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.

Inflation: Friend or Foe to the Stock Market?

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

Graph and download economic data for Producer Price Index by Industry: Investment Banking and Securities Intermediation: Brokerage Services, Equities and ETFs (PCU523120523120101) from Dec 1999 to Aug 2025 about ETF, brokers, stocks, equity, stock market, securities, services, PPI, industry, inflation, price index, indexes, price, and USA.

This panel dataset contains quarterly series on inflation targets, bands, and track records for 41 inflation targeting countries from 1990 to 2024. Data on inflation targets and bands are collected through each central bank’s historical documents and rules-based track record measures are calculated by the author to assess actual inflation outcomes with respect to the central banks’ stated policy objectives. The dataset supports research work in Zhang (2025), Zhang and Wang (2022), and Zhang (2021). Please cite the following paper when using the data: Z. Zhang, Inflation Targets, Bands, and Track Records: a Dataset of Inflation Targeting Countries, Data in Brief, Volume 61, 2025, 111753.

Other related papers: Z. Zhang, Does inflation targeting track record matter for asset prices? Evidence from stock, bond, and foreign exchange markets, Journal of International Financial Markets, Institutions and Money, Volume 101, 2025, 102141. Z. Zhang, S. Wang, Do actions speak louder than words? Assessing the effects of inflation targeting track records on macroeconomic performance, 2022, IMF Working Papers 2022/227.
Z. Zhang, Stock returns and inflation redux: An explanation from monetary policy in advanced and emerging markets, 2021, IMF Working Papers 2021/219.

The 2025 August online version has added two non-IT countries (Switzerland and China) for comparison purpose.

Czechia - Debt sec, inflation-linked, issued by central bank, in all markets at all original maturities denominated in all currencies at nominal value stocks

Sweden - Debt sec, inflation-linked, issued by central bank, in all markets at all original maturities denominated in all currencies at nominal value stocks

The Federal Reserve's balance sheet has undergone significant changes since 2007, reflecting its response to major economic crises. From a modest *** trillion U.S. dollars at the end of 2007, it ballooned to approximately **** trillion U.S. dollars by October 29, 2025. This dramatic expansion, particularly during the 2008 financial crisis and the COVID-19 pandemic—both of which resulted in negative annual GDP growth in the U.S.—showcases the Fed's crucial role in stabilizing the economy through expansionary monetary policies. Impact on inflation and interest rates The Fed's expansionary measures, while aimed at stimulating economic growth, have had notable effects on inflation and interest rates. Following the quantitative easing in 2020, inflation in the United States reached ***** percent in 2022, the highest since 1991. However, by August 2025, inflation had declined to *** percent. Concurrently, the Federal Reserve implemented a series of interest rate hikes, with the rate peaking at **** percent in August 2023, before the first rate cut since September 2021 occurred in September 2024. Financial implications for the Federal Reserve The expansion of the Fed's balance sheet and subsequent interest rate hikes have had significant financial implications. In 2024, the Fed reported a negative net income of ***** billion U.S. dollars, a stark contrast to the ***** billion U.S. dollars profit in 2022. This unprecedented shift was primarily due to rapidly rising interest rates, which caused the Fed's interest expenses to soar to over *** billion U.S. dollars in 2023. Despite this, the Fed's net interest income on securities acquired through open market operations reached a record high of ****** billion U.S. dollars in the same year.

This Economic Commentary examines the recent behavior and the longer-term properties of market-based and non-market-based inflation series, including their cyclical properties, historical revisions, and predictive power in explaining future PCE inflation. The examination reveals a statistically significant association between market-based PCE inflation and estimates of labor market slack, and a strong positive association between movements in the stock market and in some of the financial services components of non-market-based PCE inflation. Disinflation in overall PCE inflation over the course of 2023 and 2024 was largely driven by disinflation in the market-based components, coinciding with a gradual loosening in labor market conditions.

U.S. Economic and Financial Dataset

Dataset Description

This dataset combines historical U.S. economic and financial indicators, spanning the last 50 years, to facilitate time series analysis and uncover patterns in macroeconomic trends. It is designed for exploring relationships between interest rates, inflation, economic growth, stock market performance, and industrial production.

