We recently compiled a list of the 10 Best Machine Learning Stocks According to Analysts. In this article, we are going to take a look at where Symbotic Inc. (NASDAQ:SYM) stands against the other machine learning stocks.
The AI boom of the last two years has pushed mathematical computing technologies to the forefront of Wall Street and the technology industry. In its simplest form, AI is a set of programming instructions that allow software to learn from existing data and use this learning to generate new outputs using logic and other parameters. Machine learning is a subset of AI, and it uses algorithms to autonomously learn from data to provide outputs for specific use cases. Machine learning is a subset of AI, meaning that while all machine learning is AI, not all AI applications are machine learning.
Some ways in which engineers use machine learning are via classification, clustering, regression, supervised, unsupervised, and associated learning algorithms. AI, on the other hand, also involves technologies such as artificial narrow AI, general AI, and super AI. Right now, only narrow AI technologies are available, and these are limited to specific abilities such as ChatGPT being able to generate only text based outputs.
For stocks, this means that AI stocks and machine learning stocks are nearly the same. On the software side of the AI industry, those that develop AI technologies are also heavily in machine learning. On the hardware side, the same firms cater to the computational needs of AI and ML companies. It also means that for a variety of businesses, machine learning is often more suitable since it allows them to develop a customized and autonomous learning approach. And like AI, the industry is relatively nascent. Market research shows that the machine learning industry was worth $15.1 billion in 2021. From 2022 until 2029, the sector is expected to grow at a compounded annual growth rate (CAGR) of 38.8% and be worth $210 billion.
This multi billion dollar valuation for the machine learning industry benefits from the technology’s ability to adapt itself to custom business use cases. Research from McKinsey sheds light on some of these, and it also shares details about rapid cost improvements that machine learning users are experiencing. Starting from the use cases, include capital markets and education. In capital markets, machine learning can help financial institutions avoid asset mispricing losses of as much as $950 million. By using machine learning and its neural network subset, banks can reduce operational costs and portfolio risk, increase valuation accuracy, and speed up risk and valuation calculations compared to traditional Monte Carlo and risk valuation approaches.
In the education industry, an online education provider used a machine learning model to improve its student drop out (attrition) rates. The model allowed the university to identify three additional student archetypes that accounted for 70% of the students likely to leave their courses. Crucially, traditional linear models were unable to identify these groups, and machine learning enabled the university to develop a targeted approach to reduce attrition. Finally, image classification systems powered by an advanced form of machine learning called deep learning are rapidly improving their costs. Data from Stanford University shares that training costs of these systems dropped by 64% between 2015 and 2021 while training times improved by 94%.
Evaluating machine learning stock performance is trickier since there are no exclusive ETFs or stock indexes that focus only on machine learning stocks. Therefore, we’re left to use AI stocks as a proxy to also evaluate machine learning stock performance. As part of our research for this piece, Insider Monkey analyzed the year to date price performance of 39 AI stock ETFs. On average, these funds have gained 13.26% year to date while on median their price gains are 11.47%. Their performance ranges between -2.53% to 32.48%.
On the hardware side, semiconductor stocks are the primary beneficiaries of the AI surge. While their returns have stunned investors, there is some information that suggests that the sector might be overvalued. Data compiled by Aswath Damodaran shows that semiconductor stocks have an EV/EBITDA ratio of 31.6, which is the highest among all industries tracked. These stocks will also benefit from increased attention from the US government, which has earmarked $280 billion for research and production through the CHIPS and Science Act of 2022. For AI software companies, valuing them means dividing them by their size and operations. By size, the mega cap stocks have an early mover advantage in the machine learning industry and are generating as much as $4.4 billion in revenue.
Dividing them by operations means that there are AI companies that offer cloud capacity by buying GPUs and those that use this capacity. For the former, the CFO of the world’s biggest GPU provider shared in May 2024 that for “every $1” spent on AI infrastructure, “cloud providers have an opportunity to earn $5 in GPU instant hosting revenue over four years.”
