is designed for exploring relationships between interest rates, inflation, economic growth, stock market performance, and industrial production.

Key Features

• Frequency: Monthly
• Time Period: Last 50 years from Nov-24
• Sources:
  • Federal Reserve Economic Data (FRED)
  • Yahoo Finance

Dataset Feature Description

1. Interest Rate (Interest_Rate):

   • The effective federal funds rate, representing the interest rate at which depository institutions trade federal funds overnight.

2. Inflation (Inflation):

   • The Consumer Price Index for All Urban Consumers, an indicator of inflation trends.

3. GDP (GDP):

   • Real GDP measures the inflation-adjusted value of goods and services produced in the U.S.

4. Unemployment Rate (Unemployment):

   • The percentage of the labor force that is unemployed and actively seeking work.

5. Stock Market Performance (S&P500):

   • Monthly average of the adjusted close price, representing stock market trends.

6. Industrial Production (Ind_Prod):

   • A measure of real output in the industrial sector, including manufacturing, mining, and utilities.

Dataset Statistics

1. Total Entries: 599
2. Columns: 6
3. Memory usage: 37.54 kB
4. Data types: float64

Feature Overview

• Columns:
  • Interest_Rate: Monthly Federal Funds Rate (%)
  • Inflation: CPI (All Urban Consumers, Index)
  • GDP: Real GDP (Billions of Chained 2012 Dollars)
  • Unemployment: Unemployment Rate (%)
  • Ind_Prod: Industrial Production Index (2017=100)
  • S&P500: Monthly Average of S&P 500 Adjusted Close Prices

Executive Summary

This project explores the interconnected dynamics of key macroeconomic indicators and financial market trends over the past 50 years, leveraging data from the Federal Reserve Economic Data (FRED) and Yahoo Finance. The dataset integrates critical variables such as the Federal Funds Rate, Inflation (CPI), Real GDP, Unemployment Rate, Industrial Production, and the S&P 500 Index, providing a holistic view of the U.S. economy and financial markets.

The analysis focuses on uncovering relationships between these variables through time-series visualization, correlation analysis, and trend decomposition. Key findings are included in the Insights section. This project serves as a robust resource for understanding long-term economic trends, policy impacts, and market behavior. It is particularly valuable for students, researchers, policymakers, and financial analysts seeking to connect macroeconomic theory with real-world data.

Potential Use Cases

• Economic Analysis: Examine relationships between interest rates, inflation, GDP, and unemployment.
• Stock Market Prediction: Study how macroeconomic indicators influence stock market trends.
• Time Series Modeling: Perform ARIMA, VAR, or other models to forecast economic trends.
• Cyclic Pattern Analysis: Identify how economic shocks and recoveries impact key indicators.

Snap of Power Analysis

https://github.com/user-attachments/assets/1b40e0ca-7d2e-4fbc-8cfd-df3f09e4fdb8">

To ensure sufficient power, the dataset covers last 50 years of monthly data i.e., around 600 entries.

Key Insights derived through EDA, time-series visualization, correlation analysis, and trend decomposition

• Interest Rate and Inflation Dynamics: The interest Rate and inflation exhibit an inverse relationship, especially during periods of aggressive monetary tightening by the Federal Reserve.
• Economic Growth and Market Performance: GDP growth and the S&P 500 Index show a positive correlation, reflecting how market performance often aligns with overall economic health.
• Labor Market and Industrial Output: Unemployment and industrial production demonstrate a strong inverse relationship. Higher industrial output is typically associated with lower unemployment
• Market Behavior During Economic Shocks: The S&P 500 experienced sharp declines during significant crises, such as the 2008 financial crash and the COVID-19 pandemic in 2020. These events also triggered increased unemployment and contractions in GDP, highlighting the interplay between markets and the broader economy.
• Correlation Highlights: S&P 500 and GDP have a strong positive correlation. Interest rates negatively correlate with GDP and inflation, reflecting monetary policy impacts. Unemployment is negatively correlated with industrial production but positively correlated with interest rates.

Link to GitHub Repo

https:/...

China's main stock market index, the SHANGHAI, fell to 3898 points on December 2, 2025, losing 0.42% from the previous session. Over the past month, the index has declined 1.98%, though it remains 15.36% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from China. China Shanghai Composite Stock Market Index - values, historical data, forecasts and news - updated on December of 2025.