Key Features

• Frequency: Monthly
• Time Period: Last 50 years from Nov-24
• Sources:
  • Federal Reserve Economic Data (FRED)
  • Yahoo Finance

Dataset Feature Description

1. Interest Rate (Interest_Rate):

   • The effective federal funds rate, representing the interest rate at which depository institutions trade federal funds overnight.

2. Inflation (Inflation):

   • The Consumer Price Index for All Urban Consumers, an indicator of inflation trends.

3. GDP (GDP):

   • Real GDP measures the inflation-adjusted value of goods and services produced in the U.S.

4. Unemployment Rate (Unemployment):

   • The percentage of the labor force that is unemployed and actively seeking work.

5. Stock Market Performance (S&P500):

   • Monthly average of the adjusted close price, representing stock market trends.

6. Industrial Production (Ind_Prod):

   • A measure of real output in the industrial sector, including manufacturing, mining, and utilities.

Dataset Statistics

1. Total Entries: 599
2. Columns: 6
3. Memory usage: 37.54 kB
4. Data types: float64

Feature Overview

• Columns:
  • Interest_Rate: Monthly Federal Funds Rate (%)
  • Inflation: CPI (All Urban Consumers, Index)
  • GDP: Real GDP (Billions of Chained 2012 Dollars)
  • Unemployment: Unemployment Rate (%)
  • Ind_Prod: Industrial Production Index (2017=100)
  • S&P500: Monthly Average of S&P 500 Adjusted Close Prices

Executive Summary

This project explores the interconnected dynamics of key macroeconomic indicators and financial market trends over the past 50 years, leveraging data from the Federal Reserve Economic Data (FRED) and Yahoo Finance. The dataset integrates critical variables such as the Federal Funds Rate, Inflation (CPI), Real GDP, Unemployment Rate, Industrial Production, and the S&P 500 Index, providing a holistic view of the U.S. economy and financial markets.

The analysis focuses on uncovering relationships between these variables through time-series visualization, correlation analysis, and trend decomposition. Key findings are included in the Insights section. This project serves as a robust resource for understanding long-term economic trends, policy impacts, and market behavior. It is particularly valuable for students, researchers, policymakers, and financial analysts seeking to connect macroeconomic theory with real-world data.

Potential Use Cases

• Economic Analysis: Examine relationships between interest rates, inflation, GDP, and unemployment.
• Stock Market Prediction: Study how macroeconomic indicators influence stock market trends.
• Time Series Modeling: Perform ARIMA, VAR, or other models to forecast economic trends.
• Cyclic Pattern Analysis: Identify how economic shocks and recoveries impact key indicators.

Snap of Power Analysis

https://github.com/user-attachments/assets/1b40e0ca-7d2e-4fbc-8cfd-df3f09e4fdb8">

To ensure sufficient power, the dataset covers last 50 years of monthly data i.e., around 600 entries.

Key Insights derived through EDA, time-series visualization, correlation analysis, and trend decomposition

• Interest Rate and Inflation Dynamics: The interest Rate and inflation exhibit an inverse relationship, especially during periods of aggressive monetary tightening by the Federal Reserve.
• Economic Growth and Market Performance: GDP growth and the S&P 500 Index show a positive correlation, reflecting how market performance often aligns with overall economic health.
• Labor Market and Industrial Output: Unemployment and industrial production demonstrate a strong inverse relationship. Higher industrial output is typically associated with lower unemployment
• Market Behavior During Economic Shocks: The S&P 500 experienced sharp declines during significant crises, such as the 2008 financial crash and the COVID-19 pandemic in 2020. These events also triggered increased unemployment and contractions in GDP, highlighting the interplay between markets and the broader economy.
• Correlation Highlights: S&P 500 and GDP have a strong positive correlation. Interest rates negatively correlate with GDP and inflation, reflecting monetary policy impacts. Unemployment is negatively correlated with industrial production but positively correlated with interest rates.

Link to GitHub Repo

https:/...

