Gold rose to 3,357.64 USD/t.oz on August 14, 2025, up 0.03% from the previous day. Over the past month, Gold's price has risen 0.98%, and is up 36.64% compared to the same time last year, 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 August of 2025.

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}}

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.

Machine Learning Models for Gold Price Prediction

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.

How does stagflation affect gold prices?

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

Historical dataset of the 12 month LIBOR rate back to 1986. The London Interbank Offered Rate is the average interest rate at which leading banks borrow funds from other banks in the London market. LIBOR is the most widely used global "benchmark" or reference rate for short term interest rates.

Dataset

This dataset was created by Hohin WANG

Contents

These data are derived from returns submitted to the Australian Prudential Regulation Authority (APRA) by banks authorised under the Banking Act 1959. APRA assumed responsibility for the supervision and regulation of banks on 1 July 1998. Data prior to that date were submitted to the RBA.

Prior to March 2002, banks reported quarterly to APRA on the Off-balance Sheet Business Return. From that date until the end of 2007, banks reported quarterly on ARF 112.2: Capital Adequacy – Off-balance Sheet Business. Following the introduction of a new capital framework (Basel II) on 1 January 2008, the data between March 2008 and March 2011 were reported on either ARF 112.2: Capital Adequacy – Off-balance Sheet Business, ARF 112.2A: Standardised Credit Risk – Off-balance Sheet Exposures, or ARF 118.0: Off-balance Sheet Business, depending on whether the bank had been approved by APRA to use a Basel II advanced approach to credit risk. Following the revocation of Australian Prudential Standard APS150 on 30 June 2011, banks using the advanced approach to credit risk have been required to report data with reference to the Basel II framework. From June 2011, data are reported on ARF 112.2A: Standardised Credit Risk – Off-balance Sheet Exposures, ARF 118.0: Off-balance Sheet Business, or ARF 118.1: Other Off-balance Sheet Exposures, depending on whether the bank has been approved by APRA to use a Basel II advanced approach to credit risk.

‘Consolidated group’, for a locally incorporated bank, refers to the global operations of the bank and its subsidiaries, excluding those involved in insurance, funds management/trustee and non-financial business. For a foreign bank authorised to operate in Australia as a branch, the data relate to the operations of the branch only. Figures are as at the last business day of the quarter and refer to the principal amount (face value) of the transaction.

From March 2002, banks are required to report separately activity in the banking and trading books for interest rate contracts, foreign exchange contracts, and other derivative contracts. Banking and trading book figures are added to produce the data reported in the table. Before March 2002, exposures were netted across the banking and trading books (except credit derivatives). This has necessitated a break in the series.

‘Direct credit substitutes’ covers any irrevocable obligations that carry the same credit risk as a direct extension of credit. This includes the issue of guarantees, confirmation of letters of credit, standby letters of credit serving as financial guarantees for loans, securities and any other financial liabilities, and certain bills endorsed under bill endorsement lines. ‘Direct credit substitutes’ does not include credit derivatives, which are shown separately.

‘Trade- and performance-related items’ covers contingent liabilities arising from trade-related obligations secured against an underlying shipment of goods and any irrevocable obligations to make a payment to a third party if a counterparty fails to perform a contractual non-monetary obligation. This includes documentary letters of credit issued, acceptances on trade bills, shipping guarantees issued, issue of performance bonds, bid bonds, warranties, indemnities, standby letters of credit in relation to a non-monetary obligation of a counterparty under a particular transaction, and any other trade- and performance-related items.

‘Commitments and other non-market-related items’ includes lending of securities or posting of securities as collateral, assets sold with recourse, forward asset purchases, partly paid shares and securities, placements of forward deposits, underwriting facilities, standby lines of credit, redraw facilities, undrawn credit card facilities, and all other non-market-related off-balance sheet items.

‘Interest rate contracts – OTC forwards’ covers single currency over-the-counter interest rate forwards including forward rate agreements.

‘Interest rate contracts – OTC swaps’ covers single currency over-the-counter interest rate swaps.

‘Interest rate contracts – Other’ covers other single currency over-the-counter and exchange-traded interest rate contracts including interest rate options written and purchased.

‘Foreign exchange contracts – OTC forwards’ covers over-the-counter foreign exchange forwards including foreign exchange forward contracts involving gold.

‘Foreign exchange contracts – OTC swaps’ covers over-the-counter foreign exchange swaps including cross currency interest rate swaps and foreign exchange swap contracts involving gold.

‘Foreign exchange contracts – Other’ covers other over-the-counter and exchange-traded foreign exchange contracts including other foreign exchange contracts involving gold.

‘Credit derivatives’ covers all credit derivatives contracts, both where protection is purchased and protection is sold. Banks were required to report credit derivatives exposure to APRA from June 2000 following a change to the Off-balance Sheet Business Return. This has necessitated a break in the series.

‘Other off-balance sheet business’ covers equity contracts including written and purchased options positions, derivatives based on gold and precious metals, base metals, energy and other commodities, and all other derivative activity.

This dataset gathers historical macroeconomic and financial time series from Brazil and the United States, filtered for the period from 01/01/1900 to 07/20/2025 (or the earliest available date). The series include stock indices, consumer price indices (inflation), interest rates, monetary aggregates (M1 and M2), exchange rates, commodity prices, and other relevant indicators for economic, financial, and monetary policy analysis.

Sources:

• Central Bank of Brazil (SELIC, M1, M2, IPCA)

• Federal Reserve Economic Data – FRED (M1, M2, Fed Funds Rate, CPI)

• Investing.com (Ibovespa, S&P 500, Gold, USD/BRL Exchange Rate)

• Bureau of Labor Statistics – BLS (US CPI)

This dataset is ideal for time series studies, international comparative analysis, economic policy evaluation, and financial modeling.

The data and programs replicate figures from "Macroeconomic Tail Risk, Currency Crises, and the Inter-War Gold Standard", by Duley and Gai. Please see the ReadMe file for additional details.

How did countries recover from the Great Depression? In this paper, we explore the argument that leaving the gold standard helped by boosting inflationary expectations, lowering real interest rates, and stimulating interest-sensitive expenditures. We do so for a sample of 27 countries, using modern nowcasting methods and a new dataset containing more than 230,000 monthly and quarterly observations for over 1,500 variables. In those cases where the departure from gold happened on well-defined dates, inflationary expectations clearly rose in the wake of departure. IV, diff-in-diff, and synthetic matching techniques suggest that the relationship is causal.

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.

Does price of gold go up with inflation?

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

Silver fell to 38.33 USD/t.oz on August 14, 2025, down 0.48% from the previous day. Over the past month, Silver's price has risen 1.66%, and is up 35.15% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Silver - values, historical data, forecasts and news - updated on August of 2025.

CRB Index rose to 365.04 Index Points on August 11, 2025, up 0.58% from the previous day. Over the past month, CRB Index's price has fallen 2.22%, but it is still 10.58% higher than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. CRB Commodity Index - values, historical data, forecasts and news - updated on August of 2025.

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