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

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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

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This dataset contains historical stock price data for Tesla, Inc. (TSLA) starting from its IPO date, June 29, 2010, to January 1, 2025. The dataset includes daily records of Tesla's stock performance on the NASDAQ stock exchange. It is ideal for time-series analysis, stock price prediction, and understanding the long-term performance of Tesla in the stock market.

The dataset consists of the following columns:

1. Date: The trading date.
2. Open: Opening stock price on the given date.
3. High: The highest stock price during the trading day.
4. Low: The lowest stock price during the trading day.
5. Close: The closing stock price for the day.
6. Adj Close: Adjusted closing price (corrected for dividends and stock splits).
7. Volume: The number of shares traded during the day.

Use Cases of Tesla Stock Historical Data

1. Time-Series Analysis

   • Analyze trends in Tesla's stock prices over time.
   • Identify seasonality, volatility, and long-term patterns in Tesla’s performance.

2. Stock Price Prediction

   • Develop predictive models to forecast future stock prices using techniques such as ARIMA, LSTMs, or regression.

3. Investment Strategy Evaluation

   • Backtest trading strategies by simulating trades based on historical price movements.
   • Analyze returns of investment strategies such as moving averages, RSI, or Bollinger Bands.

4. Market Sentiment Analysis

   • Correlate Tesla’s stock performance with news sentiment, earnings reports, and market events.

5. Portfolio Diversification

   • Evaluate Tesla’s performance compared to other stocks or indices to assess its role in a diversified portfolio.

6. Risk Management

   • Calculate volatility, beta, and other risk metrics to assess the risk associated with investing in Tesla stock.

7. Economic and Market Studies

   • Study how macroeconomic indicators (like inflation, interest rates) influence Tesla’s stock price.
   • Analyze Tesla’s performance during major economic events such as the COVID-19 pandemic or policy changes.

8. Stock Splits and Adjustments Analysis

   • Examine the impact of Tesla’s stock splits on price and trading volume.

9. Educational Purposes

   • Serve as a dataset for academic projects, coursework, or tutorials on financial data analysis.

10. Correlation with Sector Trends

    • Compare Tesla’s stock performance with other automotive or renewable energy companies.

11. Data Visualization and Dashboarding

    • Create dashboards using tools like Tableau, Power BI, or Python libraries to visualize Tesla’s stock performance metrics.

12. A/B Testing for Financial Applications

    • Use historical stock data for controlled experiments in finance-related applications to improve decision-making tools.

An index that can be used to gauge broad financial conditions and assess how these conditions are related to future economic growth. The index is broadly consistent with how the FRB/US model generally relates key financial variables to economic activity. The index aggregates changes in seven financial variables: the federal funds rate, the 10-year Treasury yield, the 30-year fixed mortgage rate, the triple-B corporate bond yield, the Dow Jones total stock market index, the Zillow house price index, and the nominal broad dollar index using weights implied by the FRB/US model and other models in use at the Federal Reserve Board. These models relate households' spending and businesses' investment decisions to changes in short- and long-term interest rates, house and equity prices, and the exchange value of the dollar, among other factors. These financial variables are weighted using impulse response coefficients (dynamic multipliers) that quantify the cumulative effects of unanticipated permanent changes in each financial variable on real gross domestic product (GDP) growth over the subsequent year. The resulting index is named Financial Conditions Impulse on Growth (FCI-G). One appealing feature of the FCI-G is that its movements can be used to measure whether financial conditions have tightened or loosened, to summarize how changes in financial conditions are associated with real GDP growth over the following year, or both.

This dataset includes various economic indicators such as stock market performance, inflation rates, GDP, interest rates, employment data, and housing index, all of which are crucial for understanding the state of the economy. By analysing this dataset, one can gain insights into the causes and effects of past recessions in the US, which can inform investment decisions and policy-making.

There are 20 columns and 343 rows spanning 1990-04 to 2022-10

The columns are:

1. Price: Price column refers to the S&P 500 lot price over the years. The S&P 500 is a stock market index that measures the performance of 500 large companies listed on stock exchanges in the United States. This variable represents the value of the S&P 500 index from 1980 to present. Industrial Production: This variable measures the output of industrial establishments in the manufacturing, mining, and utilities sectors. It reflects the overall health of the manufacturing industry, which is a key component of the US economy.

2. INDPRO: Industrial production measures the output of the manufacturing, mining, and utility sectors of the economy. It provides insights into the overall health of the economy, as a decline in industrial production can indicate a slowdown in economic activity. This data can be used by policymakers and investors to assess the state of the economy and make informed decisions.

