Nvidia stock has dropped after earnings in six of the past eight quarters as Wednesday's report nears
NEW YORK, Aug 24. Nvidia (NVDA) has fallen after quarterly results in six of the past eight periods, including each of the last four, according to Yahoo Finance AlphaSpace analysis. The chipmaker reports Wednesday evening. Shares have outperformed the S&P 500 (^GSPC) by five percentage points over the past month, per the same data, setting a demanding standard heading into the release.
NEW YORK, Aug 24. Nvidia (NVDA) has fallen after quarterly results in six of the past eight periods, including each of the last four, according to Yahoo Finance AlphaSpace analysis. The chipmaker reports Wednesday evening. Shares have outperformed the S&P 500 (^GSPC) by five percentage points over the past month, per the same data, setting a demanding standard heading into the release.
The market is positioned for Nvidia to deliver strong numbers and for chief executive Jensen Huang to sound bullish on the call. That combination of elevated expectations and a poor post-earnings track record makes a valuation re-rating difficult. HSBC analyst Frank Lee wrote in a note ahead of the results that it is possible, though not easy.
What analysts are watching
UBS analyst Tim Arcuri, in a client note, said the numbers will carry more weight than the narrative this quarter. He argued that debates around AI infrastructure spending, return on investment, and credit risk sit largely outside Nvidia's control, pushing the focus back to the reported figures. Arcuri said he expects investors to come away from Wednesday's call with greater confidence in a path to $15 or more in earnings per share for 2027 and $20 for 2028, numbers he said should keep the stock moving higher.
Lee's case for a re-rating centers on Nvidia's positioning in open-source AI. He described the company as working to establish itself as the world's largest contributor to that field. Nvidia has said the aggregate of open-source models now ranks as the second most popular category by token generation. Lee argued that open-source small language models are becoming the preferred engine for agentic AI and on-device applications. He said their growth could lower the entry barrier for enterprise inference and expand Nvidia's addressable market from a handful of frontier AI labs to millions of individual developers and sovereign nations.
The market is also watching Nvidia's private investment portfolio. Rising valuations for privately held AI names, with Anthropic (ANTH.PVT) cited as one example, are seen as limiting downside for those holdings.