In 2024, ** percent of adults in the United States invested in the stock market. This figure has remained steady over the last few years, and is still below the levels before the Great Recession, when it peaked in 2007 at ** percent. What is the stock market? The stock market can be defined as a group of stock exchanges, where investors can buy shares in a publicly traded company. In more recent years, it is estimated an increasing number of Americans are using neobrokers, making stock trading more accessible to investors. Other investments A significant number of people think stocks and bonds are the safest investments, while others point to real estate, gold, bonds, or a savings account. Since witnessing the significant one-day losses in the stock market during the Financial Crisis, many investors were turning towards these alternatives in hopes for more stability, particularly for investments with longer maturities. This could explain the decrease in this statistic since 2007. Nevertheless, some speculators enjoy chasing the short-run fluctuations, and others see value in choosing particular stocks.

NASDAQ Listed Companies Dataset

Description:

This dataset provides comprehensive information on companies listed on the NASDAQ stock exchange. It includes essential details about each company, making it a valuable resource for financial analysis, stock market research, and investment strategies.

Features:

• symbol: The unique ticker symbol used to identify the company's stock on the NASDAQ exchange.
• name: The full name of the company.
• currency: The currency in which the company's stock is traded.
• exchange: The stock exchange where the company is listed (in this case, NASDAQ).
• mic_code: The Market Identifier Code (MIC) for the NASDAQ exchange.
• country: The country where the company is headquartered.
• type: The type of company, such as common stock or preferred stock.
• Usage: This dataset can be used for various purposes including:

Stock Market Analysis:

Analyze stock symbols, company names, and market data.

Financial Modeling:

Incorporate company details into financial models and investment strategies.

Market Research:

Understand the distribution of companies by country and currency.

Data Visualization:

Create visualizations of the NASDAQ market landscape.

Data Source:

The data is sourced from the Twelve Data API, which provides up-to-date financial and stock market information.

Notes: The dataset includes only NASDAQ-listed companies and does not cover other exchanges. Ensure to comply with any data usage policies or licensing agreements associated with the data source. Feel free to adapt the description based on the specific details and attributes of your dataset.

About this dataset

Stock market has become of the wonderful place to make money. Many loss and many gains. Many have tried to predict the price of a stock but fails miserably. Those who say they're able to do so, are the one who hide their biggest losses. If stock price cannot be determined by price alone, then there might be other way to predict it, or say to invest it in the "better" way. Otherwise Warren Buffet wouldn't as rich as he is now by luck alone. But who says we cannot play around with it and create our standard of investing in stock?

How to use this dataset

EDA RNN to predict future price Trend identifier Classifier Stock Recommendation

Features

Other Information

You also could use dataset outside this one. This dataset present all public company data in Indonesia. Might be helpful to do certain task, e.g. classification for the industry, etc.

Acknowledgement

Yahoo Finance

This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.

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.

RAG Stock: A Risky Investment

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

Techsalerator offers an extensive dataset of End-of-Day Pricing Data for all 66 companies listed on the Nairobi Securities Exchange (XNAI) in Kenya. 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 Kenya:

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 Kenya:

Nairobi Securities Exchange All Share Index (NASI): The main index that tracks the performance of all companies listed on the Nairobi Securities Exchange (NSE). NASI provides insights into the overall market performance in Kenya.

Nairobi Securities Exchange 20 Share Index (NSE 20): An index that tracks the performance of the top 20 companies by market capitalization listed on the NSE. NSE 20 is an important benchmark for the Kenyan stock market.

Safaricom PLC: A leading telecommunications company in Kenya, offering mobile and internet services. Safaricom is one of the largest and most actively traded companies on the NSE.

Equity Group Holdings PLC: A prominent financial institution in Kenya, providing banking and financial services. Equity Group is a significant player in the Kenyan financial sector and is listed on the NSE.

KCB Group PLC: Another major financial institution in Kenya, offering banking and financial services. KCB Group is also listed on the NSE and is among the key players in the country's banking industry.

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

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 Kenya?

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 Kenya 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 transfers, ACH, and wire transfers, facilitating a convenient and se...

This research was conducted on Islamic money market mutual funds registered with the OJK using three years (2015-2018) with a total sample of 36 Islamic money market mutual funds. There is one dependent variable in this research, namely the Islamic money market mutual funds’ performance, and three independent variables: asset allocation policy, investment manager performance, and risk level. The asset allocation policy variable was measured using Sharpe’s Asset Class Factor Model, the investment manager performance using the Treynor-Mazuy Model, the level of risk using the Standard Deviation Formula, and the performance of the Islamic money market mutual funds using the Shape Ratio

This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.

