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.

We compile raw data from the Datastream database for all stocks traded on the Spanish equity market. Particularly, we compile the following data series: (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) dividend yield (DY series), (vii) price-to-earnings ratio (PE series), and (viii) effective tax rate (WC08346 series). We use the filters suggested by Griffin, Kelly, and Nardari (2010) for the Datastream database to exclude assets other than ordinary shares from our sample. Hence, our sample comprises 443 companies, including all firms that started trading within the time interval under study, as well as those that were delisted. As a proxy for the risk-free rate, we use the three-month Treasury Bill rate for Spain, as provided by the OECD. Accordingly, the dataset comprises the following series:

1. Spain_9_Portfolios_SIZE_BEME: Monthly returns for 9 size-book-to-market equity portfolios, following the Fama and French (1993) methodology. (Raw data source: Datastream database)
2. Spain_9_Portfolios_DY_PE: Monthly returns for 9 dividend yield-price-to-earnings ratio, following the Fama and French (1993) methodology. (Raw data source: Datastream database)
3. Spain_9_Portfolios_SIZE_TR: Monthly returns for 9 size-effective tax rate portfolios, following the Fama and French (1993) methodology. (Raw data source: Datastream database)
4. Spain_FF_3_Factors: Monthly returns for the constituents of the three classic factors of Fama and French, following the Fama and French (1993) methodology. (Raw data source: Datastream database)
5. Spain_FF_5_Factors: Monthly returns for the constituents of the five factors of Fama and French, following the Fama and French (2015) methodology. (Raw data source: Datastream database)
6. Spain_RF: Three-month Treasury Bill rate for Spain. (Raw data source: OECD)
7. Spain_Avg_Tax_Rate: Value-weighted effective tax rate paid by companies traded in Spain. (Raw data source: Datastream database)

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.

This dataset presents an extensive record of daily historical stock prices for Tesla, Inc. (TSLA), one of the world’s most innovative and closely watched electric vehicle and clean energy companies. The data was sourced from Yahoo Finance, a widely used and trusted provider of financial market data, and covers a significant period spanning from Tesla’s initial public offering (IPO) to the most recent date available at the time of extraction.

The dataset includes critical trading metrics for each market day, such as the opening price, highest and lowest prices of the day, closing price, adjusted closing price (accounting for dividends and splits), and total trading volume. This rich dataset supports a variety of use cases, including financial market analysis, investment research, time series forecasting, development and backtesting of trading algorithms, and educational projects in data science and finance.

Dataset Features

• Date: The calendar date for each trading session (in YYYY-MM-DD format)
• Open: The opening price of TSLA shares at the start of the trading day
• High: The highest price reached during the trading session
• Low: The lowest price reached during the trading session
• Close: The last price at which the stock traded during the day
• Adj Close: The closing price adjusted for corporate actions (splits, dividends, etc.)
• Volume: The total number of TSLA shares traded on that day

Source and Collection Details

• Source: Yahoo Finance - Tesla (TSLA) Historical Data
• Collection Method: Data was downloaded using Yahoo Finance's CSV export feature for accuracy and completeness.
• Time Range: Covers from Tesla’s IPO (June 2010) to the most recent available trading day.
• Data Integrity: Minimal cleaning was performed—dates were standardized, and any duplicate or empty rows were removed; all values remain as originally reported by Yahoo Finance.

Example Use Cases

• Stock Price Prediction: Train and test time series models (ARIMA, LSTM, Prophet, etc.) to forecast Tesla’s stock prices.
• Algorithmic Trading: Backtest and evaluate trading strategies using historical price and volume data.
• Market Trend Analysis: Analyze price trends, volatility, and return rates over different periods.
• Event Study: Investigate the impact of major announcements (e.g., product launches, earnings releases) on TSLA stock price.
• Educational Projects: Use as a hands-on resource for learning finance, statistics, or machine learning.

License & Acknowledgments

• Intended Use: This dataset is provided for academic, research, and personal projects. For commercial or investment use, please verify data accuracy and consult Yahoo Finance’s terms of use.
• Acknowledgment: Data sourced from Yahoo Finance. All trademarks and copyrights belong to their respective owners.