Many items in the personal consumption expenditures (PCE) price index are influenced by developments in the labor market, but some “non-market” items tend to move with the stock market – which helped keep overall PCE inflation elevated in recent years, according to new Cleveland Fed research

During the period beginning roughly in the mid-1980s until the Global Financial Crisis (2007-2008), the U.S. economy experienced a time of relative economic calm, with low inflation and consistent GDP growth. Compared with the turbulent economic era which had preceded it in the 1970s and the early 1980s, the lack of extreme fluctuations in the business cycle led some commentators to suggest that macroeconomic issues such as high inflation, long-term unemployment and financial crises were a thing of the past. Indeed, the President of the American Economic Association, Professor Robert Lucas, famously proclaimed in 2003 that "central problem of depression prevention has been solved, for all practical purposes". Ben Bernanke, the future chairman of the Federal Reserve during the Global Financial Crisis (GFC) and 2022 Nobel Prize in Economics recipient, coined the term 'the Great Moderation' to describe this era of newfound economic confidence. The era came to an abrupt end with the outbreak of the GFC in the Summer of 2007, as the U.S. financial system began to crash due to a downturn in the real estate market.

Causes of the Great Moderation, and its downfall

A number of factors have been cited as contributing to the Great Moderation including central bank monetary policies, the shift from manufacturing to services in the economy, improvements in information technology and management practices, as well as reduced energy prices. The period coincided with the term of Fed chairman Alan Greenspan (1987-2006), famous for the 'Greenspan put', a policy which meant that the Fed would proactively address downturns in the stock market using its monetary policy tools. These economic factors came to prominence at the same time as the end of the Cold War (1947-1991), with the U.S. attaining a new level of hegemony in global politics, as its main geopolitical rival, the Soviet Union, no longer existed. During the Great Moderation, the U.S. experienced a recession twice, between July 1990 and March 1991, and again from March 2001 tom November 2001, however, these relatively short recessions did not knock the U.S. off its growth path. The build up of household and corporate debt over the early 2000s eventually led to the Global Financial Crisis, as the bursting of the U.S. housing bubble in 2007 reverberated across the financial system, with a subsequent credit freeze and mass defaults.

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.

S&P 500: A Bull or a Bear?

