This graph show how interest rates hikes by the U.S. Federal Reserve affect gold's price. While gold underperforms during the period leading up to rate hikes, its performance improves during the year after the interest rates increase.

Gold fell to 4,199.97 USD/t.oz on December 2, 2025, down 0.75% from the previous day. Over the past month, Gold's price has risen 4.93%, and is up 58.92% 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 December of 2025.

1. Size: 3,904 rows × 47 columns
2. Content: The dataset contains financial market data related to gold price prediction. It includes:
3. Stock market indices (S&P 500, Nasdaq)
4. Precious metal prices (Gold, Silver, Platinum, Palladium)
5. Economic indicators (Interest rates, GDP, CPI)
6. Foreign exchange rates (USD/CHF, EUR/USD)

Oil price data Usage: Suitable for gold price regression analysis, financial forecasting, and market trend analysis.

Explore the factors affecting gold prices, including inflation, interest rates, supply and demand dynamics, currency fluctuations, and geopolitical events, and learn how these elements influence gold's value in the global market.

Context

Real time prices in the diamond market are reflected by the so-called Diamond Financial Index (DFX) which is available on a daily base since April 2018.

As diamond prices are influenced by many factors like trade barriers, political instability, operational disruptions like mine closures or economic downturns resp. upturns, it is not an easy task to predict the development of future diamond prices.

To predict prices, indicators are needed. Empirical findings support the argument that diamond prices respond to economic downturns resp. upturns and are therefore also correlated with inflation rates and interest rates resp. fed rates. Also gold prices could be an indicator for the development of diamond prices.

Because the US are playing quite a big role in the diamond business, the following US rates can be considered:

o inflation rate (10-year breakeven inflation rate) o interest rate (10-year treasury inflation-indexed security, constant maturity, risk-free) o fed rate (effective federal funds rate)

Moreover, gold prices could be considered as an indicator.

Content

The following five datasets have been downloaded from the following websites and merged to one dataset:

o diamond price (DFX): https://www.investing.com/indices/get-diamonds-general o inflation rate: https://fred.stlouisfed.org/series/T10YIE o interest rate: https://fred.stlouisfed.org/series/DFII10 o fed rate: https://fred.stlouisfed.org/series/DFF o gold price: https://www.boerse-online.de/rohstoffe/historisch/goldpreis/usd/

To merge the datasets, date has been used as index. A few missing values in the datasets have been filled in by copying the value from the day before (see file "diamond_data_merged_with_other_variables.csv").

Please note: I added one additional version of the dataset where ID is used as index (not date). Missing values are not filled in in this version (see file "df_diamond_data_merged_with_other_variables.csv"). I would recommend using the dataset "diamond_data_merged_with_other_variables.csv" with date as index.

Inspiration

The following questions could be answered:

o How did diamond prices, inflation rate, interest rate, fed rate and gold price develop since 2018? o How is the correlation between diamond prices and inflation rate, interest rate, fed rate and gold prices? o How will diamond prices develop in the future?

When it comes to price prediction machine learning has been successful in predicting stock market prices through a host of different time series models. There is also a limited but quite restrictive application in predicting cryptocurrency prices. Often neural networks like LSTM (Long Short Term Memory) are used. LSTM oder other models, e.g. ARIMA, could be also used here.

Gold is the most popular precious metal in the investment industry. The rate of return for gold investments fluctuated significantly during the period from 2002 to 2024 but generated positive returns in most years of the observed period. The return of gold as an investment reached almost ** percent in 2024, one of the highest recorded. Why is gold valuable? Gold is a precious metal with several practical uses, particularly in technology. For example, NASA uses gold to improve its lasers and protect sensitive things in space, including a part of the visor for its astronauts. However, a large share of the demand for gold worldwide is as an investment, particularly by central banks. Gold serves the purpose of an alternative to currency because it is relatively scarce but still has enough mine production to serve the financial sector. Gold as an investment Under the Bretton Woods agreement after World War II, the world’s major currencies were tied to the value of gold. This system, called the Gold Standard, ended in 1971. Still, most countries maintain significant gold reserves. Due to this history and the overall faith in the value of gold, the average gold price tends to increase in times of recession, making it an attractive investment in uncertain times.

Between January 1971 and May 2025, gold had average annual returns of **** percent, which was only slightly more than the return of commodities, with an annual average of around eight percent. The annual return of gold was over ** percent in 2024. What is the total global demand for gold? The global demand for gold remains robust owing to its historical importance, financial stability, and cultural appeal. During economic uncertainty, investors look for a safe haven, while emerging markets fuel jewelry demand. A distinct contrast transpired during COVID-19, when the global demand for gold experienced a sharp decline in 2020 owing to a reduction in consumer spending. However, the subsequent years saw an increase in demand for the precious metal. How much gold is produced worldwide? The production of gold depends mainly on geological formations, market demand, and the cost of production. These factors have a significant impact on the discovery, extraction, and economic viability of gold mining operations worldwide. In 2024, the worldwide production of gold was expected to reach *** million ounces, and it is anticipated that the rate of growth will increase as exploration technologies improve, gold prices rise, and mining practices improve.

