Earnings Beat Forecast Using Event-Driven Score
Events such as M&A, new deals, new orders, partnerships, ESG, regulatory decisions, management and stakeholders’ changes, expansions to new markets or product categories, new products, price changes, new agreements, FDA decisions, financial reports related events, and more reflect the companies’ ongoing operation during a certain period.
To demonstrate the predictive power of events on earnings, we selected randomized 134 companies which beat the market expectation during the last earnings releases and we tested their event-driven score for the Jun-Sep 2018 period.
From 134 companies that beat the market expectation, 98 (73%) companies got an event-driven score above 0.5 which indicates a bullish trend (the event-driven score is scaled between 0-1, where below 0.5 points to a bearish event-driven score based on events during the filtered period, and above 0.5 points to bullish event-driven score). Among these companies that beat market expectations, we found events such as new products, new JV and M&A activities, increase in demand, new pricing models, entering new markets, and positive ESG events during the past quarter. A portion of these events have an impact on the same period and some will have an impact on the bottom line in the future.
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Our model predicts 73% of the companies that beat the market expectation in the earnings category, the remaining 27% can be explained by companies that had substantial events that position the companies’ event-driven score below 0.5 (a bearish territory), therefore, missing on the prediction. This is a type 2 error which spots companies that should not beat the market expectations, and in reality, beat the market expectations.
These events include ESG category, investor misleading, lawsuits, reduction in earnings guidance etc. Commonly for these events they all are involving processes and results which are unknown, therefore, they have an impact on the long-term rather than the short-term quarter results.
To conclude, each quarter has its unique events as well as dragged events from previous ones, however, data shows that events can predict which companies will outperform the market expectations and vice versa with relatively high accuracy.
Using big data and NLP technologies to capture alpha by collecting, structuring, and revealing events from news articles, press releases, and financial social media.
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