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).

Graph and download economic data for Equity Market Volatility Tracker: Macroeconomic News and Outlook: Interest Rates (EMVMACROINTEREST) from Jan 1985 to Oct 2025 about volatility, uncertainty, equity, interest rate, interest, rate, and USA.

Graph and download economic data for Interest Rates and Price Indexes; Dow Jones U.S. Total Market Index, Level (BOGZ1FL073164013Q) from Q4 1970 to Q2 2025 about mutual funds, equity, liabilities, interest rate, interest, rate, price index, indexes, price, and USA.

Graph and download economic data for Interest Rates and Price Indexes; Dow Jones U.S. Total Market Index, Percent Change in Index (BOGZ1PC073164013A) from 1971 to 2024 about mutual funds, equity, liabilities, interest rate, interest, price index, rate, indexes, price, and USA.

This is dataset combining the stock prices for S&P 500 between 1927-12-30 and 2023-03-07 and FEDs interest rates. There is no info for interest rates before 1954. V1 version filters out missing rates before 1954.

In 2025, ** 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.

The Federal National Mortgage Association, commonly known as Fannie Mae, was created by the U.S. congress in 1938, in order to maintain liquidity and stability in the domestic mortgage market. The company is a government-sponsored enterprise (GSE), meaning that while it was a publicly traded company for most of its history, it was still supported by the federal government. While there is no legally binding guarantee of shares in GSEs or their securities, it is generally acknowledged that the U.S. government is highly unlikely to let these enterprises fail. Due to these implicit guarantees, GSEs are able to access financing at a reduced cost of interest. Fannie Mae's main activity is the purchasing of mortgage loans from their originators (banks, mortgage brokers etc.) and packaging them into mortgage-backed securities (MBS) in order to ease the access of U.S. homebuyers to housing credit. The early 2000s U.S. mortgage finance boom During the early 2000s, Fannie Mae was swept up in the U.S. housing boom which eventually led to the financial crisis of 2007-2008. The association's stated goal of increasing access of lower income families to housing finance coalesced with the interests of private mortgage lenders and Wall Street investment banks, who had become heavily reliant on the housing market to drive profits. Private lenders had begun to offer riskier mortgage loans in the early 2000s due to low interest rates in the wake of the "Dot Com" crash and their need to maintain profits through increasing the volume of loans on their books. The securitized products created by these private lenders did not maintain the standards which had traditionally been upheld by GSEs. Due to their market share being eaten into by private firms, however, the GSEs involved in the mortgage markets began to also lower their standards, resulting in a 'race to the bottom'. The fall of Fannie Mae The lowering of lending standards was a key factor in creating the housing bubble, as mortgages were now being offered to borrowers with little or no ability to repay the loans. Combined with fraudulent practices from credit ratings agencies, who rated the junk securities created from these mortgage loans as being of the highest standard, this led directly to the financial panic that erupted on Wall Street beginning in 2007. As the U.S. economy slowed down in 2006, mortgage delinquency rates began to spike. Fannie Mae's losses in the mortgage security market in 2006 and 2007, along with the losses of the related GSE 'Freddie Mac', had caused its share value to plummet, stoking fears that it may collapse. On September 7th 2008, Fannie Mae was taken into government conservatorship along with Freddie Mac, with their stocks being delisted from stock exchanges in 2010. This act was seen as an unprecedented direct intervention into the economy by the U.S. government, and a symbol of how far the U.S. housing market had fallen.

Qurate Retail Inc. 8.0% Fixed Rate Cumulative Redeemable Preferred Stock is predicted to have moderate returns with low risk. The company has a strong financial position with consistent revenue and earnings growth. The preferred stock offers a fixed dividend rate, providing investors with a steady stream of income. However, the stock is subject to interest rate risk, as changes in interest rates could affect its market value.

This dataset contains historical stock price data for Tesla, Inc. (TSLA) starting from its IPO date, June 29, 2010, to January 1, 2025. The dataset includes daily records of Tesla's stock performance on the NASDAQ stock exchange. It is ideal for time-series analysis, stock price prediction, and understanding the long-term performance of Tesla in the stock market.

