Since 2003, RavenPack has pioneered investment-grade sentiment analysis in financial services. We do not believe in “one size fits all” and have developed multiple sentiment techniques where some leverage millions of rule sets while others use sophisticated machine learning algorithms. Another Wall Street Horizon proprietary metric that considers these important earnings announcement changes and therefore offers a view on corporate confidence is the Late Earnings Report Indicator (LERI).
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Research driven insights on business, policy, and markets. - Financial professionals rely on RavenPack for its speed and accuracy in analyzing large amounts of unstructured content.
- As a result of the observable differences in how advance and delay events affect stock performance, the first strategy pursued by RavenPack trades stocks just ahead of the confirmed earnings announcement date, going long on stocks that advance their earnings date and shorting those that delay.
- She is a results-driven, finance leader with more than 15 years experience in the technology industry.
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Prior to joining the firm, he was Director of Product & Strategy at Third Point, a $15 billion Event-Driven Hedge Fund, where he co-founded and led their data science team. Aakarsh was previously Senior Product Manager at FactSet, responsible for re-architecting & launching a web-based analytics platform servicing over 100,000 enterprise clients. Figure 8 shows that both mid-/large- and small-cap long-only strategies decay more slowly than short-only; additionally, advance events have higher momentum than delay events.
In the figure below, we illustrate the impact of the expanded VWAP on the open-strategy performance applying the same prompt as in the paper, revealing a distinct deterioration evident even with a shift to a 15-minute VWAP. The results also highlight variations in the open-price implementation between the March and June GPT 3.5 Turbo versions. Due to the black-box nature of the models, explaining the shift in performance becomes impractical.
Again, small-cap companies were more sensitive to the changes and therefore exhibited greater price reactions between advance and delay events than the mid-/large-cap companies. Ke, Kelly, and Xiu created a model that essentially automatically generates a dictionary of relevant words and allows for contextually specific sentiment scores. Using supervised machine learning and a method that required only a laptop and basic statistical capabilities, the researchers analyzed more than 22 million articles published from 1989 to 2017 by Dow Jones Newswires.
At the 1-day mark, annualized returns reach 8.0% for the mid-/large-caps and 19.7% for small-caps, with information ratios of 0.8 and 1.2, respectively. Investors and researchers have suspected for decades that text could be used to predict markets, some trying and failing. As other studies have demonstrated and the RavenPack study confirms, there is immense value in staying on top of corporate events like quarterly earnings reports and their https://g-markets.net/ changes. Anyone familiar with the Wall Street Horizon DateBreaks Factor or Late Earnings Report Index (LERI) knows that academic research supports the idea that companies that advance their earnings date tend to share good news on their earnings calls, while those that delay tend to share bad news. The RavenPack Earnings Dates dataset consists of Wall Street Horizon earnings calendar
change records for over 8,000 stocks globally since 2006.
Browse News Releases
We classify stock returns unexplained by the valuation model as „mispriced‟ and evaluate the efficacy of this signal. We find „mispriced‟ stocks deliver an IC of 3.8% or return of 5.1% pa, which is better than that for value factors. They also have low correlations to style factors like value and analyst sentiment. Moreover, we note that the signal works well across global regions, albeit better in larger markets. In this role, she is focused on publishing research on Wall Street Horizon event data covering 10,000 global equities in the marketplace.
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Research driven insights on business, policy, and markets. Some funds have likely been using natural language processing to trade for several years, with dubious success. A 2016 article in MIT Technology Review called analyzing language data to predict markets “one of the most promising uses of new AI techniques,” but one of the handful of funds it mentioned, Sentient, liquidated in 2018. The research by Ke, Kelly, and Xiu provides an academic framework for applying such processing to markets. 80+ fields describe every entity detected including over 20 sentiment indicators. Contextual analytics on temporal, territory, segment and similarity aspects are also delivered.
Figure 3 below shows how mid-/large-cap companies have a greater reaction to delayed dates and experience more momentum on the negative leg, while the small-caps react more to advanced dates resulting in greater momentum on the positive leg. The signal, although strong, decays relatively quickly, with the difference between the average advance and delay reactions reaching a peak in just a few days. For every entity and event detected in a story, RavenPack provides advanced analytics including relevance scoring, novelty tracking, and impact analysis.
