The Apple share market data of 10 years can be used for educational purposes in a variety of ways, such as:

To learn about the stock market and how it works. By studying the historical price movements of Apple stock, you can learn about the different factors that can affect the stock market, such as economic conditions, interest rates, and company earnings. To develop investment strategies. By analyzing the Apple share market data, you can identify patterns and trends that can help you make better investment decisions. For example, you might notice that Apple stock tends to perform well in certain economic conditions or when the company releases new products. To learn about Apple's business. By tracking the company's stock price, you can get a sense of how investors are viewing Apple's financial performance and future prospects. This information can be helpful for making decisions about whether or not to invest in Apple stock. To conduct research on financial topics. The Apple share market data can be used to support research on a variety of financial topics, such as the impact of inflation on stock prices, the relationship between stock prices and interest rates, and the performance of different investment strategies. In addition to these educational purposes, the Apple share market data can also be used for other purposes, such as:

To create trading algorithms. Trading algorithms are computer programs that automatically buy and sell stocks based on certain criteria. The Apple share market data can be used to train trading algorithms to identify profitable trading opportunities. To develop risk management strategies. Risk management strategies are used to protect investors from losses. The Apple share market data can be used to identify risks associated with investing in Apple stock and to develop strategies to mitigate those risks. To make corporate decisions. The Apple share market data can be used by companies to make decisions about their business, such as how much to invest in research and development, how to allocate capital, and when to issue new shares. Overall, the Apple share market data is a valuable resource that can be used for a variety of educational and practical purposes. If you are interested in learning more about the stock market or investing, I encourage you to explore the Apple share market data.

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.

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.

Dataset to analyze the causal relationship between the Federal Reserve's interest rate policy and financial markets, focusing specifically on the Nasdaq index

Using all stocks listed in the Tokyo Stock Exchange and macroeconomic data for Japan, the dataset comprises the following series:

1. Monthly returns for 25 size-book-to-market equity portfolios, following the Fama and French (1993) methodology. (Raw data source: Datastream database)
2. Monthly returns for 20 momentum portfolios, following the Fama and French (1993) methodology. (Raw data source: Datastream database)
3. Monthly returns for 25 price-to-cash flow-dividend yield portfolios, following the Fama and French (1993) methodology. (Raw data source: Datastream database)
4. Fama and French three-factors (RM, SMB and HML), following the Fama and French (1993) methodology. (Raw data source: Datastream database)
5. Fama and French five-factors (RM, SMB, HML, RMW, and CMA), following the Fama and French (2015) methodology for all factors, except for RMW, which is determined using the return on assets as sorting variable, as in Hou, Xue and Zhang (2014). (Raw data source: Datastream database)
6. Private final consumption expenditure, in national currency and constant prices, non-seasonally adjusted, for Japan. (Raw data source: OECD)
7. Consumer Confidence Index (CCI) for Japan. (Raw data source: OECD)
8. Three-month interest rate of the Treasury Bill for Japan. (Raw data source: OECD)
9. Gross Domestic Product (GDP) for Japan. (Raw data source: OECD)
10. Consumer Price Index (CPI) growth rate for Japan. (Raw data source: OECD)

We have produced all return series using the following data from 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) price-to-cash flow ratio (PC series), and (vii) dividend yield (DY series). We have used the generic rules suggested by Griffin, Kelly, & Nardari (2010) for excluding non-common equity securities from Datastream data. We also exclude stocks with less than twelve observations in the period from July 1992 to June 2018. Accordingly, our sample comprises a total number of 5,312 stocks.

REFERENCES:

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. Hou K, Xue C, Zhang L. (2014). Digesting anomalies: An investment approach. Review of Financial Studies, 28, 650-705.

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.

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.

Disclaimer

All information presented here is for display purpose only, and may not be complete nor accurate. This information does not constitute a financial advice, and should not be used to make any investment decisions or financial transactions. This author rejects any claims for liabilities resulting from the use, misuse, or abuse of this information. Use at your own risk.

Motivation

Due to time zone differences between Australia and most of the rest of the world, Australians have the advantage of knowing what happened at markets elsewhere in the world, before the Australian market (ASX) is open in the morning, Sydney time.