The dataset consists of the following columns:

1. Date: The trading date.
2. Open: Opening stock price on the given date.
3. High: The highest stock price during the trading day.
4. Low: The lowest stock price during the trading day.
5. Close: The closing stock price for the day.
6. Adj Close: Adjusted closing price (corrected for dividends and stock splits).
7. Volume: The number of shares traded during the day.

Use Cases of Tesla Stock Historical Data

1. Time-Series Analysis

   • Analyze trends in Tesla's stock prices over time.
   • Identify seasonality, volatility, and long-term patterns in Tesla’s performance.

2. Stock Price Prediction

   • Develop predictive models to forecast future stock prices using techniques such as ARIMA, LSTMs, or regression.

3. Investment Strategy Evaluation

   • Backtest trading strategies by simulating trades based on historical price movements.
   • Analyze returns of investment strategies such as moving averages, RSI, or Bollinger Bands.

4. Market Sentiment Analysis

   • Correlate Tesla’s stock performance with news sentiment, earnings reports, and market events.

5. Portfolio Diversification

   • Evaluate Tesla’s performance compared to other stocks or indices to assess its role in a diversified portfolio.

6. Risk Management

   • Calculate volatility, beta, and other risk metrics to assess the risk associated with investing in Tesla stock.

7. Economic and Market Studies

   • Study how macroeconomic indicators (like inflation, interest rates) influence Tesla’s stock price.
   • Analyze Tesla’s performance during major economic events such as the COVID-19 pandemic or policy changes.

8. Stock Splits and Adjustments Analysis

   • Examine the impact of Tesla’s stock splits on price and trading volume.

9. Educational Purposes

   • Serve as a dataset for academic projects, coursework, or tutorials on financial data analysis.

10. Correlation with Sector Trends

    • Compare Tesla’s stock performance with other automotive or renewable energy companies.

11. Data Visualization and Dashboarding

    • Create dashboards using tools like Tableau, Power BI, or Python libraries to visualize Tesla’s stock performance metrics.

12. A/B Testing for Financial Applications

    • Use historical stock data for controlled experiments in finance-related applications to improve decision-making tools.

NASDAQ Stock Data with Economic Indicators

Overview

This dataset comprises historical stock price data for NASDAQ-listed companies, combined with a selection of key economic indicators. It is designed to provide a comprehensive view of market behavior, facilitating financial analysis and predictive modeling. Users can explore relationships between stock performance and various economic factors.

Features

The dataset includes the following features:

• Date: The date of the recorded stock prices (formatted as YYYY-MM-DD).

• Open: The price at which the stock opened for trading on a given day.

• High: The highest price reached by the stock during the trading day.

• Low: The lowest price recorded during the trading day.

• Close: The price at which the stock closed at the end of the trading day.

• Volume: The total number of shares traded during the day.

• Interest Rate: The prevailing interest rate, which influences economic activity and stock performance.

• Exchange Rate: The exchange rate for the USD against other currencies, reflecting international market influences.

• VIX: The Volatility Index, a measure of market risk and investor sentiment, often referred to as the "fear index."

• Gold: The price of gold per ounce, which serves as a traditional safe-haven asset and is often inversely correlated with stock prices.

• Oil: The price of crude oil, an essential commodity that influences various sectors, especially transportation and manufacturing.

• TED Spread: The difference between the interest rates on interbank loans and short-term U.S. government debt, which indicates credit risk in the banking system.

• EFFR (Effective Federal Funds Rate): The interest rate at which depository institutions lend reserve balances to other depository institutions overnight, influencing overall economic activity.

Use Cases

This dataset is suitable for a variety of applications, including: - Financial Analysis: Evaluate historical trends in stock prices relative to economic indicators. - Predictive Modeling: Develop machine learning models to forecast stock price movements based on historical data and economic variables. - Time Series Analysis: Conduct analyses over different time frames (daily, weekly, monthly, yearly) to identify patterns and anomalies.

Data Source

The data is sourced from reputable financial APIs and databases: - Yahoo Finance: Historical stock prices. - Federal Reserve Economic Data (FRED): Economic indicators such as interest rates and VIX. - Alpha Vantage / Quandl: Commodity prices for gold and oil.

Conclusion

This dataset provides a rich foundation for analysts, researchers, and data scientists interested in the intersection of stock market performance and macroeconomic conditions. Its structured features and comprehensive nature make it a valuable resource for both academic and practical financial inquiries.