We compile raw data from the Datastream database for all stocks traded on the Tokyo Stock Exchance, Osaka Exchange, Fukuoka Stock Exchange, Nagoya Stock Exchange and Sapporo Securities Exchange. Particularly, we collect the following data series, on a monthly basis: (i) total return index (RI series), (ii) market value (MV series), (iii) market-to-book equity (PTBV series), and (iv) primary SIC codes. Following Griffing et al. (2010), we exclude non-common equity securities from Datastream data. Additionally, we remove all companies with less than 12 observations in RI series for the period under analysis. Hence, our sample comprises 5,627 stocks, considering all companies that started trading or were delisted in the period under analysis. We use the three-month Treasury Bill rate for Japan, as provided by the OECD database, as a proxy for the risk-free rate. Accordingly, the dataset comprises the following series:

1. Japan_25_Portfolios_MV_PTBV_M: Monthly returns for 25 size-book-to-market equity portfolios, following the Fama and French (1993) methodology. (Raw data source: Datastream database)
2. Japan_20_Portfolios_MOM_M: Monthly returns for 20 momentum portfolios rebalanced in June of each year. (Raw data source: Datastream database)
3. Japan_61_Portfolios_SECTOR_M: Monthly returns for 61 industry portfolios. (Raw data source: Datastream database)
4. Japan_RF_M: Three-month Treasury Bill rate for Japan. (Raw data source: OECD)
5. Japan_C_Q: Private final consumption expenditure, in national currency and constant prices, non-seasonally adjusted, for Japan. (Raw data source: OECD)
6. Japan_Trade_Y: Trade openness for Japan, as measured by the variation rate of exports plus imports. (Raw data source: OECD)
7. Japan_RD_Y: Variation rate of R&D investment for Japan. (Raw data source: OECD)
8. Japan_IK_Y: Investment-capital ratio for Japan., determined using the methodology suggested by Cochrane (1991) (Raw data source: OECD)
9. Japan_CCI_M: Consumer confidence index for Japan. (Raw data source: OECD)

REFERENCES:

Cochrane, J.H. (1991), Production-based asset pricing and the link between stock returns and economic fluctuations. The Journal of Finance, 46, 209-237. 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. 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.

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

The European Business Performance database describes the performance of the largest enterprises in the twentieth century. It covers eight countries that together consistently account for above 80 per cent of western European GDP: Great Britain, Germany, France, Belgium, Italy, Spain, Sweden, and Finland. Data have been collected for five benchmark years, namely on the eve of WWI (1913), before the Great Depression (1927), at the extremes of the golden age (1954 and 1972), and in 2000.The database is comprised of two distinct datasets. The Small Sample (625 firms) includes the largest enterprises in each country across all industries (economy-wide). To avoid over-representation of certain countries and sectors, countries contribute a number of firms that is roughly proportionate to the size of the economy: 30 firms from Great Britain, 25 from Germany, 20 from France, 15 from Italy, 10 from Belgium, Spain, and Sweden, and 5 from Finland. By the same token, a cap has been set on the number of financial firms entering the sample, so that they range between up to 6 for Britain and 1 for Finland.The second dataset, or Large Sample (1,167 firms), is made up of the largest firms per industry. Here industries are so selected as to take into account long-term technological developments and the rise of entirely new products and services. Firms have been individually classified using the two-digit ISIC Rev. 3.1 codes, then grouped under a manageable number of industries. To some extent and broadly speaking, the two samples have a rather distinct focus: the Small Sample is biased in favour of sheer bigness, whereas the Large Sample emphasizes industries.As far as size and performance indicators are concerned, total assets has been picked as the main size measure in the first three benchmarks, turnover in 1972 and 2000 (financial intermediaries, though, are ranked by total assets throughout the database). Performance is gauged by means of two financial ratios, namely return on equity and shareholders’ return, i.e. the percentage year-on-year change in share price based on year-end values. In order to smooth out volatility, at each benchmark performance figures have been averaged over three consecutive years (for instance, performance in 1913 reflects average performance in 1911, 1912, and 1913).All figures were collected in national currency and converted to US dollars at current year-average exchange rates.

The Google Shopping dataset is perfect for obtaining detailed product information worldwide. Easily filter by product title, seller, price, and other factors to find the exact data you need. The Google Shopping dataset includes key data points such as URL, product ID, title, description, rating, reviews count, images, seller name, delivery price, return policy, item price, total price, specifications, related items, and more.

Includes the total number of containers returned per financial year and percentage return for each material type. In addition to the number of inspections and number of non-compliant containers per financial year. For more information visit: http://www.epa.sa.gov.au/environmental_info/container_deposits

China's main stock market index, the SHANGHAI, rose to 3520 points on July 14, 2025, gaining 0.27% from the previous session. Over the past month, the index has climbed 3.86% and is up 18.35% compared to the same time last year, 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 July of 2025.