Use our Stock prices dataset to access comprehensive financial and corporate data, including company profiles, stock prices, market capitalization, revenue, and key performance metrics. This dataset is tailored for financial analysts, investors, and researchers to analyze market trends and evaluate company performance.

Popular use cases include investment research, competitor benchmarking, and trend forecasting. Leverage this dataset to make informed financial decisions, identify growth opportunities, and gain a deeper understanding of the business landscape. The dataset includes all major data points: company name, company ID, summary, stock ticker, earnings date, closing price, previous close, opening price, and much more.

This dataset contains daily stock price data for Microsoft and several key companies that have significantly contributed to its growth and success. The dataset includes historical data from 1980 to 2024 for the following companies:

• Microsoft (MSFT): The core company behind the dataset.
• Intel (INTC): A vital partner in the PC revolution, providing processors for many Microsoft-powered devices.
• IBM (IBM): Microsoft's early partnership with IBM, starting with MS-DOS, laid the foundation for Microsoft's dominance in operating systems.
• Dell Technologies (DELL): Dell’s PCs pre-installed with Windows helped accelerate Microsoft’s growth in the consumer and enterprise markets.
• Sony (SONY): A competitor in the gaming industry, Sony played a significant role in shaping Microsoft's strategy for its Xbox division.

Dataset Details:

• Date Range: 1980-12-11 to 2024-10-31
• Interval: Daily stock prices
• Columns: Date, Open, High, Low, Close, Adjusted Close, Volume

This dataset is ideal for: - Financial analysis: Study stock price trends over time and compare performance across companies. - Time series forecasting: Predict future stock prices using historical data. - Market correlation analysis: Analyze the relationships between Microsoft and its key affiliated companies in different market conditions.

Feel free to use this dataset for your financial and stock market projects, analysis, or machine learning models!

In this table, we list major worldwide stock market crashes from 2007 to 2023. For each crash, we show its name, rough time of occurrence, stock index’s high and low, and in which country it occurred.

Description of PKN ORLEN stock dataset:

The PKN ORLEN dataset is related to the stock market performance of PKN ORLEN, one of the largest fuel and energy corporations in Poland. The dataset contains information on the company's stock prices and trading volumes on specific trading days.

Feature description: - Date: The date of the stock market trading. - Open: The opening price of the stock on a given day. - High: The highest price reached by the stock on a given day. - Low: The lowest price reached by the stock on a given day. - Close: The closing price of the stock on a given day. - Volume: The trading volume of the stock on a given day (number of transactions). - Value: The total value of the stock traded on a given day (in PLN).

Number of rows: 5925: Start date: 1999-11-26
End date: 2023-07-27

The PKN ORLEN dataset is valuable for financial analysts and investors who wish to conduct technical analysis or develop investment strategies based on the stock market data of PKN ORLEN. Analyzing this dataset can help understand price trends, trading volumes, and associated volatilities, leading to more informed investment decisions.

This dataset can also be used to train and test machine learning models for forecasting stock prices, such as regression, neural networks, or ARIMA algorithms.

However, it is important to remember that stock market investments carry risks, and historical data analysis does not guarantee future results. Before making any investment decisions, it is always advisable to consult with an experienced financial advisor.

Columns in CSV:

Data (Date): Date of the stock price. Otwarcie (Open): The opening price of PKN Orlen's stock on a given day. Najwyższy (High): The highest price of PKN Orlen's stock on a given day. Najniższy (Low): Lowest price of PKN Orlen's stock reached on a given day. Zamknięcie (Close): The closing price of PKN Orlen's stock on a given day. Zmiana (Change): Difference between the closing price and the opening price of PKN Orlen's stock on a given day. Wolumen (Volume): Trading volume, representing the number of PKN Orlen's shares bought or sold on a given day. This dataset can be used for analyzing price trends, detecting changes in stock prices, or building predictive models to forecast the behavior of PKN Orlen's stock prices on the stock market. Relevant analyses and models can provide valuable insights to investors, analysts, and financial decision-makers interested in this market.

Data Source: The data is sourced from https://stooq.pl/db/, a financial data platform providing historical stock prices and market information.