Year: The year of the observation.

Month: The month of the observation.

Interest Rate: The prevailing interest rate for the given month.

Unemployment Rate: The unemployment rate in percentage terms for that time period.

Index Price: A synthetic stock market index price representing overall market trends.

We explore several explicit and alternating-direction implicit (ADI) finite difference methods for pricing compound options with early exercise opportunities. Stock prices, stock price volatilities, and interest rates are assumed to follow correlated stochastic processes.

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

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

This is not going to be an article or Op-Ed about Michael Jordan. Since 2009 we've been in the longest bull-market in history, that's 11 years and counting. However a few metrics like the stock market P/E, the call to put ratio and of course the Shiller P/E suggest a great crash is coming in-between the levels of 1929 and the dot.com bubble. Mean reversion historically is inevitable and the Fed's printing money experiment could end in disaster for the stock market in late 2021 or 2022. You can read Jeremy Grantham's Last Dance article here. You are likely well aware of Michael Burry's predicament as well. It's easier for you just to skim through two related videos on this topic of a stock market crash. Michael Burry's Warning see this YouTube. Jeremy Grantham's Warning See this YouTube. Typically when there is a major event in the world, there is a crash and then a bear market and a recovery that takes many many months. In March, 2020 that's not what we saw since the Fed did some astonishing things that means a liquidity sloth and the risk of a major inflation event. The pandemic represented the quickest decline of at least 30% in the history of the benchmark S&P 500, but the recovery was not correlated to anything but Fed intervention. Since the pandemic clearly isn't disappearing and many sectors such as travel, business travel, tourism and supply chain disruptions appear significantly disrupted - the so-called economic recovery isn't so great. And there's this little problem at the heart of global capitalism today, the stock market just keeps going up. Crashes and corrections typically occur frequently in a normal market. But the Fed liquidity and irresponsible printing of money is creating a scenario where normal behavior isn't occurring on the markets. According to data provided by market analytics firm Yardeni Research, the benchmark index has undergone 38 declines of at least 10% since the beginning of 1950. Since March, 2020 we've barely seen a down month. September, 2020 was flat-ish. The S&P 500 has more than doubled since those lows. Look at the angle of the curve: The S&P 500 was 735 at the low in 2009, so in this bull market alone it has gone up 6x in valuation. That's not a normal cycle and it could mean we are due for an epic correction. I have to agree with the analysts who claim that the long, long bull market since 2009 has finally matured into a fully-fledged epic bubble. There is a complacency, buy-the dip frenzy and general meme environment to what BigTech can do in such an environment. The weight of Apple, Amazon, Alphabet, Microsoft, Facebook, Nvidia and Tesla together in the S&P and Nasdaq is approach a ridiculous weighting. When these stocks are seen both as growth, value and companies with unbeatable moats the entire dynamics of the stock market begin to break down. Check out FANG during the pandemic. BigTech is Seen as Bullet-Proof me valuations and a hysterical speculative behavior leads to even higher highs, even as 2020 offered many younger people an on-ramp into investing for the first time. Some analysts at JP Morgan are even saying that until retail investors stop charging into stocks, markets probably don’t have too much to worry about. Hedge funds with payment for order flows can predict exactly how these retail investors are behaving and monetize them. PFOF might even have to be banned by the SEC. The risk-on market theoretically just keeps going up until the Fed raises interest rates, which could be in 2023! For some context, we're more than 1.4 years removed from the bear-market bottom of the coronavirus crash and haven't had even a 5% correction in nine months. This is the most over-priced the market has likely ever been. At the night of the dot-com bubble the S&P 500 was only 1,400. Today it is 4,500, not so many years after. Clearly something is not quite right if you look at history and the P/E ratios. A market pumped with liquidity produces higher earnings with historically low interest rates, it's an environment where dangerous things can occur. In late 1997, as the S&P 500 passed its previous 1929 peak of 21x earnings, that seemed like a lot, but nothing compared to today. For some context, the S&P 500 Shiller P/E closed last week at 38.58, which is nearly a two-decade high. It's also well over double the average Shiller P/E of 16.84, dating back 151 years. So the stock market is likely around 2x over-valued. Try to think rationally about what this means for valuations today and your favorite stock prices, what should they be in historical terms? The S&P 500 is up 31% in the past year. It will likely hit 5,000 before a correction given the amount of added liquidity to the system and the QE the Fed is using that's like a huge abuse of MMT, or Modern Monetary Theory. This has also lent to bubbles in the housing market, crypto and even commodities like Gold with long-term global GDP meeting many headwinds in the years ahead due to a...

