Data used in the paper "The emergence of critical stocks in market crash".1.
The '2015bipartite.graphml' and '2015-1_fund_stock.graphml' contains the stock networks established by the mutual funds holding data on Jun 30, 2015. While the first file has the mutual funds holding values grouped by the labels of mutual fund companies, the second one uses mutual funds holding values directly. The original data of mutual funds holding are provided by Wind Information, which is not publicly available due to Wind’s license requirement.

1. The ‘stock_style.csv’ describes which kind of investment style a stock belongs to, which is also downloaded from Wind Information.

2. The series of files named as ‘first to low *.csv’ includes the stocks which reach their limit down prices. The timing of stocks reaching limit down prices are calculated from the intraday price data provided by Thomson Reuters’ Tick History. The information of whether a stock reached its limit down price is provides by Wind Information. The original price trends data is not publicly available due to the company’s license requirement.

This statistics shows the results of a survey on which European Union countries think that major stock markets around the world will crash in 2018. Of the countries surveyed, Poland was the country most likely to think that major stock markets around the world will crash in 2018 at ** percent. The county least likely to believe that major stock markets around the world will crash in 2018, was Hungary at ** percent.

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...

such as VKOSPI

As of August 2020, the S&P 500 index had lost ** percent of its value due to the COVID-19 pandemic. However, the Great Crash, which began with Black Tuesday, remains the most significant loss in value in its history. That market crash lasted for 300 months and wiped ** percent off the index value.

In this table, we list major worldwide stock market crashes from 2007 to 2023. For each crash, we show its name, rough time of occurrence, stock index’s high and low, and in which country it occurred.

This dataset captures multi-market financial indicators that can be used to study financial crises, market stress, and economic stability. It integrates simulated data from stock, bond, and foreign exchange (forex) markets, along with volatility metrics and a binary crisis label.

The dataset provides a comprehensive view of cross-market behavior and is suitable for tasks such as crisis detection, financial risk analysis, and market interdependence studies.

Key Features Time Series Coverage:

Daily data over ~1,000 days for multiple countries

Stock Market Indicators:

Stock_Index → Simulated stock market index values

Stock_Return → Daily percentage change in stock index

Stock_Volatility → 5-day rolling standard deviation of stock returns

Bond Market Indicators:

Bond_Yield → Simulated 10-year government bond yield

Bond_Yield_Spread → Difference between long-term and short-term yields

Bond_Volatility → Simulated volatility in bond yields

Forex Market Indicators:

FX_Rate → Simulated currency exchange rate

FX_Return → Daily percentage change in exchange rate

FX_Volatility → 5-day rolling standard deviation of forex returns

Global Market Stress Indicator:

VIX → Simulated volatility index representing market stress

Target Variable:

Crisis_Label → Binary flag indicating market condition (0 = Normal, 1 = Crisis)

File Information Format: CSV

Rows: ~3,000 (1,000 days × 3 countries)

Columns: 13 (including target label)

Use Cases:

Financial crisis detection

Market stress and contagion analysis

Cross-market economic studies

This survey measures the public's attitudes towards political issues and the stock market crash of October 1987. Questions asked of respondents include whether the recent stock market crash would lead to a recession, how they would assess the condition of the national economy, whether the respondent would vote for the Democratic or the Republican candidate in the 1988 presidential election, and whether the respondent owned stock or shares in a mutual fund that invested in the stock market. Background information on individuals includes party affiliation, age, income, sex, marital status, education, and race.

From 1978 to 1981, Kuwait’s two stock markets, one the conservatively regulated “official” market and the other the unregulated Souk al-Manakh, exploded in size, growing to the point where the amount of capital actively traded exceeded that of every other country in the world except the United States and Japan. A year later, the system collapsed in an instant, causing huge real losses to the economy and financial disruption lasting nearly a decade. This Commentary examines the emergence of the Souk, the simple financial innovation that evolved to solve its rapidly increasing need for liquidity and credit, and the herculean efforts to solve the tangled problems resulting from the collapse. Two lessons of Kuwait’s crisis are that it is difficult to separate the banking and unregulated financial sectors and that regulators need detailed data on the transactions being conducted at all financial institutions to give them the understanding of the entire network they must have to maintain financial stability. If Kuwaiti officials had had transaction-by-transaction data on the trades being made in both the regulated and unregulated stock markets, then the Kuwaiti crisis and its aftermath might not have been so severe.