Context

The algorithmic trading space is buzzing with new strategies. Companies have spent billions in infrastructures and R&D to be able to jump ahead of the competition and beat the market. Still, it is well acknowledged that the buy & hold strategy is able to outperform many of the algorithmic strategies, especially in the long-run. However, finding value in stocks is an art that very few mastered, can a computer do that?

Content

This Data repo contains two datasets:

1. Example_2019_price_var.csv. I built this dataset thanks to Financial Modeling Prep API and to pandas_datareader. Each row is a stock from the technology sector of the US stock market (that is available from the aforementioned API, which is free and highly recommended). The column contains the percent price variation of each stock for the year 2019. In other words, it collects the percent price variation of each stock from the first trading day on Jan 2019 to the last trading day of Dec 2019. To compute this price variation I decided to consider the Adjusted Close Price.

2. Example_DATASET.csv. I built this dataset thanks to Financial Modeling Prep API. Each row is a stock from the technology sector of the US stock market (that is available from the aforementioned API). Each column is a financial indicator that can be found in the 2018 10-K filings of each company. There are no Nans or empty cells. Furthermore, the last column is the CLASS of each stock, where:

   1. class = 1 if the price of the stock increases during 2019
   2. class = 0 if the price of the stock decreases during 2019

In other words, the last column is used to classify each stock in buy-worthy or not, and this relationship is what should allow a machine learning model to learn to recognize stocks that will increase their value from those that won't.

NOTE: the number of stocks does not match between the two datasets because the API did not have all the required financial indicators for some stocks. It is possible to remove from Example_2019_price_var.csv those rows that do not appear in Example_DATASET.csv.

Inspiration

I built this dataset during the 2019 winter holidays period, because I wanted to answer a simple question: is it possible to have a machine learning model learn the differences between stocks that perform well and those that don't, and then leverage this knowledge in order to predict which stock will be worth buying? Moreover, is it possible to achieve this simply by looking at financial indicators found in the 10-K filings?

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.

if the stock market goes down during a recession, you should sell all of your investments to minimize your losses.

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

South African monthly The FTSE/JSE All Share Index data was procured from Bloomberg and the nominal effective exchange rate (NEER) from South African Reserve Bank (SARB) database, where the data has been seasonally adjusted specifying 2015 as the base year. Volatility measures in these markets are generated through a multivaraite EGARCH model in the WinRATS software. South African monthly consumer price index (CPI) data was procured from the International Monetary Fund’s International Financial Statistics (IFS) database, where the data has been seasonally adjusted, specifying 2010 as the base year. The inflation rate is constructed by taking the year-on-year changes in the monthly CPI figures. Inflation uncertainty was generated through the GARCH model in Eviews software. The following South African macroeconomic variables were procured from the SARB: real industrial production (IP), which is used as a proxy for real GDP, real investment (I), real consumption (C), inflation (CPI), broad money (M3), the 3-month treasury bill rate (TB3) and the policy rate (R), a measure of U.S. EPU developed by Baker et al. (2016) to account for global developments available at http://www.policyuncertainty.com/us_monthly.html.

This dataset was collected as part of a study investigating the impact of blockchain-based transaction security on investor intentions in the stock market. It comprises responses from 460 participants, including both experienced and potential investors, who provided insights into their perceptions of blockchain technology, investment behavior, and financial attitudes. The dataset includes demographic variables such as age, location, and monthly income, as well as key psychological and behavioral indicators based on the Theory of Planned Behavior (TPB).

The dataset captures multiple dimensions of investor decision-making, including money attitude (classified into avoidance, worship, status, and vigilance), subjective norms (normative beliefs and motivation to comply), perceived behavioral control, transaction security perceptions using blockchain, and investment intention. Each variable was measured using a structured questionnaire with Likert-scale responses, allowing for a quantitative analysis of investor preferences.

The dataset was processed and analyzed using Smart PLS (Partial Least Squares Structural Equation Modeling), ensuring robust validation of the proposed research model. Descriptive statistics, reliability tests, and hypothesis testing were conducted to examine the relationships between blockchain security, investor confidence, and decision-making processes. Additionally, the dataset offers insights into how financial literacy, social influence, and risk perception shape investment behavior in the presence of blockchain security mechanisms.

This dataset is valuable for researchers, financial analysts, and policymakers interested in understanding how emerging financial technologies impact investor behavior and trust in stock market transactions. It provides a foundation for further studies on financial technology adoption, fraud prevention, and regulatory frameworks aimed at enhancing investment security.

Introduction

Since Investing.com does not have an API, I decided to develop this Python package in order to retrieve historical data from the companies that integrate the Continuous Spanish Stock Market. So on, I decided to generate, via investpy, the datasets for every company so that any Data Scientist or Data Enthusiastic can handle it and abstract their own conclusions and research.