This prior knowledge provides an excellent opportunity for arbitrage. In the hands of a savvy day-trader, or a shrewd long-term investor, this information gives you the advantage of predicting the ASX, and achieve potentially significant financial gains.

Method

For the ten years period from 1/7/2010 to 30/6/2020, the daily closing prices for 41 global market indicators are collected from various reliable public-domain sources. We checked the data for error or omissions and normalised all tabulated records in a format that facilitates further analysis and visulaisation.

Those 41 market indicators are what we consider significant measures of various external factors that may affect the performance of the Australian Stock Market, as represented by the ASX200. Those indicators are:

• Nine other major stock market indices from the USA, Europe, and Asia.

• The exchange rate of the $AU against 10 world currencies that are most relevant to Australia's international trade.

• Official interest rates by the RBA and the US Feds, as indicators of affinity of foreign funds to Australia.

• Yield rates for governments-issued bonds by 10 countries from Western and Asian economies, as measures of relative availability of credit and cross-border investment. Bonds are grouped into "Short-term" (one year maturity) and "Long-term" (10 to 30 years maturity).

• Since Australia's economy is mainly an exporter of raw materials, we include prices for commodities that are most traded by Australia, as indicators for potential profitability for various relevant sectors of the ASX.

We feed relevant data to a machine learning model, which uses this data to extract heuristic parameters that are used to predict the ASX200 on daily basis, before market opens, and validates predictions at market close, with favourable results.

For more information, please visit the Tableau viz at: https://public.tableau.com/app/profile/yasser.ali.phd/viz/PredictingAustralianStockMarket/Story

This paper models the dynamics of Chinese yuan–denominated long-term interest rate swap yields. It shows that the short-term interest rate exerts a decisive influence on the long-term swap yield after controlling for various macrofinancial variables, such as core inflation, the growth of industrial production, the percent change in the equity price index, and the percentage change in the Chinese yuan exchange rate. The autoregressive distributed lag approach is applied to model the dynamics of the long-term swap yield. The findings reinforce and extend John Maynard Keynes’s conjecture that in advanced countries, as well as emerging market economies such as China, the central bank’s actions have a decisive role in setting the long-term interest rate on government bonds and over-the-counter financial instruments, such as swaps.

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.

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

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; ...).

This table contains 39 series, with data for starting from 1991 (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); Financial market statistics (39 items: Government of Canada Treasury Bills, 1-month (composite rates); Government of Canada Treasury Bills, 2-month (composite rates); Government of Canada Treasury Bills, 3-month (composite rates);Government of Canada Treasury Bills, 6-month (composite rates); ...).

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.

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.

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. 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.