3. CPI: CPI stands for Consumer Price Index, which measures the change in the prices of a basket of goods and services that consumers purchase. CPI inflation represents the rate at which the prices of goods and services in the economy are increasing.

4. Treasure Bill rate (3 month to 30 Years): Treasury bills (T-bills) are short-term debt securities issued by the US government. This variable represents the interest rates on T-bills with maturities ranging from 3 months to 30 years. It reflects the cost of borrowing money for the government and provides an indication of the overall level of interest rates in the economy.

5. GDP: GDP stands for Gross Domestic Product, which is the value of all goods and services produced in a country. This dataset is taking into account only the Nominal GDP values. Nominal GDP represents the total value of goods and services produced in the US economy without accounting for inflation.

6. Rate: The Federal Funds Rate is the interest rate at which depository institutions lend reserve balances to other depository institutions overnight. It is set by the Federal Reserve and is used as a tool to regulate the money supply in the economy.

7. BBK_Index: The BBKI are maintained and produced by the Indiana Business Research Center at the Kelley School of Business at Indiana University. The BBK Coincident and Leading Indexes and Monthly GDP Growth for the U.S. are constructed from a collapsed dynamic factor analysis of a panel of 490 monthly measures of real economic activity and quarterly real GDP growth. The BBK Leading Index is the leading subcomponent of the cycle measured in standard deviation units from trend real GDP growth.

8. Housing Index: This variable represents the value of the housing market in the US. It is calculated based on the prices of homes sold in the market and provides an indication of the overall health of the housing market.

9. Recession binary column: This variable is a binary indicator that takes a value of 1 when the US economy is in a recession and 0 otherwise. It is based on the official business cycle dates provided by the National Bureau of Economic Research.

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.

Stock Analysis Software Market Outlook

The global stock analysis software market size was valued at approximately USD 1.2 billion in 2023 and is projected to reach around USD 3.5 billion by 2032, growing at a compound annual growth rate (CAGR) of 12.5% during the forecast period. The growth of this market is driven by the increasing adoption of advanced analytics tools by individual investors and financial institutions to make informed investment decisions. The rising demand for automated trading systems and the integration of artificial intelligence (AI) and machine learning (ML) in stock analysis software are significant growth factors contributing to the market expansion.

One of the primary growth factors for the stock analysis software market is the increasing complexity and volume of financial data. With the exponential growth of data from various sources such as social media, news articles, and financial statements, investors and financial analysts require sophisticated tools to process and interpret this information accurately. Stock analysis software equipped with AI and ML algorithms can analyze vast datasets in real-time, providing valuable insights and predictive analytics that enhance investment strategies. Moreover, the growing trend of algorithmic trading, which relies heavily on high-speed data processing and automated decision-making, is further propelling the market growth.

Another crucial growth driver is the rising awareness and adoption of stock analysis software among individual investors. As more individuals seek to actively manage their investment portfolios, there is a growing demand for user-friendly and cost-effective stock analysis tools that offer comprehensive market analysis, technical indicators, and personalized investment recommendations. The proliferation of mobile applications and the increasing accessibility of cloud-based stock analysis solutions have made it easier for retail investors to access advanced analytical tools, thereby contributing to market expansion.

The integration of innovative technologies such as natural language processing (NLP) and sentiment analysis into stock analysis software is also a significant growth factor. These technologies enable the software to interpret and analyze unstructured data from news articles, social media, and other textual sources to gauge market sentiment and predict stock price movements. This capability is particularly valuable in today's fast-paced financial markets, where sentiment and news events can have a substantial impact on stock prices. The continuous advancements in AI and NLP technologies are expected to drive further innovations and improvements in stock analysis software, thereby boosting market growth.

In the evolving landscape of financial technology, Investor Relations Tools have become indispensable for companies seeking to maintain transparent and effective communication with their stakeholders. These tools facilitate seamless interaction between companies and their investors, providing real-time updates, financial reports, and strategic insights. By leveraging these tools, companies can enhance their investor engagement strategies, build trust, and foster long-term relationships with their shareholders. The integration of advanced analytics and AI-driven insights into Investor Relations Tools further empowers companies to tailor their communication strategies, ensuring that they meet the diverse needs of their investor base. As the demand for transparency and accountability in financial markets continues to grow, the adoption of sophisticated Investor Relations Tools is expected to rise, playing a crucial role in the broader ecosystem of stock analysis software.