This study examines the market return spillovers from the US market to 10 Asia-Pacific stock markets, accounting for approximately 91 per cent of the region’s GDP from 1991 to 2022. Our findings indicate an increased return spillover from the US stock market to the Asia-Pacific stock market over time, particularly after major global events such as the 1997 Asian and the 2008 global financial crises, the 2015 China stock market crash, and the COVID-19 pandemic. The 2008 global financial crisis had the most substantial impact on these events. In addition, the findings also indicate that US economic policy uncertainty and US geopolitical risk significantly affect spillovers from the US to the Asia-Pacific markets. In contrast, the geopolitical risk of Asia-Pacific countries reduces these spillovers. The study also highlights the significant impact of information and communication technologies (ICT) on these spillovers. Given the increasing integration of global financial markets, the findings of this research are expected to provide valuable policy implications for investors and policymakers.

The Long Depression was, by a large margin, the longest-lasting recession in U.S. history. It began in the U.S. with the Panic of 1873, and lasted for over five years. This depression was the largest in a series of recessions at the turn of the 20th century, which proved to be a period of overall stagnation as the U.S. financial markets failed to keep pace with industrialization and changes in monetary policy. Great Depression The Great Depression, however, is widely considered to have been the most severe recession in U.S. history. Following the Wall Street Crash in 1929, the country's economy collapsed, wages fell and a quarter of the workforce was unemployed. It would take almost four years for recovery to begin. Additionally, U.S. expansion and integration in international markets allowed the depression to become a global event, which became a major catalyst in the build up to the Second World War. Decreasing severity When comparing recessions before and after the Great Depression, they have generally become shorter and less frequent over time. Only three recessions in the latter period have lasted more than one year. Additionally, while there were 12 recessions between 1880 and 1920, there were only six recessions between 1980 and 2020. The most severe recession in recent years was the financial crisis of 2007 (known as the Great Recession), where irresponsible lending policies and lack of government regulation allowed for a property bubble to develop and become detached from the economy over time, this eventually became untenable and the bubble burst. Although the causes of both the Great Depression and Great Recession were similar in many aspects, economists have been able to use historical evidence to try and predict, prevent, or limit the impact of future recessions.

According to Cognitive Market Research, the global Date Palm market was USD 11512.2 million in 2024 and will expand at a compound annual growth rate (CAGR) of 5.60% from 2024 to 2031.

North America held the major market share of more than 40% of the global revenue, with a market size of USD 4604.88 million in 2024. The market will grow at a compound annual growth rate (CAGR) of 3.8% from 2024 to 2031.
Europe accounted for a share of over 30% of the global market size of USD 3453.66 million.
Asia Pacific held a market share of around 23% of global revenue, with a market size of USD 2647.81 million in 2024, and will grow at a compound annual growth rate (CAGR) of 7.6% from 2024 to 2031.
Latin America's Market will have more than 5% of the global revenue with a market size of USD 575.61 million in 2024 and will grow at a compound annual growth rate (CAGR) of 5.0% from 2024 to 2031.
The Middle East and Africa held the major market share of around 2% of the global revenue, with a market size of USD 230.24 million in 2024. The market will grow at a compound annual growth rate (CAGR) of 5.3% from 2024 to 2031.
Whole date product held the highest Date Palm market revenue share in 2024.

Market Dynamics of Date Palm Market

Key Drivers of Date Palm Market

Increasing Demand for Nutrient-Dense Superfoods: Date palm fruits are abundant in fiber, antioxidants, and vital minerals, becoming increasingly popular among health-conscious consumers worldwide. As awareness grows regarding natural sugar substitutes and clean-label products, the market for date-based snacks, syrups, and energy foods is witnessing significant growth.

Rising Export Opportunities: Key producers such as those in the Middle East and North Africa are enhancing exports in response to strong global demand for high-quality date varieties. Initiatives supported by governments, along with advanced packaging technologies and improved cold chain logistics, are facilitating international trade, particularly to Europe, North America, and the Asia-Pacific regions.

Growth in Organic and Sustainable Agriculture: There is an increasing inclination towards organically cultivated dates and sustainable palm farming. Consumers are prepared to pay a premium for pesticide-free, certified organic products. This trend is motivating date palm farmers to implement eco-friendly farming methods and obtain organic certifications.

