In 2024, theater attendance at cinemas run by AMC Theatres added up to approximately *** million, down from around *** million one year before. Furthermore, AMC Theatres' revenue for 2024 amounted to **** billion U.S. dollars, with **** billion dollars generated from box office admissions.

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.

AMC: Are Movie Theaters Making a Comeback?

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

Upcoming 2020 Hollywood Movies

Calendar, Production Companies, Cast and Crew

By Priyanka Dobhal [source]

About this dataset

This dataset provides a comprehensive list of the top upcoming Hollywood movies of 2021. With detailed information about each movie, including titles, production companies, cast and crew members, and sources for further reference, viewers can stay up to date on what's playing in theaters throughout the year. Discover beloved classics and modern-day blockbusters that will transport viewers to new worlds and stories for hours of entertainment!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

To use this dataset correctly here are the steps you should follow: - Read through the columns of the dataset to understand what is included in them such as “Month”, “Day”, “Title” and other columns to become familiar with the data. - Have an idea about which feature of Hollywood movies that you would like to explore further such as finding movies by a certain actors or directors or producers or release dates etcetera.
- Filter out columns needed and manipulate them according your requirements prior analysing so it will be easier to focus on valuable insights providing columns only that relates to your purpose of exploring according movie features chosen previously (ex; filter out casting director name column if isn’t related). - Analyse each row in dataset required carefully since different rows can provide important pieces of clues regarding movie features selected (ex; month column tend to tell us when a movie is usually released).

5 Once all analysis has been done feel free utilize visuals so we can draw significance relationships more efficiently between different categorical/numerical variables using charts & graphs etcetera .

6 Finally make sure that collected information relate directly towards problem statement given by conducting thorough validations from obtained results from above steps giving reliable & correct available insights related feature chosen initially making sense in context subjective scenario at hand

Research Ideas

• Creating a timeline view of the up-coming Hollywood movie releases and their associated cast, crew and production company data.
• Using production company data to analyze what genres, actors, and directors are popular this year.
• Utilizing the cast and crew data to display the most experienced actor or filmmaker within each movie

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: WIki_Movies.csv | Column name | Description | |:-----------------------|:-------------------------------------------------------------------------------| | Month | The month in which the movie is scheduled to be released. (String) | | Day | The day of the month in which the movie is scheduled to be released. (Integer) | | Title | The title of the movie. (String) | | Production company | The production company responsible for the movie. (String) | | Cast and crew | The names of the cast and crew involved in the movie. (String) | | Ref | The source from which the data was collected. (String) |

File: Hollywood Movies - 2020.csv | Column name | Description | |:-----------------------|:-----------------------------------------------------------------------| | Title | The title of the movie. (String) | | Production company | The production company responsible for the movie. (String) | | Cast and crew | The names of the cast and crew involved in the movie. (String) | | Opening | The date the movie is scheduled to be released in the US. (Date) | | Opening2 | The date the movie is scheduled to be released internationally. (Date) | | Ref. | The source from which the data was collected. (String) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Priyanka Dobhal.

According to a study held in June 2020, just 14 percent of adults said that they strongly preferred seeing a movie for the first time in a theater, and 36 percent said that they would much rather stream the film at home than visit a cinema. Preferences for watching a new release in a cinema instead of via a streaming service in the United States changed significantly between 2018 and 2020, signaling a shift in consumer behavior and potentially a risk for movie theaters in the country. Also important to note is the effect of the coronavirus on consumer confidence. There was a drop in the share of movie fans willing to visit cinemas between March and June 2020, likely the result of consumers fearing the risk of infection and feeling more comfortable viewing movies in the safety of their own home.

The World Cities Culture Forum, established in 2012, is a leading global network of civic leaders from over 40 creative cities across six continents, representing a combined population of over 245 million. The forum fosters collaborations to place culture at the core of urban development, addressing 21st-century challenges such as climate change, affordable workspaces, cultural tourism, and diversity in public spaces. Through its Global Summit, partnerships, and programs like the Leadership Exchange Programme and Digital Dialogue Masterclasses, the forum promotes cultural integration in city planning. The World Cities Culture Report 2022 provides comprehensive open-source data on culture, including over 60 datasets from 40 cities. Contextual Data: Includes demographics such as characteristics of the overall and working-age populations (including percent who were foreign born) and of the geographical area, such as the percentage of national population living in the city and the percentage of the area devoted to parks and other public green spaces. Cultural Infrastructure: Provides counts (and rates) of various facilities and venues, including art galleries, artists' studios, rehearsal spaces, bars, bookshops, cinemas, community centers, concert halls, museums, nightclubs, libraries, video game arcades, and theatres. Participation and Tourism: Focuses on cultural participation metrics, such as cinema and theatre admissions, festival attendance, museum visits, average daily attendance at the top five art exhibits, and international tourist numbers. Creative Economy: Encompasses data on book publishing, creative industries employment, film festivals, restaurant ratings, and performances. Education: Includes statistics on public library book loans, higher education levels, international student enrollment, and specialist institutes in art and design education. The source for each number is identified within the dataset. Data users can freely download selected datasets as .csv files.

According to a daily database on the cinema industry in China, Wanda Cinema Line was the largest movie theater chain with 749 cinemas under its operation. Hengdian World Cinemas followed with 449 cinemas.