From a regional perspective, North America is anticipated to hold the largest market share due to the high concentration of financial institutions, brokerage firms, and individual investors in the region. The presence of key market players and the early adoption of advanced technologies also contribute to the dominant position of North America in the global stock analysis software market. Additionally, the Asia Pacific region is expected to witness significant growth during the forecast period, driven by the increasing number of retail investors, rapid economic development, and the growing financial markets in countries such as China and India.

Component Analysis

Financial News Events Dataset - Comprehensive Description

Overview

This synthetic dataset contains 3,024 records of financial news headlines centered around major market events from February 2025 to August 2025. The dataset captures real-time market dynamics, sentiment analysis, and trading patterns across global financial markets, making it ideal for financial analysis, sentiment modeling, and market prediction tasks.

Dataset Specifications

• Total Records: 3,024 rows
• Total Features: 12 columns
• Date Range: February 1, 2025 - August 14, 2025
• File Formats: CSV, JSON, XLSX
• Data Quality: ~5% null values strategically distributed for realistic data cleaning scenarios

Column Descriptions

Key Features & Statistics

Market Events Coverage (20 Categories)

• Stock Market Crashes & Rallies
• Interest Rate Changes & Central Bank Meetings
• Corporate Earnings Reports & IPO Launches
• Government Policy Announcements
• Trade Tariffs & Geopolitical Events
• Cryptocurrency Regulations
• Supply Chain Disruptions
• Economic Data Releases

Global Market Indices (18 Major Indices)

• US Markets: S&P 500, Dow Jones, Nasdaq Composite, Russell 2000
• Indian Markets: NSE Nifty, BSE Sensex
• European Markets: FTSE 100, DAX, Euro Stoxx 50, CAC 40
• Asian Markets: Nikkei 225, Hang Seng, Shanghai Composite, KOSPI
• Others: TSX, ASX 200, IBOVESPA, S&P/TSX Composite

News Sources (18 Reputable Publications)

Major financial news outlets including Reuters, Bloomberg, CNBC, Financial Times, Wall Street Journal, Economic Times, Forbes, and specialized financial publications.

Sector Distribution (18 Business Sectors)

Technology, Finance, Healthcare, Energy, Consumer Goods, Utilities, Industrials, Materials, Real Estate, Telecommunications, Automotive, Retail, Pharmaceuticals, Aerospace & Defense, Agriculture, Transportation, Media & Entertainment, Construction.

Data Quality & Preprocessing Notes

• Realistic Null Distribution: Approximately 5% null values in key columns (Headline, Sentiment, Index_Change_Percent, News_Url) to simulate real-world data collection challenges
• Balanced Sentiment Distribution: Mix of positive, neutral, and negative sentiment classifications
• Diverse Market Conditions: Index changes ranging from -5% to +5% reflecting various market scenarios
• Volume Variability: Trading volumes span 1M to 500M to represent different market liquidity conditions

Potential Use Cases

📈 Financial Analysis

• Market sentiment analysis and trend prediction
• Correlation studies between news events and market movements
• Trading volume pattern analysis

🤖 Machine Learning Applications

• Sentiment classification model training
• Market movement prediction algorithms
• News headline generation models
• Event-driven trading strategy development

📊 Data Visualization Projects

• Interactive market sentiment dashboards
• Time-series analysis of market events
• Geographic distribution of financial news impact
• Sector-wise performance visualization

🔍 Research Applications

• Academic research on market efficiency
• News impact analysis on different sectors
• Cross-market correlation studies
• Event study methodologies

Technical Specifications

• Memory Usage: Approximately 1.5MB across all formats
• **Proces...

