End-of-day prices refer to the closing prices of various financial instruments, such as equities (stocks), bonds, and indices, at the end of a trading session on a particular trading day. These prices are crucial pieces of market data used by investors, traders, and financial institutions to track the performance and value of these assets over time. The Techsalerator closing prices dataset is considered the most up-to-date, standardized valuation of a security trading commences again on the next trading day. This data is used for portfolio valuation, index calculation, technical analysis and benchmarking throughout the financial industry. The End-of-Day Pricing service covers equities, equity derivative bonds, and indices listed on 170 markets worldwide.

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.

End-of-day prices refer to the closing prices of various financial instruments, such as equities (stocks), bonds, and indices, at the end of a trading session on a particular trading day. These prices are crucial pieces of market data used by investors, traders, and financial institutions to track the performance and value of these assets over time. The Techsalerator closing prices dataset is considered the most up-to-date, standardized valuation of a security trading commences again on the next trading day. This data is used for portfolio valuation, index calculation, technical analysis and benchmarking throughout the financial industry. The End-of-Day Pricing service covers equities, equity derivative bonds, and indices listed on 170 markets worldwide.

Euro Area's main stock market index, the EU50, rose to 5428 points on June 6, 2025, gaining 0.39% from the previous session. Over the past month, the index has climbed 3.78% and is up 7.45% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Euro Area. Euro Area Stock Market Index (EU50) - values, historical data, forecasts and news - updated on June of 2025.

End-of-day prices refer to the closing prices of various financial instruments, such as equities (stocks), bonds, and indices, at the end of a trading session on a particular trading day. These prices are crucial pieces of market data used by investors, traders, and financial institutions to track the performance and value of these assets over time. The Techsalerator closing prices dataset is considered the most up-to-date, standardized valuation of a security trading commences again on the next trading day. This data is used for portfolio valuation, index calculation, technical analysis and benchmarking throughout the financial industry. The End-of-Day Pricing service covers equities, equity derivative bonds, and indices listed on 170 markets worldwide.

Thailand's main stock market index, the SET 50, fell to 736 points on June 9, 2025, losing 0.20% from the previous session. Over the past month, the index has declined 6.81% and is down 9.30% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Thailand. Thailand Stock Market (SET50) - values, historical data, forecasts and news - updated on June of 2025.

Techsalerator offers an extensive dataset of End-of-Day Pricing Data for all 800 companies listed on the Canadian Securities Exchange (XCNQ) in Canada. This dataset includes the closing prices of equities (stocks), bonds, and indices at the end of each trading session. End-of-day prices are vital pieces of market data that are widely used by investors, traders, and financial institutions to monitor the performance and value of these assets over time.

‍

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

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

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

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

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

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

‍

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

S&P/TSX Composite Index: The primary stock market index in Canada, tracking the performance of domestic companies listed on the Toronto Stock Exchange (TSX). It provides a comprehensive view of the Canadian equity market.

Canadian Dollar (CAD): The official currency of Canada, used for transactions and trade within the country. The Canadian Dollar is also widely traded in international foreign exchange markets.

Bank of Canada: Canada's central bank responsible for monetary policy, currency issuance, and overall financial system stability. It plays a critical role in managing the country's economic and financial well-being.

Royal Bank of Canada (RBC): One of the largest and most prominent banks in Canada, offering a wide range of financial services to individuals, businesses, and institutions. RBC is a key player in the Canadian banking sector.

Canadian Government Bonds: Debt securities issued by the Canadian government to finance its operations and projects. These bonds are considered relatively safe investments and play a significant role in the country's capital markets.

‍

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

‍

Data fields included:

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

Q&A:

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

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

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

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

1. How does Techsalerator collect this data?

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

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

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

1. How do I pay for this dataset?

Techsalerator accepts various payment methods, including credit cards, direct tran...

End-of-day prices refer to the closing prices of various financial instruments, such as equities (stocks), bonds, and indices, at the end of a trading session on a particular trading day. These prices are crucial pieces of market data used by investors, traders, and financial institutions to track the performance and value of these assets over time. The Techsalerator closing prices dataset is considered the most up-to-date, standardized valuation of a security trading commences again on the next trading day. This data is used for portfolio valuation, index calculation, technical analysis and benchmarking throughout the financial industry. The End-of-Day Pricing service covers equities, equity derivative bonds, and indices listed on 170 markets worldwide.

