Wikipedia Views
A look at some of the largest firms and their daily wikipedia page views and trends.
Data is updated quarterly as data arrives after market close US-EST time.
Tutorials
are the best documentation — Wikipedia Views Tutorial
Description
This dataset provides daily Wikipedia page view data and trends for major companies, offering insights into public interest and market sentiment.
It includes metrics on view counts, relative views, and derived alpha/beta proxies to help investors gauge short-term and long-term trends in public attention towards specific stocks.
Data Access
Institutional Trading Data
This data is around 1GB if you download the entire dataset.
Filtered Dataset
Data Dictionary
Sure, let's update the markdown table with a more precise description of each feature, incorporating the detailed understanding of how alpha and beta proxies are calculated:
This table offers a succinct yet comprehensive overview of each feature, tailored to facilitate a clear understanding of the data's dimensions and their relevance in financial analysis, especially in the context of assessing public interest and market sentiment toward different financial entities.
Use Cases
This dataset is designed to provide investors with a detailed analysis of market interest and sentiment towards various financial entities, as reflected in Wikipedia page views. By analyzing page view trends and volatility, investors can gain insights into public interest and market sentiment, which are crucial factors in investment decision-making.
This dataset can be leveraged by investors for various purposes:
Market Sentiment Analysis: By analyzing trends and volatility in page views, investors can gauge public interest and sentiment towards specific tickers.
Investment Decision Support: Insights from the dataset can support buy, hold, or sell decisions based on the perceived interest and sentiment dynamics.
Risk Assessment: Variability in page views, as indicated by beta proxies, can aid in assessing the market's perception of risk associated with certain tickers.
Trend Identification: Alpha proxies provide a means to identify emerging trends in investor interest, which can be precursors to market movements.
Comparative Analysis: Comparing alpha and beta metrics across different tickers can help identify outperformers or underperformers in terms of market interest.
Last updated