Key Restrains for Date Palm Market

Sensitivity to Climate Conditions: Date palms are particularly vulnerable to extreme weather phenomena such as droughts and floods. Variability in climate can affect both the quality and quantity of yields, impacting the profitability of growers in traditional cultivation areas like the Middle East, North Africa, and South Asia.

High Labor and Maintenance Expenses: The cultivation and harvesting of date palms require significant labor. Manual tasks such as pollination, pruning, and harvesting necessitate skilled labor, which raises operational costs. This presents a considerable challenge for small-scale farmers and restricts scalability in markets sensitive to pricing.

Short Shelf Life and Storage Challenges: Fresh dates possess a relatively brief shelf life and necessitate appropriate temperature-controlled storage to avert spoilage. Insufficient storage infrastructure, particularly in developing regions, hampers market growth and results in post-harvest losses during transportation and export.

Key Trends in Date Palm Market

Innovation in Value-Added Date Products: The market is experiencing swift advancements in products derived from dates, including date syrup, energy bars, chocolates, and even non-dairy substitutes. These offerings target vegan, gluten-free, and clean-label consumer demographics, thereby broadening the market beyond conventional usage.

Government Support and Strategic Investments: Nations such as Saudi Arabia, the UAE, and Egypt are making substantial investments in research and development, export enhancement, and agricultural technology for date cultivation. Collaborations between public and private sectors, along with subsidies, are driving growth and enhancing competitiveness in international markets.

E-Commerce and Direct-to-Consumer Channels: Digital platforms are revolutionizing the marketing and sales of dates, allowing producers to connect directly with customers worldwide. High-quali...

The US_Stock_Data.csv dataset offers a comprehensive view of the US stock market and related financial instruments, spanning from January 2, 2020, to February 2, 2024. This dataset includes 39 columns, covering a broad spectrum of financial data points such as prices and volumes of major stocks, indices, commodities, and cryptocurrencies. The data is presented in a structured CSV file format, making it easily accessible and usable for various financial analyses, market research, and predictive modeling. This dataset is ideal for anyone looking to gain insights into the trends and movements within the US financial markets during this period, including the impact of major global events.

Key Features and Data Structure

The dataset captures daily financial data across multiple assets, providing a well-rounded perspective of market dynamics. Key features include:

• Commodities: Prices and trading volumes for natural gas, crude oil, copper, platinum, silver, and gold.
• Cryptocurrencies: Prices and volumes for Bitcoin and Ethereum, including detailed 5-minute interval data for Bitcoin.
• Stock Market Indices: Data for major indices such as the S&P 500 and Nasdaq 100.
• Individual Stocks: Prices and volumes for major companies including Apple, Tesla, Microsoft, Google, Nvidia, Berkshire Hathaway, Netflix, Amazon, and Meta.

The dataset’s structure is designed for straightforward integration into various analytical tools and platforms. Each column is dedicated to a specific asset's daily price or volume, enabling users to perform a wide range of analyses, from simple trend observations to complex predictive models. The inclusion of intraday data for Bitcoin provides a detailed view of market movements.

Applications and Usability

This dataset is highly versatile and can be utilized for various financial research purposes:

• Market Analysis: Track the performance of key assets, compare volatility, and study correlations between different financial instruments.
• Risk Assessment: Analyze the impact of commodity price movements on related stock prices and evaluate market risks.
• Educational Use: Serve as a resource for teaching market trends, asset correlation, and the effects of global events on financial markets.

The dataset’s daily updates ensure that users have access to the most current data, which is crucial for real-time analysis and decision-making. Whether for academic research, market analysis, or financial modeling, the US_Stock_Data.csv dataset provides a valuable foundation for exploring the complexities of financial markets over the specified period.

Acknowledgements:

This dataset would not be possible without the contributions of Dhaval Patel, who initially curated the US stock market data spanning from 2020 to 2024. Full credit goes to Dhaval Patel for creating and maintaining the dataset. You can find the original dataset here: US Stock Market 2020 to 2024.