The Dow Jones Industrial Average (DJIA) index dropped around ***** points in the four weeks from February 12 to March 11, 2020, but has since recovered and peaked at ********* points as of November 24, 2024. In February 2020 - just prior to the global coronavirus (COVID-19) pandemic, the DJIA index stood at a little over ****** points. U.S. markets suffer as virus spreads The COVID-19 pandemic triggered a turbulent period for stock markets – the S&P 500 and Nasdaq Composite also recorded dramatic drops. At the start of February, some analysts remained optimistic that the outbreak would ease. However, the increased spread of the virus started to hit investor confidence, prompting a record plunge in the stock markets. The Dow dropped by more than ***** points in the week from February 21 to February 28, which was a fall of **** percent – its worst percentage loss in a week since October 2008. Stock markets offer valuable economic insights The Dow Jones Industrial Average is a stock market index that monitors the share prices of the 30 largest companies in the United States. By studying the performance of the listed companies, analysts can gauge the strength of the domestic economy. If investors are confident in a company’s future, they will buy its stocks. The uncertainty of the coronavirus sparked fears of an economic crisis, and many traders decided that investment during the pandemic was too risky.

Statistical results of the optimal performed model before financial crisis-daily frequency.

Comprehensive 27+ years of daily stock market data for Indian indices (SENSEX & NIFTY 50) and all their constituent companies. This dataset includes OHLCV data along with pre-calculated technical indicators, making it perfect for time series analysis, algorithmic trading strategies, and machine learning applications.

Total Records: 400,000+
Companies: 80 stocks (30 SENSEX + 50 NIFTY 50)
Features: 21 columns per record

Use Cases:

Machine Learning & Prediction:

• Stock price forecasting using LSTM, GRU, or Transformers
• Next-day close price prediction
• Multi-stock portfolio prediction
• Market regime detection (bull/bear markets)

Technical Analysis:

• Backtest trading strategies (RSI, MACD, Moving Average crossovers)
• Identify support/resistance levels
• Bollinger Band squeeze patterns
• Golden Cross / Death Cross detection

Statistical Analysis:

-Correlation analysis between stocks - Volatility clustering analysis - Market crash impact studies (2008 financial crisis, 2020 COVID) - Sectoral performance comparison

Portfolio Optimization:

• Modern Portfolio Theory implementation
• Risk-return optimization
• Diversification analysis
• Sharpe ratio calculations

Education:

• Financial markets course projects
• Time series analysis tutorials
• Data science portfolio projects
• Algorithmic trading education

Company List:

SENSEX 30 Companies:

Adani Enterprises, Asian Paints, Axis Bank, Bajaj Finance, Bajaj Finserv, Bharti Airtel, HDFC Bank, HCL Technologies, Hindustan Unilever, ICICI Bank, IndusInd Bank, Infosys, ITC, Kotak Mahindra Bank, Larsen & Toubro, Mahindra & Mahindra, Maruti Suzuki, Nestle India, NTPC, ONGC, Power Grid Corporation, Reliance Industries, State Bank of India, Sun Pharmaceutical, Tata Consultancy Services, Tata Motors, Tata Steel, Tech Mahindra, Titan Company, UltraTech Cement, Wipro

NIFTY 50 Companies:

All SENSEX 30 companies plus: Adani Ports, Apollo Hospitals, Bajaj Auto, Bharat Petroleum, Britannia Industries, Cipla, Coal India, Divi's Laboratories, Dr. Reddy's Laboratories, Eicher Motors, Grasim Industries, Hero MotoCorp, Hindalco Industries, Hindustan Zinc, JSW Steel, LTIMindtree, Shriram Finance, Tata Consumer Products, Trent

Ticker Conventions: - .BO suffix = Bombay Stock Exchange (BSE) - .NS suffix = National Stock Exchange (NSE)

Citation Policy:

If you use this dataset in your research, please cite:

