A survey from June 2022 found that inflation already inspired one in five U.S. consumers to cancel at least one streaming service subscription. A further ** percent stated that they will have to drop streaming services if inflation continues at the current rate. Only ** percent of people interviewed said that inflation has no impact on their streaming behavior.

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 There a Netflix Effect on Unemployment? A Statistical Exploration

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.

NFLX Netflix Inc. Common Stock

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

Cinemas have faced a difficult operating environment in recent times. While rising discretionary incomes and mostly positive consumer sentiment have encouraged spending on cultural and recreational activities, increasing external competition has constrained the benefit that these positive economic conditions have had on cinemas. The pandemic also stripped demand for cinemas. Public health measures designed to stop the virus's spread led to cinemas being temporarily closed, with capacity constraints in place when they were allowed open. Many global film studios postponed major film releases due to the pandemic or skipped cinemas and released them on streaming services, further constraining revenue. That's why revenue has declined by an estimated 2.9% annualised over the five years through 2024-25, falling to $245.6 million. This includes an expected drop of 4.3% in 2024-25 as cinemas continue to struggle back from the pandemic with inflation weighing on consumer spending and keeping the industry operating at a loss. Cinemas have faced intense competition from alternative entertainment options, particularly subscription video on demand (SVOD) services like Netflix. These platforms offer consumers a more cost-effective way to consume content from their own home, and have expanded their libraries with exclusive original content. In response, cinemas have upgraded their infrastructure, fitting their cinemas with new screens, sound systems and more comfortable seating to provide patrons with a premium viewing experience. These investments have helped increase the average spend per patron, partially offsetting the effect of falling ticket sales. Cinemas’ pandemic recovery will drag on into the coming years. Rising discretionary incomes and positive consumer sentiment are set to support demand as consumers will have more purchasing power, while cinemas keep investing in the consumer experience in an effort to attract filmgoers back to screenings. Nevertheless, competition from alternative entertainment options is projected to intensify, with SVOD services set to continue expanding. Overall, cinema revenue is poised to expand at an annualised 1.1% through the end of 2029-30, to a projected $259.5 million, with competitive factors heavily containing growth.