The main purpose of developing investpy, the package from which these datasets have been retrieved, was to use it as the Data Extraction tool for its namesake section, for my Final Degree Project at the University of Salamanca titled "*Machine Learning for stock investment recommendation systems*". The package end up being so consistent, reliable and usable that it is going to be used as the main Data Extraction tool by another students in their Final Degree Projects named "*Recommender system of banking products*" and "*Robo-Advisor Application*".

License

MIT License

Additional Information

investpy, the Python package from which datasets were generated is currently in a development beta version, so please, if needed open an issue to solve all the possible problems the package may be causing or any dataset error. Also, any new ideas or proposals are welcome, and will be gladly implemented in the package if the are positive and useful.

For further information or any question feel free to contact me via email at alvarob96@usal.es

You can also check my Medium Publication, where I upload weekly posts related to Data Science and mainly on Data Extraction techniques via Web Scraping. In this case, you can read "investpy — a Python package for historical data extraction from the Spanish stock market" where I explain the basics on investpy development and some insights on Web Scraping with Python.

Disclaimer

This Python Package has been made for research purposes in order to fit a needs that Investing.com does not cover, so this package works like an Application Programming Interface (API) of Investing.com developed in an altruistic way. Conclude that this package is not related in any way with Investing.com or any dependant company, the only requirement for developing this package was to mention the source where data is retrieved.

Australia Assets: Stock: Money Market Financial Investment Funds: Other Accounts Receivable data was reported at 0.000 AUD mn in Dec 2024. This stayed constant from the previous number of 0.000 AUD mn for Sep 2024. Australia Assets: Stock: Money Market Financial Investment Funds: Other Accounts Receivable data is updated quarterly, averaging 0.000 AUD mn from Jun 1988 (Median) to Dec 2024, with 147 observations. The data reached an all-time high of 0.000 AUD mn in Dec 2024 and a record low of 0.000 AUD mn in Dec 2024. Australia Assets: Stock: Money Market Financial Investment Funds: Other Accounts Receivable data remains active status in CEIC and is reported by Australian Bureau of Statistics. The data is categorized under Global Database’s Australia – Table AU.AB028: SNA08: SESCA08: Funds by Sector: Financial Corporations: Money Market Financial Investment Funds: Stock.

Artificial Intelligence in Regtech Market Outlook

The global Artificial Intelligence (AI) in Regtech market size was valued at approximately USD 7.8 billion in 2023 and is projected to reach around USD 34.5 billion by 2032, growing at a Compound Annual Growth Rate (CAGR) of 18.3% during the forecast period. This robust growth is attributed to increasing regulatory scrutiny and the subsequent need for efficient compliance solutions. The market's expansion is reinforced by technological advancements in AI, which are enhancing the capabilities of Regtech solutions to address the ever-evolving regulatory landscape.

One of the primary growth factors driving the AI in Regtech market is the increasing complexity of regulatory requirements across various industries. Companies are continually faced with the challenge of staying compliant with a multitude of regulations that differ from country to country. This complexity necessitates the adoption of advanced technologies like AI to automate and streamline compliance processes. AI-powered Regtech solutions can analyze vast amounts of regulatory data and provide actionable insights, helping organizations mitigate risks and avoid costly penalties.

Another significant growth driver is the rise in financial crimes such as money laundering, fraud, and identity theft. Traditional methods of combating these issues are often inadequate due to their manual nature and the sheer volume of data that needs to be processed. AI in Regtech offers sophisticated tools for real-time monitoring, predictive analytics, and anomaly detection, enabling organizations to proactively identify and address fraudulent activities. Consequently, the increasing demand for robust fraud detection and prevention solutions is propelling market growth.

The growing emphasis on operational efficiency and cost reduction is also contributing to the market's expansion. AI technologies can automate routine compliance tasks, reducing the need for extensive human intervention and thereby lowering operational costs. Moreover, AI-driven Regtech solutions can deliver faster and more accurate results, enhancing overall efficiency. Organizations are increasingly recognizing the value of these benefits, leading to higher adoption rates of AI in Regtech solutions.

From a regional perspective, North America holds a significant share of the AI in Regtech market, driven by stringent regulatory frameworks and a high level of technological adoption. Europe is also a major market, owing to rigorous compliance requirements and strong financial sectors. The Asia Pacific region is expected to witness the highest growth rate during the forecast period, fueled by rapid digital transformation and increasing regulatory pressures in countries like China and India. Latin America and the Middle East & Africa are also emerging markets, with growing awareness and investment in Regtech solutions.

Component Analysis

In the AI in Regtech market, the component segment is categorized into software, hardware, and services. The software segment dominates the market due to the extensive adoption of AI-powered compliance and risk management solutions. These software solutions offer capabilities such as data analytics, machine learning, and natural language processing, which are crucial for automating regulatory processes. Companies are increasingly investing in AI-driven software to enhance their compliance frameworks and manage regulatory challenges more effectively.