Gold prices surged to their highest in a year amid global market volatility and a weakened US dollar, influenced by Trump's new import tariffs affecting major trading partners.

Gold prices, recently at $3,057.31, may drop to $1,820 due to supply-demand dynamics, interest rates, and new mines, says Morningstar's David Sekera.

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 January marked the 150th anniversary of a major event in American history: the discovery of gold at Sutter’s Mill, California. Fittingly, gold made news again this year by dropping past $300 an ounce to hit its lowest price in nearly two decades. While some of the subject’s interest undoubtedly springs from an almost voyeuristic fascination with the precious metal itself, gold prices are nonetheless legitimate news, since they are considered harbingers of stability or future inflation. Careful observers’ acquaintance with the gold market’s particular twists, turns, and idiosyncrasies gives them a more reasoned understanding of its uses as an economic indicator. This Economic Commentary takes the confluence of historical and current events as an excuse to refine our understanding of gold, gold prices, and inflation.

Permutable AI’s Gold Intelligence dataset (XAU) tracks the drivers of gold prices, from Federal Reserve interest rate policy and inflation trends to central bank buying and geopolitical risk. Advanced story signal detection identifies new narratives, sentiment shifts, and sustained coverage that move gold markets. With structured historical data and real-time sentiment analytics, traders and institutions can forecast gold price movements and hedge risk effectively. Delivered through the Co-Pilot API, gold market intelligence is available with millisecond latency.

Gold prices refer to the value or cost of gold in the market. Gold is a precious metal that has been highly valued throughout history and is widely recognized as a store of value and a safe haven investment. The price of gold is determined by various factors, including supply and demand dynamics, economic conditions, geopolitical events, and investor sentiment.

The price of gold is typically quoted per ounce, but it can also be measured in grams, kilograms, or other units of weight. Gold prices are influenced by several key factors:

1. Supply and Demand: The availability of gold from mining operations and recycling, as well as the demand from industries such as jewelry, technology, and central banks, plays a crucial role in determining prices. If demand exceeds supply, prices tend to rise, and vice versa.

2. Economic Factors: Gold prices are influenced by macroeconomic indicators such as interest rates, inflation, and currency fluctuations. When inflation is high, or there is economic uncertainty, investors often turn to gold as a hedge against inflation or a safe haven asset, which can drive up prices.

3. Geopolitical Events: Political instability, conflicts, trade disputes, or any major geopolitical event can impact gold prices. These events create uncertainty in financial markets, leading investors to seek the relative stability of gold, thus increasing its demand and driving up prices.

4. Investor Sentiment: Investor sentiment and market speculation can significantly affect gold prices. If investors perceive gold as an attractive investment, they may buy more, increasing demand and driving prices higher. Conversely, if investor sentiment turns negative, prices may decline as selling pressure increases.

It is important to note that gold prices can be volatile and subject to significant fluctuations over time. As a result, investors and traders closely monitor gold prices to make informed decisions about buying, selling, or holding gold as an investment. Gold prices are often tracked through live price charts, financial news outlets, and commodities exchanges around the world.

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.

Gold Price Outlook: S&P GSCI Gold index Faces Uncertain Future

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

As of 31 May 2025, MSCI U.S. had an average **-year return rate of ***** percent, whereas gold had a return rate of ***** percent. Gold mining overview In light of recent technological advancements shaping the gold mining market, global gold production has been rather stable in the last few years, hovering around ***** metric tons since 2020. Among nations, Australia holds the highest gold production, surpassing countries with the highest mine gold reserves. Gold as a financial security Known for its ability to provide diversification to investment portfolios, gold has exhibited a positive trend in its Gold’s return rate was particularly high in the early 2000s, and, despite experiencing a decline during the pandemic, it demonstrated a remarkable recovery since. Furthermore, gold serves as a valuable asset for a nation's economic stability, with the United States holding the highest amount of

As of April 2024, WisdomTree Core Physical Gold was the leading gold back exchange-traded commodity (ETC) listed on the London stock exchange, providing a return of ** percent on euro investments annually. Invesco Physical Gold A followed closely in second place, providing a return of ***** percent on investments made in euros. What is an exchange-traded commodity? An exchange-traded commodity (ETC) is a commodity such as silver, wheat, oats, and gold traded on the stock exchange. Unlike exchange-traded funds (ETFs) which allows investment in a basket of securities, ETCs allow investment in a single commodity. Gold-backed ETCs aim to track the spot price of gold. This results in the price of the ETC moving up and down in correlation with the underlying gold price. The annual return rate The return on investment (ROI) is a way to measure the performance of an investment. The ROI is calculated by dividing the amount gained or lost from an investment by the original invested amount. This number is then represented as a percentage. Different gains and losses can be generated on foreign investments due to changes in the value of the security in foreign markets. If the local home currency of an investor is rising in value, this leads to lower returns on foreign investments. Similarly, a decreasing home currency will increase the returns on foreign investments. The difference in currency performance, inflation levels in the home market or abroad, and interest rates are all factors that can lead to differing ROI rates.