When companies change the dates of their official earnings releases it has been speculated that it is because they want to delay the release of bad news or bring forward the release date for good news. Marina joined RavenPack in 2004 and is responsible for Ravenpack’s financial health and strategy by leading the finance, accounting and tax functions. She is a results-driven, finance leader with more than 15 years experience in the technology industry. RavenPack maintains a database of over 20 years of historical content that includes news and social media, industry and earnings call transcripts, insider transactions, and other regulatory filings. „The Covid pandemic has forced companies to reassess the way they monitor emerging risks,“ said Armando Gonzalez, CEO of RavenPack.
Reduce risk and increase efficiency by systematically incorporating the effects of public information into your workflows. “Corporate body language,” or the changes to these important market-moving events, is one of many cues that publicly traded companies can send to the market, both intentionally and unintentionally. These non-verbal tells reveal a lot about a company’s financial health and are very costly to miss. In the mid-/large-cap universe, there is a noticeable improvement in information ratios across longer aggregation windows when using the combined signal. Within the small-cap universe, the performance enhancement is more modest; however, information ratios remain relatively stable. Another significant finding of the RavenPack study was that advancers benefit from the signal over longer time periods, while the signal for delayers decays more quickly as determined by separating out long-only and short-only strategies.
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Using over 40,000 sources, RavenPack provides real-time news analytics, including sentiment analysis and event data focused on business and financial applications. Data includes news and social media content, allowing for comprehensive analysis of financial markets. In a recent study, the RavenPack quantitative research team explored how changes in earnings
announcement dates can offer valuable insights about ravenpack pricing stock price moves surrounding earnings
events. The research paper provided more evidence that confirms findings from previous
studies that depict earnings delays can signal weak performance, while advancing the date may
be a sign of good news. When looking at price reaction in the 20-day post-earnings period, mean excess returns are positive for advancers and negative for delayers across all market caps.
News, Jobs, and Data Sources
In our recent White Paper, RavenPack data scientists sought to test this belief by constructing strategies that bought and sold stocks when companies changed their earnings dates. We leverage RavenPack‟s news-flow database to identify corporate actions like Share Buybacks, M&A, Executive Employment, Clinical Trials, etc. that act as catalysts in either driving mean reversion or explain the persistence of stock „mispricing‟. We show that complementing the „mispricing‟ signal with corporate action news-flow helps to gain a better understanding of stock price behaviour and improves the performances of these trading strategies. Aakarsh is in charge of corporate strategy overseeing new revenue generation initiatives and investments at RavenPack.
Schedule a personalized trial to see how our actionable insights can boost your investment strategies. As mentioned in the previous two points, results were most profound for small-caps, those companies with revenues below $250M. This is not surprising considering these companies have lower liquidity and are therefore more volatile in nature. It’s important to note that this analysis doesn’t diminish the potential value of LLMs in systematic investing. While achieving Sharpe Ratios above 3 may pose challenges, the RavenPack Data Science team remains optimistic about the applications of LLMs in finance based on internal research and we anticipate sharing more of our findings on this topic throughout 2024. Peter Hafez, Chief Data Scientist at RavenPack, analyzes the impact and limitations of LLMs in stock price predictions.
Every day, we express ourselves in 500 million tweets and 64 billion WhatsApp messages. On Facebook, 864 million of us log in to post status updates, comment on news stories, and share videos. We enable our clients to quickly extract value and insights from large amounts of information. Explore News and Job Analytics with a knowledge graph across 12 million business-relevant entities. By keeping institutional investors and traders apprised of critical earnings date revisions, they
can take advantage of – or avoid – short-term volatility in a given security.
Classifying words as either positive or negative, the researchers generated article-level sentiment scores—to highlight how news likely to be perceived as positive or negative would impact stock prices. Traditionally finance researchers and market practitioners have relied on accounting data and fundamentals to predict where the market is headed. But quarterly reports arrive slowly for a market moving at warp speed, which led researchers and traders to look for other sources of predictive information, including news. To find out if news reports could be used to predict stock prices, Ke, Kelly, and Xiu borrowed machine-learning techniques used by computer scientists, who are increasingly training machines to understand text.