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This is an in-depth exploratory data analysis of Spotify's stock performance from January 1, 2018, to the present. Utilizing Python and a robust set of libraries, this project examines trends, volatility, and external influences on Spotify's stocks using data from Yahoo Finance. From trend analysis and volatility exploration to predictive modeling.

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Spotify Stock Performance Analysis

1. Introduction

This project conducts an exploratory data analysis of Spotify Technology S.A., a global leader in audio streaming and media services. The analysis delves into Spotify's stock performance, utilizing daily stock prices data from January 1, 2018, to the present date.

2. Dataset Description

The dataset includes comprehensive daily stock metrics such as opening, closing, high, low, and adjusted closing prices, along with the trading volume. The data is sourced from Yahoo Finance using Python's pandas_datareader library.

3. Objective of the Analysis

The analysis is aimed at unraveling various aspects of Spotify's stock performance through different lenses:

Trend Analysis

Examination of the evolution of Spotify's stock prices over the years. Identification of patterns or trends using rolling average impact analysis.

Volatility Exploration

Analysis of periods with high volatility in stock prices. Investigation of the potential impact of Spotify's announcements or global events on stock volatility.

Comparative Analysis

Comparison of Spotify's stock performance with major players in the Technology sector. Exploration of industry-wide trends versus Spotify-specific influences.

Influence of External Factors

Impact of global events on Spotify's stock prices. Correlation between stock performance and music industry trends or shifts in consumer behavior.

Predictive Analysis

Developing a predictive model for Spotify's future stock prices. Analysis of key factors influencing stock price movements.

Geographical Influence

Exploration of the correlation between Spotify's user base percentage and living standards. Impact of regional music trends on Spotify's global performance.

Web Scraping Insights

Gathering additional insights from news articles and financial reports. Analysis of how major announcements or controversies are reflected in stock price movements.

4. Tools and Libraries Used

  • Python
  • Pandas and Pandas_DataReader
  • Matplotlib and Seaborn for visualization
  • Additional libraries for data manipulation and analysis

About

This is an in-depth exploratory data analysis of Spotify's stock performance from January 1, 2018, to the present. Utilizing Python and a robust set of libraries, this project examines trends, volatility, and external influences on Spotify's stocks using data from Yahoo Finance. From trend analysis and volatility exploration to predictive modeling.

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