The current growing interest in the growth of the Western European economies between the end of World War II and the first oil crisis of 1973 is primarily due to the end of the Cold War and the subsequent demand for solutions for the economic problems of Central and Eastern European transition countries. It was and is discussed to what extent we could learn from the successful rebuilding of the Western European economies. In this context one area of special interest is the reconstruction of West Germany, closely accompanied by the principle of the social market economy. The recollection of this principle, and the call for a new Marshall Plan imply the idea that the Western European post-war boom in essence can be traced to a successful economic policy. It is shown how this assumption can stand up to a theoretical and empirical analysis. Using the new growth theory and the cointegration analysis both national (eg social market economy and Planification (i.e. macroeconomic framework development planning)) and international explanations (eg the Marshall Plan) of the so called ‘golden age’ are examined. It turns out that the impact of economic policies on economic growth must be put into perspective. In contrast, the importance of the different economic conditions of the countries for the explication of their growth process is underlined. Variables, inter alia: - Investment behavior of industry - Production and Export industry - Exchange Rates - Structure of the economies Data focus: Foreign trade structure, external value (foreign wholesale prices), export volume, industrial production, capital stock, long-term development (income, investment rates, openness, exchange rates), patents (patent applications in Germany, France). List of tables in the database HISTAT ZA: - Investment rates in four European countries (1880-1995) - Net fixed assets of the industry in Germany (1950-1968) - Sectoral Gross capital expenditures in Germany (1960-1976) - Sectoral Gross investment in France (1949-1965) - Export volume index of France and the Federal Republic of Germany (1950-1973) - Export volume in millions of current U.S. dollars (1951-1990) - Weighted exchange rate index in indirect rate (1950-1973) - Index of industrial production in Europe and North America (1950-1973) - Construction and equipment investment in Germany (1950-1968) - Investment rates in four European countries (1880-1995) - Sectoral gross and net capital stock in France (1950-1970) - Sectoral gross and net capital stock, investment in France (1950-1969) - Percentage of the French colonies in the French total exports (1950-1973) - Openness of four European economies (1880-1994) - Annual patent applications in the United States (1963-1995) - Real per capita income in Europe and the United States (1870-1992) - Regional structure of the French export value (1896-1973) - French sector gross investment (1960-1976) - Exchange rates in four European countries (1891-1995) Territory of investigation: Germany, France, further OECD-states. Sources: Publications of the official French and German statistics, publications of the OECD, USA and further states; scientific journals. Das aktuell wachsende Interesse an dem Wachstum der westeuropäischen Wirtschaften zwischen dem Ende des Zweiten Weltkrieges und der ersten Erdölkrise 1973 hängt in erster Linie mit dem Ende des Kalten Krieges und der darauf folgenden Nachfrage nach Lösungsansätzen für die ökonomischen Probleme der mittel- und osteuropäischen Transformationsländer zusammen. Es wurde und wird diskutiert, inwieweit sich Lehren aus dem erfolgreichen Wiederaufbau der westeuropäischen Wirtschaften ziehen ließen. Ein besonderes Interesse besaß hierbei der Wiederaufbau Westdeutschlands, eng einhergehend mit dem Prinzip der Sozialen Marktwirtschaft. Die Rückbesinnung auf diese und der Ruf nach einem neuen Marshall-Plan implizieren die Vorstellung, dass sich der westeuropäische Nachkriegsboom im Wesentlichen auf eine erfolgreiche Wirtschaftspolitik zurückführen lässt. Es wird gezeigt, inwieweit diese Annahme einer theoretischen und empirischen Analyse standhält. Mit Hilfe der neuen Wachstumstheorie und der Kointegrationsanalyse werden sowohl nationale (z.B. Soziale Marktwirtschaft und Planification) als auch internationale Erklärungsansätze (z.B. Marshall-Plan) des golden age untersucht. Es zeigt sich, dass der Einfluss der Wirtschaftspolitik auf das Wachstum relativiert werden muss. Dagegen wird die Bedeutung der unterschiedlichen Ausgangsbedingungen in den einzelnen Ländern für die Erklärung ihres Wachstumsprozesses unterstrichen. Variablen u.a.: - Investitionsverhalten der Industrie - Produktion und Export der Industrie - Wechselkurse - Struktur der Volkswirtschaften Datenschwerpunkte: Außenhandelsstruktur, Außenwert (ausländische Großhandelspreise), Exportmenge (Exportvolumen), Industrieproduktion, Kapitalstock, langfristige Entwicklung (Einkommen, Investitionsquoten, Offenheitsgrad, Wechselkurse), Patente (Patentanmeldungen Deutschland, Frankreich). Verzeichnis der Tabellen in der ZA-Datenbank HISTAT: - Investitionsquoten in vier europäischen Ländern (1880-1995) - Netto-Anlagevermögen der Industrie in der BRD (1950-1968) - Sektorale Brutto-Investitionen in Deutschland (1960-1976) - Sektorale Bruttoinvestitionen in Frankreich (1949-1965) - Index Exportvolumen Frankreichs und der BRD (1950-1973) - Exportvolumen in Mio. laufenden US Dollar (1951-1990) - Index gewichteter Wechselkurs in Mengennotierung (1950-1973) - Index Industrieproduktion in Europa und Nordamerika (1950-1973) - Bau- und Ausrüstungsinvestitionen in Deutschland (1950-1968) - Investitionsquoten in vier europäischen Ländern (1880-1995) - Sektoraler Brutto- und Nettokapitalstock in Frankreich (1950-1970) - Sektoraler Brutto- und Nettokapitalstock, Investitionen in Frankreich (1950-1969) - Anteil der französischen Kolonien am französischen Gesamtexport (1950-1973) - Offenheitsgrad von vier europäischen Volkswirtschaften (1880-1994) - Jährliche Patentanmeldungen in den USA (1963-1995) - Reales Pro-Kopf-Einkommen in Europa und den USA (1870-1992) - Regionale Struktur des französischen Exportwertes (1896-1973) - Französische sektorale Brutto-Investitionen (1960-1976) - Wechselkurse in vier europäischen Staaten (1891-1995) Veröffentlichungen öffentlicher Statistiken Frankreichs und Deutschlands, der OECD, der USA sowie weitere ausgewählte Einzelstudien; Fachzeitschriften.

