Top 10 Machine Learning Stocks Picked by Analysts

In this article, Analysts' picks for the 10 best machine learning stocks.

The AI ​​boom of the past two years has brought mathematical computing techniques to the forefront of Wall Street and the technology industry. In its simplest form, AI is a set of programming instructions that allows software to learn from existing data and use that learning to generate new outputs using logic and other parameters. Machine learning is a subset of AI, using algorithms to autonomously learn from data and 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 of the ways engineers use machine learning include classification, clustering, regression, supervised learning, unsupervised learning, and association learning algorithms. On the other hand, AI also includes technologies such as artificial narrow AI, general AI, and super AI. Currently, only narrow AI technologies are available, and these are limited to certain capabilities, such as ChatGPT being able to only generate text-based output.

For stocks, this means that AI stocks and machine learning stocks are roughly the same. On the software side of the AI ​​industry, companies that develop AI technologies also focus on machine learning. On the hardware side, the same companies serve the computational needs of AI and ML companies. It also means that machine learning is often a better fit for different businesses because it can develop customized, autonomous learning approaches. And like AI, the industry is in a relatively early stage. According to market research, the machine learning industry was worth $15.1 billion in 2021. From 2022 to 2029, the sector is expected to grow at a compound annual growth rate (CAGR) of 38.8% to be worth $210 billion.

This multi-billion dollar valuation of the machine learning industry benefits from the technology's ability to adapt to custom business use cases. A McKinsey study highlights some of these and also shares details on the rapid cost improvements machine learning users are experiencing. Starting with the use cases, they include capital markets and education. In capital markets, machine learning helps financial institutions avoid up to $950 million in losses due to asset mispricing. 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 assessment approaches.

In the education industry, an online education provider used a machine learning model to improve student dropout rates. The model allowed the university to identify three additional student archetypes that accounted for 70% of students likely to drop out of a course. Importantly, traditional linear models could not identify these groups, whereas machine learning allowed the university to develop a targeted approach to reduce dropout rates. Finally, image classification systems that leverage advanced machine learning, known as deep learning, are rapidly improving costs. According to data from Stanford University, training costs for these systems fell by 64% between 2015 and 2021, and training times have decreased by 94%.

Assessing the performance of machine learning stocks is more difficult because there are no machine learning-focused ETFs or stock indexes. only For machine learning stocks, we should also use AI stocks as a proxy to evaluate the performance of machine learning stocks. As part of the research for this article, Insider Monkey analyzed the year-to-date price performance of 39 AI stock ETFs. On average, these funds are up 13.26% year-to-date, with the median price increase being 11.47%. Performance ranges from -2.53% to 32.48%.

On the hardware side, semiconductor stocks are the biggest beneficiaries of AI's rapid growth. Their returns have surprised investors, but some information suggests the sector may be overvalued. According to data compiled by Aswath Damodaran, semiconductor stocks have an EV/EBITDA ratio of 31.6, the highest among tracked industries. These stocks will also benefit from increased attention from the U.S. government, which is allocating $280 billion for research and production through the CHIPS and Science Act of 2022. For AI software companies, to assess their value, you need to divide by size and what they do. By size, mega-cap stocks have first-mover advantage in the machine learning industry, generating as much as $4.4 billion in revenue.

Splitting it up by operations, there are AI companies that buy GPUs and provide cloud capacity, and there are AI companies that use this capacity. For the former, the CFO of the world's largest GPU provider said in May 2024 that for “every dollar” spent on AI infrastructure, cloud providers have the 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 create our list of the best machine learning stocks, we first created an initial list of 150 stocks from three AI and robotics ETFs. We then removed overlapping stocks, robotics, and companies that do not use machine learning much in their operations or operate in unrelated industries. This resulted in a final list of 78 stocks ranked by analysts' average price target upside. From these, the stocks with the highest upside were selected.

We also mentioned the number of hedge funds that bought these stocks during the same filing period. Why are we interested in stocks that hedge funds are flooding? The reason is simple: our research shows that you can outperform the market by mimicking the top holdings of the best hedge funds. Our quarterly newsletter strategy selects 14 small and large stocks each quarter and has returned 275% since May 2014, beating the benchmark by 150 percentage points (Click here for details).