Indian Stock Market Historical Data - SENSEX & NIFTY 50 (1997-2024)
Kaggle Dataset, November 2024
URL: https://www.kaggle.com/datasets/rockyt07/stock-market-sensex-nifty-all-time-dataset

Considerable research has been done on the complex stock market, however, there is very little systematic work on the impact of crisis on global stock markets. For filling in these gaps, we propose a complex network method, which analyzes the effects of the 2008 global financial crisis on global main stock index from 2005 to 2010. Firstly, we construct three weighted networks. The physics-derived technique of minimum spanning tree is utilized to investigate the networks of three stages. Regional clustering is found in each network. Secondly, we construct three average threshold networks and find the small-world property in the network before and during the crisis. Finally, the dynamical change of the network community structure is deeply analyzed with different threshold. The result indicates that for large thresholds, the network before and after the crisis has a significant community structure. Though this analysis, it would be helpful to investors for making decisions regarding their portfolios or to regulators for monitoring the key nodes to ensure the overall stability of the global stock market.

Yearly citation counts for the publication titled "The stock market crash of 2008 caused the Great Recession: Theory and evidence".

The aim of this study is to determine whether the stock indices of some developed and developing countries react similarly to the price movements in the Dow Jones Industrial Average (DJIA). In this study, the impact of DJIA on other indices during the 2008 global financial crisis, was explored by using the Vector Error Correction Model. The data used was analyzed in two periods: (1) the expansionary period; and (2) the contractionary period of the FED's policies. The results of the analysis indicate that the developed and emerging stock markets react differently to the DJIA. The results include important findings for decisions by financial investors and policy makers.

⚡ Energy Crisis and Stock Price Dataset: 2021-2024 📊

📋 About Dataset

This dataset provides a detailed view of how major energy companies' stock prices were influenced by the energy crises between 2021 and 2024. The data covers three prominent energy companies: ExxonMobil (XOM), Shell (SHEL), and BP (BP), with historical stock price information collected via the yfinance library. This dataset is particularly useful for those interested in financial analysis, market behavior, and the impact of global events on the energy sector. 🌍📉📈

📅 Date Range

• Start Date: January 1, 2021
• End Date: Present day (updated periodically)

🔍 Data Overview

The dataset contains the daily adjusted closing prices of the selected companies from January 2021 to the present. The data was gathered to analyze the impact of different energy crises, such as the fluctuations in oil and gas prices during 2021-2024, and to help provide insights into investor behavior during times of energy uncertainty.

The key columns available in each CSV file are:

💡 Potential Use Cases

This dataset can be used for various purposes including, but not limited to:

• Financial Time Series Analysis 📈: Explore trends and volatility in the stock market, particularly in the energy sector.
• Predictive Modeling 🤖: Develop models to predict future stock prices based on historical data.
• Energy Crisis Impact Studies ⚡: Assess the effect of energy crises on global markets, specifically the energy sector.
• Portfolio Analysis 💼: Evaluate the stability and performance of energy companies during different crisis periods.

📊 Data Files

Each CSV file includes the daily stock prices from January 1, 2021, to the present, with columns for open, high, low, close, adjusted close, and volume.

📂 Dataset Structure

• Directory: data/raw/
  • XOM_data.csv
  • SHEL_data.csv
  • BP_data.csv

🚀 Data Collection Process

The data for this dataset was collected using the yfinance Python library, which provides access to historical market data from Yahoo Finance. The collection script (data_collection.py) automates the download of stock data for the selected companies, saving each company's data in CSV format within the data/raw/ directory.

🔧 Tools Used

• Python 🐍: For scripting and data processing.
• yfinance 📈: To download historical stock data.
• pandas 🐼: For data manipulation and cleaning.

📜 License

The dataset is provided under the MIT License. You are free to use, modify, and distribute this dataset, provided that proper attribution is given.

