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.

What happens if us defaults on debt?

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

(Banking And Systemic Crises)

prepared by (Mohamed Abd Al-mgyd)

https://github.com/1145267383/Systemic-And-Banking-Crises

• Web APP > https://crises.herokuapp.com/

Dataset

A)20160923_global_crisis_data:

https://www.hbs.edu/behavioral-finance-and-financial-stability/data/Pages/global.aspx

This data was collected over many years by Carmen Reinhart (with her coauthors Ken Rogoff, Christoph Trebesch, and Vincent Reinhart). This data contains the banking crises of 70 countries, from 1800 AD to 2016 AD, with a total of 15,190 records and 16 variables. But the data stabilized after cleaning and adjusting to 8642 records and 17 variables.

B)Label_Country: This data contains a description of the country whether it's Developing or Developed .

Variable: Description:

1-Case: ID Number for Country.

2-Cc3: ID String for Country.

3-Country : Name Country.

4-Year: The date from 1800 to 2016.

5-Banking_Crisis: Banking problems can often be traced to a decrease the value of banks' assets.

A) due to a collapse in real estate prices or When the bank asset values decrease substantially . B) if a government stops paying its obligations, this can trigger a sharp decline in value of bonds.

6-Systemic_Crisis : when many banks in a country are in serious solvency or liquidity problems at the same time—either:

A) because there are all hits by the same outside shock. B) or because failure in one bank or a group of banks spreads to other banks in the system.

7-Gold_Standard: The Country have crisis in Gold Standard.

8-Exch_Usd: Exch local currency in USD, Except exch USD currency in GBP.

9-Domestic_Debt_In_Default: The Country have domestic debt in default.

10-Sovereign_External_Debt_1: Default and Restructurings, -Does not include defaults on WWI debt to United States and United Kingdom and post-1975 defaults on Official External Creditors.

11-Sovereign_External_Debt_2: Default and Restructurings, -Does not include defaults on WWI debt to United States and United Kingdom but includes post-1975 defaults on Official External Creditors.

12-Gdp_Weighted_Default:GDP Weighted Default for country.

13-Inflation: Annual percentages of average consumer prices.

14-Independence: Independence for country.

15-Currency_Crises: The Country have crisis in Currency.

16-Inflation_Crises: The Country have crisis in Inflation.

17-Level_Country: The description of the country whether it's Developing or Developed.

The database used includes annual frequency data for 43 countries, defined by the IMF as 24 advanced countries and 19 emerging countries, for the years 1992-2018.The database contains the fiscal stress variable and a set of variables that can be classified as follows: macroeconomic and global economy (interest rates in the US, OECD; real GDP in the US, y-o-y, OECD; real GDP in China, y-o-y, World Bank; oil price, y-o-y, BP p.l.c.; VIX, CBOE; real GDP, y-o-y, World Bank, OECD, IMF WEO; GDP per capita in PPS, World Bank); financial (nominal USD exchange rate, y-o-y, IMF IFS; private credit to GDP, change in p.p., IMF IFS, World Bank and OECD); fiscal (general government balance, % GDP, IMF WEO; general government debt, % GDP, IMF WEO, effective interest rate on the g.g. debt, IMF WEO); competitiveness and domestic demand (currency overvaluation, IMF WEO; current account balance, % GDP, IMF WEO; share in global exports, y-o-y, World Bank, OECD; gross fixed capital formation, y-o-y, World Bank, OECD; CPI, IMF IFS, IMF WEO; real consumption, y-o-y, World Bank, OECD); labor market (unemployment rate, change in p.p., IMF WEO; labor productivity, y-o-y, ILO).In line with the convention adopted in the literature, the fiscal stress variable is a binary variable equal to 1 in the case of a fiscal stress event and 0 otherwise. In more recent literature in this field, the dependent variable tends to be defined broadly, reflecting not only outright default or debt restructuring, but also less extreme events. Therefore, following Baldacci et al. (2011), the definition used in the present database is broad, and the focus is on signalling fiscal stress events, in contrast to the narrower event of a fiscal crisis related to outright default or debt restructuring. Fiscal problems can take many forms; in particular, some of the outright defaults can be avoided through timely, targeted responses, like support programs of international institutions. The fiscal stress variable is shifted with regard to the other variables: crisis_next_year – binary variable shifted by 1 year, all years of a fiscal stress coded as 1; crisis_next_period – binary variable shifted by 2 years, all years of a fiscal stress coded as 1; crisis_first_year1 – binary variable shifted by 1 year, only the first year of a fiscal stress coded as 1; crisis_first_year2 - binary variable shifted by 2 years, only the first year of a fiscal stress coded as 1.

Graph and download economic data for Delinquency Rate on All Loans, All Commercial Banks (DRALACBN) from Q1 1985 to Q1 2025 about delinquencies, commercial, loans, banks, depository institutions, rate, and USA.