This package contains the datasets and source codes used in the PhD thesis entitled Predicting the Brazilian stock market using sentiment analysis, technical indicators and stock prices. The following files are included: File Labeled.zip - financial news labeled in two classes (Positive and Negative), organized to train Sentiment Analysis models. Part of these news were initially presented in [1]. Besides the news in this file, in the related PhD thesis the training dataset was complemented with the labeled news presented in [2]. File Unlabeled.zip - general unlabeled financial news collected during the period 2010-2020 from the following online sources: G1, Folha de São Paulo and Estadão. This file contains news from the Bovespa index and from the following companies: Banco do Brasil, Itau, Gerdau and Ambev. File Stocks.zip - stock prices from the companies Banco do Brasil, Itau, Gerdau, Ambev, and the Bovespa index. The considered period ranges from 2010 to 2020. File Models.zip - contains the source codes of the models used in the PhD thesis (i.e., Multilayer Perceptron, Long Short-Term Memory, Bidirectional Long Short-Term Memory, Convolutional Neural Network, and Support Vector Machines). File Utils.zip - contains the source codes of the preprocessing step designed for the methodology of this work (i.e., load data and generate the word embeddings), alongside with stocks manipulation, and investment evaluation. [1] Carosia, A. E. D. O., Januário, B. A., da Silva, A. E. A., & Coelho, G. P. (2021). Sentiment Analysis Applied to News from the Brazilian Stock Market. IEEE Latin America Transactions, 100. DOI: 10.1109/TLA.2022.9667151 [2] MARTINS, R. F.; PEREIRA, A.; BENEVENUTO, F. An approach to sentiment analysis of web applications in portuguese. Proceedings of the 21st Brazilian Symposium on Multimedia and the Web, ACM, p. 105–112, 2015. DOI: 10.1145/2820426.2820446

In this paper we model Value-at-Risk (VaR) for daily asset returns using a collection of parametric univariate and multivariate models of the ARCH class based on the skewed Student distribution. We show that models that rely on a symmetric density distribution for the error term underperform with respect to skewed density models when the left and right tails of the distribution of returns must be modelled. Thus, VaR for traders having both long and short positions is not adequately modelled using usual normal or Student distributions. We suggest using an APARCH model based on the skewed Student distribution (combined with a time-varying correlation in the multivariate case) to fully take into account the fat left and right tails of the returns distribution. This allows for an adequate modelling of large returns defined on long and short trading positions. The performances of the univariate models are assessed on daily data for three international stock indexes and three US stocks of the Dow Jones index. In a second application, we consider a portfolio of three US stocks and model its long and short VaR using a multivariate skewed Student density.

This paper studies subsampling hypothesis tests for panel data that may be nonstationary, cross-sectionally correlated, and cross-sectionally cointegrated. The subsampling approach provides approximations to the finite sample distributions of the tests without estimating nuisance parameters. The tests include panel unit root and cointegration tests as special cases. The number of cross-sectional units is assumed to be finite and that of time-series observations infinite. It is shown that subsampling provides asymptotic distributions that are equivalent to the asymptotic distributions of the panel tests. In addition, the tests using critical values from subsampling are shown to be consistent. The subsampling methods are applied to panel unit root tests. The panel unit root tests considered are Levin, Lin, and Chu's (2002) t-test; Im, Pesaran, and Shin's (2003) averaged t-test; and Choi's (2001) inverse normal test. Simulation results regarding the subsampling panel unit root tests and some existing unit root tests for cross-sectionally correlated panels are reported. In using the subsampling approach to examine the real exchange rates of the G7 countries and a group of 26 OECD countries, we find only mixed support for the purchasing power parity (PPP) hypothesis. We then examine a panel of 17 developed stock market indexes, and also find only mixed empirical support for them exhibiting relative mean reversion with respect to the US stock market index.

Description

The Nifty 50 index is a free-float market-capitalization-weighted index of the top 50 companies listed on the National Stock Exchange of India. This means that the index is calculated by taking the market capitalization of each company and weighting it according to the free float of shares. The free float is the number of shares that are available for trading on the open market.

The data is from the NSE website and is updated daily. This means that you can use the data to track the performance of the Nifty 50 index on a daily basis. You can also use the data to identify trends in the Indian stock market. For example, if you see that the Nifty 50 index is consistently rising, this could be a sign that the Indian stock market is doing well.

The data can also be used to make investment decisions. For example, if you see that a particular company is consistently performing well, you may want to consider investing in that company. However, it is important to remember that past performance is not necessarily indicative of future results.

Overall, the data is a valuable resource for anyone who is interested in the Indian stock market. It can be used to track the performance of the Nifty 50 index, identify trends in the market, and make investment decisions.

