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.

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

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.

VIX: Fear or Opportunity?

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.

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

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.

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

United States - CBOE Equity VIX on Apple was 31.32000 Index in July of 2025, according to the United States Federal Reserve. Historically, United States - CBOE Equity VIX on Apple reached a record high of 101.69000 in March of 2020 and a record low of 12.52000 in March of 2017. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - CBOE Equity VIX on Apple - last updated from the United States Federal Reserve on July of 2025.

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

CBOE Volatility Index (VIX) time-series dataset including daily open, close, high and low. The CBOE Volatility Index (VIX) is a key measure of market expectations of near-term volatility conveyed by S...

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.

VIX Futures Signal Increased Market Volatility Ahead.

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 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 June of 2025.

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 July 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

[Keywords] Market include Xerox, GFI Genfare, Sony Corporation, Samsung SDS, Cubic Transportation Systems

Stock price volatility in Bangladesh was reported at 16.34 in 2021, according to the World Bank collection of development indicators, compiled from officially recognized sources. Bangladesh - Stock price volatility - actual values, historical data, forecasts and projections were sourced from the World Bank on July 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.

S&P 500 VIX: Fear Gauge or Market Manipulator?

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

The Mexico OTT TV and Video Market is experiencing robust growth, projected to reach a significant market size by 2033. Driven by increasing internet penetration, smartphone adoption, and a preference for on-demand content, the market's Compound Annual Growth Rate (CAGR) of 4.50% indicates sustained expansion over the forecast period (2025-2033). The market is segmented primarily by source: Subscription Video on Demand (SVOD), Transactional Video on Demand (TVOD), encompassing rentals and Download-to-Own (DTO), and Advertising-based Video on Demand (AVOD). Major players like Netflix, Amazon Prime Video, and regional providers such as Blim, Movistar Play, and Claro Video compete fiercely, offering diverse content libraries tailored to local preferences. The rise of mobile viewing and the increasing affordability of data plans are key trends fueling this growth. However, factors like fluctuating currency exchange rates, competition from traditional television, and concerns about piracy pose challenges to continued market expansion. The market's strength lies in its diverse content offerings and the significant growth potential for SVOD, driven by the young and increasingly digitally-savvy population of Mexico. The historical period (2019-2024) likely showcased substantial initial growth laying the groundwork for the continued expansion forecasted through 2033. The continued success of the Mexican OTT market hinges on providers adapting to changing consumer preferences. This includes offering localized content, improving user experience on mobile devices, and developing innovative pricing strategies to attract a broader range of consumers. Furthermore, addressing concerns around internet accessibility and affordability in more rural areas is crucial for unlocking the full potential of the market. The focus on original programming and strategic partnerships with local content creators will be instrumental in maintaining a competitive edge and driving further growth. While the presence of established international players ensures competitiveness, the success of regional providers will largely depend on their ability to differentiate themselves through unique content and pricing strategies targeted at the Mexican consumer. Recent developments include: March 2022: TelevisaUnivision's new streaming service ViX, which brings the world's largest offering of Spanish-language entertainment, news, and sports content, became available to all users in the United States, Mexico, and most Spanish-speaking Latin America. ViX users can stream original programming and top live sports and news free of charge in the first broadcast-quality ad-supported offering for Mexico.. Key drivers for this market are: High Penetration of Smart TVs and the Presence of Major OTT Providers. Potential restraints include: High Penetration of Smart TVs and the Presence of Major OTT Providers. Notable trends are: OTT industry is expected to register a significant growth in the market.

Stock price volatility in Malaysia was reported at 16.13 in 2021, according to the World Bank collection of development indicators, compiled from officially recognized sources. Malaysia - Stock price volatility - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.