Using all stocks listed on the Japanese equity market and macroeconomic data for Japan, the dataset comprises the following series:

1. Japan_25_Portfolios_MV_PTBV: Monthly returns for 25 size-book-to-market equity portfolios, following the Fama and French (1993) methodology. (Raw data source: Datastream database)
2. Japan_25_Portfolios_MV_PE: Monthly returns for 25 size-PE portfolios, following the Fama and French (1993) methodology. (Raw data source: Datastream database)
3. Japan_50_Portfolios_SECTOR: Monthly returns for 50 industry portfolios. (Raw data source: Datastream database)
4. Japan_3 Factors: Fama and French three-factors (RM, SMB and HML), following the Fama and French (1993) methodology. (Raw data source: Datastream database)
5. Japan_5 Factors: Fama and French five-factors (RM, SMB, HML, RMW, and CMA), following the Fama and French (2015) methodology. (Raw data source: Datastream database)
6. Japan_NUCLEAR_Y: Instrument in years with a value of 1 when a nuclear disaster has occurred somewhere in the world and 0 otherwise. (Raw data source: Bloomberg and BBC News)
7. Japan_NUCLEAR_M: Instrument in months with a value of 1 when a nuclear disaster has occurred somewhere in the world and 0 otherwise. (Raw data source: Bloomberg and BBC News)
8. Japan_RF_M: Three-month interest rate of the Treasury Bill for Japan. (Raw data source: OECD)
9. Company data: Names and general data of the companies that constitute the sample. (Raw data source: Datastream database)
10. Number of stocks in portfolios: Number of stocks included each year in Japan_25_Portfolios_MV_PTBV, Japan_25_Portfolios_MV_PE and Japan_50_Portfolios_SECTOR. (Raw data source: Datastream database)

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-earnings ratio (PE series), and (vii) industry (SECTOR 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. Accordingly, our sample comprises a total number of 5,212 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.

This is the dataset from Momentum Strategies in Frontier Equity Markets: A Comparative Analysis of Traditional and Alternative Strategies (2025). The research investigates the presence of the momentum effect and the performance of momentum strategies in frontier equity markets.The dataset consists of 11 xlsx files, each containing data of a different frontier market, and another xlsx file containing total return data of the S&P Frontier Benchmark Index (BMI), which is used as the market proxy in this study. Each frontier market file has several sheets covering monthly data of several companies, including closing prices, total returns, market capitalization, trading volume, shares outstanding, turnover ratio, etc. The period of data collection is from March 2003 to December 2023.

This dashboard shows information about how the Rod catch returns: salmon and sea trout catches service is currently performing.

This is a "beta" service. The dashboard shows number of digital transactions, total cost of transactions, cost per transaction and take-up of digital services. Performance Dashboards are likely to be used by many people, including:

government service managers and their teams journalists students and researchers members of the public interested in how public services are performing The service also provides the option of a download of the data. Attribution statement:

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.

Abstract

This study sought to develop a user-friendly decision-making tool to explore country-specific estimates for costs and economic consequences of different options for scaling screening and psychosocial interventions for women with common perinatal mental health problems in Malawi. We developed a simple simulation model using a structure and parameter estimates that were established iteratively with experts, based on published trials, international databases and resources, statistical data, best practice guidance and intervention manuals. The model projects annual costs and returns to investment from 2022 to 2026. The study perspective is societal, including health expenditure and productivity losses. Outcomes in the form of health-related quality of life are measured in Disability Adjusted Life Years, which were converted into monetary values. Economic consequences include those that occur in the year in which the intervention takes place. Results suggest that the net benefit is relatively small at the beginning but increases over time as learning effects lead to a higher number of women being identified and receiving (cost‑)effective treatment. For a scenario in which screening is first provided by health professionals (such as midwives) and a second screening and the intervention are provided by trained and supervised volunteers to equal proportions in group and individual sessions, as well as in clinic versus community setting, total costs in 2022 amount to US$ 0.66 million and health benefits to US$ 0.36 million. Costs increase to US$ 1.03 million and health benefits to US$ 0.93 million in 2026. Net benefits increase from US$ 35,000 in 2022 to US$ 0.52 million in 2026, and return-on-investment ratios from 1.05 to 1.45. Results from sensitivity analysis suggest that positive net benefit results are highly sensitive to an increase in staff salaries. This study demonstrates the feasibility of developing an economic decision-making tool that can be used by local policy makers and influencers to inform investments in maternal mental health

