Key information about Algeria Algiers Stock Exchange

• Algeria Algiers Stock Exchange closed at 3,832.6 points in Nov 2025, compared with 3,837.3 points at the previous month end
• Algeria Equity Market Index: Month End: Algiers Stock Exchange data is updated monthly, available from Sep 1999 to Nov 2025, with an average number of 1,289.5 points
• The data reached an all-time high of 19,423.9 points in Feb 2000 and a record low of 369.7 points in Apr 2006

Starting September 2011, the formula for calculating Algiers Stock Exchange Index is amended to contain an adjustment coefficient

Selected technical indicators and their formulas (Type 2).

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.

Key information about Chile IPSA

• Chile IPSA closed at 7,332.1 points in Feb 2025, compared with 7,199.6 points at the previous month end
• Chile Equity Market Index: Month End: Santiago Stock Exchange: IPSA data is updated monthly, available from Jan 2003 to Feb 2025, with an average number of 4,023.4 points
• The data reached an all-time high of 7,332.1 points in Feb 2025 and a record low of 1,002.0 points in Jan 2003

Santiago Stock Exchange provides daily data on several major stock market indices, but the IPSA index is the one most closely monitored by analysts. Co-branding between S & P Dow Jones Indices (S & P DJI) and Santiago Stock Exchange has been effective as of August 6, 2018. Calculation and maintenance of the index will be taken over by S & P pursuant to the Operating Agreement and Index Licensing signed by both parties in August 2016. Historical values and components of the index will not be modified to maintain continuity.


Further information about Chile IPSA

• In the latest reports, Santiago Stock Exchange recorded a monthly P/E ratio of 12.6 in Jan 2025

View market daily updates and historical trends for US Total Market Capitalization as % of GDP (DISCONTINUED). from United States. Source: Wilshire. Track…

Stock prices for various companies are obtained from Google Finance through the utilization of the googlefinance() function, and are stored in an .xlsx file format. The stock data is classified and categorized into individual sheets, which correspond to a specific company. The table contains data for each day from the beginning of data collection up to March 2023, including the opening, high, low, and closing prices for the stock, as well as the volume of trades. The prices are denominated in the local currency of the respective country. Drive Stocks file link: https://docs.google.com/spreadsheets/d/1ElCXYXv-NjAmMKy7fQ1bjI05q1xij5hZ2DCLrJs0A5w/edit?usp=share_link

Alongside the stock data, two other files are used: the Inflation consumer prices (annual %) and the Wholesale price index (2010 = 100).

• The Wholesale price index is a measure of the average price of a basket of goods and services in a given economy, including both agricultural and industrial goods at various stages of production and distribution, and may also include import duties. The Laspeyres formula is typically used to calculate the index.

• The Inflation consumer prices (annual %) file measures the annual percentage change in the cost to the average consumer of acquiring a basket of goods and services. The basket may be fixed or changed at specified intervals, such as yearly. The Laspeyres formula is typically used to calculate the index. Both of these files provide valuable context for understanding the performance of the stock market and the broader economic conditions that may be affecting it.

Wholesale price index and Inflation consumer prices are uncleared on propose. The cleaned version of the financial data is also included.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Liberty Formula 1 (Series C) assigned short-term Ba1 & long-term B1 forecasted stock rating.

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Indicators and their formulas.

Selection and calculation methods of structural variables for financial system stress index and energy market index.

This table contains figures on non-financial balance sheets. The balance sheets show the market value of non-financial assets. Changes in the value of non-financial assets are also presented in this table. These changes are, for example, the result of price changes or the result of purchases minus sales of non-financial assets. Non-financial balance sheets are part of the National Accounts. The balance sheets are presented by different types of assets for the economy as a whole and for the different institutional sectors in the Dutch economy. Figures of the sectors households and non-profit institutions serving households (NPISH) are from reporting year 2013 onwards no longer separately published. Only their aggregate will be published. The reason for this change is that reliable estimates for NPISH for recent years are no longer available. Data available from: 1995 Status of the figures: The figures for the most recent reporting year 2016 are provisional. The status of the figures for other years is final. Changes as from 11 August 2017: Provisional figures on the reporting year 2016 have been added. - The volume-indices of the inventories of the general government were mistakenly displayed with a dot (.). This has now been corrected and the volume-indices for the base year are now set to 100. - Due to data on the reporting years 1995-2001 becoming available, this table has been expanded. The years 1995-2000 are completely new, for the year 2001 only the closing balance sheet was displayed. Now figures on opening balance sheet, revaluation, capital formation, other changes in volume and statistical discrepancy are added for this reporting year. Changes as from 25 October 2016: A number of corrections have been applied as a result of mistakes in the calculations for the years 2002, 2003, 2004, 2009, 2011, 2012 and 2015. These mistakes did not result in any changes in the totals for the closing balance sheet, but led to incorrect aggregations of sectors or type of non-financial asset. Furthermore the calculation method of the volume indices have been harmonised for the capital stock and non-financial balance sheets. Moreover, the volume index will now be calculated on the basis of rounded figures. Because of these changes in method a maximum difference of 85.6 percentage points occurs for series of less than 100 mln. A maximum difference of 16.2 percentage points occurs for series larger than 100 mln. Volume indices of series which contain 0 mln of non-financial assets every year are set at 100, rather than hidden. The calculation method of consumer durables has been changed as well, to account for the purchase of lease cars by consumers. Correction as of 5 February 2016: As a result of a mistake in the calculation the opening balance sheet of 2006 is not equal to the closing balance sheet of 2005. The mistake has been corrected. Correction as of 4 November 2015: The volume-indices of total of non-financial assets 2010-2014 have been changed because consumer durables do not belong to this category and were previously included. When will new figures be published? Provisional data are published 6 months after the end of the reporting year. Final data are released 18 months after the end of the reporting year. Since the end of June 2016 the release and revision policy of the national accounts have been changed. References to additional information about these changes can be found in section 3.

The Chinese economy has undergone a long-term transition reform, but there is still a planned economy characteristic in the financial sector, which is financial repression. Due to the existence of financial repression, China’s actual interest rate level should be lower than the Consumer Price Index (CPI). However, based on official China’s interest rates and CPI, over half of the years China’s actual interest rate remained higher than CPI by our calculation from 1999 to 2022. This is inconsistent with the financial repression that exists in China, and the main reason is the calculation methods of China’s CPI. China’s CPI measurement system originated from the planned economy era, which did not fully consider the rise in housing purchase prices, so the current CPI measurement system can be more realistically presented by taking the rise in housing prices into consider. The core idea of this study is to mining relevant official statistical data and calculate the proportion of Chinese residents’ expenditure on purchasing houses to their total expenditure. By taking the proportion of house purchases as the weight of house price factor, and taking the proportion of other consumption as the weight of official CPI, the Generalized CPI (GCPI) is formulated. The GCPI is then compared with market interest rates to determine the actual interest rate situation in China over the past 20 years. This study has found that if GCPI is used as a measure, China’s real interest rates have been negative for most years since 1999. Chinese residents have suffered the negative effects of financial repression over the past 20 years, and their property income cannot keep up with the actual losses caused by inflation. Therefore, it is believed that China’s CPI calculation method should be adjusted to take into account the rise in housing prices, so China’s actual inflation level could be more accurately reflected. In view of the above, deepening interest rate marketization reform and expand channels for financial investment are the future development goals of China’s financial system.