Unlock a powerful, new data source. Stocksnips News Sentiment is derived by analyzing SEC filings, earnings transcripts, media alerts and analyst opinions from 20 reliable sources to ensure a high signal-to-noise ratio.
Turn news sentiment into actionable insight. Aggregate news sentiment scores are assessed at the discrete snippet level using a financially trained machine learning model and has proven to be less noisy compared to social media and other type of sentiment signals. Mean weighted correlation was 0.23 (sentiment change vs price change) for 629 randomly selected tickers from the Russell 1000 universe.
Enhance alpha. Stocksnips proprietary sentiment signal has proven predictive value as it incorporates a memory-based attenuation model to ensure that the impact of past and current news is weighted appropriately and is a leading indicator of price changes.
Stocksnips News Sentiment V3 leverages an AI platform that analyzes multiple news sources to create a quantified news sentiment signal for US Equities. Stocksnips sentiment is a proprietary signal generated from the count of positive and negative snippets extracted from news articles. The Sentiment score incorporates an attenuation model that accounts for source and decay of news and is expressed as a % positive as well as a sentiment trend indicator value ranging from -100 to +100. Stocksnips News Sentiment has generated significant alpha when blended with other factors like growth, value and business momentum and has been validated by third party firms and researchers.
StockSnips crawls, aggregates and analyzes news related to public companies listed on Nasdaq and NYSE. Using Big Text Analytics and Machine Learning, StockSnips AI platform transforms unstructured textual data into quantified real-time news sentiment data that can be used to predict stock returns. By sifting through and summarizing millions of SEC filings and news publications, StockSnips efficiently and affordably delivers concise and unbiased financial sentiment to investors.
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