5 Set-It-and-Forget-It Stocks to Buy According to Financial Media

In this article, we discuss the 5 set-it-and-forget-it stocks to buy according to financial media. To read the overview of set-it-and-forget-it stocks and portfolios, go directly to the 11 Set-It-and-Forget-It Stocks to Buy According to Financial Media.

5. NVIDIA Corporation (NASDAQ:NVDA)

Number of Hedge Fund Holders: 180

NVIDIA Corporation (NASDAQ:NVDA) is one of the most successful manufacturers of computer graphics processors, chipsets, and more. It operates through two segments, the Graphics Processing Unit segment and the Tegra Processor segment.

According to Insider Monkey’s database, hedge fund sentiment was positive toward NVIDIA Corporation (NASDAQ:NVDA)’s stock. In Q3, 180 hedge funds were bullish on the stock, up from 175 in Q2. With 14.04 million shares worth $6.1 billion, Rajiv Jain’s GQG Partners was the most significant stakeholder in the company.

Over the last three months, 34 Wall Street analysts covered NVIDIA Corporation (NASDAQ:NVDA), and 31 maintained a Buy rating on the stock. The average price target of $661.35 had an upside of 35.44% as of the December 22 market close.

Blue Tower Asset Management mentioned NVIDIA Corporation (NASDAQ:NVDA) in its third quarter 2023 investor letter. Here is what it said:

“In addition to the use of larger datasets, the training speed of AI models has increased dramatically. NVIDIA Corporation (NASDAQ:NVDA)’s stock almost tripled in the first 3 quarters of this year with a 197% gain, and a large reason for this is the huge role they have played in recent AI improvements. Nvidia’s single GPU AI training speed performance has increased by a dramatic 1000x in 10 years with only 2.5x coming from Moore’s Law3 driven increases in chip density. Besides better chip manufacturing, there were three other improvement factors at play: simplifications in number representation for the weights of the neural networks, more complex mathematical instructions for reducing the computational overhead involved in mathematical calculations, and increased neuron sparsity (in neural networks, some neurons are useless and can be pruned from the network without reducing performance significantly). In addition to these single GPU improvements, Nvidia also made improvements in data center scale architecture that allows groups of GPUs to work more efficiently together.

It is noteworthy that the vast majority of the improvement came from hardware architectural and software data improvements, rather than transition density. These improvements were likely the low-hanging fruit of training speed improvements as researchers will eventually converge on an ideal architecture. The simplification of going from 32-bit to 8-bit floating point numbers for measuring weights is a one-time gain that can’t be repeated again. I expect the rate of improvement to slow down over the next ten years and eventually approach the levels of Moore’s Law improvements in chip efficiency. The historical trend for computer hardware is for it to eventually be commoditized, and I believe this will eventually occur for Nvidia’s GPUs as well.”