News in 41 languages,
indexed for your agents

Our API analyzes the full content of news articles, not just the headlines, to determine relevancy to a stock and its sentiment. We analyze over 1,000,000 articles per month from trusted global sources.

We use a custom Large Language Model (LLM) to provide granular intraday sentiment on a detailed 11-point scale, offering a more nuanced view than simple positive/negative scores.

Our API analyzes news content from 41 languages, including Chinese, Japanese, and Arabic. We provide specific backtesting datasets for US and Brazil markets, with custom markets available.

Alternative data refers to non-traditional data sources that can provide an information edge in financial analysis. For Reclever.ai, the primary source of alternative data is the full, unstructured text of global news articles. By analyzing this data, we uncover signals and sentiment shifts that are not apparent in standard financial statements or market price data.

Reclever.ai measures news sentiment using a custom-built Generative AI model. Instead of a simple positive, negative, or neutral score, we provide a granular 11-point sentiment scale. Our model analyzes the full content of an article, not just the headline, to determine its relevance and true emotional tone concerning a specific financial asset. This provides a more nuanced and accurate signal for investment strategies.

Generative AI, specifically our custom Large Language Model (LLM), is at the core of our service. It allows us to move beyond keyword-based analysis and truly understand the context, subtleties, and nuances of financial news in over 41 languages. This technology enables us to parse millions of unstructured articles and convert them into structured, actionable sentiment data that quantitative firms can use to identify unique, alpha-generating opportunities.

Headlines can be misleading or lack crucial context. The true sentiment and impact of a news event are often detailed within the body of the article. Our Generative AI analyzes the full text to ensure the sentiment score is based on a comprehensive understanding of the information, leading to a more robust and reliable signal.

The sentiment data is delivered via an easy-to-integrate API in JSON format. All data is mapped to specific financial instruments using the OpenFIGI standard for ticker symbols. We also provide historical, back-testable datasets for markets like the US, Japan and Brazil, allowing firms to validate their data against their trading strategies.

Investment managers use news sentiment data as a key input for their decision-making process. It can serve as an early warning system for negative events, a confirmation signal for a positive thesis, or a way to identify undervalued assets that have positive but yet-unrecognized sentiment momentum. For example, a rising sentiment score for a company's products, even before sales numbers are reported, can be a leading indicator of future growth.

In quantitative trading, news sentiment is used as a predictive feature in algorithmic models. Quants use historical sentiment data, like our backtesting datasets, to find statistical correlations between sentiment shifts and subsequent stock price movements. A model might be designed to automatically long stocks with a sustained high sentiment score while shorting those with a sudden, sharp decline in sentiment, all executed systematically at high speed.

Alpha represents investment returns that are not attributable to general market movement. Sentiment data can generate alpha by providing an 'information edge'. Because our AI analyzes the full text of news articles in near real-time, it can capture nuanced market perception shifts faster than human analysts and before that information is fully reflected in the stock price. This time advantage allows trading strategies to act on new information first, which is a classic source of alpha.

Fund managers seek alpha from various sources. These include informational advantages (knowing something before the market does), analytical advantages (interpreting public information better or faster), and behavioral advantages (exploiting market irrationality). High-quality alternative data, like the granular news sentiment provided by Reclever.ai, directly contributes to analytical advantages.

A simple positive/negative score is a blunt instrument. A granular 11-point scale allows quantitative models to detect subtle but significant changes. For instance, a shift from 'very positive' to just 'positive' can be a powerful leading indicator of decelerating momentum, even though the sentiment is still positive overall. This nuance is often where alpha is found.