Hardware, though a smaller segment compared to software, plays a critical role in supporting the deployment of AI in Regtech solutions. High-performance computing hardware, such as GPUs and servers, is essential for running complex AI algorithms and processing large datasets. Organizations are investing in advanced hardware to ensure that their AI systems operate efficiently and deliver accurate results. The growth in cloud computing and edge computing technologies is also driving the demand for specialized hardware in the Regtech market.

Services constitute a vital component of the AI in Regtech market, encompassing consulting, implementation, and support services. As organizations adopt AI-powered Regtech solutions, they often require expert guidance to integrate these technologies into their existing systems. Consulting services help companies understand their regulatory requirements and devise effective compliance strategies. Implementation services assist in deploying and customizing AI solutions, while support services ensure the ongoing maintenance and optimization of these sy

Australia Liabilities: Stock: Money Market Financial Investment Funds: Long Term Loans & Placements: Banks data was reported at 0.000 AUD mn in Dec 2024. This stayed constant from the previous number of 0.000 AUD mn for Sep 2024. Australia Liabilities: Stock: Money Market Financial Investment Funds: Long Term Loans & Placements: Banks data is updated quarterly, averaging 1.000 AUD mn from Jun 1988 (Median) to Dec 2024, with 147 observations. The data reached an all-time high of 58.000 AUD mn in Dec 2016 and a record low of 0.000 AUD mn in Dec 2024. Australia Liabilities: Stock: Money Market Financial Investment Funds: Long Term Loans & Placements: Banks data remains active status in CEIC and is reported by Australian Bureau of Statistics. The data is categorized under Global Database’s Australia – Table AU.AB028: SNA08: SESCA08: Funds by Sector: Financial Corporations: Money Market Financial Investment Funds: Stock.

Assets: Stock: Non Money Market Financial Investment Funds: Derivatives Issued data was reported at 24,298.000 AUD mn in Dec 2024. This records a decrease from the previous number of 36,055.000 AUD mn for Sep 2024. Assets: Stock: Non Money Market Financial Investment Funds: Derivatives Issued data is updated quarterly, averaging 2,808.000 AUD mn from Jun 1988 (Median) to Dec 2024, with 147 observations. The data reached an all-time high of 45,996.000 AUD mn in Mar 2020 and a record low of 0.000 AUD mn in Mar 1994. Assets: Stock: Non Money Market Financial Investment Funds: Derivatives Issued data remains active status in CEIC and is reported by Australian Bureau of Statistics. The data is categorized under Global Database’s Australia – Table AU.AB030: SNA08: SESCA08: Funds by Sector: Financial Corporations: Non Money Market Financial Investment Funds: Stock.

Australia Assets: Stock: Money Market Financial Investment Funds: Loans & Placements Borrowed by: Private Non Financial Investment Funds data was reported at 0.000 AUD mn in Dec 2024. This stayed constant from the previous number of 0.000 AUD mn for Sep 2024. Australia Assets: Stock: Money Market Financial Investment Funds: Loans & Placements Borrowed by: Private Non Financial Investment Funds data is updated quarterly, averaging 0.000 AUD mn from Jun 1988 (Median) to Dec 2024, with 147 observations. The data reached an all-time high of 6.000 AUD mn in Sep 2006 and a record low of 0.000 AUD mn in Dec 2024. Australia Assets: Stock: Money Market Financial Investment Funds: Loans & Placements Borrowed by: Private Non Financial Investment Funds data remains active status in CEIC and is reported by Australian Bureau of Statistics. The data is categorized under Global Database’s Australia – Table AU.AB028: SNA08: SESCA08: Funds by Sector: Financial Corporations: Money Market Financial Investment Funds: Stock.

Australia Assets: Stock: Money Market Financial Investment Funds: Deposits Accepted by: Banks data was reported at 14,030.000 AUD mn in Dec 2024. This records an increase from the previous number of 13,166.000 AUD mn for Sep 2024. Australia Assets: Stock: Money Market Financial Investment Funds: Deposits Accepted by: Banks data is updated quarterly, averaging 6,774.000 AUD mn from Jun 1988 (Median) to Dec 2024, with 147 observations. The data reached an all-time high of 14,243.000 AUD mn in Jun 2020 and a record low of 649.000 AUD mn in Mar 1994. Australia Assets: Stock: Money Market Financial Investment Funds: Deposits Accepted by: Banks data remains active status in CEIC and is reported by Australian Bureau of Statistics. The data is categorized under Global Database’s Australia – Table AU.AB028: SNA08: SESCA08: Funds by Sector: Financial Corporations: Money Market Financial Investment Funds: Stock.

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.

Uranium Energy (YCA): The Sun's Yellow Cake, or Just a Fool's Gold Investment?

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