The global calendar market is poised for robust expansion, projected to reach an estimated USD 3,200 million by 2025, with a compelling Compound Annual Growth Rate (CAGR) of 4.5% through 2033. This sustained growth trajectory is underpinned by a confluence of factors, including the enduring utility of calendars as organizational tools in both personal and professional spheres, alongside their evolving role as decorative and promotional items. The demand for wall and desk calendars remains significant, driven by their tactile appeal and visual presence in homes, offices, and educational institutions. Furthermore, the increasing adoption of digital technologies has not diminished the market's vitality but rather spurred innovation, with businesses leveraging calendars for brand visibility and customer engagement through personalized and custom-designed products. The factory direct sales channel is a significant contributor, reflecting the efficiency of bulk orders for corporate gifting and promotional campaigns. The market's growth is further propelled by evolving consumer preferences and the increasing importance of marketing and branding strategies. Online sales channels are experiencing remarkable growth, offering greater accessibility and a wider selection for consumers worldwide. This shift necessitates a strong digital presence for manufacturers and distributors. While the market benefits from the constant need for time management and organizational aids, it also faces challenges. The rise of digital calendars and planning apps presents a substitute for traditional paper-based products, requiring the physical calendar market to continuously emphasize its unique advantages, such as aesthetic appeal, permanence, and the tangible experience it offers. Emerging economies, particularly in the Asia Pacific region, are expected to be significant growth engines due to increasing disposable incomes and a burgeoning corporate sector. Innovations in design, material sustainability, and the integration of augmented reality features in physical calendars are also anticipated to shape market dynamics and drive future demand.

While the global coronavirus (COVID-19) pandemic caused all major stock market indices to fall sharply in March 2020, both the extent of the decline at this time, and the shape of the subsequent recovery, have varied greatly. For example, on March 15, 2020, major European markets and traditional stocks in the United States had shed around ** percent of their value compared to January *, 2020. However, Asian markets and the NASDAQ Composite Index only shed around ** to ** percent of their value. A similar story can be seen with the post-coronavirus recovery. As of November 14, 2021 the NASDAQ composite index value was around ** percent higher than in January 2020, while most other markets were only between ** and ** percent higher. Why did the NASDAQ recover the quickest? Based in New York City, the NASDAQ is famously considered a proxy for the technology industry as many of the world’s largest technology industries choose to list there. And it just so happens that technology was the sector to perform the best during the coronavirus pandemic. Accordingly, many of the largest companies who benefitted the most from the pandemic such as Amazon, PayPal and Netflix, are listed on the NADSAQ, helping it to recover the fastest of the major stock exchanges worldwide. Which markets suffered the most? The energy sector was the worst hit by the global COVID-19 pandemic. In particular, oil companies share prices suffered large declines over 2020 as demand for oil plummeted while workers found themselves no longer needing to commute, and the tourism industry ground to a halt. In addition, overall share prices in two major stock exchanges – the London Stock Exchange (as represented by the FTSE 100 index) and Hong Kong (as represented by the Hang Seng index) – have notably recovered slower than other major exchanges. However, in both these, the underlying issue behind the slower recovery likely has more to do with political events unrelated to the coronavirus than it does with the pandemic – namely Brexit and general political unrest, respectively.

Snapshot img

Event Logistics Market Size 2025-2029

The event logistics market size is valued to increase USD 1.58 billion, at a CAGR of 5.9% from 2024 to 2029. Growth of large-scale events will drive the event logistics market.

Major Market Trends & Insights

APAC dominated the market and accounted for a 33% growth during the forecast period.
By Event Type - Entertainment events segment was valued at USD 1.73 billion in 2023
By End-user - Corporates and enterprises segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: 54.70 million
Market Future Opportunities: USD 1.58 billion 
CAGR : 5.9%
APAC: Largest market in 2023

Market Summary

The market encompasses the planning, coordination, and execution of logistical operations for various events, from small corporate gatherings to large-scale international conferences. This dynamic market is fueled by the growing demand for seamless event experiences, with core technologies and applications, such as digital and smart logistics solutions, playing a pivotal role. Service types, including transportation, accommodation, catering, and security, are continually evolving to meet the needs of event organizers. Regulations and geopolitical risks pose challenges, while the adoption of digital solutions and the growth of large-scale events offer significant opportunities.
According to recent studies, the digital transformation of event logistics is expected to reach a market share of over 30% by 2026. In related markets such as the transportation and hospitality industries, the integration of technology is also driving innovation and growth. The ongoing unfolding of these trends and patterns underscores the continuous evolution of the market.

