Financial firms have long understood that actionable information is increasingly found in the oceans of news and digital content available. In the nearly 20 years since the technology firm was founded, RavenPack has built a sterling reputation on Wall Street for the unparalleled breadth and quality of its low-latency text processing and data products. With Edge, RavenPack sets a new standard for its traditional user base, and further extends its reach by helping non-financial firms better mitigate risk exposures in investments, supply chain, client compliance, reputation management, competitive analysis, and sustainability. Capable of understanding content in 13 different languages, Edge can extract insights from all types of documents —from short news articles to complex legal filings. 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. As Figure 6 shows, companies that advanced an earnings date outperformed those that delayed after the change was made.
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- 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.
- 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.
- We highlight the limitations of valuation models in that stock price can be driven by sentiment, which is difficult to capture, or due to errors in forecasting earnings or discount rates which limits the usefulness of valuation models.
To complement the slew of new reports, Wall Street Horizon looks at recent findings from RavenPack that continue to highlight the important information that can be culled from the timing of earnings dates. Internal research within RavenPack suggests that the outcomes can be sensitive not only to the version of the GPT model they used but also to the strategy implementation. The robust performance depicted in the paper relies heavily on the assumption of attaining the open-price, a scenario proven impractical in real-world contexts.
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We attempt to rationalise whether a stock is „cheap‟ or „expensive‟ for a reason, or „mispriced‟. We highlight the limitations of valuation models in that stock price can be driven by sentiment, which is difficult to capture, or due to errors in forecasting earnings or discount rates which limits the usefulness of valuation models. We show that identifying additional drivers of returns like sentiment, management quality, earnings visibility and leverage helps to discriminate between stocks that are „mispriced‟ and „cheap/expensive‟ for a reason.
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The company’s products allow clients to enhance returns, reduce risk and increase efficiency by systematically incorporating the effects of public information in their models or workflows. RavenPack’s clients include the most successful hedge funds, banks, and asset managers in the world. Financial professionals rely on RavenPack for its speed and accuracy in analyzing large amounts of unstructured content. The company’s products allow clients to enhance returns, reduce risk or increase efficiency by incorporating the effects of public information in their models or workflows.
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Conceptually this approach is not different from an alpha model; however, its advantage is that we start with a valuation framework, which is how fundamental analysts evaluate investment opportunities. Additionally a valuation approach broadens the appeal to investors who view investment decisions outside the dimensions of styles. To us, ravenpack pricing this approach appeals to both fundamental and quantitative managers, i.e. a „quantamental‟ approach to stock selection. This website is using a security service to protect itself from online attacks. There are several actions that could trigger this block including submitting a certain word or phrase, a SQL command or malformed data.
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Again, this is true when looking at the calendar change long-short strategy that measures stock movement in reaction to the earnings date change before the quarterly report is released. While small-caps continue to outperform here (and decay more slowly), annualized returns and information ratios are maximized at the 10-day aggregation window. When using this long-short strategy across different aggregation windows and market caps (Figure 4), the 1-day aggregation window achieves the best results.
It looks at the number of outlier earnings date confirmations and whether companies are confirming earnings dates that are later than they have historically reported, or earlier. The historical baseline for this indicator is 100, meaning that anything above this average suggests companies are confirming later earnings announcements and below this average indicates companies https://g-markets.net/ are confirming dates that are earlier. Wall Street Horizon found that quarters that begin with a high LERI reading end up reporting lower S&P 500 EPS YoY growth than those with lower LERI readings. Combining the two strategies by using both price movement in reaction to earnings calendar change events and earnings announcement events proved to perform best.
News, Jobs, and Data Sources
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.
Finding 2: Changes in Earnings Announcement Dates Are Followed by Outsized Price Reactions
Promising results in the realm of stock price predictions using LLMs have been showcased in the now-famous paper Can ChatGPT Forecast Stock Price Movements? Enter your email to receive regular updates on research and articles on this topic. RavenPack delivers structured analytics on published content from high-quality sources including gated content and over 40,000 web and social media sources. A single word in a news report—a well-placed “undervalue,” for example—can drive a company’s stock price up or down.
Investors can benefit if they can figure out which words matter within a few days, research suggests. This is important as identifying between these competing hypotheses can aid investors in making rational decisions on whether to exploit the market’s misunderstanding of the stocks’ future potential or to avoid these companies as they are “cheap” or “expensive” for a genuine reason. A stronger contribution came from long positions, while the signals from short positions decayed more quickly. A new white paper, “Trading the Earnings Calendar,” (RavenPack, 2022), provides further research on this topic using Wall Street Horizon data, with five major findings that you should consider ahead of earnings season.
Her research has been widely featured in financial news outlets including regular appearances on networks such as CNBC and Fox Business to talk about corporate earnings and the economy. Ms. Short earned a BA in International Relations and English from Fairfield University. Both mid-/large- and small-caps long-only strategies decay more slowly than short-only, and advance events have higher momentum than delay events. This is consistent with findings from Figure 6 where the short-only strategy does not perform well, as delayed events do not deviate from zero significantly. RavenPack also found that stocks not only reacted to what was being shared in quarterly earnings reports as predicted by the confirmed earnings date, but that stocks also reacted to the earnings date changes themselves (prior to the actual report being released). The second strategy focused on the price reactions around these sequential changes in earnings announcement dates.