Boston, MA, March 08, 2022 (GLOBE NEWSWIRE) -- Cmind Inc (“Cmind”), an AI driven technology firm that focuses on key corporate event prediction, today announced its latest research report, “Natural Language Processing and EPS Prediction”. Produced in collaboration with Northeastern University finance professor, John Bai, the study makes a strong case for using natural language processing (NLP) with traditional corporate financials to accurately predict public company earnings beats and misses. Click HERE to request a complimentary copy of the research.
The Cmind EPS Predictor leverages over ten (10) years of EPS beat/miss history, forty (40) years of company quarterly financials, four (4) years of earnings transcripts, twenty (20) years of corporate governance variables, and forty (40) years of macroeconomic variables. These signals are then trained by Cmind’s powerful machine learning algorithm which is optimized for time series modelling and NLP. These techniques together enable the Cmind EPS Predictor to achieve a 70% and higher accuracy rate in predicting earnings beats/ misses on over 4,000 publicly traded companies.
During the Q4 2021 earnings season, Cmind is happy to report that it has achieved over 70% accuracy in its EPS beat/miss predictions as of February 9, 2022, with many successes in high-profile results (e.g., McDonald’s miss; Amazon’s beat). This is a notable achievement given the recent spike in market volatility and uncertainty in the macroeconomic environment.
Additional Report Highlights:
- Cmind EPS Beat/Miss Predictor achieves average accuracy over 80% for Information Technology companies over the last 8 quarters. Big data from a wide range of sources is frequent but often unstructured, calling for “smart” and automatic ways of ingesting and analyzing the data
- New frontier algorithms exploiting both NLP and traditional finance signals significantly improve prediction accuracy
- Cmind continues to improve its prediction accuracy by ingesting and annotating some of the most recently available social media data (e.g., WallStreetBets, Twitter, Glassdoor)
“The earnings season is always challenging as we need to monitor so many different companies at the same time” said Dr. Henry Ma, CFA, President and Chief Investment Officer of Julex Capital. A quantitative investment firm based in Boston, Julex Capital manages a small cap strategy with a five-star rating from Morningstar and among the top performers in the manager databases like eVestment and Informa Financial Intelligence. The firm is an early user of Cmind’s EPS Predictor. “We have found ourselves in a much better position managing our portfolios since we started incorporating Cmind’s products into our investment process.” Dr. Ma further added “instead of hiring a group of analysts, we use Cmind products as a cost effective way to help generate new ideas as well as manage the exposures of existing portfolios.”
“The availability of large quantities of data today provides a unique opportunity,” said Professor Bai. “Existing products in the industry are mostly manual, and do not use available data to its fullest potential, and that’s what makes Cmind’s approach unique..”
About Professor John Bai
Mr. John Bai is an associate professor of finance and Gary Gregg research fellow at Northeastern University. Professor Bai has published numerous articles in top finance and management journals and is frequently interviewed by NPR on corporate finance and investment related issues.
About Cmind
Cmind is an AI-enabled technology firm that focuses on generating predictive signals for key corporate events such as earnings and revenue beat/miss, credit rating changes, ESG risks, among others by utilizing proprietary annotation schemes and continuously optimized algorithms that reflect newly available financial data, news, and management commentaries.
