Cmind uses natural language processing (NLP) and machine
Boston, MA, March 08, 2022 (GLOBE NEWSWIRE) — Cmind Inc (“Cmind”), an AI-driven technology company focused on predicting key business events, 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 the case for using natural language processing (NLP) with traditional business finance to accurately predict beats and misses profits of public enterprises. Click HERE to request a free copy of the research.
The Cmind EPS Predictor is based on over ten (10) years of EPS beat/miss history, forty (40) years of company quarterly financial data, four (4) years of transcripts on earnings, 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, optimized for time series modeling and NLP. Together, these techniques allow the Cmind EPS Predictor to achieve an accuracy rate of 70%+ in predicting earnings beat/fail on over 4,000 publicly traded companies.
In the fourth quarter 2021 earnings season, Cmind is pleased to report that it has achieved over 70% accuracy in its EPS beat/fail forecast as of February 9, 2022, with numerous successes in earnings. high profile (e.g. McDonald’s miss; Amazon’s beat). This is a remarkable achievement given the recent spike in market volatility and uncertainty in the macroeconomic environment.
Additional highlights from the report:
- Cmind EPS Beat/Miss Predictor achieves an average accuracy of over 80% for IT companies over the past 8 quarters. Big data from a wide range of sources is frequent but often unstructured, requiring “smart” and automatic ways to ingest and analyze the data
- New frontier algorithms leveraging both NLP and traditional financial signals dramatically improve prediction accuracy
- Cmind continues to improve the accuracy of its predictions by ingesting and annotating some of the most recent data available on social media (eg WallStreetBets, Twitter, Glassdoor)
“Earnings season is always challenging because we have to monitor so many different companies simultaneously,” said Dr. Henry Ma, CFA, Chairman and Chief Investment Officer of Julex Capital. A Boston-based quantitative investment firm, Julex Capital runs a small-cap strategy with a five-star rating from Morningstar and among the top performers in manager databases like eVestment and Informa Financial Intelligence. The company is one of the first users of Cmind’s EPS predictor. “We have found ourselves in a much better position to manage our portfolios since we started integrating Cmind’s products into our investment process.” Dr. Ma further added “instead of hiring a group of analysts, we are using Cmind products as a cost-effective way to help generate new ideas as well as manage existing portfolio exposures.”
“The availability of large amounts of data today provides a unique opportunity,” said Professor Bai. “Existing products in the industry are mostly manual and don’t use available data to their full potential, and that’s what makes Cmind’s approach unique.”
About Professor John Bai
Mr. John Bai is Associate Professor of Finance and Gary Gregg Fellow at Northeastern University. Professor Bai has published extensively in top finance and management journals and is frequently interviewed by NPR on issues related to corporate finance and investing.
Cmind is an AI-powered technology company focused on generating predictive signals for key business events such as beat/miss earnings and revenue, credit rating changes, ESG risks, among other things by using proprietary annotation schemes and continuously optimized algorithms that reflect new available financial results. data, news and management commentary.