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

The aim of this investigation is, to describe the development of the German Stock Market during the inter-war period. Causes for the so called change of the stock exchange functions are analysed. The author wants to make a contribution on special aspects of the economic history of the Weimar Republic and the following NS-regime. In his investigation the researcher analyses the activities of the involved players in a historical-institutional framework. The Study’s subjectIn the year 1890 the constitution of security exchange markets and stock markets has been the object of political debate and there has been discussed similar questions according to this topic in public and in policy as today. A current question is about the possibilities to boost the functionality of the security exchange and stock markets, not least in the face of Germany’s position in the global economy. In 1896 as a result of massive political conflicts a stock exchange act has arisen that disappointed the representatives of liberal trading interests because of the restriction of the stock market system’s autonomy and the prohibition of certain forms of trade. In 1908 an amendment to the stock exchange act has been adopted by the parliament. The stock market act in this new form has had validity until today. After the years of the hyperinflation deep changes of the stock market processes has been taken place. This changes can be described as a change of function. The economic-historical study at hand deals with the description of the development of the German security exchange markets during the interwar period. Reasons of the functional changes, which means mainly the decrease in importance, are analysed. In this context the primary investigator’s analysis contributes also to specific aspects of the economic history of the Weimar Republic and the Nazi empire. Due to a lack of date the needed statistical information concerning the period of interest is not available and therefore a statistical analysis cannot meet cliometric requirements. Therefore, the study’s concept is primary a desciptive one. On the basis of the quantitative information an identification of the functional change and the definition of stages of this process is made. The researcher tries to carve out the factors which have led to the functional change particularly during the period between 1924 and 1939. In this context the annual reports of banks, reports of the Chamber of Commerce and Industry, contributions of professional journals, and documents of authorities charged with the stock exchange market, are the empirical basis for the investigation. The researcher analyzed the effects of the banking sector’s concentration-process on the stock exchange market and assessed quantitatively the functional change. On the basis of the collected time series for the period of the late 19th century until 1939 the investigator analyzed the activities at the stock markets. First, the focus on interest is on the development of investments and securities issues. Then information on the securities turnover of German capital market before 1940 are given on the basis of an estimation procedure, developed by the researcher. The sepcial conditions during the inflation between 1914 and 1923 are discussed separately and the long term effects of this hyper-inflation on the stock exchange are identified. The effects of the taxation of stock exchange market visits and the high transaction costs are discussed, too. Used sources for the investigation have been:Archives of German Public Authorities:- finance ministry of the German Reich,- imperial chancellery- Reich´s ministry of economics- reference files of the German Reichsbank- Imperial commissioner of the stock market in Berlin Official Statistics, statistics of trade associations, chambers of commerce, enterprises, the press, and scientific publications. Finally, the author made estimates and calculations. The Study’s data:Data tables are accessible via the search- and download-system HISTAT unter the Topic ‘State: Finances and Taxes’ (= Staat: Finanzen und Steuern). The Study’s data are diveded into the following parts: A. Quantitative Indicators on the Change of Functions (Quantitative Indikatoren des Funktionswandels) A.1 Structure of floatation (Struktur der Wertpapieremission ausgewählter Zeitspannen (1901-1939).)A.2 Tax revenues of exchange turnover (Börsenumsatzsteueraufkommen (1885-1939).)A.3 Vergleich des unkorrigierten mit einem fiktiv möglichen Börsenumsatzsteueraufkommen (1906-1913).A.4 Estimation of everage tax rates (Geschätzte Durchschnittssteuersätze (1884-1913).)A.5 Amount of stock companies of the German Empire (Zahl der Aktiengesellschaften im Deutschen Reich zu bestimmten Jahren (1886-1939).)A.6 Shares listed on the Berlin stock exchange at the end of the year (Die zum Jahresende an der Berliner Börse notierten Aktien (1926-1939).)A.7 Reports und Lombards der Berliner Großbanken in ...

Slovenia - Debt sec, inflation-linked, issued by central bank, in all markets at all original maturities denominated in all currencies at nominal value stocks

Overview with Chart & Report: Bank of Japan's L Money Stock y/y reflects a year-over-year change in the entire quantity of money circulating in the country's economy. The Money Stock indicator characterizes inflation in the

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.

Context

The stock market has consistently proven to be a good place to invest in and save for the future. There are a lot of compelling reasons to invest in stocks. It can help in fighting inflation, create wealth, and also provides some tax benefits. Good steady returns on investments over a long period of time can also grow a lot more than seems possible. Also, thanks to the power of compound interest, the earlier one starts investing, the larger the corpus one can have for retirement. Overall, investing in stocks can help meet life's financial aspirations.

It is important to maintain a diversified portfolio when investing in stocks in order to maximise earnings under any market condition. Having a diversified portfolio tends to yield higher returns and face lower risk by tempering potential losses when the market is down. It is often easy to get lost in a sea of financial metrics to analyze while determining the worth of a stock, and doing the same for a multitude of stocks to identify the right picks for an individual can be a tedious task. By doing a cluster analysis, one can identify stocks that exhibit similar characteristics and ones which exhibit minimum correlation. This will help investors better analyze stocks across different market segments and help protect against risks that could make the portfolio vulnerable to losses.

Objective

Trade&Ahead is a financial consultancy firm who provide their customers with personalized investment strategies. They have hired you as a Data Scientist and provided you with data comprising stock price and some financial indicators for a few companies listed under the New York Stock Exchange. They have assigned you the tasks of analyzing the data, grouping the stocks based on the attributes provided, and sharing insights about the characteristics of each group

