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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.
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)
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)
Stock price prediction
Portfolio optimization
Algorithmic trading
Market sentiment analysis
Risk management
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
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
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Gold rose to 3,369.59 USD/t.oz on July 16, 2025, up 1.34% from the previous day. Over the past month, Gold's price has fallen 0.53%, but it is still 36.90% higher than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Gold - values, historical data, forecasts and news - updated on July of 2025.
Open Database License (ODbL) v1.0https://www.opendatacommons.org/licenses/odbl/1.0/
License information was derived automatically
EGPB - An Event-based Gold Price Benchmark Dataset
This benchmark dataset consists of 8030 rows and 36 variables sourced from multiple credible economic websites, covering a period from January 2001 to December 2022. This dataset can be utilized to predict gold prices specifically or to aid any economic field that is influenced by the variables in this dataset.
Key variables & Features include:
• Previous gold prices
• Future gold prices with predictions for one day, one week, and one month
• Oil prices
• Standard & Poor's 500 Index (S&P 500)
• Dow Jones Industrial (DJI)
• US dollar index
• US treasury
• Inflation rate
• Consumer price index (CPI)
• Federal funds rate
• Silver prices
• Copper prices
• Iron prices
• Platinum prices
• Palladium prices
Additionally, the dataset considers global events that may impact gold prices, which were categorized into groups and collected from three distinct sources: the Al-Jazeera website spanning from 2022 to 2019, the Investing website spanning from 2018 to 2016, and the Yahoo Finance website spanning from 2007 to 2001.
These events data were then divided into multiple groups:
• Economic data
• Politics
• logistics
• Oil
• OPEC
• Dollar currency
• Sterling pound currency
• Russian ruble currency
• Yen currency
• Euro currency
• US stocks
• Global stocks
• Inflation
• Job reports
• Unemployment rates
• CPI rate
• Interest rates
• Bonds
These events were encoded using a numeric value, where 0 represented no events, 1 represented low events, 2 represented high events, 3 represented stable events, 4 represented unstable events, and 5 represented events that were observed during the day but had no effect on the dataset.
Cite this dataset: Farah Mansour and Wael Etaiwi, "EGPBD: An Event-based Gold Price Benchmark Dataset," 2023 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), Tenerife, Canary Islands, Spain, 2023, pp. 1-7, doi: 10.1109/ICECCME57830.2023.10252987.
@INPROCEEDINGS{10252987, author={Mansour, Farah and Etaiwi, Wael}, booktitle={2023 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)}, title={EGPBD: An Event-based Gold Price Benchmark Dataset}, year={2023}, volume={}, number={}, pages={1-7}, doi={10.1109/ICECCME57830.2023.10252987}}
In 2021, the inflation rate in Ghana amounted to about 9.98 percent compared to the previous year. Ghana’s inflation peaked at almost 17.5 percent in 2016 and is predicted to decrease to 8 percent by 2030. Steady is best for inflationAccording to economists, a steady inflation rate between two and three percent is desirable to achieve a stable economy in a country. Inflation is the increase in the price level of consumer goods and services over a certain time period. A high inflation rate is often caused by excessive money supply and can turn into hyperinflation, i.e. if inflation occurs too quickly and rapidly, it can devalue currency and cause a recession and even economic collapse. This scenario is currently taking place in Venezuela , for example. The opposite of inflation, the decrease in the price level of goods and services below zero percent, is called deflation. While hyperinflation devalues money, deflation usually increases its value. Both events can damage an economy severely. Is Ghana’s economy at risk?Ghana’s economy is considered quite stable and fast-growing, and is rich in oil, diamonds, and gold. After struggling in the years around 2015 due to increased government spending and plummeting oil prices, it is now on an upswing again. This is also reflected in the decreasing inflation rate, and other key indicators like unemployment and rapid GDP growth support this theory. However, Ghana’s government debt is still struggling with the consequences of the 2015 crisis and forecast to keep skyrocketing during the next few years.
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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.
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)
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)
Stock price prediction
Portfolio optimization
Algorithmic trading
Market sentiment analysis
Risk management
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
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