🙌 Contributions

Contributions are welcome! If you have any suggestions or improvements, feel free to fork the repository and make a pull request. Let's make this dataset even more comprehensive and insightful together. 💪🌟

📧 Contact

For any questions or further information, feel free to reach out:

I hope this dataset helps you uncover new insights about the relationship between energy crises and stock prices! If you find it helpful, don't forget to give it a ⭐️ on Kaggle! 😊✨

Throughout the 1920s, prices on the U.S. stock exchange rose exponentially, however, by the end of the decade, uncontrolled growth and a stock market propped up by speculation and borrowed money proved unsustainable, resulting in the Wall Street Crash of October 1929. This set a chain of events in motion that led to economic collapse - banks demanded repayment of debts, the property market crashed, and people stopped spending as unemployment rose. Within a year the country was in the midst of an economic depression, and the economy continued on a downward trend until late-1932.

It was during this time where Franklin D. Roosevelt (FDR) was elected president, and he assumed office in March 1933 - through a series of economic reforms and New Deal policies, the economy began to recover. Stock prices fluctuated at more sustainable levels over the next decades, and developments were in line with overall economic development, rather than the uncontrolled growth seen in the 1920s. Overall, it took over 25 years for the Dow Jones value to reach its pre-Crash peak.

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.

Financial News Events Dataset - Comprehensive Description

Overview

This synthetic dataset contains 3,024 records of financial news headlines centered around major market events from February 2025 to August 2025. The dataset captures real-time market dynamics, sentiment analysis, and trading patterns across global financial markets, making it ideal for financial analysis, sentiment modeling, and market prediction tasks.

Dataset Specifications

• Total Records: 3,024 rows
• Total Features: 12 columns
• Date Range: February 1, 2025 - August 14, 2025
• File Formats: CSV, JSON, XLSX
• Data Quality: ~5% null values strategically distributed for realistic data cleaning scenarios

Column Descriptions

Key Features & Statistics

Market Events Coverage (20 Categories)

• Stock Market Crashes & Rallies
• Interest Rate Changes & Central Bank Meetings
• Corporate Earnings Reports & IPO Launches
• Government Policy Announcements
• Trade Tariffs & Geopolitical Events
• Cryptocurrency Regulations
• Supply Chain Disruptions
• Economic Data Releases

Global Market Indices (18 Major Indices)

• US Markets: S&P 500, Dow Jones, Nasdaq Composite, Russell 2000
• Indian Markets: NSE Nifty, BSE Sensex
• European Markets: FTSE 100, DAX, Euro Stoxx 50, CAC 40
• Asian Markets: Nikkei 225, Hang Seng, Shanghai Composite, KOSPI
• Others: TSX, ASX 200, IBOVESPA, S&P/TSX Composite

News Sources (18 Reputable Publications)

Major financial news outlets including Reuters, Bloomberg, CNBC, Financial Times, Wall Street Journal, Economic Times, Forbes, and specialized financial publications.

Sector Distribution (18 Business Sectors)

Technology, Finance, Healthcare, Energy, Consumer Goods, Utilities, Industrials, Materials, Real Estate, Telecommunications, Automotive, Retail, Pharmaceuticals, Aerospace & Defense, Agriculture, Transportation, Media & Entertainment, Construction.

Data Quality & Preprocessing Notes

• Realistic Null Distribution: Approximately 5% null values in key columns (Headline, Sentiment, Index_Change_Percent, News_Url) to simulate real-world data collection challenges
• Balanced Sentiment Distribution: Mix of positive, neutral, and negative sentiment classifications
• Diverse Market Conditions: Index changes ranging from -5% to +5% reflecting various market scenarios
• Volume Variability: Trading volumes span 1M to 500M to represent different market liquidity conditions

Potential Use Cases

📈 Financial Analysis

• Market sentiment analysis and trend prediction
• Correlation studies between news events and market movements
• Trading volume pattern analysis

🤖 Machine Learning Applications

• Sentiment classification model training
• Market movement prediction algorithms
• News headline generation models
• Event-driven trading strategy development

📊 Data Visualization Projects

• Interactive market sentiment dashboards
• Time-series analysis of market events
• Geographic distribution of financial news impact
• Sector-wise performance visualization

🔍 Research Applications

• Academic research on market efficiency
• News impact analysis on different sectors
• Cross-market correlation studies
• Event study methodologies

Technical Specifications

• Memory Usage: Approximately 1.5MB across all formats
• **Proces...

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.

Canadian Equities: Market Upswing or Downturn in the Cards?

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