With these details in mind, let’s take a look at the top machine learning stocks according to analysts.
Our Methodology
To make our list of the best machine learning stocks, we first compiled an initial list of 150 stocks from three AI and robotics ETFs. Then, repeated entries, robotics, and firms that either do not significantly use machine learning in their operations or operate in unrelated industries were removed. This led to a final list of 78 stocks which were ranked by their average analyst share price target upside. Out of these, the stocks with the highest upside were chosen.
We also mentioned the number of hedge funds that had bought these stocks during the same filing period. Why are we interested in the stocks that hedge funds pile into? The reason is simple: our research has shown that we can outperform the market by imitating the top stock picks of the best hedge funds. Our quarterly newsletter’s strategy selects 14 small-cap and large-cap stocks every quarter and has returned 275% since May 2014, beating its benchmark by 150 percentage points (see more details here).
A warehouse automation system in operation, with robotic arms managing inventory efficiently.
Symbotic Inc. (NASDAQ:SYM)
Number of Hedge Fund Investors in Q1 2024: 24
Analyst Average Share Price Target: $55.38
Upside: 40%
Symbotic Inc. (NASDAQ:SYM) is a specialty process automation firm which focuses on the needs of the warehousing and the logistics industry. It offers machine learning to its customers through platforms such as its AI powered robotic warehouse process automation products. Symbotic Inc. (NASDAQ:SYM) is one of the few firms of its kind with a pure play focus on warehouse automation, which offers it a significant competitive moat. This is evidenced by the firm’s $23 billion order backlog. Its financial heft is further bolstered by $546 million in cash and no long term debt, allowing Symbotic Inc. (NASDAQ:SYM) to ensure a steady flow of revenue even as the broader cloud sector struggles. One of Symbotic Inc. (NASDAQ:SYM)’s biggest customers is the retail giant Walmart, which has teamed up with the firm to deploy AI automation across its warehouses.
One key application of machine learning is image recognition, and on this front, here’s what Symbotic Inc. (NASDAQ:SYM)’s management had to say during its Q2 2024 earnings call:
So we’ve been working on this for about three years now, but putting vision on our bots and that combined with NVIDIA chips that we’re using, allows us to recognize boxes that may be deformed, but still recognize what the product is. And so that makes our bots much more able to pick irregular cases. And as you know, we’re one of the only people, maybe the only one that puts our box directly on shelves. We don’t put them on trays. That requires a lot of expertise and a lot of knowledge. And so we’ve been on this journey for a while, and now about 40% of our bots in our network are vision-enabled. And so there’s a bunch of work for the AI to catch up with. Recognizing 1,000 different pictures of a single box and saying, oh, that’s XY’s product.
And the shape, I didn’t recognize it before because we were just using sensors, but now with vision, we can actually recognize that. So that’s one thing. The other thing we did is that we changed the routing algorithms for our bots, and they will also be vision-enabled so that they’re more reliable, so that if something happens, like a bot gets stuck on, a broken case or something that we can now route around it. And we always could do that a little bit, but now we can do it much better. And then to be able to actually see the bot in front of us is also innovative. The other thing we’ve done is we have started work on perishable testing. And so we think that’s going to go fairly well because there’s not a lot of new things we have to do on perishables, but we want to test what happens when a bot runs over yogurt.
So things like that, and then the next thing after that will be testing bots in a frozen environment. So those are a couple of things we’ve been doing.
Overall SYM ranks 8th on our list of the best machine learning stocks to buy. You can visit 10 Best Machine Learning Stocks According to Analysts to see the other machine learning stocks that are on hedge funds’ radar. While we acknowledge the potential of SYM as an investment, our conviction lies in the belief that AI stocks hold greater promise for delivering higher returns, and doing so within a shorter timeframe. If you are looking for an AI stock that is more promising than SYM but that trades at less than 5 times its earnings, check out our report about the cheapest AI stock.
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Disclosure: None. This article is originally published at Insider Monkey.