What will be the Size of the Event Logistics Market during the forecast period?

Get Key Insights on Market Forecast (PDF) Request Free Sample

How is the Event Logistics Market Segmented and what are the key trends of market segmentation?

The event logistics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Event Type

  Entertainment events
  Sports events
  Trade fairs and expos
  Corporate events
  Others


End-user

  Corporates and enterprises
  Entertainment companies
  Government and public sector
  Sports organizations
  Others


Service Type

  Transportation and freight
  On-site setup and handling
  Warehousing and storage
  Customs and compliance
  Others


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    Australia
    China
    India
    Japan


  South America

    Brazil


  Rest of World (ROW)

By Event Type Insights

The entertainment events segment is estimated to witness significant growth during the forecast period.

The market encompasses a significant and intricate segment dedicated to managing the complexities of various types of events, particularly entertainment events. This category comprises concerts, music festivals, film festivals, theater productions, live shows, and touring performances. These events necessitate the transportation of substantial volumes of equipment, including audio-visual gear, stage props, lighting rigs, costumes, instruments, and promotional materials, often across cities, countries, or even continents. The logistical challenges are amplified by tight turnaround times between shows. Effective execution of entertainment events hinges on the precise coordination of transport, customs clearance, setup, and dismantling within narrow timeframes. Logistics providers must be adept at handling last-minute changes, rerouting, and special cargo handling, as a considerable portion of the equipment is high-value, fragile, or custom-made.

Moreover, sustainability is increasingly becoming a crucial aspect of event planning, with a growing emphasis on reducing carbon footprints and minimizing waste. Event marketing automation, data privacy compliance, attendee engagement tools, and resource allocation models are essential components of modern event logistics. Contract negotiation strategies, event sponsorship acquisition, exhibitor management tools, accessibility event planning, digital ticketing solutions, company management platforms, supplier relationship management, crowd management strategies, event registration systems, lead generation strategies, venue management software, security management systems, virtual event platforms, emergency response planning, event staffing solutions, registration data analytics, event content management, post-event evaluation metrics, event technology integration, hybrid event management, transportation route planning, real-time event tracking, risk assessment pro

The global capital exchange ecosystem market, valued at $1.06 trillion in 2025, is projected to experience robust growth, driven by increasing global trade, the rise of fintech innovations, and a growing preference for digital trading platforms. The market's Compound Annual Growth Rate (CAGR) of 5.80% from 2025 to 2033 signifies a consistently expanding market opportunity. Key segments, including the primary and secondary markets, contribute significantly to this growth, with the primary market fueled by Initial Public Offerings (IPOs) and other new listings, while the secondary market thrives on the continuous trading of existing securities. The diverse range of stock and bond types (common, preferred, growth, value, defensive stocks; government, corporate, municipal, mortgage bonds) caters to a broad spectrum of investor profiles and risk appetites. Technological advancements, including high-frequency trading algorithms and improved data analytics, are further enhancing market efficiency and liquidity. However, regulatory hurdles, geopolitical uncertainties, and cybersecurity threats remain as potential restraints on market growth. The strong presence of established exchanges like the New York Stock Exchange (NYSE), NASDAQ, and the London Stock Exchange, alongside emerging players in Asia and other regions, contributes to the market's competitive landscape. Regional growth will likely be influenced by economic development, regulatory frameworks, and investor confidence, with North America and Asia Pacific anticipated to maintain leading positions. The future of the capital exchange ecosystem hinges on adaptation and innovation. The increasing integration of blockchain technology and decentralized finance (DeFi) is expected to reshape trading infrastructure and potentially challenge traditional exchange models. Increased regulatory scrutiny globally will likely necessitate further transparency and improved risk management practices by exchanges. Furthermore, the growing prominence of Environmental, Social, and Governance (ESG) investing will influence investment strategies and, consequently, trading activity across various asset classes. The market's future success will depend on its ability to effectively manage risks, embrace technological innovation, and meet the evolving needs of a diverse and increasingly sophisticated investor base. Continued growth is anticipated, driven by both established and emerging markets. Recent developments include: In December 2023, Defiance ETFs, introduced the Defiance Israel Bond ETF (NYSE Arca: CHAI) to facilitate investors' access to the Israeli bond market. CHAI commenced trading on the New York Stock Exchange. The ETF, CHAI, mirrors the MCM (Migdal Capital Markets) BlueStar Israel Bond Index, enabling investors to tap into both Israel government and corporate bonds. This index specifically monitors the performance of bonds, denominated in USD and shekels, issued by either the Israeli government or Israeli corporations., In January 2024, the National Stock Exchange (NSE) saw a 22% rise in its investor base, increasing from 70 million to 85.4 million during the calendar year 2023. This growth highlights the increasing participation of retail investors in the stock market.. Key drivers for this market are: Automating all processes, Regulatory Landscape. Potential restraints include: Automating all processes, Regulatory Landscape. Notable trends are: Increasing Stock Exchanges Index affecting Capital Market Exchange Ecosystem.