Data Dictionary

Ticker Symbol: An abbreviation used to uniquely identify publicly traded shares of a particular stock on a particular stock market Company: Name of the company GICS Sector: The specific economic sector assigned to a company by the Global Industry Classification Standard (GICS) that best defines its business operations GICS Sub Industry: The specific sub-industry group assigned to a company by the Global Industry Classification Standard (GICS) that best defines its business operations Current Price: Current stock price in dollars Price Change: Percentage change in the stock price in 13 weeks Volatility: Standard deviation of the stock price over the past 13 weeks ROE: A measure of financial performance calculated by dividing net income by shareholders' equity (shareholders' equity is equal to a company's assets minus its debt) Cash Ratio: The ratio of a company's total reserves of cash and cash equivalents to its total current liabilities Net Cash Flow: The difference between a company's cash inflows and outflows (in dollars) Net Income: Revenues minus expenses, interest, and taxes (in dollars) Earnings Per Share: Company's net profit divided by the number of common shares it has outstanding (in dollars) Estimated Shares Outstanding: Company's stock currently held by all its shareholders P/E Ratio: Ratio of the company's current stock price to the earnings per share P/B Ratio: Ratio of the company's stock price per share by its book value per share (book value of a company is the net difference between that company's total assets and total liabilities)

This stock market dataset is designed for financial analysis and predictive modeling. It includes historical stock prices, technical indicators, macroeconomic factors, and sentiment scores to help in developing and testing machine learning models for stock trend prediction.

Dataset Features: Column Description Stock Random stock ticker (AAPL, GOOG, etc.) Date Random business date Open Open price High High price Low Low price Close Close price Volume Trading volume SMA_10 10-day Simple Moving Average RSI Relative Strength Index (10-90 range) MACD MACD indicator (-5 to 5) Bollinger_Upper Upper Bollinger Band Bollinger_Lower Lower Bollinger Band GDP_Growth Random GDP growth rate (2.5% to 3.5%) Inflation_Rate Inflation rate (1.5% to 3.0%) Interest_Rate Interest rate (0.5% to 5.0%) Sentiment_Score Random sentiment score (-1 to 1) Next_Close Next day's closing price (for regression) Target Binary classification (1: Price Increase, 0: Price Decrease)

Key Features: Stock Prices: Open, High, Low, Close, and Volume data. Technical Indicators: Simple Moving Average (SMA), Relative Strength Index (RSI), MACD, and Bollinger Bands. Macroeconomic Factors: Simulated GDP growth, inflation rate, and interest rates. Sentiment Scores: Randomized sentiment values between -1 and 1 to simulate market sentiment. Target Variables: Next-day close price (for regression) and price movement direction (for classification).

Poland - Debt sec, inflation-linked, issued by central bank, in all markets at all original maturities denominated in all currencies at nominal value stocks

Techsalerator offers an extensive dataset of End-of-Day Pricing Data for all 19 companies listed on the South Pacific Stock Exchange (XSPS) in Fiji. 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.

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

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.

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

South Pacific Stock Exchange (SPSE): The South Pacific Stock Exchange is the main stock exchange in Fiji. It facilitates the trading of shares of domestic companies and provides a platform for investors to participate in Fiji's capital markets.

Fijian Dollar (FJD): The Fijian Dollar is the official currency of Fiji. It is commonly abbreviated as FJD and is used for transactions within the country. The currency's value is influenced by economic conditions in Fiji and global factors.

Reserve Bank of Fiji (RBF): The central bank of Fiji responsible for formulating and implementing monetary policy, regulating financial institutions, and maintaining financial stability. The RBF's decisions impact interest rates, inflation, and overall economic conditions in Fiji.

Government Bonds: The Fijian government issues bonds to raise funds for various projects and operations. These bonds offer a fixed return to investors over a specified period. Government bonds are considered relatively safe investments and play a role in financing government activities.

Fiji National Provident Fund (FNPF): The FNPF is Fiji's national superannuation fund, providing retirement savings and social security services to Fijian citizens. It invests in various assets, including equities, bonds, and real estate, with the aim of generating returns for its members.

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If you're interested in accessing Techsalerator's End-of-Day Pricing Data for Fiji, 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.

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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 Fiji ?

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 Fiji?

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 Fiji 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. W...

Finland - Debt sec, inflation-linked, issued by central bank, in all markets at all original maturities denominated in all currencies at nominal value stocks