The benchmark interest rate in the United Kingdom was last recorded at 4 percent. This dataset provides - United Kingdom Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Stock Price Time Series for Japan Real Estate Investment Corp. Japan Real Estate Investment Corporation (the "Company") was established on May 11, 2001 pursuant to Japan's Act on Investment Trusts and Investment Corporations ("ITA"). The Company was listed on the real estate investment trust market of the Tokyo Stock Exchange ("TSE") on September 10, 2001 (Securities Code: 8952). Since its IPO, the size of the Company's assets (total acquisition price) has grown steadily, expanding from 92.8 billion yen to 1,167.7 billion yen as of March 31, 2025. Over the same period, the Company's portfolio has also increased from 20 properties to 77 properties. During the March 2025 period (October 1, 2024 to March 31, 2025), the Japanese economy continued to demonstrate a gradual recovery, despite some lingering stagnation in capital investment and personal consumption due to inflation and other factors. On the other hand, given the policy rate hikes by the Bank of Japan, the shift in global interest rates to a lowering phase, the impact of U.S. policy trends, such as trade policy and other factors, interest rate trends, overseas political and economic developments, and price trends, including resource prices, will continue to bear watching. In the office leasing market, demand continues to grow for leases driven by business expansion and relocations aimed at improving location. As a result, the vacancy rate in central Tokyo continues to decline gradually. In addition, rent levels are rising at an accelerating rate. In light of the prevailing conditions in the leasing market, the Company is striving to attract new tenants through strategic leasing activities and to further enhance the satisfaction level of existing tenants by adding value to its portfolio properties with the aim of maintaining and improving the occupancy rate and realizing sustainable income growth across the entire portfolio. In the real estate trading market, despite the Bank of Japan normalizing its monetary policy, the appetite for property acquisition among both domestic and foreign investors remains firm, backed ma

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.

We investigate high-frequency reactions in the Eurozone stock market and the UK stock market during the time period surrounding the European Central Bank (ECB) and the Bank of England (BoE)'s interest rate decisions assessing how these two markets react and co-move influencing each other.

The effects are quantified by measuring linear and non-linear transfer entropy combined with a Bivariate Empirical Mode Decomposition (BEMD) from a dataset of 1-minute prices for the Euro Stoxx 50 and the FTSE 100 stock indices.

We uncover that central banks' interest rate decisions induce an upsurge in intraday volatility that is more pronounced on ECB announcement days and there is a significant information flow between the markets with prevalent direction going from the market where the announcement is made towards the other.

The benchmark interest rate in Pakistan was last recorded at 11 percent. This dataset provides - Pakistan Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Using all stocks listed in the London Stock Exchange for the period from January 1989 to December 2018, the dataset comprises the following series:

1. Annual returns for 20 asset growth portfolios, following Fama and French (1993) methodology.
2. Annual returns for 25 portfolios size-book to market equity, following Fama and French (1993) methodology.
3. Annual returns for 62 industry portfolios, using two-digit SIC codes.
4. Fama and French (1993) factors for their three-factor model (RM, SMB and HML).
5. Fama and French (2015) factors for their five-factor model (RM, SMB, HML, RMW, and CMA).
6. Variation of the Amihid illiquidy measure for the London Stock Exchange, following Amihud (2002) methodology.
7. Three-month interest rate of the Treasury Bill for the United Kingdom, as provided by the OECD database.

We have produced these series using the following data from Thomson Reuters Datastream: (i) total return index (RI series), (ii) market value (MV series), (iii) market-to-book equity (PTBV series), (iv) total assets (WC02999 series), (v) return on equity (WC08301 series), (vi) tax rate (WC08346 series), (vii) primary SIC codes, (viii) turnover by volume (VO series), and (ix) the market price (P series). Following Griffin et al. (2010), we use the generic rules provided by the authors for excluding non-common equity securities from Datastream data.

REFERENCES: Amihud, Y. (2002). Illiquidity and stock returns: Cross-section and time-series effects. Journal of Financial Markets, 5, 31–56. Fama, E. F. and French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33, 3–56. Fama, E. F. and French, K. R. (2015). A five-factor asset pricing model. Journal of Financial Economics, 116, 1–22. Griffin, J. M., Kelly, P., and Nardari, F. (2010). Do market efficiency measures yield correct inferences? A comparison of developed and emerging markets. Review of Financial Studies, 23, 3225–3277.

The benchmark interest rate in India was last recorded at 5.50 percent. This dataset provides - India Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This table contains 38 series, with data starting from 1957 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 item: Canada), Rates (38 items: Bank rate; Chartered bank administered interest rates - prime business; Chartered bank - consumer loan rate; Forward premium or discount (-), United States dollars in Canada: 1 month; ...).

The dataset appears to focus on the following features:

Stock Market Data:

Open, High, Low, Close: Daily price movements of a stock or index. Volume: The number of shares traded. Economic Indicators:

InterestRate: Reflects monetary policy trends, impacting the financial market. ExchangeRate: Represents currency exchange rates, which influence international trade. VIX (Volatility Index): A measure of market sentiment and expected volatility. TEDSpread: The difference between the interest rates on interbank loans and short-term government debt; used to gauge financial risk. EFFR (Effective Federal Funds Rate): Represents the interest rate banks charge each other for overnight loans. Commodity Prices:

Gold: Often considered a safe-haven asset. Oil: Key economic input, reflecting energy market trends. Likely Analysis or Tasks in the Notebook Descriptive Statistics: Summarizing trends in stock prices, trading volumes, and economic indicators.

