Understanding price movements in markets requires vigilance across an ever-increasing number of sources. However, quickly extracting valuable and usable information from thousands of disparate conversations is not an easy task. Pluribus Labs does this heavy lifting for you. Our team of data scientists, quantitative analysts, engineers and financial professionals go through a rigorous process to extract insights from the most relevant social media conversations. We deliver these predictive analytics to you via a real-time streaming API. With Pluribus Labs Market Mood, it’s easy to systematically integrate powerful sentiment analytics into your investment workflow.
Pluribus Labs’ methodology for deriving sentiment is proprietary from the ground up – we leverage deep experience in quantitative finance, data science and financial analytics to produce predictive insights on the market.
Our data sources…
Data sources for the US market include both social media platforms (Twitter and StockTwits) and traditional media outlets. We continue to integrate relevant data sources as additional data becomes available.
Our sentiment dictionaries are built and maintained in-house and based on an extensive data corpus. They are tailored specifically to financial markets and take into account trading and industry-related nuances.
US Market Mood
Sentiment analytics for 3,500 US stocks
Sentiment analytics aggregated at the company, industry and sector level
Discussion themes that drive sentiment for a given sector and time period, derived via our topic extraction technology
China Market Mood
Sentiment analytics for the overall China market
Themes that drive sentiment, derived via our topic extraction technology
For front-end applications, Market Mood visualizations enable you to keep pace with changes in sentiment and stay abreast of market moving discussions for orders in flight.
Scan relationships between company and sector moves.
Track momentum and reversals relative to sector.
Identify outsize discussion volume and tradable stories.