AI Research
History Says the Nasdaq Will Soar: 2 Artificial Intelligence (AI) Stocks to Buy Now, According to Wall Street
Most Wall Street analysts see substantial upside in these technology stocks.
Anticipating what the stock market will do in any given year is impossible, but investors can lean into long-term trends. For instance, the Nasdaq Composite (^IXIC 0.09%) soared 875% in the last 20 years, compounding at 12% annually, due to strength in technology stocks. That period encompasses such a broad range of market and economic conditions that similar returns are quite plausible in the future.
Indeed, the rise of artificial intelligence (AI) should be a tailwind for the technology sector, and most Wall Street analysts anticipate substantial gains in these Nasdaq stocks:
- Among 31 analysts who follow AppLovin (APP -1.86%), the median target price of $470 per share implies 40% upside from the current share price of $335.
- Among 39 analysts that follow MongoDB (MDB -3.48%), the median target price of $275 per share implies 34% upside from the current share price of $205.
Here’s what investors should know about AppLovin and MongoDB.
Image source: Getty Images.
AppLovin: 40% upside implied by the median target price
AppLovin builds adtech software that helps developers market and monetize applications across mobile and connected TV campaigns. The company is also piloting ad tech tools for e-commerce brands. Importantly, its platform leans on a sophisticated AI engine called Axon to optimize campaign results by matching advertiser demand with the best publisher inventory.
AppLovin has put a great deal of effort into building its Axon recommendation engine. The company started acquiring video game studios several years ago to train the underlying machine learning models that optimize targeting, and subsequent upgrades have encouraged media buyers to spend more on the platform over time.
Morgan Stanley analyst Brian Nowak recently called AppLovin the “best executor” in the adtech industry. In particular, he called attention to superior ad targeting capabilities driven by its “best-in-class” machine learning engine, which has led to outperformance versus the broader in-app advertising market since 2023.
AppLovin reported strong first-quarter financial results. Revenue increased 40% to $1.4 billion, as strong sales growth in the advertising segment offset a decline in sales in the mobile games segment. Generally accepted accounting principles (GAAP) net income increased 149% to $1.67 per diluted share. And management guided for 69% advertising sales growth in the second quarter.
Wall Street estimates AppLovin’s earnings will increase at 53% annually through 2026. That makes the current valuation of 61 times earnings look rather inexpensive. Investors should pounce on the opportunity to buy this stock today. Personally, I would start with a small position and add shares periodically.
MongoDB: 34% upside implied by the median target price
MongoDB is the most popular document database. Whereas traditional relational databases (also called SQL databases) store information in structured rows and columns, the document model is more scalable and flexible. It supports structured data, but also unstructured data like emails, social media posts, images, videos, and websites.
Every application requires a database. It is where information can be stored, modified, and retrieved when needed. But the document model is particularly well suited to analytics, content management, e-commerce, payments, and artificial intelligence applications due to its superior scalability and flexibility. MongoDB is leaning into demand for AI.
Last year, the company introduced MAAP (MongoDB AI Application Program), a collection of resources and reference architectures that help programmers build applications with AI capabilities. Additionally, MongoDB recently acquired Voyage AI, a company that develops embedding and reranking models that make AI applications more accurate and reliable.
CEO Dev Ittycheria told analysts: “MongoDB now brings together three things that modern AI-powered applications need: real-time data, powerful search, and smart retrieval. By combining these into one platform, we make it dramatically easier for developers to build intelligent, responsive apps without stitching together multiple systems.”
MongoDB reported encouraging first-quarter financial results, exceeding estimates on the top and bottom lines. Customers climbed 16% to 57,100, the highest net additions in six years. Revenue increased 22% to $549 million, a sequential acceleration, and non-GAAP earnings jumped 96% to $1.00 per diluted share.
Going forward, Grand View Research estimates the database management system market will increase at 13% annually through 2030. MongoDB should grow faster as it continues to gain market share. That makes the present valuation of 7.8 times sales look reasonable, especially when the three-year average is 13.2 times sales. Patient investors should feel comfortable buying a small position today.
AI Research
Canadian Scientists Pioneer Made-in-Canada Quantum-powered AI Solution
Insider Brief
- A Canadian-led research team from TRIUMF and the Perimeter Institute has developed a quantum-assisted AI model to simulate particle collisions more efficiently, addressing global computational challenges.
- The study demonstrates that combining deep learning with quantum computing—using technology from D-Wave—can significantly reduce the time and cost of high-energy physics simulations.
- Published in npj Quantum Information, the work supports future upgrades to CERN’s Large Hadron Collider and underscores Canada’s growing leadership in quantum and AI-driven scientific research.
