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 HubSpot, Inc. (NYSE:HUBS) 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 team of software developers gathered around a monitor discussing a new CRM platform.
HubSpot, Inc. (NYSE:HUBS)
Number of Hedge Fund Investors in Q1 2024: 55
Analyst Average Share Price Target: $663
Upside: 40%
HubSpot, Inc. (NYSE:HUBS) is a cloud computing company that focuses on customer relationship management (CRM) products. The consumer facing nature of its products, which allow businesses to meet their customer management also creates significant room for HubSpot, Inc. (NYSE:HUBS) to integrate machine learning into its platform. One such use case comes through HubSpot, Inc. (NYSE:HUBS)’s Marketing Hub which uses machine learning for advertising. Yet, due to the growth intense nature of the cloud industry, the firm remains vulnerable to any growth slowdown, or a market downturn that affects its recurring revenue. To combat the slow industry in H1 2024, HubSpot, Inc. (NYSE:HUBS) has introduced 70 AI features for its customers, reduced its entry level prices, and attempted to beef up retention by allowing customers to upgrade their packages easily. Its shares did fall in July, but this was unrelated to fundamentals as investors reacted negatively to the report that Google parent Alphabet wasn’t buying HubSpot, Inc. (NYSE:HUBS).
HubSpot, Inc. (NYSE:HUBS)’s management commented on its new features during the Q1 2024 the earnings call:
Customers are looking at consolidating on our platform. They want to be very, very sure, and so there’s, like some additional proof-of-concepts there. In terms of AI though, we have, it’s been about a year, right? Last year, about this time, is when we launched our first set of products. And, most of the products are now in GA. In fact, in April, as part of our new spotlight, we launched about 70 additional AI features. And, what we are seeing, in terms of adoption is there are customers who are leading with AI.
They’re looking at like every single department within the go-to-market function saying, how can I leverage AI? And, those customers are moving fast. And, we can see that in terms of content use case adoption. We can see that in terms of service, summarization as well as deflection of call adoption, and we can see that in terms of Sales Hub, adoption of AI features. But then there are, a whole set of customers who are either just getting out of experimenting or figuring out the first set of use cases and then getting value, and that is going to take time. So, our focus this year is all about driving repeat usage. From a product perspective, you’re focused on driving repeat usage. From a go-to-market perspective, we are focused on educating customers how their data is being used, where they can drive productivity benefits as well as growth benefits.
Overall HUBS ranks 7th 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 HUBS 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 HUBS 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.