Visualization: Using line plots, candlestick charts, or correlation heatmaps to explore relationships between variables.

Predictive Modeling: Potential machine learning models to forecast stock prices or analyze the relationship between indicators and market performance.

Economic Insights: Investigating how factors like interest rates, exchange rates, and commodity prices impact the Nasdaq index.

This data collection is one in a series of financial surveys of consumers conducted annually since 1946. In a nationally representative sample, the head of each family unit was interviewed. Starting in 1966, in order to examine the effect that increased car ownership was having on American families, the data collected in this series were organized so that they could be analyzed by both family unit and car unit. The 1968 data are based on car unit. Survey questions regarding automobiles included number of drivers and car owners in the family, make and model of each car, purchase method, car financing and installment debt, and expectations of car purchases in the coming year. Other questions in the 1968 survey covered the respondent's attitudes toward national economic conditions (e.g., the effect of income tax, interest rates, the stock market, Vietnam War involvement, and relations with other communist countries on United States business) and price activity, as well as the respondent's own financial situation. Other questions examined the family unit head's occupation, and the nature and amount of the family's income, debts, liquid assets, changes in liquid assets, savings, investment preferences, and actual and expected purchases of major durables. In addition, the survey explored in detail the subject of housing, e.g., previous and present home ownership, value of respondent's dwelling, and mortgage information. Personal data include age and education of head, household composition, and occupation. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR at https://doi.org/10.3886/ICPSR07448.v3. We highly recommend using the ICPSR version as have made this dataset available in multiple data formats.

Unternehmensfinanzierung. Nutzung von Krediten. Schwierigkeiten bei Kreditaufnahme. Themen: Wichtigstes Problem des Unternehmens; Innovation im letzten Jahr: Einführung eines neuen Produkts, Verbesserung des Produktionsprozesses, neue Organisation des Managements oder neuer Vertriebsweg; finanzielle Situation des Unternehmens; Veränderung der Unternehmensindikatoren wie Lohnsteuer, Umsatz, Materialkosten, Zinskosten, Gewinn und Marge, Veränderung zwischen Fremdkapital und Unternehmensvermögen; Nutzung von internen oder externen Finanzierungsquellen (Eigenmittel, Überziehungs- und Bankkredite etc.); Veränderungen in der Nutzung externer Finanzierungsquellen; Einfluss folgender Finanzierungsmittel auf die Notwendigkeit externer Finanzierung: Anlageinvestitionen, Vorratsinvestitionen oder mangelnde Eigenmittel; Beantragung von Bank- und Handelskrediten oder sonstige Außenfinanzierung; Erhalt der kompletten oder nur Teile der beantragten Finanzmittel; Veränderung in der Verfügbarkeit von Finanzmitteln; Veränderung der Bankfinanzierung in preislichen und nichtpreislichen Konditionen; Beurteilung der Veränderung der Verfügbarkeit von Finanzmitteln durch die Wirtschaftslage, unternehmerische Situation oder die Einstellung der Kreditgeber; Höhe des letzten Kreditantrags; Erhalt des letzten Kredits von einer Bank oder einer Privatperson; Verwendungszweck des Kredits; Unternehmenswachstum in den letzten drei Jahren; Wahrscheinlichkeit des zukünftigen Umsatzwachstums; Verhandlung mit Kapitalanlegern/Venture-Capital-Firmen; präferierte Form der Außenfinanzierung (Bankkredit, Kredit aus anderer Quelle, Kapitalbeteiligung, Darlehen); Höhe des gewünschten Finanzierungsbeitrags; Hauptgrund für mögliche Ablehnung einer gewünschten Finanzierung; erwartete Veränderung der verfügbaren Finanzierungsmittel des Unternehmens; geplanter Börsengang des Unternehmens; Haupthindernis für einen Börsengang. Demographie: Angaben zum Unternehmen: Anzahl der Mitarbeiter, Unternehmensgröße, Art des Unternehmens, Hauptgeschäftsfeld des Unternehmens, Branche, Jahr der Eintragung, Eigentümerstruktur; Jahresumsatz im eigenen Land in 2008. Zusätzlich verkodet wurde: Befragten-ID; Land; NACE-Code; Gewichtungsfaktor. Access to finance of small and medium enterprises. Topics: most important problem of the company; introduction (in the last twelve months) of new or significantly improved: products or services, production processes, organisational methods, marketing strategies; development of the following indicators in the last six months: turnover, labour cost, other cost, net interest expenses, profit, mark up; development of the amount of debt compared to the assets in the last six months; use of selected sources of financing in the last six months: internal funds, grants from public sources, bank or credit cards overdraft, bank loan, trade credit, other loan, leasing, issue of debt securities, subordinated loans, equity issuance, other; development of the need for the following types of external financing in the last six months: bank loans, trade credit, equity investment, issue of debt securities, other; impact of selected issues on the company’s need for external financing in the last six months: fixed investment, inventories and working capital, internal funds, corporate restructuring; application for selected sources of external financing in the last six months: bank loan, trade credit, other; success of the application for the aforementioned means of financing: received all financing requested, received only part of the financing requested, refused because of too high cost, refusal of application; development of the availability of the following means of financing for the own company over the last six months: bank loans, trade credit, equity investments, issue of debt securities, other; development of selected issues regarding terms and conditions of bank financing: level of interest rates, level of other cost, available size of loan or credit line, available loan maturity, collateral requirements, other; development of the following factors over the last six months: general economic outlook, access to public financial support, company-specific outlook, company’s capital, company’s credit history, willingness of banks to provide loans, willingness of business partners to provide trade credits, willingness of investors to invest in equity or debt securities issued by the company; size of last loan; provider of last loan; purpose of the loan; development of turnover in the last three years; expected development of turnover in the next three years; confidence to obtain desired results with regard to financing from: banks, equity investors; preferred type of external financing; aimed amount of financing; most important limiting factor with regard to financing; expected development of selected types of financing over the next six months: internal funds, bank loans, equity investments, trade credit, issue of debt securities, other; aims to be listed on a stock market within the next two years; main obstacles to be listed on a stock market. Demography: information about the company: number of employees, company size, kind of enterprise, main activity of the company, company sector, year of company registration, ownership structure; turnover of the company in the own country in 2008. Additionally coded was: respondent ID; country; NACE-Code; weighting factor.

