The S&P 500 VIX index is expected to remain elevated in the near term due to ongoing geopolitical uncertainties and concerns about the economic impact of the COVID-19 pandemic. However, the index could experience some volatility as investors assess the latest economic data and earnings reports. The index may experience a decline if positive economic signals emerge, or if market participants become more confident in the long-term outlook for the economy. Conversely, the index may experience a rise if geopolitical risks intensify or if economic data continues to disappoint.

VIX Daily Price Data

Overview

Contains historical data of the VIX Volatility Index from 2000 - 2025. The data is obtained from the yfinance api created by yahoo finance and contains the daily price data for the VIX.

The dataset contains the daily Open, Close, High, and Low of the VIX.

Columns Open: Starting price level of VIX for the day Close: Final price level of VIX for the day High: Highest price level of VIX for the day Low: Lowest price level of VIX for the day

The VIX is an index that measures near term volatility expectations for the S&P 500 gathered from SPX options data. VIX was created and maintained by CBOE.

Uses

This data can be used to train models on predicting the market's volatility forecasts. The VIX can also be compared to the realized historical volatility over a period of time.

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.

Is the Market on Edge? VIX Reveals

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

United States - CBOE Volatility : VIX was 15.48000 Index in July of 2025, according to the United States Federal Reserve. Historically, United States - CBOE Volatility : VIX reached a record high of 82.69000 in March of 2020 and a record low of 9.14000 in November of 2017. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - CBOE Volatility : VIX - last updated from the United States Federal Reserve 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.

Is the VIX Index a Reliable Gauge of Market Volatility?

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.

CBOE Volatility Index Options & Futures 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.

Is the S&P 500 VIX Index Signaling Market Volatility?

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 repository contains the supplementary materials for a deep learning study on stock price forecasting and trading strategy enhancement using volatility indicators.

The provided dataset and code support a CNN-GRU hybrid model designed to predict stock prices and evaluate trading strategies, with a focus on the Volatility Index (VIX) as an additional feature.

Included are two versions of the feature datasets (with and without VIX), preprocessed technical indicators (SMA, EMA, MACD, RSI, etc.), and the full implementation code in a Jupyter Notebook. The code enables reproduction of the experimental results, including model training, forecasting, and trading performance analysis.

These materials are shared to support research transparency, reproducibility, and reuse by other researchers in the fields of financial forecasting and applied deep learning.

Please refer to the included `README.txt` and `requirements.txt` for usage instructions and software dependencies.

**Data sources**:
- Historical stock prices: Yahoo Finance
- VIX data: Chicago Board Options Exchange (CBOE)

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.

VIX Forecast: Elevated Volatility Expected Amidst Market Uncertainty, S&P 500 VIX index Indicates.

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

Stock price volatility in Indonesia was reported at 21.77 in 2021, according to the World Bank collection of development indicators, compiled from officially recognized sources. Indonesia - Stock price volatility - actual values, historical data, forecasts and projections were sourced from the World Bank 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.

Volatility May Rise, Signaling Uncertainty for S&P 500 VIX index.

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

Context

Content

Ticker Description 0 GC=F Gold 1 SI=F Silver 2 CL=F Crude Oil 3 ^GSPC S&P500 4 PL=F Platinum 5 HG=F Copper 6 DX=F Dollar Index 7 ^VIX Volatility Index 8 EEM MSCI EM ETF 9 EURUSD=X Euro USD 10 ^N100 Euronext100 11 ^IXIC Nasdaq 12 ^BSESN Bse sensex 13 ^NSEI Nifty 50 14 ^DJI Dow

Stock price volatility in France was reported at 24.62 in 2021, according to the World Bank collection of development indicators, compiled from officially recognized sources. France - Stock price volatility - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

Stock price volatility in Pakistan was reported at 17.28 in 2021, according to the World Bank collection of development indicators, compiled from officially recognized sources. Pakistan - Stock price volatility - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

Stock price volatility in Oman was reported at 9.421 in 2021, according to the World Bank collection of development indicators, compiled from officially recognized sources. Oman - Stock price volatility - actual values, historical data, forecasts and projections were sourced from the World Bank on August of 2025.

Stock price volatility in Poland was reported at 26.57 in 2021, according to the World Bank collection of development indicators, compiled from officially recognized sources. Poland - Stock price volatility - actual values, historical data, forecasts and projections were sourced from the World Bank on August of 2025.

Stock price volatility in Sri Lanka was reported at 22.24 in 2021, according to the World Bank collection of development indicators, compiled from officially recognized sources. Sri Lanka - Stock price volatility - actual values, historical data, forecasts and projections were sourced from the World Bank on August of 2025.

Summary statistics for the log return of S&P 500 index, VIX, USDX, and gold.

Stock price volatility in Ireland was reported at 24.79 in 2021, according to the World Bank collection of development indicators, compiled from officially recognized sources. Ireland - Stock price volatility - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

Stock price volatility in Vietnam was reported at 22.5 in 2021, according to the World Bank collection of development indicators, compiled from officially recognized sources. Vietnam - Stock price volatility - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.