10. Alibaba Group Holding Limited (NYSE:BABA)

Number of hedge fund investors in Q1 2024: 103

Analyst average price target: $107.12

Increase: 33%

Alibaba Group Holding Limited (NYSE:BABA) is one of the world's largest e-commerce retailers. The company commands about 40% of the market in China, the world's second most populous country. Its size, as evidenced by its $34 billion in cash, gives Alibaba Group Holding Limited (NYSE:BABA) ample resources to weather the slowing Chinese economy. In addition, the company is also a technology conglomerate, diversifying its revenues through high-growth industries such as cloud computing and machine learning. However, Alibaba Group Holding Limited (NYSE:BABA) faces stiff competition from Chinese e-commerce startups such as Pinduoduo, which has thrived in the struggling Chinese economy by offering discounted products and bulk-buying offers. Most of Alibaba Group Holding Limited's (NYSE:BABA) businesses, including marketing services, travel advice, and restaurant guides, are all cyclical in nature. This means stocks are more vulnerable to more bad news about the Chinese economy, as shown by their 2% drop in July after the economy grew 4.7% in the second quarter, below expectations of 5.1%.

Moreover, Alibaba Group Holding Limited (NYSE:BABA) has a forward P/E ratio of just 9.23, which is significantly lower than its US peer Amazon's 40.16. This large difference reflects investor sentiment surrounding Chinese stocks. If the US-China trade tensions continue, especially regarding tariffs and semiconductor regulations, this could pose further problems for Alibaba Group Holding Limited (NYSE:BABA). Here's what Artisan Partners had to say about the company in their Q1 2024 investor letter:

Alibaba shares fell 7% during the quarter. There is nothing particularly new about Alibaba. There was no meaningful news that caused the stock to fall. December quarter earnings were good, with both sales and profits up 5%. While not a very exciting level of growth typically, it is enough to justify a modest valuation of 4-5 times the company's EBIT. As I noted in my recent letter, this is a valuation level typically given to companies in decline, and Alibaba is not a company in decline. Management has continually implemented changes aimed at increasing shareholder value. Over the past year, management has made changes, adjusted the corporate structure, explored asset separation, monetized the balance sheet, and improved capital allocation. All of these measures have yet to be fully reflected in the stock price. This stock could double and still remain cheap.

9. Teradata Corporation (NYSE:TDC)

Number of hedge fund investors in Q1 2024: 33

Analyst average price target: $46.73

Increase: 37%

Teradata Corporation (NYSE:TDC) is a data-focused cloud company that enables businesses to run analytics across multiple clouds. Users can embed machine learning directly into the database through the Teradata ClearScape Analytics platform, so companies don't need to run a separate runtime environment for their machine learning requirements. Three key principles for evaluating cloud computing stocks are revenue growth, cost control as measured by free cash flow, and recurring revenue. Weakness on this front means stock prices will fall, and this is true for Teradata Corporation (NYSE:TDC). The company's shares are down 20% year to date, driven by a 21% drop in February and a 13% drop in May. The February drop was driven by a 1% decline in annual recurring revenue. Teradata Corporation (NYSE:TDC)'s ARR of $1.48 billion was below the low end of guidance of $1.498 billion. In May, Teradata Corporation (NYSE:TDC) ARR increased 48% to $528, while management's guidance was 53% to 57%. These factors indicate that market expectations for cloud providers are high, suggesting that even strong growth may not satisfy investors.

However, Teradata Corporation (NYSE:TDC) offers enterprises the ability to simultaneously manage large, complex data sets. Ariel Investments acknowledged these strengths in its Q1 2024 investor letter, stating:

We acquired an American software company. Teradata Corporation (TDC)is a provider of business analytics solutions, hybrid cloud products, and consulting services. Teradata stands out for its ability to manage and analyze large, complex datasets, delivering the highest number of concurrent users, the lowest cost per query, and deep insights across a variety of operational datasets. While the market is focused on near-term setbacks in the company's transformation to a cloud-based computing model, we believe Teradata's technology advantages, modern cloud services, and installed base ensure a solid long-term trajectory in key growth areas.