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.

How accurate is machine learning in stock market? (TD Stock Forecast)

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 dataset appears to focus on the following features:

Stock Market Data:

Open, High, Low, Close: Daily price movements of a stock or index. Volume: The number of shares traded. Economic Indicators:

InterestRate: Reflects monetary policy trends, impacting the financial market. ExchangeRate: Represents currency exchange rates, which influence international trade. VIX (Volatility Index): A measure of market sentiment and expected volatility. TEDSpread: The difference between the interest rates on interbank loans and short-term government debt; used to gauge financial risk. EFFR (Effective Federal Funds Rate): Represents the interest rate banks charge each other for overnight loans. Commodity Prices:

Gold: Often considered a safe-haven asset. Oil: Key economic input, reflecting energy market trends. Likely Analysis or Tasks in the Notebook Descriptive Statistics: Summarizing trends in stock prices, trading volumes, and economic indicators.

Visualization: Using line plots, candlestick charts, or correlation heatmaps to explore relationships between variables.

Predictive Modeling: Potential machine learning models to forecast stock prices or analyze the relationship between indicators and market performance.

Economic Insights: Investigating how factors like interest rates, exchange rates, and commodity prices impact the Nasdaq index.

The aim of this study is to investigate the effects of monetary policy on financial asset prices in Poland. Following Gürkaynak et al. (2005) I test how many factors adequately explain the variability of short-term interest rates around MPC meetings, finding that there are two such factors. The first one has a structural interpretation as a “current interest rate change” factor, and the second one as a “future interest rate changes” factor, with the latter related to MPC communication. Regression analysis shows that, controlling for foreign interest rates and global risk aversion, both MPC actions and communication matter for government bond yields, and that communication is more important for stock prices. Furthermore, the foreign exchange rate used to depreciate (appreciate) after MPC statements signalling tighter (easier) future monetary policy. However, the effect disappeared at the end of the sample. For most of the sample the exchange rate would appreciate (depreciate) or would not change in a statistically significant manner after an increase (a decrease) of the current interest rate. The results indicate that not only changes of the current interest rate but also MPC communication matters for financial asset prices in Poland. It has important implications for the conduct of monetary policy, especially in a low inflation and low interest rate environment.

TATA Motors

The TATA Motors Stock Price Dataset provides historical stock price and trading data for TATA Motors Limited, a prominent automotive company in India.

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This dataset spans from January 3, 2000, to September 2, 2023, offering insights into TATA Motors' stock performance over more than two decades.

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It includes daily records of open, high, low, close prices, adjusted close prices, and trading volumes. Investors, analysts, and researchers can use this dataset for various analyses, including trend identification, volatility assessment, and predictive modeling for stock price movements.

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Description:

The Open, High, Low and Close prices together form the price range for the stock on a given trading day. "Open" is the starting price, "High" is the highest price, "Low" is the lowest price, and Close is the final price at which the stock traded.

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The Adj Close price is particularly important for long-term analysis because it adjusts for events that can impact the stock's historical prices. This adjusted price allows you to assess the stock's true performance over time.

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The Volume column is essential for understanding the level of market activity on a specific day. High trading volumes can indicate increased market interest and potentially greater price volatility.

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By analyzing these columns and their historical trends, you can gain insights into how TATA Motors' stock has performed over time, identify patterns, and make informed investment decisions. Traders and investors often use this data to perform technical analysis, create trading strategies, and assess the stock's risk and potential for returns.

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This chapter reviews the behavior of financial asset prices in relation to consumption. The chapter lists some important stylized facts that characterize US data, and relates them to recent developments in equilibrium asset pricing theory. Data from other countries are examined to see which features of the US experience apply more generally. The chapter argues that to make sense of asset market behavior one needs a model in which the market price of risk is high, time-varying, and correlated with the state of the economy. Models that have this feature, including models with habit formation in utility, heterogeneous investors, and irrational expectations, are discussed. The main focus is on stock returns and short-term real interest rates, but bond returns are also considered.