Description of data set

Iteratively, information was gathered from desk-based searches and from talking to and exchanging emails with experts in the maternal health field to establish a model structure and the parameter values. This included the development of an information request form that presents a list of parameters, parameter values and details about how the values were estimated and the data sources. We collected information on: Intervention’s effectiveness; prevalence rates; population and birth estimates; proportion of women attending services; salaries and reimbursement rates for staff and volunteers; details about training, supervision, intervention delivery (e.g., frequency, duration); unit costs, and data needed to derive economic consequences (e.g. women’s income, health weights). Data were searched from the following sources: published randomised controlled trials and meta-analyses; WHO published guidance and intervention manual; international databases and resources (WHO-CHOICE, Global Burden of Disease Database; International Monetary Fund; United Nations Treasury, World Bank, Global Investment Framework for Women’s and Children’s Health). We consulted two groups of experts: one group included individuals with clinical, research or managerial expertise in funding, managing, delivering, or evaluating screening of common mental health problems and PSIs; the second group included individuals from the Malawi Government, Ministry of Health Reproductive Health Unit and Non-Communicable Disease Committee and Mental Health Unit. The first group of experts provided information from research and administrative data systems concerned with implementing and evaluating screening for maternal mental health and the delivery of interventions. The second group of experts from the Malawi Government provided information on unit costs for hospital use and workforce data, as well as information on how training and supervision might be delivered at scale. Individuals were identified by colleagues of this team based or part-time based in Malawi, which included a psychiatrist specialising in perinatal mental health (co-author RS) and the coordinator of the African Maternal Mental Health Alliance (co-author DN), an organisation concerned with disseminating information and evidence on perinatal mental health to policy makers and influencers, and the wider public.

Russia's main stock market index, the MOEX, fell to 2642 points on July 11, 2025, losing 3.31% from the previous session. Over the past month, the index has declined 3.94% and is down 11.21% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Russia. Russia Stock Market Index MOEX CFD - values, historical data, forecasts and news - updated on July of 2025.

CRB Index rose to 373.34 Index Points on July 11, 2025, up 1.06% from the previous day. Over the past month, CRB Index's price has risen 0.59%, and is up 9.33% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. CRB Commodity Index - values, historical data, forecasts and news - updated on July of 2025.

Includes the total number of containers returned per financial year and percentage return for each material type. In addition to the number of inspections and number of non-compliant containers per financial year. For more information visit: http://www.epa.sa.gov.au/environmental_info/container_deposits

Using all stocks listed in the Australian Securities Exchange and macroeconomic data for Australia, the dataset comprises the following series:

1. Monthly returns for 20 size-price to cash flow portfolios, following the Fama and French (1993) methodology. (Raw data source: Datastream database)
2. Monthly returns for 25 size-book to market equity portfolios, following the Fama and French (1993) methodology. (Raw data source: Datastream database)
3. Monthly returns for 41 industry portfolios. (Raw data source: Datastream database)
4. Private final consumption expenditure, in national currency and constant prices, non-seasonally adjusted, for Australia. (Raw data source: OECD)
5. Fama and French (1993) factors (RM, SMB and HML), following the Fama and French (1993) methodology. (Raw data source: Datastream database)
6. Fama and French (2015) factors (RM, SMB, HML, RMW, and CMA), following the Fama and French (2015) methodology. (Raw data source: Datastream database)
7. Three-month interest rate of the Treasury Bill for Australia. (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) price-to-cash flow ratio (PC series), (v) primary SIC codes, and (vi) tax rate (WC08346 series). We use the rules suggested by Griffin, Kelly, & Nardari (2010) for excluding non-common equity securities from Datastream data.

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.

This dashboard shows information about how the Generic operator returns: water abstraction service is currently performing.

This is a "beta" service. The dashboard shows number of digital transactions, total cost of transactions, cost per transaction and take-up of digital services. Performance Dashboards are likely to be used by many people, including:

government service managers and their teams journalists students and researchers members of the public interested in how public services are performing The service also provides the option of a download of the data. Attribution statement:

AgriProfit$ cost and returns profiles give an insight into the productive and economic performance of Alberta cereal, oilseed, forage and grazing crops, reported by soil or grass-type zone. These benchmark include field observations from the period 2008 to 2010. Averaging over a three-year span smooths out extremes in prices, yields and expenses and gives a better reflection of the crops’ intermediate term costs and profit potential.