Smart Insider monitors new director changes at listed companies, allowing our clients to view the compositions of boardrooms and monitor changes as they occur for the stocks that matter to them.

We track internal movement in company for director changes as they move positions within a firm or transition to a new one as well as monitor key dates and biographical information. Insights derived from aggregated share transactions across multiple boards can expose patterns and activity that might otherwise remain below the radar.

In addition to key dates for director and board changes, our people profiles go beyond by including biographies and pictures. This comprehensive visual profile provides clients with a holistic understanding of the characteristics of directors and senior officers.

We provide tailored data delivery to meet client needs, including scheduled desktop reports, nightly updates via FTP, API or Snowflake.

Investor Relations and HR departments can receive concise boardroom personnel changes across peer companies. Fund managers can get portfolio/watchlist updates and sector-specific information.

Sample dataset can be provided upon request.

Tags: Corporate Actions Data, Management Changes, Director Insights, Internal Movement in Companies, Board Changes, Stock Holdings Data, Salary Data, Insider Score.

The prediction of stock prices has long been a captivating subject in academic research. This study aims to forecast the prices of prominent stocks in five key industries of the Chinese A-share market by leveraging the synergistic power of deep learning techniques and investor sentiment analysis. To achieve this, a sentiment multi-classification dataset is for the first time constructed for China’s stock market, based on four types of sentiments in modern psychology. The significant heterogeneity of sentiment changes in the sectors’ leading stock markets is trained and mined using the Bi-LSTM-ATT model. The impact of multi-classification investor sentiment on stock price prediction was analyzed using the CNN-Bi-LSTM-ATT model. It finds that integrating sentiment indicators into the prediction of industry leading stock prices can enhance the accuracy of the model. Drawing upon four fundamental sentiment types derived from modern psychology, our dataset provides a comprehensive framework for analyzing investor sentiment and its impact on forecasting the stock prices of China’s A-share market.

Techsalerator offers an extensive dataset of End-of-Day Pricing Data for all 14 companies listed on the Belarus Currency and Stock Exchange (BCSE) in Belarus. This dataset includes the closing prices of equities (stocks), bonds, and indices at the end of each trading session. End-of-day prices are vital pieces of market data that are widely used by investors, traders, and financial institutions to monitor the performance and value of these assets over time.

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Top 5 used data fields in the End-of-Day Pricing Dataset for Belarus:

1. Equity Closing Price :The closing price of individual company stocks at the end of the trading day.This field provides insights into the final price at which market participants were willing to buy or sell shares of a specific company.

2. Bond Closing Price: The closing price of various fixed-income securities, including government bonds, corporate bonds, and municipal bonds. Bond investors use this field to assess the current market value of their bond holdings.

3. Index Closing Price: The closing value of market indices, such as the Botswana stock market index, at the end of the trading day. These indices track the overall market performance and direction.

4. Equity Ticker Symbol: The unique symbol used to identify individual company stocks. Ticker symbols facilitate efficient trading and data retrieval.

5. Date of Closing Price: The specific trading day for which the closing price is provided. This date is essential for historical analysis and trend monitoring.

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Top 5 financial instruments with End-of-Day Pricing Data in Belarus:

Belarusian Currency: The Belarusian ruble (BYN) is the official currency of Belarus. Monitoring the exchange rate of the Belarusian ruble against major foreign currencies provides insights into the country's economic stability and trade dynamics.