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Venture Capital Investment Market Size 2025-2029

The venture capital investment market size is forecast to increase by USD 2920.2 billion, at a CAGR of 37.9% between 2024 and 2029.

The Venture Capital (VC) investment market is experiencing significant growth, particularly in the biotech sector, driven by advancements in technology and innovation. This trend is fueled by an increasing number of high-net-worth individuals (HNWIs) worldwide, who are seeking to diversify their portfolios and invest in promising startups. However, this market faces challenges, including foreign exchange volatility, which can impact the returns on investments made across borders. As HNWIs continue to invest in VC funds, they bring not only capital but also expertise and industry connections, further enhancing the potential for successful ventures.
Simultaneously, biotech companies, with their innovative solutions, are attracting substantial VC interest, presenting significant opportunities for growth and returns. Navigating foreign exchange risks and identifying promising biotech startups will be crucial for VC firms seeking to capitalize on these trends and outperform their competitors.

What will be the Size of the Venture Capital Investment Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The venture capital (VC) investment market continues to evolve, shaped by dynamic market conditions and diverse sector applications. Dividend yields and capital gains remain key drivers for investors, as they seek to maximize returns. Big data and growth hacking are increasingly integral to investment theses, enabling industry analysis and informed decision-making. Limited partnerships (LPs) and funds collaborate, with GPs overseeing operations and risk management. Deal sourcing and due diligence are essential components of the investment process, ensuring portfolio companies align with the fund's objectives. Revenue growth and marketing strategies are critical for portfolio companies, as they aim to scale and attract investment.

Term sheets outline investment details, while advisory boards provide strategic guidance. Financial modeling and cash flow management are essential for effective fund management. Technology infrastructure, including AI, cloud computing, and blockchain technology, underpins innovation and growth. Joint ventures and technology licensing offer opportunities for collaboration and expansion. Sales strategy and burn rate analysis help optimize portfolio performance. Private equity and data analytics provide valuable insights for investment opportunities. Stock options and Series A and B funding rounds offer potential for significant returns. Legal agreements and intellectual property (IP) rights are crucial for protecting investments and ensuring long-term success.

How is this Venture Capital Investment Industry segmented?

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

Sector

  Software
  Pharmaceutical and biotechnology
  Media and entertainment
  Medical devices and equipments
  Others


Type

  First-time venture funding
  Follow-on venture funding


Variant

  Institutional Investors
  Corporate venture capital
  Private equity firms
  Angel investors
  Others


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    The Netherlands
    UK


  APAC

    China
    India
    Japan


  Rest of World (ROW)

By Sector Insights

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

The market has witnessed significant activity in the software industry, with a focus on disruptive technologies such as Artificial Intelligence (AI), Machine Learning (ML), and Blockchain technology. VC firms have invested billions of dollars in these areas, with some companies achieving unicorn status. The software sector includes application software, system infrastructure software, software as a service (SaaS), operating systems, database software, and analytics software. The growing number of entrepreneurs and businesses, estimated to be over 450 million and 300 million, respectively, is fueling the growth of the software segment in the market. VC funds have been actively involved in Series A funding, providing capital for early-stage startups, and Series B funding, for growth-stage companies.

Limited partnerships (LPs) have been essential in providing capital for these funds. Risk management is a critical factor in venture capital investment, with due diligence, financial modeling, and market analysis being crucial c

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