Date: 25 May, 2023

Data This data is related to share market and I personally collecting this data on NSE official website with the help of web scrapping. This data helps you to enhancing the trading skills also you can build the project with this real time data.

Lumber fell to 602.62 USD/1000 board feet on June 6, 2025, down 0.40% from the previous day. Over the past month, Lumber's price has risen 11.57%, and is up 18.02% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Lumber - values, historical data, forecasts and news - updated on June of 2025.

Rhodium increased 1,000 USD/t oz. or 21.86% since the beginning of 2025, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Rhodium - values, historical data, forecasts and news - updated on March of 2025.

Uranium traded flat at 70.50 USD/Lbs on June 6, 2025. Over the past month, Uranium's price has risen 0.57%, but it is still 19.34% lower than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Uranium - values, historical data, forecasts and news - updated on June of 2025.

This database contains data on top 30 cm soil biogeochemical properties from soil cores (minimum length of 30 cm) sampled in seagrass (n=201 cores) and adjacent unvegetated patches (n=39) around Australia.

Average biogeochemical properties per core along with information about type of environment, biotic characteristics and environmental conditions.

In particular, the variables included in sheet 1 are:

- Core ID (column A): core code

- Location (column B): name of the region of the sampling site.

- Latitude / Longitude (columns C, D): latitude and longitude

- Bioregion (column E): classified the sampling sites according to their location in Temperate-Southern Oceans and Tropical-Indo Pacific, following Short et al. (2007) classification.

- Coastal geomorphic setting (column F): classified sampling sites in estuarine settings, if influenced by riverine inputs, or coastal settings, in case located in open waters, no influence by rivers.

- Vegetated vs. bare (column G): refers to the vegetated vs. unvegetated condition of the sampling patch.

- Genus (column H): refers to the genus of the dominant species, in the case of vegetated patches.

- Species size (column I): classify vegetated sampling sites by the size of the dominat species in considering species of Posidonia and Amphibolis as large species, and those of Halodule, Halophila, Ruppia, Zostera, Cymodocea and Syringodium as small species (Kiminster et al., 2015).

- Water depth (m) (column J): depth of the sampling site. 0 for intertidal meadows.

- Dominant wind fetch (km) (column K): fetch in the direction of the dominant wind, calculated with ‘fetchR’ package, using the Australian coastline shapefile from GADM database (www.gadm.org, version 2.0) and the dominant wind for each location obtained from the Bureau of Meteorology (http://www.bom.gov.au/climate/data/). Fetch estimations are provided only for coastal locations or outer estuarine locations due to the spatial resolution of the Australian coastline.

- Air Tª (Cº)_Annual avg. (column L): Annual average air temperature from 1995-2005 at the sampling location, extracted from Australian Bureau of Meteorology: http://www.bom.gov.au/jsp/ncc/climate_averages/temperature/index.jsp.

- Solar exposure (MJ m-2)_Annual avg. (column M): Annual mean solar exposure extracted from 1990-2011, extracted from Australian Bureau of Meteorology:http://www.bom.gov.au/jsp/ncc/climate_averages/solar-exposure/index.jsp

- Rainfall (mm)_Decadal avg. (column N): Decadal average rainfall from 1996 to 2005 extracted from Australian Bureau of Meteorology: http://www.bom.gov.au/jsp/ncc/climate_averages/decadal-rainfall/index.jsp?maptype=1&period=9605&product=totals

- Deviation from natural state (column O): index to estimate the level of human pressure, calculated based on the intensity of land use (adapted from Lenzen, M., and S. A. Murray. 2006. A modified ecological footprint method and its application to Australia. Ecol. Econ. 37: 229–255.). Land use data was obtained from the Australian Bureau of Agriculture and Resource Economics and Sciences at https://data.gov.au/dataset/ds-dga-bba36c52-d5cc-4bd4-ac47-f37693a001f6/details

.

-Top 30 cm C_org stock (g cm-2) (column P): cumulative soil C_org stocks within the top 30 cm of soil (decompressed depth).

- Top 30 cm_13dC_avg (column Q): average C_org isotopic signature (d13C) within the top 30 cm of soil (decompressed depth).

- Top 30 cm_13dC_SE (column R): standard error of C_org isotopic signature (d13C) within the top 30 cm of soil (decompressed depth).

- Top 30 cm_% Silt & Clay_avg. (column S): average mud content (%) within the top 30 cm of soil (decompressed depth).

- Top 30 cm_% Silt & Clay_SE (column T): standard error of mud content (%) within the top 30 cm of soil (decompressed depth).