This attachment contains data linked to the research article titled "Dynamic Heterogeneous Panel Analysis of Financial Market Disciplinary Effects on Fiscal Balance".The dataset contains cyclically adjusted primary balance, long-term interest rate, interest payment as a share of revenue, effective borrowing cost, lagged public debt as a share of GDP, fiscal rule index, VXO index, EMU dummy, and partial sums of positive and negative changes in the long-term interest rate, interest payment, effective borrowing cost, and strucural primary balance.

Begin-Period-Cashflow Time Series for Japan Real Estate Investment Corp. Japan Real Estate Investment Corporation (the "Company") was established on May 11, 2001 pursuant to Japan's Act on Investment Trusts and Investment Corporations ("ITA"). The Company was listed on the real estate investment trust market of the Tokyo Stock Exchange ("TSE") on September 10, 2001 (Securities Code: 8952). Since its IPO, the size of the Company's assets (total acquisition price) has grown steadily, expanding from 92.8 billion yen to 1,167.7 billion yen as of March 31, 2025. Over the same period, the Company's portfolio has also increased from 20 properties to 77 properties. During the March 2025 period (October 1, 2024 to March 31, 2025), the Japanese economy continued to demonstrate a gradual recovery, despite some lingering stagnation in capital investment and personal consumption due to inflation and other factors. On the other hand, given the policy rate hikes by the Bank of Japan, the shift in global interest rates to a lowering phase, the impact of U.S. policy trends, such as trade policy and other factors, interest rate trends, overseas political and economic developments, and price trends, including resource prices, will continue to bear watching. In the office leasing market, demand continues to grow for leases driven by business expansion and relocations aimed at improving location. As a result, the vacancy rate in central Tokyo continues to decline gradually. In addition, rent levels are rising at an accelerating rate. In light of the prevailing conditions in the leasing market, the Company is striving to attract new tenants through strategic leasing activities and to further enhance the satisfaction level of existing tenants by adding value to its portfolio properties with the aim of maintaining and improving the occupancy rate and realizing sustainable income growth across the entire portfolio. In the real estate trading market, despite the Bank of Japan normalizing its monetary policy, the appetite for property acquisition among both domestic and foreign investors remains firm, backed ma