PRESS RELEASE — In a landmark achievement for Canadian science, a team of scientists led by TRIUMF and the Perimeter Institute for Theoretical Physics have unveiled transformative research that – for the first time – merges quantum computing techniques with advanced AI to model complex simulations in a fast, accurate and energy-efficient way.
“This is a uniquely Canadian success story,” said Wojciech Fedorko, Deputy Department Head, Scientific Computing at TRIUMF. “Uniting the expertise from our country’s research institutions and industry leaders has not only advanced our ability to carry out fundamental research, but also demonstrated Canada’s ability to lead the world in quantum and AI innovation.”
Traditional simulations of particle collisions are already both time-consuming and costly, often running on massive supercomputers for weeks or months. By leveraging quantum processes and technology made possible by California-based D-Wave Quantum Inc., the researchers were able to create a new “quantum-assisted” generative model capable of running simulations and open new opportunities to cost-effectively analyze rapidly growing data sets.
The research, published today in npj Quantum Information, is part of a worldwide effort to create the tools needed to accommodate upgrades to CERN’s particle accelerator, the Large Hadron Collider (LHC), and alleviate a computational bottleneck that would impact researchers all over the world.
“Our method shows that quantum and AI technologies developed here in Canada can solve real-world scientific bottlenecks,” said Javier Toledo-Marín, joint appointee at TRIUMF and Perimeter Institute. “By combining deep learning with quantum technology, we are forging a new path for both theoretical experimentation and technological application.”
In addition to TRIUMF and Perimeter, contributions to the published research came from the National Research Council of Canada (NRC), the University of British Columbia and the University of Virginia, showcasing not only the wealth of research talent and scientific ingenuity across the country, but also the international collaboration that places Canada at the forefront of worldwide scientific innovation.
AI Research
Apple Researchers Create an AI Model That Uses Behavioural Data from Wearables to Predict Health Signals
Apple researchers, in collaboration with the University of Southern California, have developed a new artificial intelligence (AI) model that tracks behavioural data over sensor signals. The new research builds on prior work by the Apple Heart and Movement Study (AHMS) and was aimed at understanding if behavioural data, such as sleep pattern and step count, can be a better determinant of a person’s health compared to traditional indices such as heart rate and blood oxygen level. As per the paper, the AI model performed surprisingly well, even if with some caveats.
New Apple Study Shows Benefits of Moving Beyond Traditional Health Data
The study, titled “Beyond Sensor Data: Foundation Models of Behavioral Data from Wearables Improve Health Predictions” was published in the pre-print journal arXiv and is yet to be peer reviewed. The researchers set out to develop an AI model, dubbed Wearable Behaviour Model (WBM), that relies on processed behavioural data from wearables such as how long a person sleeps and their REM cycles, daily steps taken and gait, and how their activity pattern changes over the week.
Traditionally, to predict or assess someone’s health, wearable health research has typically focused on raw sensor readings such as continuous heart rate monitoring, blood oxygen levels, and body temperature. The study believes that while this data can be useful at times, it also lacks the full context about the individual and can have inconsistencies.
Regardless, so far, behavioural data, which is also something most wearables process, has not been used in systems as a reliable indicator of a person’s health. There are two main reasons for it, according to the study. First, this data is much more voluminous compared to sensor data, and as a result, it can also be very noisy. Second, creating algorithms and systems that can collect and analyse this data and reliably make health predictions is very challenging.
This is where a large language model (LLM) comes in and solves the analysis problem. To solve the noise in data, researchers fed the model with structured and processed data. The data itself comes from more than 1,62,000 Apple Watch users who participated in the AHMS research, totalling more than 2.5 billion hours of wearable data.
Once trained, the AI model used 27 different behavioural metrics, which were grouped into categories such as activity, cardiovascular health, sleep, and mobility. It was then tested across 57 different health-related tasks, such as finding out if someone had a particular medical condition (diabetes or heart disease) and tracking temporary health changes (recovery from injury or infection). Compared to the baseline accuracy, researchers claimed that WMB outperformed in 39 out of 47 outcomes.
Comparison between performance of the WBM model the test model and the combination of both
Photo Credit: Apple
The findings from the model were then compared with another test model that was only fed raw heart data, also known as photoplethysmogram (PPG) data. Interestingly, when individually compared, there was no clear winner. However, when researchers combined the two models, the accuracy of prediction and health analysis was measured to be higher.
Researchers believe combining traditional sensor data with behavioural data could improve the accuracy in the prediction of health conditions. The study stated that behavioural data metrics are easier of interpret, align better with real-life health outcomes, and are less affected by technical errors.
Notably, the study also highlighted several key limitations. The data was taken from Apple Watch users in the US, and the broader global population was not represented in this. Additionally, due to the high price of wearable devices that accurately collect and store behavioural data, accessibility of preventive healthcare also becomes a challenge.
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