In 2024, Rolex was the largest player in the global luxury watch market with more than ********* retail market share in the luxury watch industry. Other well-known Swiss brands Cartier and Omega followed Rolex in the ranking with market shares of * percent and * percent, respectively. World capital of luxury watches   The concept of luxury watches is almost synonymous with Swiss watches. Renowned for their precision, quality and durability, watches from Switzerland are in high demand all around the globe. The domination of Swiss watches in the luxury watch market, which is a multi-billion-dollar industry, is further attested by the ranking of leading luxury watch brands based on their annual sales. The worldwide success of Swiss watch brands also plays an important role for the economy of Switzerland, as the country generated almost ** billion Swiss francs from the export of luxury watches in 2023.    Future of traditional watches  Even though Swiss watches are perceived, sold, and owned as luxury goods items, the future of traditional, analog watches may not be as bright as the economic figures indicate. In 2019, Apple Watch alone outperformed the entire Swiss watch industry, which could indicate a shift in consumer choices towards more digital options.

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.

Watches of Switzerland Group (WOSG) Soaring or Drowning in the Luxury Market?

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