Hello community, This dataset combines historical gold prices (2010–2025) with technical indicators (SMA, RSI, MACD, Bollinger Bands) and ARIMA forecasts for September 2025.

It was developed in R and visualized in Tableau as part of my learning journey in data analytics and financial markets. I’d love to hear your feedback, suggestions, and ideas for improving the models or extending them with additional macroeconomic variables (DXY, interest rates, etc.).

Hola comunidad, Este conjunto de datos combina precios históricos del oro (2010–2025) con indicadores técnicos (SMA, RSI, MACD, Bandas de Bollinger) y pronósticos ARIMA para septiembre de 2025.

Fue desarrollado en R y visualizado en Tableau como parte de mi camino de aprendizaje en analítica de datos y mercados financieros. Me encantaría conocer sus comentarios, sugerencias e ideas para mejorar los modelos o ampliarlos con variables macroeconómicas adicionales (DXY, tasas de interés, etc.).

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.

Philadelphia Gold and Silver Index: The Future of Precious Metals?

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

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The raw data that is used in this dataset is the basic OHLC time series dataset for a gold market of the last 20 years collected and verified from different exchanges. This dataset contains over 8677 daily candle prices (rows) and in order to make it wealthy, extra datasets were merged with it to provide more details to each data frame. The sub-datasets contain historical economic information such as interest rates, inflation rates, and others that are highly related and affecting the gold market movement.

Raw dataset:

Time Range: 1988-08-01 to 2023-11-10 Number of data entries: 4050 Number of features: 4 (open, high, low, close OHLC daily candle price)

What are done to prepare this dataset : 1. Starting Exploratory Data Analysis (EDA) for all the raw datasets. 2. Find and fill in missing days. 3. Merge all the datasets into one master dataset based on the time index. 4. Verify the merge process. 5. Check and remove Duplicates. 6. Check and fill in missing values. 7. Including the basic technical indicators and price moving averages. 8. Outliers Inspection and treatment by different methods. 9. Adding targets. 10. Feature Analysis to identify the importance of each feature. 11. Final check.

After data preparation and feature engineering:

Time Range: 1999-12-30 to 2023-10-01

Number of data entries: 8677

Number of featuers: 28

Features list: open, high, low, close (OHLC daily candle price) dxy_open, dxy_close, dxy_high, dxy_low, fred_fedfunds, usintr, usiryy (Ecnomic inducators) RSI, MACD, MACD_signal, MACD_hist, ADX, CCI (Technical indicators) ROC SMA_10, SMA_20, EMA_10, EMA_20, SMA_50, EMA_50, SMA_100, SMA_200, EMA_100, EMA_200 (Moving avrages)

Targets List: next_1_day_price next_3_day_price next_7_day_price next_30_day_price next_1_day_Price_Change next_3_day_Price_Change next_7_day_Price_Change next_30_day_Price_Change next_30_day_Price_Change next_1_day_price_direction( Up, Same ,Down) next_3_day_price_direction( Up, Same ,Down) next_7_day_price_direction( Up, Same ,Down) next_30_day_price_direction( Up, Same ,Down)

Abbreviations of Features: dxy = US Dollar Index fred_fedfunds= Effective Federal Funds Rate usintr= US Interest Rate usiryy= US Inflation Rate YOY RSI= Relative Strength Index MACD= Moving Average Convergence Divergence ADX= Avrerage Directional Index CCI=Commodity Channel Index ROC= Rate of Change SMA= Simple Moving Average EMA= Exponential Moving Average

Forecasting gold prices remains vital in financial markets, given gold’s dual role as both a hedge against inflation and a safe-haven asset during economic uncertainty. This study proposes a hybrid model integrating SARIMA, LSTM, and RF to improve predictive accuracy by capturing both linear and nonlinear dependencies in historical gold price data. SARIMA models linear trends and seasonal components, LSTM captures nonlinear patterns from SARIMA residuals, and RF refines predictions using macroeconomic indicators such as the USD Index, Federal Interest Rate, US CPI, Oil Prices, S&P 500 Index, and Bond Yields. Utilizing real-world data, the model effectively tracks market trends with reduced forecasting errors, indicating continued price fluctuations and potential long-term growth. The findings provide valuable insights for investors and policymakers, with future research focusing on additional macroeconomic factors and advanced hybrid forecasting techniques. This study introduces a hybrid SARIMA–LSTM–RF model that enhances the accuracy of gold price forecasting by capturing both linear and nonlinear market dynamics. By integrating macroeconomic indicators such as the USD Index, Federal Interest Rate, CPI, Oil Prices, S&P 500 Index, and Bond Yields, the model effectively reflects real-world financial interactions influencing gold prices. The results demonstrate reduced prediction errors and improved tracking of short-term fluctuations as well as long-term growth trends. These findings provide valuable implications for investors and policymakers in managing financial risk and optimizing investment portfolios, while contributing to the advancement of hybrid forecasting frameworks for complex financial time series.