William Blair Comments on McGraw Hill FY2026 Earnings

McGraw Hill, Inc. (NYSE:MH) – Equities research analysts at William Blair issued their FY2026 earnings per share (EPS) estimates for shares of McGraw Hill in a note issued to investors on Monday, August 18th. William Blair analyst S. Sheldon anticipates that the company will post earnings per share of $1.21 for the year. William Blair has a “Outperform” rating on the stock. William Blair also issued estimates for McGraw Hill’s FY2027 earnings at $1.60 EPS.

MH has been the subject of several other reports. BTIG Research began coverage on McGraw Hill in a research report on Monday. They issued a “buy” rating and a $19.00 price objective for the company. JPMorgan Chase & Co. began coverage on McGraw Hill in a research report on Monday. They issued an “overweight” rating and a $21.00 price objective for the company. Rothschild & Co Redburn began coverage on McGraw Hill in a research report on Monday. They issued a “buy” rating and a $28.60 price objective for the company. Stifel Nicolaus began coverage on McGraw Hill in a research report on Monday. They issued a “buy” rating and a $19.00 price objective for the company. Finally, BMO Capital Markets began coverage on McGraw Hill in a research report on Monday. They issued an “outperform” rating and a $24.00 price objective for the company. One research analyst has rated the stock with a Strong Buy rating, twelve have assigned a Buy rating and one has given a Hold rating to the stock. Based on data from MarketBeat, McGraw Hill presently has a consensus rating of “Buy” and a consensus target price of $20.88.

McGraw Hill Trading Up 4.9%

Shares of NYSE:MH opened at $15.31 on Wednesday. McGraw Hill has a 12-month low of $12.55 and a 12-month high of $17.25.

McGraw Hill Company Profile

At McGraw Hill, our purpose is to unlock the potential of each learner at every stage of life. Our mission is to support educators, learners and professionals around the world with trusted, high-quality content and digital solutions that use data and learning science to adapt to each student as they progress towards their goals.