Belarusian Stock Exchange Index (BASE): The BASE index represents the performance of companies listed on the Belarusian Currency and Stock Exchange (BCSE). It tracks the overall trends in the Belarusian stock market.

Company A: A prominent Belarusian company in a key sector such as energy, manufacturing, or technology. The stock of this company contributes to the diversity of the market and reflects trends in the respective sector.

Company B: A leading Belarusian company in the agriculture or food processing sector. The stock of this company reflects the performance of the agricultural industry in Belarus.

Company C: A significant Belarusian company in the banking or financial services sector. The stock of this company is influential in the financial sector of Belarus.

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If you're interested in accessing Techsalerator's End-of-Day Pricing Data for Belarus, please contact info@techsalerator.com with your specific requirements. Techsalerator will provide you with a customized quote based on the number of data fields and records you need. The dataset can be delivered within 24 hours, and ongoing access options can be discussed if needed.

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Data fields included:

Equity Ticker Symbol Equity Closing Price Bond Ticker Symbol Bond Closing Price Index Ticker Symbol Index Closing Price Date of Closing Price Equity Name Equity Volume Equity High Price Equity Low Price Equity Open Price Bond Name Bond Coupon Rate Bond Maturity Index Name Index Change Index Percent Change Exchange Currency Total Market Capitalization Dividend Yield Price-to-Earnings Ratio (P/E) ‍

Q&A:

1. How much does the End-of-Day Pricing Data cost in Belarus ?

The cost of this dataset may vary depending on factors such as the number of data fields, the frequency of updates, and the total records count. For precise pricing details, it is recommended to directly consult with a Techsalerator Data specialist.

1. How complete is the End-of-Day Pricing Data coverage in Belarus?

Techsalerator provides comprehensive coverage of End-of-Day Pricing Data for various financial instruments, including equities, bonds, and indices. Thedataset encompasses major companies and securities traded on Belarus exchanges.

1. How does Techsalerator collect this data?

Techsalerator collects End-of-Day Pricing Data from reliable sources, including stock exchanges, financial news outlets, and other market data providers. Data is carefully curated to ensure accuracy and reliability.

1. Can I select specific financial instruments or multiple countries with Techsalerator's End-of-Day Pricing Data?

Techsalerator offers the flexibility to select specific financial instruments, such as equities, bonds, or indices, depending on your needs. While the dataset focuses on Botswana, Techsalerator also provides data for other countries and international markets.

1. How do I pay for this dataset?

Techsalerator accepts various payment methods, including credit cards, direct transfers, ACH, and wire transfers, facilitating a conveni...

Abstract Purpose: The objective of this study is to analyze the impact of changes in credit ratings on the long-term return of Brazilian firms. Design/methodology/approach: We conducted an event study to measure how stock prices in the Brazilian stock exchange (B3) react to rating upgrades and downgrades by Moody’s and S&P. Findings: Our sample presents positive and significant returns measured by the BHAR for ratings downgrades and non-significant ones for upgrades. Our data also show the important role of the previous rating in explaining these results in a non-linear fashion. Originality/value: Our research makes an important contribution to the theory of market efficiency, analyzing the degree of information present in the announcements of credit ratings changes. We also present results for Brazilian companies, correcting gaps pointed out in previous methodologies.

A stock market is a non-stationary complex system. The stock interactions are important for understanding the state of the market. However, our knowledge on the stock interactions on the minute timescale is limited. Here we apply the random matrix theory and methods in complex networks to study the stock interactions and sector interactions. Further, we construct a new kind of cross-correlation matrix to investigate the correlation between the stock interactions at different minutes within one trading day. Based on 50 million minute-to-minute price data in the Shanghai stock market, we discover that the market states in the morning and afternoon are significantly different. The differences mainly exist in three aspects, i.e. the co-movement of stock prices, interactions of sectors and correlation between the stock interactions at different minutes. In the afternoon, the component stocks of sectors are more robust and the structure of sectors is firmer. Therefore, the market state in the afternoon is more stable. Furthermore, we reveal that the information of the sector interactions can indicate the financial crisis in the market, and the indicator based on the empirical data in the afternoon is more effective.

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.

Apogee Therapeutics: Analysts Project Significant Growth Potential for (APGE).

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