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 PROS Holdings, Inc. (NYSE:PRO) 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 businessperson using modern technology to analyze and research airline performance.
PROS Holdings, Inc. (NYSE:PRO)
Number of Hedge Fund Investors in Q1 2024: 17
Analyst Average Share Price Target: $40.56
Upside: 61%
PROS Holdings, Inc. (NYSE:PRO) is a small Texas based company which provides revenue and sales optimization products to the airline industry. It offers machine learning for product and pricing optimization through its PROS AI platform. Since it’s a SaaS company, key to PROS Holdings, Inc. (NYSE:PRO)’s hypothesis is its ability to grow revenue and beef up margins. On this front, the stock price is influenced by PROS Holdings, Inc. (NYSE:PRO)’s goal to become a Rule of 40 firm by 2026 by delivering at least 16% to 21% in revenue growth and 19% to 24% in free cash flow margin. Coupling this with the firm’s business model which is mostly levered towards customer facing firms that are sensitive to economic conditions, PROS Holdings, Inc. (NYSE:PRO)’s growth and recurring revenue is contingent on a vibrant economy. While this might mean that the firm can find it difficult to keep up with its growth trend (revenue growth was 10% in Q1), PROS Holdings, Inc. (NYSE:PRO) is making some moves to expand its market presence.
These include a partnership with tech giant Microsoft to offer CoPilot for machine learning and AI driven pricing information. PROS Holdings, Inc. (NYSE:PRO)’s management elaborated on this platform during the Q1 2024 earnings call where it shared:
In Q1, we officially launched the PROS Copilot for Sales Plugin in partnership with Microsoft. In today’s environment, customers place a premium on speed and efficiency, and research shows that 35% to 50% of sales go to the vendor that responds first.
The PROS Copilot for Sales Plugin seamlessly integrates PROS AI-powered quote insights into Microsoft Copilot for Sales, empowering sellers to deliver fast, personalized offers to customers directly from email threads. PROS is the first vendor to integrate quote insights into Microsoft’s Copilot for Sales, uniquely harnessing the power of data from across PROS Smart CPQ, Microsoft 365 apps, and CRM platforms to drive AI- powered offers that win. Our platform innovations are resonating, evidenced by our wins across industries. I will share a few of our exciting wins, starting with new customers. In Q1, ECE Group and Les Schwab made the strategic decision to adopt the PROS Platform. ECE Group, a global leader in real estate management, chose to activate Smart CPQ to power offers for their retail rental spaces to accelerate time-to-quote and drive a better customer experience.
Overall PRO ranks 2nd 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 PRO 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 PRO 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.
